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Developmental coordination disorder (DCD), which is characterized by an inability to perform age-appropriate fine (hand writing and shoelace tying) and gross motor skills (playing sport and getting dressed) (1), affects approximately 6% of school-aged children, making it the most common childhood movement disorder (1). Such a broad range of deficits not only impacts performance of daily tasks, but also contributes to secondary long-term health consequences, including reduced engagement in physical activity and social activities (2, 3), and increased risk of low self-esteem, anxiety, and depression (3, 4). Coordination difficulties seen in 50–70% of children with DCD persist into adolescence and adulthood (1). Although the behavioral motor impairments experienced by children who have DCD are well known, the etiology, and neurological origin that has been long suspected to contribute to such deficits, underlying these impairments remain unclear (5). Among the motor impairments seen in DCD, we specifically focused on clumsy manual dexterity in the current study. Further, we evaluated the internal model deficit (6–9) and mirror-neuron system (MNS) deficit (10, 11), i.e., two putative pathophysiological mechanisms thought to impede fine motor skills in DCD, consequently affecting manual dexterity.

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Internal modeling deficits (IMDs) have been proposed as a neurological cause of DCD (12, 13). According to the IMD hypothesis, the sensory–motor integration in the internal model is dysfunctional in children with DCD, which reduces their ability to use predictive motor control (12, 13). Before slow, sensory–motor feedback becomes available, internal models provide stability to the motor system by predicting the outcome of movements. This allows rapid online correction (7, 8). More specifically, during the generation of a motor plan, the motor command is generated by the motor cortex and relayed to the body. An efference copy of this motor command is concurrently generated as a corollary discharge and relayed to the parietal lobe and cerebellum. Then, the predicted and actual sensory feedback is compared with somatic events and visuospatial integration, which are processed at the level of the cerebellum and parietal cortex, respectively. Mismatch between motor predictions (e.g., efference copy, predicted sensory feedback) and actual sensory feedback generates error signals are generated, which correct/modulate the unfolding motor output commands in real time. The parietal cortex and cerebellum is the neural basis for the internal model for online correction (14).

The IMD hypothesis of DCD is supported by several studies that demonstrated deficits in the predictive control of manual action, posture, gait, and eye movements. Paradigms, including the covert orienting of visuospatial attention (15–17), mental rotation of limb-versus object-based stimuli (18–20), grip force and anticipatory postural adjustments (21–23), predictive control of eye movements (24, 25), imagined or simulated pointing (26, 27), and rapid online control of reaching movements (6–9), have been applied to test the IMD hypothesis. The results of these studies converge on the argument that children with DCD have difficulty representing a predictive model of a prospective action, based on the integration of visuospatial/somatic information and motor programming, i.e., sensory–motor integration. Deficits in internal modeling in DCD have also been attributed to dysfunctions in the parietal lobe and cerebellum by previous brain imaging studies (28–34).

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However, it has not been clarified whether the time window of visuo–motor integration in the internal model in children with DCD differs from those with typical development (TD). The time window of visuo–motor integration, i.e., the visuo–motor temporal integration ability, can be quantitatively examined using the delayed visual feedback detection task (35). The delay-detection threshold (DDT) and steepness of the probability curve for delay detection, which will simply be referred to as steepness herein, can be determined from this task. The DDT, which is the time delay at which the rate of delay detection is 50%, indicates the extent to which the brain allows temporal discrepancy in different modalities of sensation. The steepness indicates the mechanism by which the brain integrates multisensory signals; a greater steepness indicates a more strict or precise judgment (35). Therefore, a lower DDT and higher steepness represents a highly sensitive visuo–motor temporal integration. Brain imaging and neuromodulation studies revealed that detection of delayed visual feedback for self-generated movement is based on comparison of motor signals (e.g., motor predictions, actual proprioceptive feedback) and actual visual feedback, possibly in the parietal cortex and cerebellum (36–39).

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Since previous studies suggested that DCD has an internal model deficit and parietal cortex and cerebellum dysfunction, children with DCD can be expected to have the difficulty with visuo–motor temporal integration. More specifically, the current study hypothesized that DDT was elevated, while steepness was decreased for children with DCD, in comparison to children with TD. Experimental task 1 (the delayed visual feedback detection task) in the present study was designed to examine this hypothesis.

There is recent evidence that MNS deficits are also associated with DCD (10, 11, 40). It is postulated that the human MNS consists of the pars opercularis of the inferior frontal gyrus (IFG), adjacent ventral premotor cortex, and inferior parietal lobule. The MNS is the neural basis of action observation and imitation (41) and plays an integrative role in observational learning. It forms a core circuit for action imitation, aiding learning, and acquisition of new skills via modeling others’ behavior and actions (42). Thus, deficits in MNS function interfere with a child’s ability to learn or imitate movements they observe. In fact, several previous studies reported that, compared to children with TD, the performance of imitating meaningful or meaningless intransitive or transitive gestures of children with DCD was significantly reduced (43–47). In addition, an fMRI study conducted by Reynolds et al. (10) reported that the activity of the IFG during action imitation significantly decreased in children with DCD, compared to those with TD. However, the imitation test used in previous studies were adult apraxia assessment batteries, which require advanced cognitive function, and thus, may not be suitable to evaluate a child’s imitation ability (48).

The MNS is responsible for automatic imitation, i.e., the unconscious tendency to mimic the behavior of others (41, 49–52). However, it has not been clarified whether the automatic-imitation function in MNS differs between children with DCD and TD. The automatic-imitation function can be quantitatively investigated using a motor interference task (motor contagion task), which can even be effectively used to assess 4-year-old children (53). In the motor interference task, subjects make sinusoidal right arm movements in the vertical plane while observing another human right arm making either congruent or incongruent movements (i.e., in the vertical or horizontal plane, respectively). The subject experiences interference when observing the incongruent movement executed by the other human. The interference effects (automatic-imitation effects, motor contagion effects) refer to the increased movement fluctuations occurring in the orthogonal (i.e., in the horizontal plane) direction (54). The magnitude of the interference effect has been used as an index of MNS functioning in many previous studies (50, 52, 54–63). In fact, Catmur et al. (64) revealed that disruptive, theta burst transcranial magnetic stimulation of the IFG selectively impaired automatic imitation of abduction movements of the index and little fingers. This previous study provided evidence for the causal relationship between the MNS and automatic imitation.

Since previous studies have suggested that there is dysfunction of the MNS in DCD, children with DCD are expected to have a decline in automatic-imitation function. More specifically, the hypothesis of the current study was that there would be a decline in the interference effect in children with DCD, compared to those with TD. This hypothesis was examined using Experimental task 2 (the motor interference task) in the present study.

Children with DCD often develop depressive symptoms due to motor coordination disorders (65–71). Further, they are also often diagnosed with other developmental disorders, the most common of which is attention-deficit hyperactivity disorder (ADHD), which affects 50% of the population that show both disorders (72–75). Autism-spectrum disorder (ASD) is also indicated as a comorbidity (76–78).

Therefore, the current study cohort was divided into two groups using the manual dexterity test of the Movement Assessment Battery for Children-2nd edition (M-ABC2), which is the international standard evaluation battery for DCD. Since the focus of the current study was on manual dexterity in DCD, the two experimental tasks used evaluated the participants’ upper limbs and hands. The manual dexterity test of the M-ABC2 was used to separate children with probable DCD (pDCD) from those with TD. Children with clumsy manual dexterity were assigned to the pDCD group, while those who were not clumsy were assigned to the TD group. We investigated whether visuo–motor temporal integration ability (Experimental task 1) and automatic-imitation function (self-other visuo–motor integration ability, Experimental task 2) differed between the two groups. In addition, we investigated whether depressive symptoms, ASD traits, and ADHD traits differed among the two groups. Furthermore, correlation and multiple regression analyses were performed to extract factors affecting clumsy manual dexterity.

The purpose of the current study was to investigate factors affecting clumsy manual dexterity, provide useful behavioral markers for understanding the neural mechanism of DCD, and promote the development of new neurorehabilitation techniques to improve clumsy manual dexterity.

Materials and Methods

Participants

Participants were recruited from public preschools (i.e., nursery schools and kindergartens), primary schools, and secondary schools, in Osaka, Japan. Children with motor coordination problems were introduced and observed by school teachers and parents. The exclusion criteria included being diagnosed with the following: a general medical condition (e.g., cerebral palsy, muscular dystrophy, hemiplegia, or a degenerative disorder), visual impairment, or intellectual disability {according to criterion D of the DCD diagnostic criteria in the Diagnostic and statistical manual of mental disorders 5th edition [DSM-5 (1)]}. Eligibility was confirmed by interviewing parents and the results of the regular checkup, which was provided by the school doctor at each school. A total of 71 children, with an average age ± SD of 9.8 ± 2.6 years (range: 4.2–15.0 years, boys = 57, right handers = 60) participated in the current study.

Participants were assigned to the pDCD group (n = 29, mean percentile ± SD = 4.5 ± 3.5, range: 0.5–9.0 percentile) based on the following: scoring in or below the 15th percentile of the M-ABC2 manual dexterity test; motor coordination problems, which affected activities of daily living and school life, identified by the teachers and parents (criterion B of the DSM-5); and motor coordination problems since early development, identified by the parents (criterion C of the DSM-5). Conversely, even with criterion B and C, participants scoring above the 16th percentile on the M-ABC2 manual dexterity test were assigned to the TD group (n = 42, mean percentile ± SD = 53.0 ± 22.7, range: 16.0–84.0 percentile).

There were no significant differences between the groups in terms of sex (pDCD: 25 boys; TD: 32 boys; χ² = 1.087, χ²(0.95) = 3.841, p = 0.297), age [pDCD: 9.6 ± 2.1 years; TD: 9.9 ± 2.9 years; t(68.691) = −0.570, p = 0.570], and dominant hand (pDCD: 24 right handers; TD: 36 right handers; χ² = 0.114, χ²(0.95) = 3.841, p = 0.735). None of the participants had been previously diagnosed with developmental disorder (e.g., ASD, ADHD, learning disorder, pervasive developmental disorder) or depression.

The local ethic committee of the Graduate School and Faculty of Health Sciences at Kio University approved the present study (approval number: H27-33). Parents of the participants provided signed, informed consent.

Procedures

The manual dexterity test, Experimental task 1 and 2, and the depression self-rating scale for children (DSRS-C) were conducted in each school’s prescribed rooms. The order of implementation of these tasks was randomized for each child. All the tasks were completed in approximately 90 min per child. Concurrently, in another room, the child’s parents responded to the social communication questionnaire (SCQ), ADHD Rating Scale (ADHD-RS), and Developmental Coordination Disorder Questionnaire (DCDQ).

The Manual Dexterity Test of the Movement Assessment Battery for Children-2nd Edition (M-ABC2)

The manual dexterity test of the M-ABC2 (79) is a standardized, age-adjusted test to identify motor problems in children using different tasks for different age bands. The M-ABC2 had good test retest reliability (minimum value at any age is 0.75), inter-rater value (0.70), and concurrent validity (79). This test has three age bands, which encompasses the following age ranges: 3–6, 7–10, and 11–16 years. In the current study, each child received three sub-tests that were appropriate for their age bands. The sub-tests were as follows: age band 1 (3–6 years), posting coins test, threading beads test, and drawing trail I test; age band 2 (7–10 years), placing pegs test, threading lace test, and drawing trail II test; and age band 3 (11–16 years): turning pegs test, triangle with nuts and bolts test, and drawing trail III test. Based on the examiner’s manual of M-ABC2, the percentile was calculated from the obtained raw scores. The percentile reflected the degree of manual dexterity for each age year, where an increased percentile represented improvement of manual dexterity within each age-group. All the assessments were administrated by a specifically trained, certified physical therapist.

Experimental Design-1: Delayed Visual Feedback Detection Task

Experiment-1: Task

The delayed visual feedback detection task was performed using the preferred hand of each child (Figure 1). The participants were instructed to observe the reflection in the mirror with the following instruction: “please observe your own hand reflected in the mirror.” Subsequently, the participants opened and closed their hand once, in a continuous and smooth manner, based on the child’s own volition, after the experimenter had orally informed them of the start of a trial. The self-generated movement was regarded when setting the following 18 delay conditions using a video delay-inserting device: 33, 67, 100, 133, 167, 200, 233, 267, 300, 333, 367, 400, 433, 467, 500, 533, 567, and 600 ms. All 18 delay conditions were treated as one set and performed four times; their presentation order was randomized. Consistent with previous studies (35, 39), trials without delay were not included. Thus, each participant completed a total of 72 randomized trials with 18 delay conditions per four sets, which corroborated with previous studies (35, 39).

Figure 1. Experiment-1 (Delayed visual feedback detection task). In this study, a similar experimental setup as Shimada et al. (35) was used (Figure 1). The child’s preferred hand was placed under a two-way mirror, so the child was unable to directly see his/her hands. The image of the hand, which was reflected in the two-sided mirror, was filmed with a video camera (FDR-AXP35, Sony, Tokyo, Japan). The movie of the photographed hand was further reflected from an installed monitor (LMD-A240, Sony, Tokyo, Japan) onto the two-sided mirror via a video delay-inserting device (EDS-3306, FOR-A YEM ELETEX, Tokyo, Japan). Thus, the child observed the delayed image of their own hand reflected in the mirror at the position where their own hand would be. In addition, the setup included a blackout curtain so that the child would not be able to see outside the experimental chamber. The intrinsic delay of the visual feedback in this experimental setting was 33.7 ms as measured by a time lag check device (EDD-5200, FOR-A YEM ELETEX, Tokyo, Japan).

During the delayed visual feedback detection task, the participants only looked at the reflection of their hand in the mirror, and not their real hand. Thus, the participants could feel their own hand moving while watching the display of the delayed mirror reflection of the same movement. Each participant had to determine if the visual feedback was synchronous or asynchronous, relative to the movement of the preferred hand that was based on their own intention. Immediately following the trial, the participant had to orally state if the movement was “delayed” or “not delayed” by the forced-choice method. A 10-s rest time was set between each trial. A previous study (80), which examined the developmental change of visuo–motor temporal integration ability in children using the same task as in this study, demonstrated that there was no relationship between reaction time/movement speed and visuo–motor temporal integration ability. Thus, the reaction time and movement speed was not recorded in the present study.

The delayed visual feedback detection task was conducted after sufficient explanation and practice to ensure that the children adequately understood the task. Further, before the task, all participants confirmed that they could distinguish between a minimum delay of 33 ms and a maximum delay of 600 ms.

Experiment-1: Data Analysis

The logistic curve was fitted to each participant’s response on the visual feedback delay-detection task (35, 81) using the following formula: P(t) = 1/1 + exp(−a(t − DDT)), where t was the visual feedback delay length, P(t) was the probability of delay detection, a was the steepness of the fitted curve, and DDT was the observer’s DDT representing the delay length at which the probability of delay detection was 50%. In the current experiment, t P(t) served as the independent variable and observed value, respectively. The curve was fitted using a nonlinear least squares method (a trust-region algorithm) with the Curve Fitting toolbox in Matlab R2014b (MathWorks Inc., Natick, Massachusetts, USA) to estimate a and DDT.

Experimental Design-2: Motor Interference Task

Experiment-2: Task

The motor interference task, which was created based on previous studies (53, 54, 59, 82), was conducted as outlined in Figure 2. The task was to draw a vertical line iteratively with a preferred hand’s index finger on the Tablet PC (Surface Pro 4, Microsoft), matching the metronome sound of 100 BPM (beats/min). The drawing distance was 260 mm. For the task, children sat on a chair and installed the Tablet PC on a horizontal desk at an appropriate height. Two conditions, i.e., the congruent and incongruent conditions, were set for the task. In the congruent condition, the child repeatedly drew a vertical line on the Tablet PC while observing the facing experimenter performing the congruent movement (repeatedly drawing a vertical line). In the incongruent condition, the child repeatedly drew vertical line on the Tablet PC while observing the facing experimenter performing an incongruent movement (repeatedly drawing a horizontal).

Figure 2. Experiment-2 (Motor interference task). Using the index finger on the preferred hand, the child drew repetitive vertical lines on the Tablet PC, matching the metronome sound of 100 BPM (beats per minute). Arrow, represents the direction of movement. (A) Congruent condition: child and experimenter performed repetitive vertical movements. Child repeatedly drew vertical line on the Tablet PC while observing the facing experimenter performing congruent movement (repeating vertical line drawing). (B) Incongruent condition: child performed repetitive vertical movement, while experimenter performs repetitive horizontal movement. Child repeatedly drew vertical line on the Tablet PC while observing the facing experimenter performing incongruent movement (repeating horizontal line drawing). Prior to this experiment, the child was given sufficient explanation and carried out enough practice to understand this task. The child demonstrated that they could repeatedly draw a vertical line on the Tablet PC, matching the metronome sound, before starting the trials, even in the absence of the experimenter. This process was repeated as necessary, and the child’s understanding of this task was confirmed. The child had two practice trials for each condition to gain proficiency in the task.

The experimenter was the same person for the entire cohort and for all trials. The motion trajectory drawn by the child was recorded on the Tablet PC, but was programmed so that the movement locus could not be seen by the child. The child was instructed to repeat their vertical movement, regardless of the experimenter’s movements. The experimenter carried out repetitive vertical line drawing (congruent condition) or repetitive horizontal line drawing (incongruent condition), facing the child. The movement distance of the experimenter was also 260 mm. The experimenter performed the iterative movement using the hand on the same side as the hand used by the child, according to the same 100 BPM metronome sound (e.g., if the child moved the right hand, the experimenter also used the right hand and vice versa). The experimenter closed their eyes during the repetitive movement to avoid being influenced by observing the participant’s movement. The participant conducted two trials for each condition and each trial was 40-s long. The order of trials was randomized. This experimental task was conducted after sufficient explanation and practice to ensure that the participant understood the task.

Experiment-2: Data Analysis

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The motion trajectory drawn by the participant was analyzed using MATLAB R2014b (MathWorks Inc., Natick, Massachusetts, USA). The first and final five movements of each trial were excluded from analysis. The error value of each reciprocating trajectory was calculated using the following formula: Error value = [SD of vertical-plane data (pixel)/SD of horizontal-plane data (pixel)] × 100 (83, 84). The mean error value for each condition was calculated. An increase in the error value indicated that the participant’s movement trajectory became distorted toward the horizontal direction, while a decrease indicated a more vertical movement trajectory. The interference effect was calculated by subtracting the mean error value of the congruent condition from the mean error value of the incongruent condition (82). An increase in the interference effect indicated an increase in automatic imitation.

Questionnaires

The Depression Self-Rating Scale for Children

Depression in the participants was assessed using the DSRS-C (85, 86), which is a screening test for depression that is composed of 18 items related to the mental condition of the children during the week prior to the test. The scale of the Japanese version has reliable internal consistency (87). Responses were recorded at the following three levels: (1) always; (2) sometimes, and (3) never. The total score ranged from 0 to 36, and a higher score indicated a greater level of depression (87).

The Social Communication Questionnaire

The SCQ (88), which is a parent/caregiver report, measures characteristic ASD behaviors, including deficits in social functioning and communication, in children over the age of 4 years. The SCQ includes 40 “yes or no” questions that are derived from the Autism Diagnostic Interview Revised (89). The questions assess the child’s functioning over the past 3 months, as well as at 4–5 years of age. Subscales of the SCQ include the following: communication, reciprocal social interaction, and restricted, repetitive, and stereotyped patterns of behavior. The measure has demonstrated good internal consistency and concurrent validity (88). Higher scores on the SCQ indicate higher levels of autistic traits. The sum of the scores for all 40 questions was calculated to create the total score in the current study.

The Attention-Deficit Hyperactivity Disorder Rating Scale

The ADHD-RS-IV (90) is composed of 18 items and scored by a parent/caregiver. Each item corresponds to one of the 18 symptoms in the DSM-IV criteria. Previous research confirmed sufficient reliability and validity for the home form of the ADHD-RS-IV (91). Using a 4-point Likert scale, the children’s parents/guardians rated each item as follows: (0), “not at all or rarely”; (1), “sometimes”; (2), “often”; or (3), “very often.” Therefore, the higher the score and percentile on the ADHD-RS, the more ADHD symptoms the child displayed. This study employed the Japanese version of the ADHD-RS home form (92). The Japanese version of the ADHD-RS demonstrated good reliability and validity (93). In the current study, the sum of the scores for the 18 items was calculated to create a total score (94), and according to the criteria of the Japanese version of the ADHD-RS, the percentile score was calculated from the total score (92).

The Developmental Coordination Disorder Questionnaire

The DCDQ is a parent questionnaire designed to screen for pediatric DCD (95, 96). Nakai et al. (97) developed the Japanese version of the DCDQ (DCDQ-J), which is widely applicable to Japanese children (98). The DCDQ-J, which is a 15-item parent rating scale, encompasses the following three factors: general coordination (five items), control during movement (six items), and fine motor skills (four items). Each item is scored using a 5-point scale as follows: (1), “not at all like your child”; (2), “a bit like your child”; (3), “moderately like your child”; (4), “quite a bit like your child”; and (5), “extremely like your child.” Higher scores indicated better motor coordination.

Statistical Analysis

Inter-group comparisons between the pDCD group and the TD group, and correlation and multiple regression analyses, to detect factors related to clumsy manual dexterity, were performed using SPSS ver. 24 (SPSS, Chicago, IL, USA). The significance level was set at P < 0.05.

Inter- and Intra-Group Comparison

Since the Shapiro–Wilk test demonstrated the normal distribution of DSRS-C scores, an independent t-test was used to compare the two groups. However, since the DDT, steepness (Experiment-1), interference effect (Experiment-2), and SCQ, ADHD-RS, and DCDQ scores, were not normally distributed (according to the Shapiro–Wilk test), Mann–Whitney U tests were used to compare these measures between the two groups. In addition, a comparison of error values of congruent and incongruent conditions of Experiment-2 was performed using a Wilcoxon signed-rank test.

Correlation Analysis

A correlation analysis for each measured variable was performed using a Spearman’s correlation coefficient rank test.

Multiple Regression Analysis

A multiple regression analysis (stepwise method), with manual dexterity (percentile) as the dependent variable and measured items as the independent variables, was performed to extract factors with a significant influence on manual dexterity. Independent variables included DDT/steepness for Experiment-1, error value of the congruent condition for Experiment-2, and the SCQ score, percentile score of ADHD-RS, and DCDQ score.

Results

Inter- and Intra-Group Comparison Results

The delay-detection probability curves of the pDCD and TD groups are shown in Figure 3A, while comparative results for Experiment-1 are shown in Figure 3B. Compared to the TD group, the DDT was significantly elevated (p < 0.001) and the steepness was significantly flatter (p = 0.005) in the pDCD group.

Figure 3. Results of Experiment-1: delayed visual feedback detection task. (A) The delay-detection probability curve of each group. White, typical development (TD) group; black, probable developmental coordination disorder (pDCD) group. (B) The mean DDT or steepness of each group. Horizontal axis shows each group. Black, pDCD group; white, TD group. Error bars represent the SEM. Left, comparison of the mean DDT of each group; Right, comparison of the mean steepness of each group. DDT, delay-detection threshold; steepness, steepness of the probability curve for delay detection (**p < 0.01; *p < 0.05).

The inter-group and intra-group comparison results of Experiment-2 are shown in Figure 4A. Typical examples of the drawing trajectories of each group are shown in Figure 4B. Compared to the TD group, the error value for the congruent condition was significantly increased (p = 0.003) and the interference effect was significantly decreased (p = 0.044) in the pDCD group. There was no significant difference in the error value during in the incongruent condition between the two groups (p = 0.352). Compared to the congruent condition, the error value during the incongruent condition increased in the TD group (p < 0.001). In contrast, there was no significant difference in the error value between the congruent and the incongruent conditions in the pDCD group (p = 0.094).

Figure 4. Experiment-2 (Motor interference task) results. (A) The mean error value of each condition and interference effect of each group. Horizontal axis shows each condition and interference effect of each group. Black, probable developmental coordination disorder (pDCD) group; white, typical development (TD) group. Error bars represent the SEM. Left, the comparison of the mean error value of each condition in each group; right, the comparison result of the mean interference effect of each group (**p < 0.01; *p < 0.05; n.s. not significant). (B) Typical examples of the drawing trajectories of each group. The movement trajectory of a (a) congruent condition and (b) incongruent condition of one example in the TD group (a right-handed 11-year-old boy; manual dexterity = 84th percentile), and a (c) congruent condition and (d) incongruent condition of one example in the pDCD group (a right-handed 11-year-old boy; manual dexterity = 2nd percentile).

The inter-group comparison of DSRS-C, SCQ, ADHD-RS, and DCDQ are shown in Figure 5. The DSRS-C score [t(69) = 2.551, p = 0.013], SCQ score (p = 0.012), and ADHD-RS percentile score (p = 0.003) were significantly higher in the pDCD group, compared with the TD group. The DCDQ score of the pDCD group was significantly lower than that of the TD group (p = 0.009).

Figure 5. Results from the questionnaires. Score (error bars represent the SEM) for each questionnaire (indicated on the horizontal axis), categorized according to the group. Black, probable developmental coordination disorder (pDCD) group; white, typical developmental (TD) group. DSRS-C, depression self-rating scale for children; SCQ, social communication questionnaire; ADHD-RS, attention-deficit hyperactivity disorder rating scale; DCDQ, developmental coordination disorder questionnaire (**p < 0.01; *p < 0.05).

Correlation Analysis Results

The correlation analysis results are shown in Table 1. There was a significant correlation between manual dexterity (percentile), and DDT (r = −0.577, p < 0.001), steepness (r = 0.371, p = 0.001) for Experiment-1, error value of the congruent condition for Experiment-2 (r = −0.468, p < 0.001), SCQ score (r = −0.305, p = 0.010), ADHD-RS percentile score (r = −0.325, p = 0.006), and DCDQ score (r = 0.356, p = 0.002). There was a significant correlation between age, and DDT (r = −0.343, p = 0.003), steepness (r = 0.310, p = 0.008), error value of the congruent condition (r = −0.451, p < 0.001), error value of the incongruent condition (r = −0.438, p < 0.001), SCQ score (r = −0.273, p = 0.021), and ADHD-RS percentile score (r = −0.240, p = 0.044). There was a significant correlation between DDT, and steepness (r = −0.435, p < 0.001), error value of the congruent condition (r = 0.461, p < 0.001), error value of the incongruent condition (r = 0.238, p = 0.046), ADHD-RS percentile score (r = 0.329, p = 0.005), and DCDQ score (r = −0.378, p = 0.001). There was a significant correlation between steepness, and error value of the congruent condition (r = −0.395, p = 0.001), and error value of the incongruent condition (r = −0.414, p < 0.001). There was a significant correlation between error value of the congruent condition, and error value of the incongruent condition (r = 0.706, p < 0.001), SCQ score (r = 0.349, p = 0.003), ADHD-RS percentile score (r = 0.281, p = 0.018), and DCDQ score (r = −0.458, p < 0.001). There was a significant correlation between error value of the incongruent condition, and the interference effect (r = 0.642, p < 0.001), and DCDQ score (r = −0.313, p = 0.008). There was a significant correlation between SCQ score, and ADHD-RS percentile score (r = 0.503, p < 0.001), and DCDQ score (r = −0.585, p < 0.001). There was a significant correlation between ADHD-RS percentile score and DCDQ score (r = −0.539, p < 0.001).

Multiple Regression Analysis Results

Table 2 shows the results of the multiple regression analysis, where manual dexterity (percentile) was the dependent variable, and DDT/steepness for the Experiment-1, error value of the congruent condition for the Experiment-2, the SCQ score, percentile score of ADHD-RS, and DCDQ score were independent variables. The DDT (β = −0.482, p < 0.001) and SCQ score (β = −0.219, p = 0.035) were significant independent variables. The following multiple regression equation revealed the relationship between manual dexterity (percentile) and the DDT/SCQ score: manual dexterity (percentile) = 84.747 + (−0.156 × DDT) + (−0.691 × SCQ) (R = 0.570, R² = 0.325, p < 0.001). There was no multicollinearity between the DDT and SCQ scores.

Table 2. Multiple regression analysis results.

Discussion

The participants in the current study were classified as either the pDCD group or TD group, based on if the children had clumsiness of manual dexterity or not, respectively, based on the manual dexterity test of M-ABC2. There were no significant differences in sex, age, and handedness, among the two groups. However, in the pDCD group, the DDT and steepness, which reflected the visuo–motor temporal integration ability, were prolonged and decreased, respectively. In addition, in the pDCD group, the interference effect reflecting the automatic-imitation function was decreased. The correlation analysis showed a significant correlation between manual dexterity (percentile), and DDT/steepness for the delayed visual feedback detection task, error value of the congruent condition for the motor interference task, ASD traits, and ADHD traits. Furthermore, the multiple regression analysis revealed that DDT and ASD traits were significantly predictors for manual dexterity in children.

Experiment-1: Delayed Visual Feedback Detection Task

The results demonstrated that children with pDCD had a significantly longer DDT and lower steepness of the probability curve for delay detection, compared with children with TD. Thus, children with pDCD had a deficit visuo–motor temporal integration. There was also a significant correlation between manual dexterity (percentile) and DDT/steepness, where a decrease in manual dexterity was associated with a decline in visuo–motor temporal integration ability. Further, DDT was the independent variable that was most predictive of manual dexterity. Although previous studies revealed a deficit in visuo–motor integration in DCD (12), there has been no study that has focused only on the temporal aspect in visuo–motor integration. The delayed visual feedback detection task reflects visuo–motor temporal integration ability, where extended DDT and decline of steepness indicates poor visuo–motor temporal integration. Therefore, the current study supported the IMD hypothesis and provided additional evidence that children with clumsy of manual dexterity have a deficit of visuo–motor temporal integration. DCD is a frequent comorbid disorder of ASD and ADHD. Further, DCD frequently accompanies depressive symptoms. Therefore, we also evaluated ASD traits, ADHD traits, and depression symptoms in the current study. The SCQ is a useful screening test that corroborated well with results from the Autism Diagnostic Interview-Revised and Autism Diagnostic Observation Schedule, which are the gold standards of ASD diagnosis. Further, in children, the ADHD-RS and DSRS-C are useful screening tests for the diagnosis of ADHD and depression, respectively. Thus, it is worth noting that the DDT, which was measured by the delayed visual feedback detection task, was the most significant factor that predicted the severity of clumsiness in children, over these standard evaluation batteries.

An important role of the internal model is to generate error signals between motor predictions and actual sensory feedback, to correct motor commands online. Importantly, error signals can also act as a training signal, refining the accuracy of predictive models. This iterative process is fundamental for motor learning (99). Thus, a major mismatch in motor predictions/actual proprioceptive feedback and actual visual feedback, during the initiation of self-generated movement, can cause unsuccessful movement. In other words, the reduced ability of the visuo–motor temporal integration impedes the generation of error signals, which may cause movement failure. In fact, artificially delaying visual feedback from self-generated hand movement causes a decrease in hand motor performance and hampers adaptive motor learning. Previous studies reported a decrease in motor performance during writing, drawing, star or maze tracing (100), steering (101), manual tracking (102), and pegboard (103), reaching (104), and sequential motor tasks (105), when visual feedback from self-generated movement is substantially delayed. Moreover, studies have demonstrated that a delay in visual feedback slows the rate and extent of prism adaptation (106, 107). Further, another study demonstrated that a delay in visual feedback decreased muscle activity (108). The findings of the current study corroborated with these previous studies, suggesting that a diminished ability to integrate hand movement and visual feedback in a temporal sequence is detrimental to manual dexterity.

Previous studies on adults using the delayed visual feedback detection task, similar to this study, indicated that the parietal cortex and cerebellum were the neural bases for visuo–motor temporal integration (36–39). Further, fMRI studies suggested that the parietal cortex and cerebellar regions may be key structures implementing the comparison between predicted movements and actual sensory feedback that is required for explicit agency judgments (14, 109–111). These previous studies showed that the parietal lobe and cerebellum play a role in the detection of delayed visual feedback from self-generated movement. Therefore, the current results may be interpreted as reflecting parietal and cerebellar dysfunction/developmental failure in children with clumsy manual movements.

Experiment-2: Motor Interference Task

The error value during the incongruent condition was significantly increased, compared with the congruent condition, in children with TD. However, there were no differences between the congruent condition and the incongruent condition in children with pDCD. Further, in comparison to children with TD, the interference effect was significantly lower in the pDCD group. These results indicated a deficit in automatic imitation in children with clumsy manual dexterity. Previous studies also demonstrated that DCD caused an obstacle to conscious imitation (43–47) due to MNS inactivation (10). The current study corroborated with previous studies and is the first to show a decrease in automatic-imitation function in children with clumsy manual dexterity, compared with children with TD. Automatic imitation is the basic function of the MNS (41, 49–52). The current results strongly suggested that the MNS is dysfunctional in DCD, as the children with pDCD demonstrated deficits in automatic imitation.

Questionnaires

The DSRS-C score of the pDCD was significantly higher than the TD group, which indicated that children who were clumsy had more depressive symptoms. Poor motor skills are associated with a range of psychosocial issues, including internalizing problems, i.e., anxiety and depression (69). The current study also corroborated with previous studies, which demonstrated that children with DCD have a higher level of depressive symptoms compared with those with TD (68, 71).

The SCQ score of the pDCD group was also significantly higher than the TD group. There was significant correlation between manual dexterity (percentile) and the SCQ score. Furthermore, multiple regression analysis showed that SCQ was a significant independent variable that predicted manual dexterity (percentile). The SCQ is an evaluation battery of social cognitive functions related to ASD. Therefore, the current findings demonstrated that children with pDCD showed high ASD traits, and that increasing ASD traits was associated with worsening manual dexterity. Sumner et al. (78) investigated the extent of overlap of these problems in children aged 7–10 years, who were diagnosed with either ASD or DCD. Compared to the control cohort (children with TD), the motor and social difficulties of children with ASD or DCD showed considerable overlap. Furthermore, they indicated that motor skill predicted social functioning for both ASD and DCD. The present study revealed that social functioning reflected by SCQ predicted motor skill. Other studies also reported that motor function and social cognitive function were correlated in children (112, 113). The current results, in concert with previous studies, demonstrated the important relationship between motor function and social cognitive function in children.

Compared with children with TD, the percentile scores of ADHD-RS was significantly higher in the pDCD group. There was a significant correlation between manual dexterity (percentile) and ADHD-RS score. The connection between ADHD and DCD has been recognized for several decades (74), with an estimated overlap between both disorders of 50% (72, 73, 75). Thus, the current findings showed that children with clumsiness of manual dexterity have high ADHD traits, similar to previous studies.

The DCDQ score of the pDCD group was significantly lower than the TD group. There was a significant correlation between manual dexterity (percentile) and DCDQ score. A previous study showed that the percentile measured by M-ABC2 and the DCDQ score were significantly correlated (96). Since the DCDQ is a subjective assessment of the child’s motor function by the parents, these results reflected the parents’ understanding of their child’s motor ability.

Correlation analysis showed a significant correlation between manual dexterity, and DDT, ASD traits, and ADHD traits. In addition, correlation analysis also showed a significant correlation between DDT and ADHD traits, and between ADHD traits and ASD traits. These results strongly suggested that manual dexterity, time window for visuo–motor integration, ASD traits, and ADHD traits are related to bidirectionality.

General Discussion

Visuo–motor temporal integration is the ability to integrate self-generated movement and visual information, while automatic imitation also reflects the integrated function of self and other movements. Thus, both are a reflection of visuo–motor integration. Thus, the present study revealed that children with clumsy manual dexterity, i.e., pDCD, had deficits in visuo–motor integration. The parietal lobes and cerebellum are the neural bases of visuo–motor temporal integration and automatic imitation. Thus, dysfunction of these brain regions may lead to difficulties in visuo–motor integration, which in turn could lead to the development of clumsiness in children.

However, multiple regression analysis highlighted visuo–motor temporal integration (DDT), but not automatic imitation, as a significant predictor of manual dexterity in children. This may be due to the influence of the characteristics of the two experimental tasks, where the delayed visual feedback detection task was a cognitive task that required judgment. The motor interference task, however, reflected automatic imitation, which does not require cognitive function. Many previous studies have reported that the child’s manual dexterity ability is related to cognitive functions, such as math, reading, and science (114–116). The correlation between manual dexterity and cognitive processing is observed both in children with DCD and in children without DCD (117). Therefore, although this explanation is speculative, DDT obtained by the delayed visual feedback detection task, which required cognitive function, may more accurately predict manual dexterity.

In conclusion, compared with the TD group, the pDCD group showed significant deficits in visuo–motor temporal integration and automatic imitation. Thus, the results of this study concurrently supported both the IMD hypothesis and MNS deficits hypothesis. However, given that multiple regression analysis revealed that the index of visuo–motor temporal integration ability was the most important predictor for the clumsiness of manual dexterity, we strongly support the IMD hypothesis.

Limitations of the Current Study

The current study had several limitations, which must be noted. The pDCD group in the current study did not completely satisfy the DCD diagnostic criterion A in DSM-5. The DCD diagnostic criteria A in DSM-5 requires a M-ABC2 total test score that is in or below the 15th percentile. However, the criteria in the current study required an M-ABC2 manual dexterity test component score in or below the 15th percentile because this study specifically focused on manual dexterity. Therefore, the current results are limited to the results of children with difficulty of manual dexterity. Further research by participants who fully satisfy the DSM-5 DCD diagnostic criteria A through D is necessary.

The study focused on the children’s manual dexterity, classified the children with pDCD based on manual dexterity, and revealed their deficits in visuo–motor temporal integration ability and automatic-imitation function. The relationship between other movement dysfunctions that are observed with DCD, i.e., gross motor skills and balance disorders, and visuo–motor temporal integration and automatic imitation, however, are unknown.

In the current study, two experimental tasks showed that children with pDCD have deficits in visuo–motor temporal integration and automatic imitation. However, the findings of both tasks of delayed visual feedback detection task and motor interference task may be explained by the automatization deficit, attention deficit, and timing deficit hypotheses. In automatization deficit hypothesis, a dual task of motor and cognitive function is generally used, which in the past has been verified as the etiology of developmental dyslexia (118), and recently was suggested as a pathogenesis of DCD (119). The attention-deficit hypothesis has been discussed in the context of ADHD (120). The timing deficit hypothesis, which has been mainly verified as the etiology of ADHD (121), has also been suggested to underlie DCD (122). However, several studies investigating the automatization deficit of DCD also reported negative results (16, 123–127). We also evaluated ADHD traits (using ADHD-RS), which were previously shown to be strongly related to the attention-deficit and timing deficit hypotheses, but were not a significant predictor of manual dexterity in the current study. Therefore, we believe that the current results cannot really be attributed to either of these hypotheses. However, further research is required to verify these findings, using a new experiment method that controls for dual task conditions, attention, and timing.

This study also did not measure the intelligence quotient (IQ) of children. Thus, there is a possibility that IQ may have affected the current results. However, the participants in this study attended regular classes at public preschools, public primary schools, or public secondary schools, and they had not been diagnosed with an intellectual disability. Thus, IQ was assumed to have no effect on the current results. Nonetheless, it remains to be measured in a future study to yield more definitive conclusions.

Self-generated movement has two components, motor predictions and proprioceptive feedback. Thus, whether visuo–motor temporal integration as was measured in this study indicates the ability to integrate motor predictions and visual feedback or the ability to integrate proprioceptive feedback and visual feedback is not clear. A future study needs to clearly contrast passive movement (a task to detect delayed visual feedback from proprioception) and active movement (a task to detect delayed visual feedback from active movement, which was used in this study).

Future Directions

Although deficits in the cerebellum (28, 29, 33, 34) and parietal lobe (29–34) are thought to underlie DCD, this has not yet been confirmed (128). Therefore, visuo–motor temporal integration may serve as psychophysical marker of DCD in future neuroimaging studies that evaluate the neural signature of DCD.

The current study suggested that improving visuo–motor temporal integration may be effective as rehabilitation for DCD. Therefore, it is necessary to develop new neurorehabilitation techniques that will promote visuo–motor temporal integration. We specifically focused on rehabilitation using stochastic resonance. Stochastic resonance is a phenomenon in which random frequency noise, which is less than or equal to the sensory threshold, is added to the body, the rhythm activity in the peripheral or central nervous system is superimposed on noise and the sensitivity of the somatosensory sense increases. Therefore, it might be hypothesized that stochastic resonance may improve the visuo–motor temporal integration function and motor function of children with DCD. This hypothesis needs to be investigated by a randomized controlled trial of children with DCD.

Ethics Statement

The experimental procedures were approved by the local ethics committee of the Graduate School and Faculty of Health Sciences at Kio University (approval number: H27-33). There were no foreseeable risks to the participants; no personally identifying information was collected. Participants (i.e., each child’s parents) provided background information and gave written informed consent. The procedures complied with the ethical standards of the 1964 Declaration of Helsinki regarding the treatment of human participants in research.

Author Contributions

SN collected and analyzed the data, and wrote the manuscript. AS, TT, TS, and EF assisted in collecting data. SN designed the study. YN, DA, EF, MO, SS, SM, and AN designed and supervised the study. All authors read and approved the manuscript.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

The authors wish to acknowledge and thank all the children who participated in this study.

Funding

This work was supported by JSPS KAKENHI, Grant-in-Aid for Young Scientists (B) (Grant Number 16K16453), JSPS KAKENHI, Grant-in-Aid for Scientific Research (C) (Grant Number 16K09981), and JSPS KAKENHI, Grant-in-Aid for Scientific Research on Innovative Areas “Understanding brain plasticity on body representations to promote their adaptive functions” (Grant Number 15H01671, 17H05915).

References

1. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders: DSM5. 5th ed. Washington, DC: American Psychiatric Association (2013).

2. Poulsen AA, Ziviani JM. Can I play too? Physical activity engagement of children with developmental coordination disorders. Can J Occup Ther (2004) 71(2):100–7. doi:10.1177/000841740407100205

PubMed Abstract CrossRef Full Text Google Scholar

3. Zwicker JG, Harris SR, Klassen AF. Quality of life domains affected in children with developmental coordination disorder: a systematic review. Child Care Health Dev (2013) 39(4):562–80. doi:10.1111/j.1365-2214.2012.01379.x

PubMed Abstract CrossRef Full Text Google Scholar

4. Jarus T, Lourie-Gelberg Y, Engel-Yeger B, Bart O. Participation patterns of school-aged children with and without DCD. Res Dev Disabil (2011) 32(4):1323–31. doi:10.1016/j.ridd.2011.01.033

PubMed Abstract CrossRef Full Text Google Scholar

5. Brown-Lum M, Zwicker JG. Brain imaging increases our understanding of developmental coordination disorder: a review of literature and future directions. Curr Dev Dis Rep (2015) 2(2):131–40. doi:10.1007/s40474-015-0046-6

CrossRef Full Text Google Scholar

6. Fuelscher I, Williams J, Enticott PG, Hyde C. Reduced motor imagery efficiency is associated with online control difficulties in children with probable developmental coordination disorder. Res Dev Disabil (2015) 45:239–52. doi:10.1016/j.ridd.2015.07.027

PubMed Abstract CrossRef Full Text Google Scholar

7. Hyde C, Wilson PH. Dissecting online control in developmental coordination disorder: a kinematic analysis of double-step reaching. Brain Cogn (2011) 75(3):232–41. doi:10.1016/j.bandc.2010.12.004

CrossRef Full Text Google Scholar

8. Hyde C, Wilson P. Online motor control in children with developmental coordination disorder: chronometric analysis of double-step reaching performance. Child Care Health Dev (2011) 37(1):111–22. doi:10.1111/j.1365-2214.2010.01131.x

CrossRef Full Text Google Scholar

9. Hyde CE, Wilson PH. Impaired online control in children with developmental coordination disorder reflects developmental immaturity. Dev Neuropsychol (2013) 38(2):81–97. doi:10.1080/87565641.2012.718820

PubMed Abstract CrossRef Full Text Google Scholar

10. Reynolds JE, Licari MK, Billington J, Chen Y, Aziz-Zadeh L, Werner J, et al. Mirror neuron activation in children with developmental coordination disorder: a functional MRI study. Int J Dev Neurosci (2015) 47(Pt B):309–19. doi:10.1016/j.ijdevneu.2015.10.003

CrossRef Full Text Google Scholar

11. Reynolds JE, Thornton AL, Elliott C, Williams J, Lay BS, Licari MK. A systematic review of mirror neuron system function in developmental coordination disorder: imitation, motor imagery, and neuroimaging evidence. Res Dev Disabil (2015) 47:234–83. doi:10.1016/j.ridd.2015.09.015

CrossRef Full Text Google Scholar

12. Adams IL, Lust JM, Wilson PH, Steenbergen B. Compromised motor control in children with DCD: a deficit in the internal model? – A systematic review. Neurosci Biobehav Rev (2014) 47:225–44. doi:10.1016/j.neubiorev.2014.08.011

PubMed Abstract CrossRef Full Text Google Scholar

13. Wilson PH, Ruddock S, Smits-Engelsman B, Polatajko H, Blank R. Understanding performance deficits in developmental coordination disorder: a meta-analysis of recent research. Dev Med Child Neurol (2013) 55(3):217–28. doi:10.1111/j.1469-8749.2012.04436.x

PubMed Abstract CrossRef Full Text Google Scholar

14. Blakemore SJ, Sirigu A. Action prediction in the cerebellum and in the parietal lobe. Exp Brain Res (2003) 153(2):239–45. doi:10.1007/s00221-003-1597-z

PubMed Abstract CrossRef Full Text Google Scholar

15. Tsai CL, Pan CY, Cherng RJ, Hsu YW, Chiu HH. Mechanisms of deficit of visuospatial attention shift in children with developmental coordination disorder: a neurophysiological measure of the endogenous Posner paradigm. Brain Cogn (2009) 71(3):246–58. doi:10.1016/j.bandc.2009.08.006

PubMed Abstract CrossRef Full Text Google Scholar

16. Wilmut K, Brown JH, Wann JP. Attention disengagement in children with developmental coordination disorder. Disabil Rehabil (2007) 29(1):47–55. doi:10.1080/09638280600947765

PubMed Abstract CrossRef Full Text Google Scholar

17. Wilson PH, Maruff P, McKenzie BE. Covert orienting of visuospatial attention in children with developmental coordination disorder. Dev Med Child Neurol (1997) 39(11):736–45. doi:10.1111/j.1469-8749.1997.tb07375.x

PubMed Abstract CrossRef Full Text Google Scholar

18. Williams J, Thomas PR, Maruff P, Butson M, Wilson PH. Motor, visual and egocentric transformations in children with developmental coordination disorder. Child Care Health Dev (2006) 32(6):633–47. doi:10.1111/j.1365-2214.2006.00688.x

PubMed Abstract CrossRef Full Text Google Scholar

19. Williams J, Thomas PR, Maruff P, Wilson PH. The link between motor impairment level and motor imagery ability in children with developmental coordination disorder. Hum Mov Sci (2008) 27(2):270–85. doi:10.1016/j.humov.2008.02.008

PubMed Abstract CrossRef Full Text Google Scholar

20. Williams J, Omizzolo C, Galea MP, Vance A. Motor imagery skills of children with attention deficit hyperactivity disorder and developmental coordination disorder. Hum Mov Sci (2013) 32(1):121–35. doi:10.1016/j.humov.2012.08.003

PubMed Abstract CrossRef Full Text Google Scholar

21. Jover M, Schmitz C, Centelles L, Chabrol B, Assaiante C. Anticipatory postural adjustments in a bimanual load-lifting task in children with developmental coordination disorder. Dev Med Child Neurol (2010) 52(9):850–5. doi:10.1111/j.1469-8749.2009.03611.x

PubMed Abstract CrossRef Full Text Google Scholar

22. Jucaite A, Fernell E, Forssberg H, Hadders-Algra M. Deficient coordination of associated postural adjustments during a lifting task in children with neurodevelopmental disorders. Dev Med Child Neurol (2003) 45(11):731–42. doi:10.1017/S0012162203001373

PubMed Abstract CrossRef Full Text Google Scholar

23. Pereira HS, Landgren M, Gillberg C, Forssberg H. Parametric control of fingertip forces during precision grip lifts in children with DCD (developmental coordination disorder) and DAMP (deficits in attention motor control and perception). Neuropsychologia (2001) 39(5):478–88. doi:10.1016/S0028-3932(00)00132-9

PubMed Abstract CrossRef Full Text Google Scholar

24. Katschmarsky S, Cairney S, Maruff P, Wilson PH, Currie J. The ability to execute saccades on the basis of efference copy: impairments in double-step saccade performance in children with developmental co-ordination disorder. Exp Brain Res (2001) 136(1):73–8. doi:10.1007/s002210000535

PubMed Abstract CrossRef Full Text Google Scholar

25. Langaas T, Mon-Williams M, Wann JP, Pascal E, Thompson C. Eye movements, prematurity and developmental co-ordination disorder. Vision Res (1998) 38(12):1817–26. doi:10.1016/S0042-6989(97)00399-4

PubMed Abstract CrossRef Full Text Google Scholar

26. Lewis M, Vance A, Maruff P, Wilson P, Cairney S. Differences in motor imagery between children with developmental coordination disorder with and without the combined type of ADHD. Dev Med Child Neurol (2008) 50(8):608–12. doi:10.1111/j.1469-8749.2008.03030.x

PubMed Abstract CrossRef Full Text Google Scholar

27. Maruff P, Wilson P, Trebilcock M, Currie J. Abnormalities of imaged motor sequences in children with developmental coordination disorder. Neuropsychologia (1999) 37(11):1317–24. doi:10.1016/S0028-3932(99)00016-0

PubMed Abstract CrossRef Full Text Google Scholar

28. Debrabant J, Gheysen F, Caeyenberghs K, Van Waelvelde H, Vingerhoets G. Neural underpinnings of impaired predictive motor timing in children with developmental coordination disorder. Res Dev Disabil (2013) 34(5):1478–87. doi:10.1016/j.ridd.2013.02.008

PubMed Abstract CrossRef Full Text Google Scholar

29. Debrabant J, Vingerhoets G, Van Waelvelde H, Leemans A, Taymans T, Caeyenberghs K. Brain connectomics of visual-motor deficits in children with developmental coordination disorder. J Pediatr (2016) 169:21–7.e2. doi:10.1016/j.jpeds.2015.09.069

PubMed Abstract CrossRef Full Text Google Scholar

30. Kashiwagi M, Iwaki S, Narumi Y, Tamai H, Suzuki S. Parietal dysfunction in developmental coordination disorder: a functional MRI study. Neuroreport (2009) 20(15):1319–24. doi:10.1097/WNR.0b013e32832f4d87

PubMed Abstract CrossRef Full Text Google Scholar

31. McLeod KR, Langevin LM, Goodyear BG, Dewey D. Functional connectivity of neural motor networks is disrupted in children with developmental coordination disorder and attention-deficit/hyperactivity disorder. Neuroimage Clin (2014) 4:566–75. doi:10.1016/j.nicl.2014.03.010

PubMed Abstract CrossRef Full Text Google Scholar

32. Querne L, Berquin P, Vernier-Hauvette MP, Fall S, Deltour L, Meyer ME, et al. Dysfunction of the attentional brain network in children with developmental coordination disorder: an fMRI study. Brain Res (2008) 1244:89–102. doi:10.1016/j.brainres.2008.07.066

CrossRef Full Text Google Scholar

33. Zwicker JG, Missiuna C, Harris SR, Boyd LA. Brain activation of children with developmental coordination disorder is different than peers. Pediatrics (2010) 126(3):e678–86. doi:10.1542/peds.2010-0059

PubMed Abstract CrossRef Full Text Google Scholar

34. Zwicker JG, Missiuna C, Harris SR, Boyd LA. Brain activation associated with motor skill practice in children with developmental coordination disorder: an fMRI study. Int J Dev Neurosci (2011) 29(2):145–52. doi:10.1016/j.ijdevneu.2010.12.002

PubMed Abstract CrossRef Full Text Google Scholar

35. Shimada S, Qi Y, Hiraki K. Detection of visual feedback delay in active and passive self-body movements. Exp Brain Res (2010) 201(2):359–64. doi:10.1007/s00221-009-2028-6

PubMed Abstract CrossRef Full Text Google Scholar

36. Balslev D, Nielsen FA, Lund TE, Law I, Paulson OB. Similar brain networks for detecting visuo-motor and visuo-proprioceptive synchrony. Neuroimage (2006) 31(1):308–12. doi:10.1016/j.neuroimage.2005.11.037

PubMed Abstract CrossRef Full Text Google Scholar

37. Leube DT, Knoblich G, Erb M, Grodd W, Bartels M, Kricher TT. The neural correlates of perceiving one’s own movements. Neuroimage (2003) 20(4):2084–90. doi:10.1016/j.neuroimage.2003.07.033

CrossRef Full Text Google Scholar

38. MacDonald PA, Paus T. The role of parietal cortex in awareness of self-generated movements: a transcranial magnetic stimulation study. Cereb Cortex (2003) 13(9):962–7. doi:10.1093/cercor/13.9.962

PubMed Abstract CrossRef Full Text Google Scholar

39. Shimada S, Hiraki K, Oda I. The parietal role in the sense of self-ownership with temporal discrepancy between visual and proprioceptive feedbacks. Neuroimage (2005) 24(4):1225–32. doi:10.1016/j.neuroimage.2004.10.039

PubMed Abstract CrossRef Full Text Google Scholar

40. Licari MK, Billington J, Reid SL, Wann JP, Elliott CM, Winsor AM, et al. Cortical functioning in children with developmental coordination disorder: a motor overflow study. Exp Brain Res (2015) 233(6):1703–10. doi:10.1007/s00221-015-4243-7

PubMed Abstract CrossRef Full Text Google Scholar

41. Rizzolatti G, Craighero L. The mirror-neuron system. Annu Rev Neurosci (2004) 27:169–92. doi:10.1146/annurev.neuro.27.070203.144230

PubMed Abstract CrossRef Full Text Google Scholar

42. Billard A, Arbib M. Mirror neurons and the neural basis for learning by imitation: computational modeling. In: Stamenov M, Gallese V, editors. Mirror Neurons and the Evolution of Brain and Language. (Vol. 42), Amsterdam/Philadelphia: John Benjamins Publishing Company (2002). p. 343–52.

43. Dewey D. Error analysis of limb and orofacial praxis in children with developmental motor deficits. Brain Cogn (1993) 23(2):203–21. doi:10.1006/brcg.1993.1055

PubMed Abstract CrossRef Full Text Google Scholar

44. Hill EL. A dyspraxic deficit in specific language impairment and developmental coordination disorder? Evidence from hand and arm movements. Dev Med Child Neurol (1998) 40(6):388–95. doi:10.1111/j.1469-8749.1998.tb08214.x

PubMed Abstract CrossRef Full Text Google Scholar

45. Hill EL, Bishop DMV, Nimmo-Smith I. Representational gestures in developmental coordination disorder and specific language impairment: error-types and the reliability of ratings. Hum Mov Sci (1998) 17(4–5):655–78. doi:10.1016/S0167-9457(98)00017-7

CrossRef Full Text Google Scholar

46. Sinani C, Sugden DA, Hill EL. Gesture production in school vs. clinical samples of children with developmental coordination disorder (DCD) and typically developing children. Res Dev Disabil (2011) 32(4):1270–82. doi:10.1016/j.ridd.2011.01.030

PubMed Abstract CrossRef Full Text Google Scholar

47. Zoia S, Pelamatti G, Cuttini M, Casotto V, Scabar A. Performance of gesture in children with and without DCD: effects of sensory input modalities. Dev Med Child Neurol (2002) 44(10):699–705. doi:10.1111/j.1469-8749.2002.tb00273.x

PubMed Abstract CrossRef Full Text Google Scholar

48. Reynolds JE, Kerrigan S, Elliott C, Lay BS, Licari MK. Poor imitative performance of unlearned gestures in children with probable developmental coordination disorder. J Mot Behav (2017) 49:378–87. doi:10.1080/00222895.2016.1219305

PubMed Abstract CrossRef Full Text Google Scholar

49. Blakemore SJ, Frith C. The role of motor contagion in the prediction of action. Neuropsychologia (2005) 43(2):260–7. doi:10.1016/j.neuropsychologia.2004.11.012

PubMed Abstract CrossRef Full Text Google Scholar

50. Capa RL, Marshall PJ, Shipley TF, Salesse RN, Bouquet CA. Does motor interference arise from mirror system activation? The effect of prior visuomotor practice on automatic imitation. Psychol Res (2011) 75(2):152–7. doi:10.1007/s00426-010-0303-6

CrossRef Full Text Google Scholar

51. Jeannerod M. Neural simulation of action: a unifying mechanism for motor cognition. Neuroimage (2001) 14(1 Pt 2):S103–9. doi:10.1006/nimg.2001.0832

PubMed Abstract CrossRef Full Text Google Scholar

52. Spengler S, Brass M, Kühn S, Schütz-Bosbach S. Minimizing motor mimicry by myself: self-focus enhances online action-control mechanisms during motor contagion. Conscious Cogn (2010) 19(1):98–106. doi:10.1016/j.concog.2009.12.014

PubMed Abstract CrossRef Full Text Google Scholar

53. Marshall PJ, Bouquet CA, Thomas AL, Shipley TF. Motor contagion in young children: exploring social influences on perception-action coupling. Neural Netw (2010) 23(8–9):1017–25. doi:10.1016/j.neunet.2010.07.007

PubMed Abstract CrossRef Full Text Google Scholar

54. Kilner JM, Paulignan Y, Blakemore SJ. An interference effect of observed biological movement on action. Curr Biol (2003) 13(6):522–5. doi:10.1016/S0960-9822(03)00165-9

PubMed Abstract CrossRef Full Text Google Scholar

55. Becchio C, Pierno A, Mari M, Lusher D, Castiello U. Motor contagion from gaze: the case of autism. Brain (2007) 130(Pt 9):2401–11. doi:10.1093/brain/awm171

PubMed Abstract CrossRef Full Text Google Scholar

56. Bird G, Leighton J, Press C, Heyes C. Intact automatic imitation of human and robot actions in autism spectrum disorders. Proc Biol Sci (2007) 274(1628):3027–31. doi:10.1098/rspb.2007.1019

PubMed Abstract CrossRef Full Text Google Scholar

57. Bouquet CA, Gaurier V, Shipley T, Toussaint L, Blandin Y. Influence of the perception of biological or non-biological motion on movement execution. J Sports Sci (2007) 25(5):519–30. doi:10.1080/02640410600946803

PubMed Abstract CrossRef Full Text Google Scholar

58. Bouquet CA, Shipley TF, Capa RL, Marshall PJ. Motor contagion: goal-directed actions are more contagious than non-goal-directed actions. Exp Psychol (2011) 58(1):71–8. doi:10.1027/1618-3169/a000069

PubMed Abstract CrossRef Full Text Google Scholar

59. Gowen E, Stanley J, Miall RC. Movement interference in autism-spectrum disorder. Neuropsychologia (2008) 46(4):1060–8. doi:10.1016/j.neuropsychologia.2007.11.004

PubMed Abstract CrossRef Full Text Google Scholar

60. Kilner J, Hamilton AF, Blakemore SJ. Interference effect of observed human movement on action is due to velocity profile of biological motion. Soc Neurosci (2007) 2(3–4):158–66. doi:10.1080/17470910701428190

PubMed Abstract CrossRef Full Text Google Scholar

61. Press C, Gillmeister H, Heyes C. Sensorimotor experience enhances automatic imitation of robotic action. Proc Biol Sci (2007) 274(1625):2509–14. doi:10.1098/rspb.2007.0774

PubMed Abstract CrossRef Full Text Google Scholar

62. Press C, Bird G, Walsh E, Heyes C. Automatic imitation of intransitive actions. Brain Cogn (2008) 67(1):44–50. doi:10.1016/j.bandc.2007.11.001

PubMed Abstract CrossRef Full Text Google Scholar

63. Stanley J, Gowen E, Miall RC. Effects of agency on movement interference during observation of a moving dot stimulus. J Exp Psychol Hum Percept Perform (2007) 33(4):915–26. doi:10.1037/0096-1523.33.4.915

PubMed Abstract CrossRef Full Text Google Scholar

64. Catmur C, Walsh V, Heyes C. Associative sequence learning: the role of experience in the development of imitation and the mirror system. Philos Trans R Soc Lond B Biol Sci (2009) 364(1528):2369–80. doi:10.1098/rstb.2009.0048

PubMed Abstract CrossRef Full Text Google Scholar

65. Cairney J, Veldhuizen S, Szatmari P. Motor coordination and emotional-behavioral problems in children. Curr Opin Psychiatry (2010) 23(4):324–9. doi:10.1097/YCO.0b013e32833aa0aa

PubMed Abstract CrossRef Full Text Google Scholar

66. Caçola P. Physical and mental health of children with developmental coordination disorder. Front Public Health (2016) 4:224. doi:10.3389/fpubh.2016.00224

PubMed Abstract CrossRef Full Text Google Scholar

67. Gillberg IC, Gillberg C. Children with preschool minor neurodevelopmental disorders. IV: behaviour and school achievement at age 13. Dev Med Child Neurol (1989) 31(1):3–13. doi:10.1111/j.1469-8749.1989.tb08406.x

PubMed Abstract CrossRef Full Text Google Scholar

68. Lingam R, Jongmans MJ, Ellis M, Hunt LP, Golding J, Emond A. Mental health difficulties in children with developmental coordination disorder. Pediatrics (2012) 129(4):e882–91. doi:10.1542/peds.2011-1556

PubMed Abstract CrossRef Full Text Google Scholar

69. Mancini VO, Rigoli D, Cairney J, Roberts LD, Piek JP. The elaborated environmental stress hypothesis as a framework for understanding the association between motor skills and internalizing problems: a mini-review. Front Psychol (2016) 7:239. doi:10.3389/fpsyg.2016.00239

PubMed Abstract CrossRef Full Text Google Scholar

70. Missiuna C, Cairney J, Pollock N, Campbell W, Russell DJ, Macdonald K, et al. Psychological distress in children with developmental coordination disorder and attention-deficit hyperactivity disorder. Res Dev Disabil (2014) 35(5):1198–207. doi:10.1016/j.ridd.2014.01.007

PubMed Abstract CrossRef Full Text Google Scholar

71. Piek JP, Rigoli D, Pearsall-Jones JG, Martin NC, Hay DA, Bennett KS, et al. Depressive symptomatology in child and adolescent twins with attention-deficit hyperactivity disorder and/or developmental coordination disorder. Twin Res Hum Genet (2007) 10(4):587–96. doi:10.1375/twin.10.4.587

PubMed Abstract CrossRef Full Text Google Scholar

72. Gillberg C, Gillberg IC, Rasmussen P, Kadesjö B, Söderström H, Råstam M, et al. Co-existing disorders in ADHD – implications for diagnosis and intervention. Eur Child Adolesc Psychiatry (2004) 13:I80–92. doi:10.1007/s00787-004-1008-4

CrossRef Full Text Google Scholar

73. Goulardins JB, Rigoli D, Licari M, Piek JP, Hasue RH, Oosterlaan J, et al. Attention deficit hyperactivity disorder and developmental coordination disorder: two separate disorders or do they share a common etiology. Behav Brain Res (2015) 292:484–92. doi:10.1016/j.bbr.2015.07.009

PubMed Abstract CrossRef Full Text Google Scholar

74. Hellgren L, Gillberg C, Gillberg IC, Enerskog I. Children with deficits in attention, motor control and perception (DAMP) almost grown up: general health at 16 years. Dev Med Child Neurol (1993) 35(10):881–92. doi:10.1111/j.1469-8749.1993.tb11565.x

PubMed Abstract CrossRef Full Text Google Scholar

75. Visser J. Developmental coordination disorder: a review of research on subtypes and comorbidities. Hum Mov Sci (2003) 22(4–5):479–93. doi:10.1016/j.humov.2003.09.005

PubMed Abstract CrossRef Full Text Google Scholar

76. Green D, Charman T, Pickles A, Chandler S, Loucas T, Simonoff E, et al. Impairment in movement skills of children with autistic spectrum disorders. Dev Med Child Neurol (2009) 51(4):311–6. doi:10.1111/j.1469-8749.2008.03242.x

PubMed Abstract CrossRef Full Text Google Scholar

77. McPhillips M, Finlay J, Bejerot S, Hanley M. Motor deficits in children with autism spectrum disorder: a cross-syndrome study. Autism Res (2014) 7(6):664–76. doi:10.1002/aur.1408

PubMed Abstract CrossRef Full Text Google Scholar

78. Sumner E, Leonard HC, Hill EL. Overlapping phenotypes in autism spectrum disorder and developmental coordination disorder: a cross-syndrome comparison of motor and social skills. J Autism Dev Disord (2016) 46(8):2609–20. doi:10.1007/s10803-016-2794-5

PubMed Abstract CrossRef Full Text Google Scholar

79. Henderson SE, Sugden DA, Barnett AL. Movement Assessment Battery for Children-2 (Second Edition). United Kingdom: Harcourt Assessment (2007).

80. Jaime M, Longard J, Moore C. Developmental changes in the visual-proprioceptive integration threshold of children. J Exp Child Psychol (2014) 125:1–12. doi:10.1016/j.jecp.2013.11.004

PubMed Abstract CrossRef Full Text Google Scholar

81. Afraz SR, Kiani R, Esteky H. Microstimulation of inferotemporal cortex influences face categorization. Nature (2006) 442(7103):692–5. doi:10.1038/nature04982

PubMed Abstract CrossRef Full Text Google Scholar

82. Cook J, Swapp D, Pan X, Bianchi-Berthouze N, Blakemore SJ. Atypical interference effect of action observation in autism spectrum conditions. Psychol Med (2014) 44(4):731–40. doi:10.1017/S0033291713001335

PubMed Abstract CrossRef Full Text Google Scholar

83. Garbarini F, Rabuffetti M, Piedimonte A, Pia L, Ferrarin M, Frassinetti F, et al. ‘Moving’ a paralysed hand: bimanual coupling effect in patients with anosognosia for hemiplegia. Brain (2012) 135(Pt 5):1486–97. doi:10.1093/brain/aws015

PubMed Abstract CrossRef Full Text Google Scholar

84. Garbarini F, Pia L, Piedimonte A, Rabuffetti M, Gindri P, Berti A. Embodiment of an alien hand interferes with intact-hand movements. Curr Biol (2013) 23(2):R57–8. doi:10.1016/j.cub.2012.12.003

CrossRef Full Text Google Scholar

85. Birleson P. The validity of depressive disorder in childhood and the development of a self-rating scale: a research report. J Child Psychol Psychiatry (1981) 22(1):73–88. doi:10.1111/j.1469-7610.1981.tb00533.x

CrossRef Full Text Google Scholar

86. Birleson P, Hudson I, Buchanan DG, Wolff S. Clinical evaluation of a self-rating scale for depressive disorder in childhood (Depression Self-Rating Scale). J Child Psychol Psychiatry (1987) 28(1):43–60. doi:10.1111/j.1469-7610.1987.tb00651.x

PubMed Abstract CrossRef Full Text Google Scholar

87. Murata T, Shimizu A, Mori Y, Oushima S. Childhood depressive state in the school situation: consideration from the Birleson’s Scale (in Japanese). Jpn J Psychiatry (1996) 1:131–8.

88. Rutter M, Bailey A, Lord C. The Social Communication Questionnaire: Manual. Torrance: Western Psychological Services (2003).

89. Le Couteur A, Lord C, Rutter M. The Autism Diagnostic Interview-Revised (ADI-R). Los Angeles: Western Psychological Services (2003).

90. DuPaul GJ, Power TJ, Anastopoulos AD, Reid R. ADHD Rating Scale-IV: Checklists, Norms, and Clinical Interpretation. New York: Guilford Press (1998).

91. DuPaul GJ, Power TJ, McGoey KE, Ikeda MJ, Anastopoulos AD. Reliability and validity of parent and teacher ratings of attention-deficit/hyperactivity disorder symptoms. J Psychoeduc Assess (1998) 16:55–68. doi:10.1177/073428299801600104

CrossRef Full Text Google Scholar

92. DuPaul GJ, Power TJ, Anastopoulos AD, Reid R. In: Ichikawa H, Tanaka Y, editors. ADHD Rating Scale-IV: Checklists, Norms, and Clinical Interpretation. Akashi-shoten, Tokyo: Akashi-shoten (2008). [Original work published 1998, in Japanese].

93. Tani I, Okada R, Ohnishi M, Nakajima S, Tsujii M. Japanese version of home form of the ADHD-RS: an evaluation of its reliability and validity. Res Dev Disabil (2010) 31(6):1426–33. doi:10.1016/j.ridd.2010.06.016

PubMed Abstract CrossRef Full Text Google Scholar

94. Faries DE, Yalcin I, Harder D, Heiligenstein JH. Validation of the ADHD rating scale as a clirlician administered and scored instrument. J Atten Disord (2001) 5(2):107–15. doi:10.1177/108705470100500204

CrossRef Full Text Google Scholar

95. Wilson BN, Kaplan BJ, Crawford SG, Campbell A, Dewey D. Reliability and validity of a parent questionnaire on childhood motor skills. Am J Occup Ther (2000) 54(5):484–93. doi:10.5014/ajot.54.5.484

PubMed Abstract CrossRef Full Text Google Scholar

96. Wilson BN, Crawford SG, Green D, Roberts G, Aylott A, Kaplan BJ. Psychometric properties of the revised developmental coordination disorder questionnaire. Phys Occup Ther Pediatr (2009) 29(2):182–202. doi:10.1080/01942630902784761

PubMed Abstract CrossRef Full Text Google Scholar

97. Nakai A, Yoshizawa M, Kawatani M, Wilson BW. Cross-cultural adaptation of the developmental coordination disorder questionnaire 2007 (DCDQ’07) for Japanese children. Paper Presented at the DCD VIII: Developmental Coordination Disorder International Conference. Maryland, USA (2009).

98. Nakai A, Miyachi T, Okada R, Tani I, Nakajima S, Onishi M, et al. Evaluation of the Japanese version of the developmental coordination disorder questionnaire as a screening tool for clumsiness of Japanese children. Res Dev Disabil (2011) 32(5):1615–22. doi:10.1016/j.ridd.2011.02.012

PubMed Abstract CrossRef Full Text Google Scholar

99. Davidson PR, Wolpert DM. Widespread access to predictive models in the motor system: a short review. J Neural Eng (2005) 2(3):S313–9. doi:10.1088/1741-2560/2/3/S11

PubMed Abstract CrossRef Full Text Google Scholar

100. Smith WM, McCrary JW, Smith KU. Delayed visual feedback and behavior. Science (1960) 132(3433):1013–4. doi:10.1126/science.132.3433.1013

PubMed Abstract CrossRef Full Text Google Scholar

101. Smith KU, Kaplan R. Effects of visual feedback delay on simulated automobile steering. J Mot Behav (1970) 2(1):25–36. doi:10.1080/00222895.1970.10734861

PubMed Abstract CrossRef Full Text Google Scholar

102. Miall RC, Weir DJ, Stein JF. Visuomotor tracking with delayed visual feedback. Neuroscience (1985) 16(3):511–20. doi:10.1016/0306-4522(85)90189-7

PubMed Abstract CrossRef Full Text Google Scholar

103. Fujisaki W. Effects of delayed visual feedback on grooved pegboard test performance. Front Psychol (2012) 3:61. doi:10.3389/fpsyg.2012.00061

PubMed Abstract CrossRef Full Text Google Scholar

104. Botzer L, Karniel A. Feedback and feedforward adaptation to visuomotor delay during reaching and slicing movements. Eur J Neurosci (2013) 38(1):2108–23. doi:10.1111/ejn.12211

PubMed Abstract CrossRef Full Text Google Scholar

105. Kulpa JD, Pfordresher PQ. Effects of delayed auditory and visual feedback on sequence production. Exp Brain Res (2013) 224(1):69–77. doi:10.1007/s00221-012-3289-z

PubMed Abstract CrossRef Full Text Google Scholar

106. Kitazawa S, Kohno T, Uka T. Effects of delayed visual information on the rate and amount of prism adaptation in the human. J Neurosci (1995) 15(11):7644–52.

PubMed Abstract Google Scholar

107. Tanaka H, Homma K, Imamizu H. Physical delay but not subjective delay determines learning rate in prism adaptation. Exp Brain Res (2011) 208(2):257–68. doi:10.1007/s00221-010-2476-z

PubMed Abstract CrossRef Full Text Google Scholar

108. Imaizumi S, Asai T, Kanayama N, Kawamura M, Koyama S. Agency over a phantom limb and electromyographic activity on the stump depend on visuomotor synchrony: a case study. Front Hum Neurosci (2014) 8:545. doi:10.3389/fnhum.2014.00545

PubMed Abstract CrossRef Full Text Google Scholar

109. Blakemore SJ, Frith CD, Wolpert DM. The cerebellum is involved in predicting the sensory consequences of action. Neuroreport (2001) 12(9):1879–84. doi:10.1097/00001756-200107030-00023

CrossRef Full Text Google Scholar

110. Farrer C, Frith CD. Experiencing oneself vs another person as being the cause of an action: the neural correlates of the experience of agency. Neuroimage (2002) 15(3):596–603. doi:10.1006/nimg.2001.1009

PubMed Abstract CrossRef Full Text Google Scholar

111. Farrer C, Franck N, Georgieff N, Frith CD, Decety J, Jeannerod M. Modulating the experience of agency: a positron emission tomography study. Neuroimage (2003) 18(2):324–33. doi:10.1016/S1053-8119(02)00041-1

PubMed Abstract CrossRef Full Text Google Scholar

112. Hilton CL, Zhang Y, Whilte MR, Klohr CL, Constantino J. Motor impairment in sibling pairs concordant and discordant for autism spectrum disorders. Autism (2012) 16(4):430–41. doi:10.1177/1362361311423018

PubMed Abstract CrossRef Full Text Google Scholar

113. MacDonald M, Lord C, Ulrich D. The relationship of motor skills and adaptive behavior skills in young children with autism spectrum disorders. Res Autism Spectr Disord (2013) 7(11):1383–90. doi:10.1016/j.rasd.2013.07.020

PubMed Abstract CrossRef Full Text Google Scholar

114. Grissmer D, Grimm KJ, Aiyer SM, Murrah WM, Steele JS. Fine motor skills and early comprehension of the world: two new school readiness indicators. Dev Psychol (2010) 46(5):1008–17. doi:10.1037/a0020104

PubMed Abstract CrossRef Full Text Google Scholar

115. Pagani LS, Fitzpatrick C, Archambault I, Janosz M. School readiness and later achievement: a French Canadian replication and extension. Dev Psychol (2010) 46(5):984–94. doi:10.1037/a0018881

PubMed Abstract CrossRef Full Text Google Scholar

116. Wassenberg R, Feron FJ, Kessels AG, Hendriksen JG, Kalff AC, Kroes M, et al. Relation between cognitive and motor performance in 5- to 6-year-old children: results from a large-scale cross-sectional study. Child Dev (2005) 76(5):1092–103. doi:10.1111/j.1467-8624.2005.00899.x

PubMed Abstract CrossRef Full Text Google Scholar

117. Asonitou K, Koutsouki D, Kourtessis T, Charitou S. Motor and cognitive performance differences between children with and without developmental coordination disorder (DCD). Res Dev Disabil (2012) 33(4):996–1005. doi:10.1016/j.ridd.2012.01.008

CrossRef Full Text Google Scholar

118. Fawcett AJ, Nicolson RI. Automatisation deficits in balance for dyslexic children. Percept Mot Skills (1992) 75(2):507–29. doi:10.2466/pms.1992.75.2.507

PubMed Abstract CrossRef Full Text Google Scholar

119. Schott N, El-Rajab I, Klotzbier T. Cognitive-motor interference during fine and gross motor tasks in children with developmental coordination disorder (DCD). Res Dev Disabil (2016) 57:136–48. doi:10.1016/j.ridd.2016.07.003

PubMed Abstract CrossRef Full Text Google Scholar

120. Hemgren E, Persson K. Deficits in motor co-ordination and attention at 3 years of age predict motor deviations in 6.5-year-old children who needed neonatal intensive care. Child Care Health Dev (2009) 35(1):120–9. doi:10.1111/j.1365-2214.2008.00896.x

PubMed Abstract CrossRef Full Text Google Scholar

121. Marx I, Weirich S, Berger C, Herpertz SC, Cohrs S, Wandschneider R, et al. Living in the fast lane: evidence for a global perceptual timing deficit in childhood ADHD caused by distinct but partially overlapping task-dependent cognitive mechanisms. Front Hum Neurosci (2017) 20(11):122. doi:10.3389/fnhum.2017.00122

PubMed Abstract CrossRef Full Text Google Scholar

122. Rosenblum S, Regev N. Timing abilities among children with developmental coordination disorders (DCD) in comparison to children with typical development. Res Dev Disabil (2013) 34(1):218–27. doi:10.1016/j.ridd.2012.07.011

PubMed Abstract CrossRef Full Text Google Scholar

123. Tsai CL, Pan CY, Cherng RJ, Wu SK. Dual-task study of cognitive and postural interference: a preliminary investigation of the automatization deficit hypothesis of developmental co-ordination disorder. Child Care Health Dev (2009) 35(4):551–60. doi:10.1111/j.1365-2214.2009.00974.x

PubMed Abstract CrossRef Full Text Google Scholar

124. Chen FC, Tsai CL, Stoffregen TA, Chang CH, Wade MG. Postural adaptations to a suprapostural memory task among children with and without developmental coordination disorder. Dev Med Child Neurol (2012) 54(2):155–9. doi:10.1111/j.1469-8749.2011.04092.x

PubMed Abstract CrossRef Full Text Google Scholar

125. Cherng RJ, Liang LY, Chen YJ, Chen JY. The effects of a motor and a cognitive concurrent task on walking in children with developmental coordination disorder. Gait Posture (2009) 29(2):204–7. doi:10.1016/j.gaitpost.2008.08.003

PubMed Abstract CrossRef Full Text Google Scholar

126. Laufer Y, Ashkenazi T, Josman N. The effects of a concurrent cognitive task on the postural control of young children with and without developmental coordination disorder. Gait Posture (2008) 27(2):347–51. doi:10.1016/j.gaitpost.2007.04.013

PubMed Abstract CrossRef Full Text Google Scholar

127. Biotteau M, Chaix Y, Albaret JM. Procedural learning and automatization process in children with developmental coordination disorder and/or developmental dyslexia. Hum Mov Sci (2015) 43:78–89. doi:10.1016/j.humov.2015.07.005

PubMed Abstract CrossRef Full Text Google Scholar

128. Biotteau M, Chaix Y, Blais M, Tallet J, Péran P, Albaret JM. Neural signature of DCD: a critical review of MRI neuroimaging studies. Front Neurol (2016) 7:227. doi:10.3389/fneur.2016.00227

PubMed Abstract CrossRef Full Text Google Scholar

Amyotrophic lateral sclerosis (ALS), also known as motor neurone disease (MND) or Lou Gehrig's disease, is a specific disease that causes the death of neurons controlling voluntary muscles.^[4][9] Some also use the term motor neuron disease for a group of conditions of which ALS is the most common.^[2] ALS is characterized by stiff muscles, muscle twitching, and gradually worsening weakness due to muscles decreasing in size.^[2] It may begin with weakness in the arms or legs, or with difficulty speaking or swallowing.^[10] About half of the people affected develop at least mild difficulties with thinking and behavior and most people experience pain.^[11][12] Most eventually lose the ability to walk, use their hands, speak, swallow, and breathe.^[5]

The cause is not known in 90% to 95% of cases,^[4] but is believed to involve both genetic and environmental factors.^[13] The remaining 5–10% of cases are inherited from a person's parents.^[3] About half of these genetic cases are due to one of two specific genes.^[4] The underlying mechanism involves damage to both upper and lower motor neurons.^[2] The diagnosis is based on a person's signs and symptoms, with testing done to rule out other potential causes.^[4]

No cure for ALS is known.^[4] The goal of treatment is to improve symptoms.^[11] A medication called riluzole may extend life by about two to three months.^[6]Non-invasive ventilation may result in both improved quality and length of life.^[5]Mechanical ventilation can prolong survival but does not stop disease progression.^[14] A feeding tube may help.^[15] The disease can affect people of any age, but usually starts around the age of 60 and in inherited cases around the age of 50.^[3] The average survival from onset to death is two to four years, though this can vary.^[5][16] About 10% survive longer than 10 years.^[4] Most die from respiratory failure.^[3] In Europe, the disease affects about two to three people per 100,000 per year.^[8] Rates in much of the world are unclear.^[17] In the United States, it is more common in white people than black people.^[18]

Descriptions of the disease date back to at least 1824 by Charles Bell.^[19] In 1869, the connection between the symptoms and the underlying neurological problems was first described by Jean-Martin Charcot, who in 1874 began using the term amyotrophic lateral sclerosis.^[19] It became well known in the United States in the 20th century when in 1939 it affected the baseball player Lou Gehrig and later worldwide following the 1963 diagnosis of cosmologistStephen Hawking.^[20][21] The first ALS gene was discovered in 1993 while the first animal model was developed in 1994.^[22][23] In 2014, videos of the Ice Bucket Challenge went viral on the Internet and increased public awareness of the condition.^[24]

• 1Classification
• 2Signs and symptoms
• 3Cause
  • 3.2Environmental factors
• 4Pathophysiology
• 5Diagnosis
• 6Management
  • 6.2Breathing support
• 8History
• 10Research

Classification[edit]

ALS is a motor neuron disease, also spelled 'motor neurone disease', which is a group of neurological disorders that selectively affect motor neurons, the cells that control voluntary muscles of the body.^[2] Motor neuron diseases include amyotrophic lateral sclerosis (ALS), primary lateral sclerosis (PLS), progressive muscular atrophy (PMA), progressive bulbar palsy, pseudobulbar palsy, and monomelic amyotrophy (MMA).^[25]

ALS itself can be classified a few different ways: by how fast the disease progresses (slow vs fast progressors), by whether it is inherited or sporadic, and by where it starts.^[4] In about 25% of cases, muscles in the face, mouth, and throat are affected first, because motor neurons in the part of the brain stem called the medulla oblongata (formerly called the 'bulb') start to die first along with lower motor neurons. This form is called 'bulbar onset'. In about 5% of cases muscles in the trunk of the body are affected first.^[3] In most cases the disease spreads and affects other spinal cord regions. A few people with ALS have symptoms that are limited to one spinal cord region for at least 12 to 24 months before spreading to a second region; these regional variants of ALS are associated with a better prognosis.^[26]

Classical ALS, PLS, and PMA[edit]

ALS can be classified by the types of motor neurons that are affected. Typical or 'classical' ALS involves neurons in the brain (upper motor neurons) and in the spinal cord (lower motor neurons).^[27] Primary lateral sclerosis (PLS) involves only upper motor neurons, and progressive muscular atrophy (PMA) involves only lower motor neurons. There is debate over whether PLS and PMA are separate diseases or simply variants of ALS.^[11]

Classic ALS accounts for about 70% of all cases of ALS and can be subdivided into spinal-onset and bulbar-onset ALS.^[11] Spinal-onset ALS, also called limb-onset ALS, begins with weakness in the arms and legs^[10] and accounts for about two-thirds of all cases of classic ALS.^[11] Bulbar-onset ALS begins with weakness in the muscles of speech, chewing, and swallowing^[27] and accounts for about one-third of all cases of classic ALS.^[11] It is associated with a worse prognosis than spinal-onset ALS; a population-based study found that bulbar-onset ALS has a median survival of 2.0 years and a 10-year survival rate of 3%, while spinal-onset ALS has a median survival of 2.6 years and a 10-year survival rate of 13%.^[28]

Primary lateral sclerosis (PLS) accounts for about 5% of all cases of ALS and affects upper motor neurons in the arms and legs.^[16] However, more than 75% of people with apparent PLS develop lower motor neuron signs within four years of symptom onset, meaning that a definite diagnosis of PLS cannot be made until then.^[29] PLS has a better prognosis than classic ALS, as it progresses slower, results in less functional decline, does not affect the ability to breathe, and causes less severe weight loss.^[16]

Progressive muscular atrophy (PMA) accounts for about 5% of all cases of ALS and affects lower motor neurons in the arms and legs.^[16] While PMA is associated with longer survival on average than classic ALS, it still progresses to other spinal cord regions over time, eventually leading to respiratory failure and death.^[11] Upper motor neuron signs can develop late in the course of PMA, in which case the diagnosis might be changed to classic ALS.^[29]

Regional variants[edit]

Regional variants of ALS have symptoms that are limited to a single spinal cord region for at least a year; they progress slower than classic ALS and are associated with longer survival. Examples include flail arm syndrome, flail leg syndrome, and isolated bulbar ALS. Flail arm syndrome and flail leg syndrome are often considered to be regional variants of PMA because they only involve lower motor neurons. Isolated bulbar ALS can involve upper and/or lower motor neurons. These regional variants of ALS cannot be diagnosed at the onset of symptoms; a failure of the disease to spread to other spinal cord regions for an extended period of time (at least 12 months) must be observed.^[26]

Flail arm syndrome, also called brachial amyotrophic diplegia,^[a] is characterized by lower motor neuron damage in the cervical spinal cord only, leading to gradual onset of weakness in the proximal arm muscles and decreased or absent reflexes. Flail leg syndrome, also called leg amyotrophic diplegia,^[b] is characterized by lower motor neuron damage in the lumbosacral spinal cord only, leading to gradual onset of weakness in the legs and decreased or absent reflexes. Isolated bulbar ALS is characterized by upper and/or lower motor neuron damage in the bulbar region only, leading to gradual onset of difficulty with speech (dysarthria) and swallowing (dysphagia); breathing (respiration) is generally preserved, at least initially. Two small studies have shown that people with isolated bulbar ALS may live longer than people with bulbar-onset ALS.^[26]

Age of onset[edit]

ALS can also be classified based on the age of onset. While the peak age of onset is 58 to 63 for sporadic ALS and 47 to 52 for familial ALS,^[3] about 10% of all cases of ALS begin before age 45 ('young-onset' ALS), and about 1% of all cases begin before age 25 (juvenile ALS).^[27] People who develop young-onset ALS are more likely to be male, less likely to have a bulbar onset of symptoms, and more likely to have a slower progression of disease.^[29] Juvenile ALS is more likely to be familial than adult-onset ALS; genes known to be associated with juvenile ALS include ALS2, SETX, SPG11, FUS, and SIGMAR1. Although most people with juvenile ALS live longer than those with adult-onset ALS, some of them have specific mutations in FUS and SOD1 that are associated with a poor prognosis.^[30] Late onset (after age 65) is associated with a more rapid functional decline and shorter survival.^[31]

Respiratory onset[edit]

Respiratory-onset ALS is a rare variant that accounts for about 3% of all cases of ALS,^[11] in which the initial symptoms are difficulty breathing (dyspnea) with exertion, at rest, or while lying down (orthopnea).^[32] Spinal and bulbar symptoms tend to be mild or absent at the beginning. It is more common in males.^[16] Respiratory-onset ALS has the worst prognosis of any ALS variant; in a population-based study, those with respiratory-onset had a median survival of 1.4 years and 0% survival at 10 years.^[28]

Signs and symptoms[edit]

The disorder causes muscle weakness, atrophy, and muscle spasms throughout the body due to the degeneration of the upper motor and lower motor neurons. Individuals affected by the disorder may ultimately lose the ability to initiate and control all voluntary movement,^[5] although bladder and bowel function and the extraocular muscles (the muscles responsible for eye movement) are usually spared^[33][c] until the final stages of the disease.^[35]

Cognitive or behavioral dysfunction is present in 30–50% of individuals with ALS.^[36] Around half of people with ALS will experience mild changes in cognition and behavior, and 10–15% will show signs of frontotemporal dementia.^[5]Repeating phrases or gestures, apathy, and loss of inhibition are frequently reported behavioral features of ALS.^[37]Language dysfunction, executive dysfunction, and troubles with social cognition and verbal memory are the most commonly reported cognitive symptoms in ALS; a meta-analysis found no relationship between dysfunction and disease severity.^[38] However, cognitive and behavioral dysfunctions have been found to correlate with reduced survival in people with ALS and increased caregiver burden; this may be due in part to deficits in social cognition.^[38] About half the people who have ALS experience emotional lability, in which they cry or laugh for no reason; it is more common in those with bulbar-onset ALS.^[5]

Pain is a symptom experienced by most people with ALS and can take the form of neuropathic pain (pain caused by nerve damage), spasticity, muscle cramps, and nociceptive pain caused by reduced mobility and muscle weakness; examples of nociceptive pain in ALS include contractures (permanent shortening of a muscle or joint), neck pain, back pain, shoulder pain, and pressure ulcers.^[12]

Sensory nerves and the autonomic nervous system are generally unaffected, meaning the majority of people with ALS maintain hearing, sight, touch, smell, and taste.^[4]

Initial symptoms[edit]

The start of ALS may be so subtle that the symptoms are overlooked.^[4] The earliest symptoms of ALS are muscle weakness or muscle atrophy. Other presenting symptoms include trouble swallowing or breathing, cramping, or stiffness of affected muscles; muscle weakness affecting an arm or a leg; or slurred and nasal speech. The parts of the body affected by early symptoms of ALS depend on which motor neurons in the body are damaged first.^[39]

In limb-onset ALS, the first symptoms are in arms or the legs. If the legs are affected first, people may experience awkwardness, tripping, or stumbling when walking or running; this is often marked by walking with a 'dropped foot' that drags gently on the ground. If the arms are affected first, they may experience difficulty with tasks requiring manual dexterity, such as buttoning a shirt, writing, or turning a key in a lock.^[39]

In bulbar-onset ALS, the first symptoms are difficulty speaking or swallowing. Speech may become slurred, nasal in character, or quieter. There may be difficulty with swallowing and loss of tongue mobility. A smaller proportion of people experience 'respiratory-onset' ALS, where the intercostal muscles that support breathing are affected first.^[3]

Over time, people experience increasing difficulty moving, swallowing (dysphagia), and speaking or forming words (dysarthria). Symptoms of upper motor neuron involvement include tight and stiff muscles (spasticity) and exaggerated reflexes (hyperreflexia), including an overactive gag reflex. An abnormal reflex commonly called Babinski's sign also indicates upper motor neuron damage. Symptoms of lower motor neuron degeneration include muscle weakness and atrophy, muscle cramps, and fleeting twitches of muscles that can be seen under the skin (fasciculations). However, twitching is more of a side effect than a diagnostic symptom; it either occurs after or accompanies weakness and atrophy.^[4]

Progression[edit]

Although the initial symptoms and rate of progression vary from person to person, the disease eventually spreads to unaffected regions and the affected regions become more affected. Most people eventually are not able to walk or use their hands and arms, lose the ability to speak and swallow food and their own saliva, and begin to lose the ability to cough and to breathe on their own.^[5]

The rate of progression can be measured using the 'ALS Functional Rating Scale Revised (ALSFRS-R)', a 12-item instrument survey administered as a clinical interview or self-reported questionnaire that produces a score between 48 (normal function) and 0 (severe disability);^[40] it is the most commonly used outcome measure in clinical trials and is used by doctors to track disease progression.^[41] Though the degree of variability is high and a small percentage of people have a much slower disorder, on average, people with ALS lose about 0.9 FRS points per month. A survey-based study among clinicians showed that they rated a 20% change in the slope of the ALSFRS-R as being clinically meaningful.^[42]

Disease progression tends to be slower in people who are younger than 40 at onset,^[43] are mildly obese,^[44] have symptoms restricted primarily to one limb, and those with primarily upper motor neuron symptoms.^[28] Conversely, progression is faster and prognosis poorer in people with bulbar-onset ALS, respiratory-onset ALS and frontotemporal dementia.^[28]

Late stages[edit]

Difficulties with chewing and swallowing make eating very difficult and increases the risk of choking or of aspirating food into the lungs. In later stages of the disorder, aspiration pneumonia can develop, and maintaining a healthy weight can become a significant problem that may require the insertion of a feeding tube. As the diaphragm and intercostal muscles of the rib cage that support breathing weaken, measures of lung function such as vital capacity and inspiratory pressure diminish. In respiratory-onset ALS, this may occur before significant limb weakness is apparent. Most people with ALS die of respiratory failure or pneumonia.^[3]

Although respiratory support can ease problems with breathing and prolong survival, it does not affect the progression of ALS. Most people with ALS die between two and four years after the diagnosis.^[5] Around half of people with ALS die within 30 months of their symptoms beginning, and about 20% of people with ALS live between five and 10 years after symptoms begin.^[3] Guitarist Jason Becker has lived since 1989 with the disorder, while cosmologistStephen Hawking lived for 55 more years following his diagnosis, but they are considered unusual cases.^[45]

Most people with ALS die in their own home, with their breath stopping while they sleep.^[9]

Cause[edit]

Though the exact cause of ALS is unknown, genetic factors and environmental factors are thought to be of roughly equal importance.^[13] The genetic factors are better understood than the environmental factors; no specific environmental factor has been definitively shown to cause ALS. A liability threshold model for ALS proposes that cellular damage accumulates over time due to genetic factors present at birth and exposure to environmental risks throughout life.^[17]

Genetics[edit]

ALS can be classified as familial or sporadic, depending on whether or not there is a family history of the disease.^[16] There is no consensus among neurologists on the exact definition of familial ALS. The strictest definition is that a person with ALS must have two or more first-degree relatives (children, siblings, or parents) who also have ALS. A less strict definition is that a person with ALS must have at least one first-degree or second-degree relative (grandparents, grandchildren, aunts, uncles, nephews, nieces or half-siblings) who also has ALS.^[46] Familial ALS is usually said to account for 10% of all cases of ALS, though estimates range from 5%^[47] to 20%.^[48] Higher estimates use a broader definition of familial ALS and examine the family history of people with ALS more thoroughly.^[46]

In sporadic ALS, there is no family history of the disease.^[35] Sporadic ALS and familial ALS appear identical clinically and pathologically and are similar genetically;^[48] about 10% of people with sporadic ALS have mutations in genes that are known to cause familial ALS.^[11] In light of these parallels, the term 'sporadic ALS' has been criticized as misleading because it implies that cases of sporadic ALS are only caused by environmental factors; the term 'isolated ALS' has been suggested as a more accurate alternative.^[48]

More than 20 genes have been associated with familial ALS, of which four account for the majority of familial cases:^[49]C9orf72 (40%), SOD1 (20%), FUS (1–5%), and TARDBP (1–5%).^[11] The genetics of familial ALS are better understood than the genetics of sporadic ALS;^[11] as of 2016, the known ALS genes explained about 70% of familial ALS and about 15% of sporadic ALS.^[50][51] Overall, first-degree relatives of an individual with ALS have a 1% risk of developing ALS.^[13][52] ALS has an oligogenic mode of inheritance, meaning that mutations in two or more genes are required to cause disease.^[22]

B 169 Belt

ALS and frontotemporal dementia (FTD) are now considered to be part of a common disease spectrum (FTD–ALS) because of genetic, clinical, and pathological similarities.^[53] Genetically, C9orf72 repeat expansions account for about 40% of familial ALS and 25% of familial FTD.^[22] Clinically, 50% of people with ALS have some cognitive and/or behavioral impairments and 5–15% have FTD, while 40% of people with FTD have some motor neuron symptoms and 12.5% have ALS.^[11] Pathologically, abnormal aggregations of TDP-43 protein are seen in up to 97% of ALS patients and up to 50% of FTD patients.^[54] Other genes known to cause FTD-ALS include CHCHD10, SQSTM1, and TBK1.^[49]

Environmental factors[edit]

Where no family history of the disease is present — around 90% of cases — no cause is known. Possible associations for which evidence is inconclusive include military service and smoking.^[36] Although studies on military history and ALS frequency are inconsistent, there is weak evidence for a positive correlation.^[55] Various proposed factors include exposure to environmental toxins (inferred from geographical deployment studies), as well as alcohol and tobacco use during military service.^[55]

A 2016 review of 16 meta-analyses concluded that there was convincing evidence for an association with chronic occupational exposure to lead; suggestive evidence for farming, exposure to heavy metals other than lead, beta-carotene intake, and head injury; and weak evidence for omega-three fatty acid intake, exposure to extremely low frequency electromagnetic fields, pesticides, and serum uric acid.^[56]

In a 2017 study by the United States Centers for Disease Control and Prevention analyzing U.S. deaths from 1985 to 2011, occupations correlated with ALS deaths were white collar, such as in management, financial, architectural, computing, legal, and education jobs.^[57] Other potential risk factors remain unconfirmed, including chemical exposure, electromagnetic field exposure, occupation, physical trauma, and electric shock.^[58][59] There is a tentative association with exposure to various pesticides, including the organochlorine insecticidesaldrin, dieldrin, DDT, and toxaphene.^[60][61][62]

Head injury[edit]

A 2015 review found that moderate to severe traumatic brain injury is a risk factor for ALS, but whether mild traumatic brain injury increases rates was unclear.^[63] A 2017 meta-analysis found an association between head injuries and ALS; however, this association disappeared when the authors considered the possibility of reverse causation, which is the idea that head injuries are an early symptom of undiagnosed ALS, rather than the cause of ALS.^[64]

Physical activity[edit]

A 2005 systematic review found no relationship between the amount of physical activity and the risk of developing ALS, but did find that increased leisure time physical activity was strongly associated with an earlier age of onset of ALS.^[65] A 2007 review concluded that physical activity was probably not a risk factor for ALS.^[66] A 2009 review found that the evidence for physical activity as a risk factor for ALS was limited, conflicting, and of insufficient quality to come to a firm conclusion.^[67] A 2014 review concluded that physical activity in general is not a risk factor for ALS, that soccer and American football are possibly associated with ALS, and that there was not enough evidence to say whether or not physically demanding occupations are associated with ALS.^[68] A 2016 review found the evidence inconclusive and noted that differences in study design make it difficult to compare studies, as they do not use the same measures of physical activity or the same diagnostic criteria for ALS.^[69]

Sports[edit]

Both soccer and American football have been identified as risk factors for ALS in several studies, although this association is based on small numbers of ALS cases.^[70] A 2012 retrospective cohort study of 3,439 former NFL players found that their risk of dying from neurodegenerative causes was three times higher than the general US population, and their risk of dying from ALS or Alzheimer's disease was four times higher.^[71] However, this increased risk was calculated on the basis of two deaths from Alzheimer's disease and six deaths from ALS out of 334 deaths total in this cohort, meaning that this study does not definitively prove that playing American football is a risk factor for ALS.^[72] Some NFL players thought to have died from ALS may have actually had chronic traumatic encephalopathy (CTE), a neurodegenerative disorder associated with multiple head injuries that can present with symptoms that are very similar to ALS.^[63][d]

Soccer was identified as a possible risk factor for ALS in a retrospective cohort study of 24,000 Italian soccer players who played between 1960 and 1996. There were 375 deaths in this group, including eight from ALS. Based on this information and the incidence of ALS, it was calculated that the soccer players were 11 times more likely to die from ALS than the general Italian population.^[17] However, this calculation has been criticized for relying on an inappropriately low number of expected cases of ALS in the cohort.^[67] When the lifetime risk of developing ALS was used to predict the number of expected cases, soccer players were no more likely to die of ALS than the general population.^[17]

Brinkmann B 169 Manual Dexterity Pdf

Smoking[edit]

Smoking is possibly associated with ALS. A 2009 review concluded that smoking was an established risk factor for ALS.^[75] A 2010 systematic review and meta-analysis concluded that there was not a strong association between smoking and ALS, but that smoking might be associated with a higher risk of ALS in women.^[76] A 2011 meta-analysis concluded that smoking increases the risk of ALS versus never smoking. Among smokers, the younger they started smoking, the more likely they were to get ALS; however, neither the number of years smoked nor the number of cigarettes smoked per day affected their risk of developing ALS.^[77]

Pathophysiology[edit]

Neuropathology[edit]

The defining feature of ALS is the death of both upper motor neurons (located in the motor cortex of the brain) and lower motor neurons (located in the brainstem and spinal cord).^[78] In ALS with frontotemporal dementia, neurons throughout the frontal and temporal lobes of the brain die as well.^[35] The pathological hallmark of ALS is the presence of inclusion bodies (abnormal aggregations of protein) in the cytoplasm of motor neurons. In about 97% of people with ALS, the main component of the inclusion bodies is TDP-43 protein;^[10] however, in those with SOD1 or FUS mutations, the main component is SOD1 protein or FUS protein, respectively.^[27] The gross pathology of ALS, which are features of the disease that can be seen with the naked eye, include skeletal muscle atrophy, motor cortex atrophy, sclerosis of the corticospinal and corticobulbar tracts, thinning of the hypoglossal nerves (which control the tongue), and thinning of the anterior roots of the spinal cord.^[10] Besides for the death of motor neurons, two other characteristics common to most ALS variants are focal initial pathology, meaning that symptoms start in a single spinal cord region, and progressive continuous spread, meaning that symptoms spread to additional regions over time. Prion-like propagation of misfolded proteins from cell to cell may explain why ALS starts in one area and spreads to others.^[27]

Biochemistry[edit]

It is still not fully understood why neurons die in ALS, but this neurodegeneration is thought to involve many different cellular and molecular processes.^[11] The genes known to be involved in ALS can be grouped into three general categories based on their normal function: protein degradation, the cytoskeleton, and RNA processing. Mutant SOD1 protein forms intracellular aggregations that inhibit protein degradation. Cytoplasmic aggregations of wild-type (normal) SOD1 protein are common in sporadic ALS.^[35] It is thought that misfolded mutant SOD1 can cause misfolding and aggregation of wild-type SOD1 in neighboring neurons in a prion-like manner.^[10] Other protein degradation genes that can cause ALS when mutated include VCP, OPTN, TBK1, and SQSTM1. Three genes implicated in ALS that are important for maintaining the cytoskeleton^[35] and for axonal transport^[10] include DCTN1, PFN1, and TUBA4A.^[35]

There are a number of ALS genes that encode for RNA-binding proteins. The first to be discovered was TDP-43 protein,^[35] a nuclear protein that aggregates in the cytoplasm of motor neurons in almost all cases of ALS; however, mutations in TARDBP, the gene that codes for TDP-43, are a rare cause of ALS.^[10]FUS codes for FUS, another RNA-binding protein with a similar function to TDP-43, which can cause ALS when mutated.^[22] It is thought that mutations in TARDBP and FUS increase the binding affinity of the low-complexity domain, causing their respective proteins to aggregate in the cytoplasm. Once these mutant RNA-binding proteins are misfolded and aggregated, they may be able to misfold normal protein both within and between cells in a prion-like manner.^[35] This also leads to decreased levels of RNA-binding protein in the nucleus, which may mean that their target RNA transcripts do not undergo the normal processing. Other RNA metabolism genes associated with ALS include ANG, SETX, and MATR3.^[10]

C9orf72 is the most commonly mutated gene in ALS and causes motor neuron death through a number of mechanisms.^[35] The pathogenic mutation is a hexanucleotide repeat expansion (a series of six nucleotides repeated over and over);^[54] people with 30 repeats are normal, while people with hundreds or thousands of repeats can have familial ALS, frontotemporal dementia, or sometimes sporadic ALS. The three mechanisms of disease associated with these C9orf72 repeats are deposition of RNA transcripts in the nucleus, translation of the RNA into toxic dipeptide repeat proteins in the cytoplasm, and decreased levels of the normal C9orf72 protein.^[35]

Excitotoxicity, or nerve cell death caused by high levels of intracellular calcium due to excessive stimulation by the excitatory neurotransmitter glutamate, is a mechanism thought to be common to all forms of ALS. Motor neurons are more sensitive to excitotoxicity than other types of neurons because they have a lower calcium-buffering capacity and a type of glutamate receptor (the AMPA receptor) that is more permeable to calcium. In ALS, there are decreased levels of excitatory amino acid transporter 2 (EAAT2), which is the main transporter that removes glutamate from the synapse; this leads to increased synaptic glutamate levels and excitotoxicity. Riluzole, a drug that modestly prolongs survival in ALS, inhibits glutamate release from pre-synaptic neurons; however, it is unclear if this mechanism is responsible for its therapeutic effect.^[10]

Diagnosis[edit]

No test can provide a definite diagnosis of ALS, although the presence of upper and lower motor neuron signs in a single limb is strongly suggestive.^[4] Instead, the diagnosis of ALS is primarily based on the symptoms and signs the physician observes in the person and a series of tests to rule out other diseases.^[4] Physicians obtain the person's full medical history and usually conduct a neurologic examination at regular intervals to assess whether symptoms such as muscle weakness, atrophy of muscles, hyperreflexia, and spasticity are worsening.^[4] A number of biomarkers are being studied for the condition, but so far are not in general medical use.^[80][81]

Diagnostic criteria[edit]

The diagnosis of ALS is based on the El Escorial Revised criteria and the Awaji criteria.^[10] The original El Escorial criteria had four levels of diagnostic certainty, based on how many of the four spinal cord regions were involved: bulbar, cervical, thoracic, and lumbar. Definite ALS was defined as upper motor neuron (UMN) and lower motor neuron (LMN) signs in three spinal cord regions, probable ALS as UMN and LMN signs in two regions, possible ALS as UMN and LMN signs in only one region, and suspected ALS as LMN signs only. The El Escorial Revised criteria, also known as the Airlie House criteria, dropped the 'suspected ALS' category and added a 'laboratory-supported probable ALS' category. The Awaji criteria give abnormal EMG tests the same weight as clinical signs of LMN dysfunction in making the diagnosis of ALS,^[29] thus making the 'laboratory-supported probable ALS' category unnecessary. The only three categories in the Awaji criteria are definite ALS, probable ALS, and possible ALS.^[82]

The El Escorial Revised criteria are specific for ALS, which means that someone who meets the criteria is very likely to have ALS; however, they are not especially sensitive for ALS, which means that someone who does not meet the criteria can still have ALS. Their sensitivity is particularly poor in the early stages of ALS. The Awaji criteria have better sensitivity than the El Escorial Revised criteria, especially for bulbar-onset ALS.^[29] A 2012 meta-analysis found that the El Escorial Revised criteria had a sensitivity of 62.2%, while the Awaji criteria had a sensitivity of 81.1%; both sets of criteria had a specificity of about 98%.^[83] The El Escorial criteria were designed to standardize patient groups for clinical trials^[84] but are not as useful in clinical practice; possible ALS as described by the El Escorial criteria is almost always clinically ALS.^[10]

Differential diagnosis[edit]

Because symptoms of ALS can be similar to those of a wide variety of other, more treatable diseases or disorders, appropriate tests must be conducted to exclude the possibility of other conditions.^[4] One of these tests is electromyography (EMG), a special recording technique that detects electrical activity in muscles.^[4] Certain EMG findings can support the diagnosis of ALS.^[4] Another common test measures nerve conduction velocity (NCV).^[4] Specific abnormalities in the NCV results may suggest, for example, that the person has a form of peripheral neuropathy (damage to peripheral nerves) or myopathy (muscle disease) rather than ALS. While a magnetic resonance imaging (MRI) is often normal in people with early stage ALS, it can reveal evidence of other problems that may be causing the symptoms, such as a spinal cord tumor, multiple sclerosis, a herniated disk in the neck, syringomyelia, or cervical spondylosis.^[4]

Based on the person's symptoms and findings from the examination and from these tests, the physician may order tests on blood and urine samples to eliminate the possibility of other diseases, as well as routine laboratory tests.^[4] In some cases, for example, if a physician suspects the person may have a myopathy rather than ALS, a muscle biopsy may be performed.^[4]

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Brinkmann B169 Manual Dexterity Test

A number of infectious diseases can sometimes cause ALS-like symptoms,^[4] including human immunodeficiency virus (HIV), human T-lymphotropic virus (HTLV), Lyme disease, and syphilis.^[11] Neurological disorders such as multiple sclerosis, post-polio syndrome, multifocal motor neuropathy, CIDP, spinal muscular atrophy, and spinal and bulbar muscular atrophy can also mimic certain aspects of the disease and should be considered.^[4]

ALS must be differentiated from the 'ALS mimic syndromes', which are unrelated disorders that may have a similar presentation and clinical features to ALS or its variants.^[85] Because of the prognosis carried by this diagnosis and the variety of diseases or disorders that can resemble ALS in the early stages of the disease, people with ALS symptoms should always obtain a specialist neurological opinion in order to rule out alternative diagnoses. Myasthenic syndrome, also known as Lambert–Eaton syndrome, can mimic ALS, and its initial presentation can be similar to that of myasthenia gravis (MG), a treatable autoimmune disease sometimes mistaken for ALS.^[86][87]Benign fasciculation syndrome is another condition that mimics some of the early symptoms of ALS, but is accompanied by normal EMG readings and no major disablement.^[88]

Most cases of ALS, however, are correctly diagnosed, with the error rate of diagnosis in large ALS clinics being less than 10%.^[89][90] One study examined 190 people who met the MND/ALS diagnostic criteria, complemented with laboratory research in compliance with both research protocols and regular monitoring. Thirty of these people (16%) had their diagnosis completely changed during the clinical observation development period.^[91] In the same study, three people had a false negative diagnosis of MG, which can mimic ALS and other neurological disorders, leading to a delay in diagnosis and treatment. MG is eminently treatable; ALS is not.^[92]

Management[edit]

There is no cure for ALS. Management focuses on treating symptoms and providing supportive care, with the goal of improving quality of life and prolonging survival.^[11] This care is best provided by multidisciplinary teams of healthcare professionals; attending a multidisciplinary ALS clinic is associated with longer survival, fewer hospitalizations, and improved quality of life.^[5]Riluzole prolongs survival by about 2–3 months.^[6]Edaravone slows functional decline slightly in a small number of people with ALS;^[93] it is expensive and must be administered by daily IV infusions that may decrease quality of life.^[94] Other medications may be used to manage other symptoms.^[95]

Non-invasive ventilation (NIV) is the main treatment for respiratory failure in ALS.^[10] In people with normal bulbar function, it prolongs survival by about seven months and improves quality of life. One study found that NIV is ineffective for people with poor bulbar function^[96] while another suggested that it may provide a modest survival benefit.^[11] Many people with ALS have difficulty tolerating NIV.^[97] Invasive ventilation is an option for people with advanced ALS when NIV is not enough to manage their symptoms.^[5] While invasive ventilation prolongs survival, disease progression and functional decline continue.^[14] It may decrease the quality of life of people with ALS and/or their caregivers.^[15][14] Invasive ventilation is more commonly used in Japan than North America or Europe.^[98]

Physical therapy can promote functional independence^[99][100] through aerobic, range of motion, and stretching exercises.^[95] Occupational therapy can assist with activities of daily living through adaptive equipment.^[101] Speech therapy can assist people with ALS who have difficulty speaking.^[100] Preventing weight loss and malnutrition in people with ALS improves both survival and quality of life.^[11] Initially, difficulty swallowing (dysphagia) can be managed by dietary changes and swallowing techniques. A feeding tube should be considered if someone with ALS loses 5% or more of their body weight or if they cannot safely swallow food and water.^[10] The feeding tube is usually inserted by percutaneous endoscopic gastrostomy (PEG). There is weak evidence that PEG tubes improve survival.^[102] PEG insertion is usually performed with the intent of improving quality of life.^[15]

Palliative care should begin shortly after someone is diagnosed with ALS.^[103] Discussion of end-of-life issues gives people with ALS time to reflect on their preferences for end-of-life care and can help avoid unwanted interventions or procedures. Hospice care can improve symptom management at the end of life and increases the likelihood of a peaceful death.^[15] In the final days of life, opioids can be used to treat pain and dyspnea, while benzodiazepines can be used to treat anxiety.^[14]

Medications[edit]

Riluzole has been found to modestly prolong survival by about 2–3 months.^[104][6] It may have a greater survival benefit for those with bulbar-onset ALS.^[6] Riluzole's mechanism of action is poorly understood. It may work by decreasing release of the excitatory neurotransmitter glutamate from pre-synaptic neurons.^[10] The most common side effects are nausea and a lack of energy (asthenia).^[6] People with ALS should begin treatment with riluzole as soon as possible following their diagnosis, according to guidelines from the European Federation of Neurological Societies.^[103]

Edaravone has been shown to modestly slow the decline in function (as measured by the Revised ALS Functional Rating Scale) in a small group of people with early-stage ALS who meet very strict criteria;^[e][f] there is no evidence that edaravone is effective in all people with ALS.^[93] Its mechanism of action in ALS is unknown.^[106] It may work by protecting motor neurons from oxidative stress.^[107] The most common side effects are bruising and gait disturbance.^[106] Treatment with edaravone is expensive (about $140,000 per year in the United States); it is also intensive and requires daily hour-long IV infusions for 10 days in a two-week period, followed by two weeks off the drug. Fatigue from these daily infusions or from daily travel to an infusion center may decrease quality of life.^[94] There is debate over whether all people with ALS should receive edaravone.^[16] People with ALS should be made aware of the time commitment, high cost, and limited therapeutic benefit before beginning treatment.^[94]

Other medications may be used to help reduce fatigue, ease muscle cramps, control spasticity, and reduce excess saliva and phlegm.^[95]Gabapentin, pregabalin, and tricyclic antidepressants (e.g. amitriptyline) can be used for neuropathic pain, while nonsteroidal anti-inflammatory drugs (NSAIDs), acetaminophen, and opioids can be used for nociceptive pain.^[12] Depression can be treated with selective serotonin reuptake inhibitors (SSRIs) or tricyclic antidepressants,^[10] while benzodiazepines can be used for anxiety.^[5] There are no medications to treat cognitive impairment/frontotemporal dementia (FTD); however, SSRIs and antipsychotics can help treat some of the symptoms of FTD.^[10]Baclofen and tizanidine are the most commonly used oral drugs for treating spasticity; an intrathecal baclofen pump can be used for severe spasticity.^[10]Atropine, scopolamine, amitriptyline or glycopyrrolate may be prescribed when people with ALS begin having trouble swallowing their saliva (sialorrhea).^[10] A 2012 review found no evidence that medications are effective at reducing muscle cramps experienced by people with ALS; however, many of the 20 studies analyzed were too small to come to a definite conclusion about efficacy.^[108] A 2017 review concluded that mexiletine was safe and effective for treating cramps in ALS based on a randomized controlled trial from 2016.^[106]

Breathing support[edit]

Non-invasive ventilation[edit]

Non-invasive ventilation (NIV) is the primary treatment for respiratory failure in ALS^[10] and was the first treatment shown to improve both survival and quality of life in people with ALS.^[5] NIV uses a face or nasal mask connected to a ventilator that provides intermittent positive pressure to support breathing. Continuous positive pressure is not recommended for people with ALS because it makes breathing more difficult.^[14] Initially, NIV is used only at night^[5] because the first sign of respiratory failure is decreased gas exchange (hypoventilation) during sleep; symptoms associated with this nocturnal hypoventilation include interrupted sleep, anxiety, morning headaches, and daytime fatigue. As the disease progresses, people with ALS develop shortness of breath when lying down, during physical activity or talking, and eventually at rest.^[109] Other symptoms include poor concentration, poor memory, confusion, respiratory tract infections, and a weak cough. Respiratory failure is the most common cause of death in ALS.^[5]

It is important to monitor the respiratory function of people with ALS every three months, because beginning NIV soon after the start of respiratory symptoms is associated with increased survival. This involves asking the person with ALS if they have any respiratory symptoms and measuring their respiratory function.^[5] The most commonly used measurement is upright forced vital capacity (FVC), but it is a poor detector of early respiratory failure and is not a good choice for those with bulbar symptoms, as they have difficulty maintaining a tight seal around the mouthpiece. Measuring FVC while the person is lying on their back (supine FVC) is a more accurate measure of diaphragm weakness than upright FVC.^[97] Sniff nasal inspiratory pressure (SNIP) is a rapid, convenient test of diaphragm strength that is not affected by bulbar muscle weakness.^[14] If someone with ALS has signs and symptoms of respiratory failure, they should undergo daytime blood gas analysis^[5] to look for hypoxemia (low oxygen in the blood) and hypercapnia (too much carbon dioxide in the blood).^[14] If their daytime blood gas analysis is normal, they should then have nocturnal pulse oximetry to look for hypoxemia during sleep.^[5]

Non-invasive ventilation prolongs survival longer than riluzole. A 2006 randomized controlled trial found that NIV prolongs survival by about 48 days and improves quality of life; however, it also found that some people with ALS benefit more from this intervention than others. For those with normal or only moderately impaired bulbar function, NIV prolongs survival by about seven months and significantly improves quality of life. For those with poor bulbar function, NIV neither prolongs survival nor improves quality of life, though it does improve some sleep-related symptoms.^[96] Despite the clear benefits of NIV, about 25–30% of all people with ALS are unable to tolerate it, especially those with cognitive impairment or bulbar dysfunction.^[97] Results from a large 2015 cohort study suggest that NIV may prolong survival in those with bulbar weakness, and so NIV should be offered to all people with ALS, even if it is likely that they will have difficulty tolerating it.^[11]

Invasive ventilation[edit]

Invasive ventilation bypasses the nose and mouth (the upper airways) by making a cut in the trachea (tracheostomy) and inserting a tube connected to a ventilator.^[14] It is an option for people with advanced ALS whose respiratory symptoms are poorly managed despite continuous NIV use.^[5] While invasive ventilation prolongs survival, especially for those younger than 60, it does not treat the underlying neurodegenerative process. The person with ALS will continue to lose motor function, making communication increasingly difficult and sometimes leading to locked-in syndrome, in which they are completely paralyzed except for their eye muscles.^[14] About half of the people with ALS who choose to undergo invasive ventilation report a decrease in their quality of life^[15] but most still consider it to be satisfactory. However, invasive ventilation imposes a heavy burden on caregivers and may decrease their quality of life.^[14] Attitudes toward invasive ventilation vary from country to country; about 30% of people with ALS in Japan choose invasive ventilation, versus less than 5% in North America and Europe.^[98]

Therapy[edit]

Physical therapy plays a large role in rehabilitation for individuals with ALS. Specifically, physical, occupational, and speech therapists can set goals and promote benefits for individuals with ALS by delaying loss of strength, maintaining endurance, limiting pain, improving speech and swallowing, preventing complications, and promoting functional independence.^[99][100]

Occupational therapy and special equipment such as assistive technology can also enhance people's independence and safety throughout the course of ALS.^[101] Gentle, low-impact aerobic exercise such as performing activities of daily living, walking, swimming, and stationary bicycling can strengthen unaffected muscles, improve cardiovascular health, and help people fight fatigue and depression. Range of motion and stretching exercises can help prevent painful spasticity and shortening (contracture) of muscles. Physical and occupational therapists can recommend exercises that provide these benefits without overworking muscles, because muscle exhaustion can lead to worsening of symptoms associated with ALS, rather than providing help to people with ALS.^[95] They can suggest devices such as ramps, braces, walkers, bathroom equipment (shower chairs, toilet risers, etc.), and wheelchairs that help people remain mobile. Occupational therapists can provide or recommend equipment and adaptations to enable ALS people to retain as much safety and independence in activities of daily living as possible.^[101]

People with ALS who have difficulty speaking may benefit from working with a speech-language pathologist.^[100] These health professionals can teach people adaptive strategies such as techniques to help them speak louder and more clearly. As ALS progresses, speech-language pathologists can recommend the use of augmentative and alternative communication such as voice amplifiers, speech-generating devices (or voice output communication devices) or low-tech communication techniques such as head mounted laser pointers, alphabet boards or yes/no signals.^[100]

Nutrition[edit]

Preventing weight loss and malnutrition in people with ALS improves both survival and quality of life.^[11] Weight loss in ALS is caused by muscle wasting due to motor neuron death, increased resting energy expenditure, and decreased food intake. Difficulty swallowing (dysphagia) develops in about 85% of people with ALS at some point over the course of their disease and is a major cause of decreased food intake, leading to malnutrition and weight loss.^[14] It is important to regularly assess the weight and swallowing ability of people with ALS.^[5] Initially, dysphagia can be managed by dietary changes and modified swallowing techniques.^[10] Difficulty swallowing liquids usually develops first and can be managed by switching to thicker liquids like fruit nectar or smoothies, or by adding fluid thickeners to thin fluids like water and coffee. People with ALS should eat soft, moist foods, which tend to be easier to swallow than dry, crumbly, or chewy foods.^[109] They should also be instructed on proper head posture during swallowing, which can make swallowing easier.^[10] There is tentative evidence that high-calorie diets may prevent further weight loss and improve survival.^[106]

A feeding tube should be considered if someone with ALS loses 5% or more of their body weight or if they cannot safely swallow food and water.^[10] This can take the form of a gastrostomy tube, in which a tube is placed through the wall of the abdomen into the stomach, or a nasogastric tube, in which a tube is placed through the nose and down the esophagus into the stomach.^[14] A gastrostomy tube is more appropriate for long-term use^[5] than a nasogastric tube, which is uncomfortable and can cause esophageal ulcers.^[14] The feeding tube is usually inserted by percutaneous endoscopic gastrostomy (PEG). There is some evidence that a PEG tube should be inserted before vital capacity drops below 50% of expected, as a low vital capacity may be associated with a higher risk of complications. However, a large 2015 study showed that PEG insertion is safe in people with advanced ALS and low vital capacities, as long as they are on NIV during the procedure.^[106]

There is weak evidence that PEG tubes improve survival.^[102] PEG insertion is usually performed with the intent of improving quality of life^[15] by sustaining nutrition and medication intake.^[5] This reduces the risk of weight loss and dehydration, and can decrease anxiety from extended mealtimes^[15] and decreased oral food intake.^[5]

End-of-life care[edit]

Palliative care, which relieves symptoms and improves quality of life without treating the underlying disease, should begin shortly after someone is diagnosed with ALS.^[103] Early discussion of end-of-life issues gives people with ALS time to reflect on their preferences for end-of-life care and can help avoid unwanted interventions or procedures.^[15] Once they have been fully informed about all aspects of various life-prolonging measures, they can fill out advanced directives indicating their attitude toward noninvasive ventilation, invasive ventilation, and feeding tubes.^[106] Late in the disease course, difficulty speaking due to muscle weakness (dysarthria) and cognitive dysfunction may impair their ability to communicate their wishes regarding care.^[10] Continued failure to solicit the preferences of the person with ALS may lead to unplanned and potentially unwanted emergency interventions, such as invasive ventilation. If people with ALS or their family members are reluctant to discuss end-of-life issues, it may be useful to use the introduction of gastrostomy or noninvasive ventilation as an opportunity to bring up the subject.^[15]

Hospice care, or palliative care at the end of life, is especially important in ALS because it helps to optimize the management of symptoms and increases the likelihood of a peaceful death.^[15] It is unclear exactly when the end-of-life phase begins in ALS, but it is associated with significant difficulty moving, communicating, and, in some cases, thinking.^[10] Although many people with ALS fear choking to death (suffocating),^[15] they can be reassured that this occurs rarely, about 0–3% of the time. About 90% of people with ALS die peacefully.^[110] In the final days of life, opioids can be used to treat pain and dyspnea, while benzodiazepines can be used to treat anxiety.^[14]

Epidemiology[edit]

ALS is the most common motor neuron disease in adults and the third most common neurodegenerative disease^[22] after Alzheimer's disease and Parkinson's disease.^[111] Worldwide the number of people who develop ALS yearly is estimated to be 1.9 people per 100,000 per year, while the number of people who have ALS at any given time is estimated to be about 4.5 people per 100,000.^[112] In Europe, the number of new cases a year is about 2.6 people per 100,000, while the number affected is 7–9 people per 100,000.^[8] The lifetime risk of developing ALS is 1:350 for European men and 1:400 for European women. Men have a higher risk mainly because spinal-onset ALS is more common in men than women.^[17] The number of those with ALS in the United States in 2015 was 5.2 people per 100,000; and was higher in whites, males, and people over 60 years old.^[18] The number of new cases is about 0.8 people per 100,000 per year in east Asia and about 0.7 people per 100,000 per year in south Asia. About 80% of ALS epidemiology studies have been conducted in Europe and the United States, mostly in people of northern European descent.^[10] There is not enough information to determine the rates of ALS in much of the world, including Africa, parts of Asia, India, Russia, and South America.^[17] There are several geographic clusters in the Western Pacific where the prevalence of ALS was reported to be 50–100 times higher than the rest of the world, including Guam, the Kii Peninsula of Japan, and Western New Guinea. The incidence in these areas has decreased since the 1960s;^[1] the cause remains unknown.^[17]

People of all races and ethnic backgrounds may be affected by ALS,^[18] but it is more common in whites than in Africans, Asians, or Hispanics.^[113] In the United States in 2015, the prevalence of ALS in whites was 5.4 people per 100,000, while the prevalence in blacks was 2.3 people per 100,000. The Midwest had the highest prevalence of the four US Census regions with 5.5 people per 100,000, followed by the Northeast (5.1), the South (4.7), and the West (4.4). The Midwest and Northeast likely had a higher prevalence of ALS because they have a higher proportion of whites than the South and West.^[18] Ethnically mixed populations may be at a lower risk of developing ALS; a study in Cuba found that people of mixed ancestry were less likely to die from ALS than whites or blacks.^[114] There are also differences in the genetics of ALS between different ethnic groups; the most common ALS gene in Europe is C9orf72, followed by SOD1, TARDBP, and FUS, while the most common ALS gene in Asia is SOD1, followed by FUS, C9orf72, and TARDBP.^[115]

ALS can affect people at any age,^[36] but the peak incidence is between 50–75 years^[11] and decreases dramatically after 80 years.^[3] The reason for the decreased incidence in the elderly is unclear. One thought is that people who survive into their 80s may not be genetically susceptible to developing ALS; alternatively, ALS in the elderly might go undiagnosed because of comorbidities (other diseases they have), difficulty seeing a neurologist, or dying quickly from an aggressive form of ALS.^[114] In the United States in 2015, the lowest prevalence was in the 18–39 age group, while the highest prevalence was in the 70–79 age group.^[18] Sporadic ALS usually starts around the ages of 58 to 63 years, while familial ALS starts earlier, usually around 47 to 52 years.^[3] The number of ALS cases worldwide is projected to increase from 222,801 in 2015 to 376,674 in 2040, an increase of 69%. This will largely be due to the aging of the world's population, especially in developing countries.^[113]

History[edit]

Descriptions of the disease date back to at least 1824 by Charles Bell.^[19] In 1850, François-Amilcar Aran was the first to describe a disorder he named 'progressive muscular atrophy', a form of ALS in which only the lower motor neurons are affected.^[116] In 1869, the connection between the symptoms and the underlying neurological problems were first described by Jean-Martin Charcot, who initially introduced the term amyotrophic lateral sclerosis in his 1874 paper.^[19] Flail arm syndrome, a regional variant of ALS, was first described by Alfred Vulpian in 1886. Flail leg syndrome, another regional variant of ALS, was first described by Pierre Marie and his student Patrikios in 1918.^[117]

In 1945, American naval doctors reported that ALS was 100 times more prevalent among the Chamorro people of Guam than in the rest of the world. In 1956 the variant of ALS endemic to Guam was named 'amyotrophic lateral sclerosis/parkinsonism dementia complex' (ALS/PDC), as it had the typical symptoms of ALS accompanied by parkinsonism-like symptoms; the name in the local language is lytico-bodig disease. Despite a number of genetic and environmental studies, the cause of ALS/PDC remains unknown. Rates peaked in the early 1950s and steadily declined thereafter, and by 1985 the incidence of ALS/PDC in Guam was about the same as the rest of the world.^[118]

The first gene to be associated with ALS was SOD1, which was identified in 1993.^[22] This led to the development of the first animal model of ALS, the transgenicSOD1 mouse, in 1994.^[23] In December 1995, riluzole became the first FDA-approved drug for ALS. It was then approved in Europe in 1996 and in Japan in 1998.^[94] In 1996, the ALS Functional Rating Scale (ALSFRS) was first published; it was a 10-item questionnaire that measured the ability of people with ALS to perform activities of daily living.^[119] In 1999, the scale was changed to give more weight to respiratory symptoms. The resulting Revised ALS Functional Rating Scale (ALSFRS-R) is a 12-item questionnaire that replaces the single question about breathing with a question each about dyspnea, orthopnea, and respiratory insufficiency.^[120]

In 2006, it was discovered that the protein TDP-43 is a major component of the inclusion bodies seen in both ALS and frontotemporal dementia (FTD), which provided evidence that ALS and FTD are part of a common disease spectrum. This led to the discovery in 2008 that mutations in TARDBP, the gene that codes for TDP-43, are a cause of familial ALS.^[22] In 2011, noncoding repeat expansions in C9orf72 were found to be a major cause of ALS and FTD.^[10] Edaravone was approved to treat ALS in Japan and South Korea in 2015 and in the United States in 2017.^[107] As of 2017, it has not been approved to treat ALS in Europe.^[106]

Diagnostic criteria[edit]

In the 1950s, electrodiagnostic testing (EMG and NCV) began to be used to evaluate clinically suspected ALS. In 1969 Edward H. Lambert published the first EMG/NCS diagnostic criteria for ALS, consisting of four findings he considered to strongly support the diagnosis.^[121] In 1990, the World Federation of Neurology (WFN) held a meeting at El Escorial, Spain, to come up with precise diagnostic criteria for ALS to help standardize clinical trials; the resulting 'El Escorial' criteria were published in 1994.^[122] In 1998, the WFN held another meeting to revise the criteria at Airlie House in Warrenton, Virginia; the resulting 'Airlie House' or 'El Escorial Revised' criteria were published in 2000.^[123] In 2006, a meeting was held on Awaji Island in Japan to discuss how to use EMG and NCV tests to help diagnose ALS earlier; the resulting 'Awaji' criteria were published in 2008.^[82]

Name[edit]

Other names for ALS include Charcot's disease, Lou Gehrig's disease, and motor neurone disease.^[1]Amyotrophic comes from the Greek word amyotrophia: a- means 'no', myo refers to 'muscle', and trophia means 'nourishment'. Therefore, amyotrophia means 'no muscle nourishment,'^[125] which describes the loss of signals motor neurons usually send to muscle cells;^[126] this leads to the characteristic muscle atrophy seen in people with ALS. Lateral identifies the areas in a person's spinal cord where the affected motor neurons that control muscle are located. Sclerosis means 'scarring' or 'hardening' and refers to the death of the motor neurons in the spinal cord.^[125]

ALS is sometimes referred to as 'Charcot's disease' because Jean-Martin Charcot was the first to connect the clinical symptoms with the pathology seen at autopsy. The term is ambiguous and can also refer to Charcot–Marie–Tooth disease and Charcot joint disease.^[127] The British neurologist Russell Brain coined the term 'motor neurone disease' in 1933 to reflect his belief that ALS, progressive bulbar palsy, and progressive muscular atrophy were all different forms of the same disease.^[128] In some countries, especially the United States, ALS is called 'Lou Gehrig's disease',^[124] after the famous American baseball player Lou Gehrig, who developed ALS in 1938, had to stop playing baseball in 1939, and died from it in 1941.^[129]

B 169

In the United States, the terms 'ALS' or 'Lou Gehrig's disease' refer to all forms of the disease, including classical ALS, progressive bulbar palsy, progressive muscular atrophy, and primary lateral sclerosis.^[130] In Europe, the term 'ALS' also refers to all forms of the disease.^[31] In the United Kingdom and Australia, the term 'motor neurone disease' refers to all forms of the disease, and 'ALS' only refers to classical ALS, meaning the form with both upper and lower motor neuron involvement.^[130]

Society and culture[edit]

In August 2014, a challenge went viral online, commonly known as the 'ALS Ice Bucket Challenge'.^[131] Contestants fill a bucket full of ice and water, then state who nominated them to do the challenge, and nominate three other individuals of their choice to take part in it. The contestants then dump the buckets of ice and water onto themselves. However, it can be done in a different order. The contestants then donate at least US$10 (or a similar amount in their local currency) to ALS research at the ALS Association, the ALS Therapy Development Institute, ALS Society of Canada or Motor Neurone Disease Association in the UK. Any contestants who refuse to have the ice and water dumped on them are expected to donate at least US$100 to ALS research. As of July 2015, the Ice Bucket Challenge had raised $115 million for the ALS Association.^[132] Many celebrities have taken part in the challenge.^[133] The Ice Bucket Challenge was credited with helping to raise funds that contributed to the discovery that the gene NEK1 may potentially contribute to the development for ALS.^[134][135]

Research[edit]

Model organisms[edit]

Many different organisms are used as models for studying ALS, including Saccharomyces cerevisiae (a species of yeast),^[79]Caenorhabditis elegans (a roundworm), Drosophila melanogaster (the common fruit fly), Danio rerio (the zebrafish), Mus musculus (the house mouse), and Rattus norvegicus (the common rat).^[11] None of these models perfectly represents ALS in humans, partly because most animal models are based on gene overexpression, meaning that multiple copies of the mutant human gene are inserted into the transgenic model, and partly because the human nervous system is very different from that of other animals.^[10]

The first animal model for ALS was the SOD1^G93A transgenic mouse,^[g] which was developed in 1994. It expresses about 20–24 copies of the mutant human SOD1 gene^[136] and reproduces most of the clinical and pathological findings seen in ALS.^[137] Although there are now over 20 different SOD1 mouse models, the SOD1^G93A model remains both the most widely used SOD1 mouse model^[136] and the most widely used ALS mouse model overall.^[23] Much of the present understanding of ALS pathophysiology came from studying mouse models that overexpress mutant SOD1,^[136] especially SOD1^G93A mice.^[23] However, many drug targets that were shown to be effective in the SOD1^G93A transgenic mouse failed in clinical trials in humans; other SOD1 models have had similar problems.^[136] Most of these drugs were identified as potentially effective based on a single study in a rodent SOD1 model and then failed in clinical trials in patients who primarily had sporadic ALS.^[79] It is thought that these clinical trials failed because SOD1 mutations account for only 2% of all ALS cases^[136] and because the pathology of SOD1 ALS is thought to be distinct from all other types of ALS; it lacks the abnormal aggregations of TDP-43 protein and/or FUS protein seen in nearly all other cases of ALS.^[22]

Brinkmann B169 Manual Dexterity Activities

As of 2018, there are about 20 TARDBP mouse models, a dozen FUS mouse models, and a number of C9orf72, PFN1, and UBQLN2 mouse models. There are also new methods of developing animal models, including viral transgenesis, in which viruses are used to deliver mutant genes to an animal model, and CRISPR/Cas9, which can be used to give an animal model multiple mutated genes. Both of these methods are faster and cheaper than traditional methods of genetically engineering mice; they also allow scientists to study the effects of a mutation in mice of different genetic backgrounds, which better represents the genetic diversity seen in humans.^[23]

Cellular models used to study ALS include the yeast Saccharomyces cerevisiae and rat or mouse motor neurons in culture. Small-animal models include the fruit fly, the roundworm C. elegans, and the zebrafish. Of the three, the fruit fly is the most widely used; it has a rapid life-cycle, short lifespan, a sophisticated nervous system, and many genetic tools available. C. elegans has a short life-cycle, is easy to manipulate genetically, and has a simple but well-understood nervous system. The zebrafish has transparent embryos that can be injected with DNA or RNA and has a lifespan of up to two years.^[79]Induced pluripotent stem cells (iPSCs) can be used to convert skin fibroblasts into motor neurons.^[11] It is now possible to generate iPSCs from people with ALS, which can then be converted into spinal motor neurons, which are useful for studying disease mechanisms and for testing potential drugs for ALS. iPSCs allow sporadic ALS to be modeled, which cannot be done with animal models.^[79]

Treatments[edit]

From the 1960s until 2014, about 50 drugs for ALS were tested in randomized controlled trials (RCTs);^[h] of these, riluzole was the only one that showed a slight benefit in improving survival. Drugs tested and not shown to be effective in clinical trials in humans include antiviral drugs, anti-excitotoxic drugs, growth factors, neurotrophic factors, anti-inflammatory drugs, antioxidants, anti-apoptotic drugs, and drugs to improve mitochondria function.^[138]

An analysis of 23 large phase II and phase III RCTs that failed between 2004 and 2014 concluded that there were many potential reasons for their lack of success. These trials in humans went ahead on the basis of positive results in SOD1 transgenic mice, which are not a good animal model for sporadic ALS. Additionally, in most preclinical studies the SOD1 mice were given the drug during the presymptomatic stage; this makes the results less likely to apply to people with ALS, who begin treatment well after their symptoms begin. Positive results in small phase II studies in humans could also be misleading and lead to failure in phase III trials. Other potential issues included the drug not reaching its intended site of action in the central nervous system and drug interactions between the study drug and riluzole.^[138]

Repetitive transcranial magnetic stimulation had been studied in ALS in small and poorly designed clinical trials; as of 2013, evidence was insufficient to know whether rTMS is safe or effective for ALS.^[139] One 2016 review of stem-cell therapy trials found tentative evidence that intraspinal stem cell implantation was relatively safe and possibly effective.^[140] A 2016 Cochrane review of cell-based therapies found that there was insufficient evidence to speculate about efficacy.^[141]Masitinib has been approved as an orphan drug in Europe and the United States, with studies ongoing as of 2016.^[142]Beta-adrenergic agonist drugs have been proposed as a treatment for their effects on muscle growth and neuroprotection, but research in humans is insufficient to determine their efficacy.^[143]

Cause[edit]

With the discovery that TDP-43, FUS, and C9orf72 can cause ALS as well as related forms of frontotemporal dementia (FTD/ALS)^[144][145] there has been intense effort to understand how these mutations cause disease, and whether other protein dysfunction may be important. As of 2013 it appeared that differences in the methylation of arginine residues in FUS protein may be relevant, and methylation status may be a way to distinguish some forms of FTD from ALS.^[146]

Notes[edit]

1. ^Additional names for flail arm syndrome include the scapulohumeral form of ALS, Vulpian–Bernart syndrome, hanging arm syndrome, and neurogenic man-in-a-barrel syndrome.^[16]
2. ^Additional names for flail leg syndrome that involves both lower legs (bilateral distal involvement) include pseudopolyneuritic ALS, Patrikios syndrome, Marie-Patrikios ALS, and the peroneal form of ALS.^[16]
3. ^According to one cohort study, 11.5% of people with ALS have extraocular muscle dysfunction.^[34]
4. ^In 2013, the NFL reached a $765 million agreement to compensate more than five thousand former NFL players for concussion-related injuries and illnesses.^[73] Some NFL players involved in the legal settlement complained that the NFL was not doing enough to help players. The judge in the case concurred, and in 2015 the NFL agreed to pay an unlimited amount of damages for players found to have ALS, Parkinson's disease, Alzheimer's disease, or dementia.^[74]
5. ^The criteria are 'scores of at least 2 points on all 12 items of ALSFRS-R, forced vital capacity of 80% or more, definite or probable ALS according to the revised El Escorial criteria, and disease duration of 2 years or less.'^[93]
6. ^Based on population-based ALS registries, it is estimated that less than 7% of people with ALS meet these criteria.^[105]
7. ^'G93A' means that the 93rd amino acid residue in the SOD1 protein has been changed from glycine to alanine.
8. ^For the complete list, see Amyotrophic lateral sclerosis research#Past clinical trials.

References[edit]

1. ^ ^abcWijesekera LC, Leigh PN (February 2009). 'Amyotrophic lateral sclerosis'. Orphanet Journal of Rare Diseases. 3 (4): 1–22. doi:10.1186/1750-1172-4-3. PMC2656493. PMID19192301.
2. ^ ^abcdef'Motor Neuron Diseases Fact Sheet'. National Institute of Neurological Disorders and Stroke. Archived from the original on 13 April 2014. Retrieved 7 November 2010.
3. ^ ^abcdefghijklKiernan MC, Vucic S, Cheah BC, Turner MR, Eisen A, Hardiman O, Burrell JR, Zoing MC (March 2011). 'Amyotrophic lateral sclerosis'. Lancet. 377 (9769): 942–55. doi:10.1016/s0140-6736(10)61156-7. PMID21296405.
4. ^ ^{abcdefghijklmnopqrstuvwx}'Amyotrophic Lateral Sclerosis (ALS) Fact Sheet'. National Institute of Neurological Disorders and Stroke. 19 September 2014. Archived from the original on 5 January 2017. Retrieved 2 January 2015.
5. ^ ^{abcdefghijklmnopqrstuvwx}Hobson EV, McDermott CJ (September 2016). 'Supportive and symptomatic management of amyotrophic lateral sclerosis'. Nature Reviews. Neurology. 12 (9): 526–38. doi:10.1038/nrneurol.2016.111. PMID27514291.
6. ^ ^abcdefgMiller RG, Mitchell JD, Moore DH (March 2012). 'Riluzole for amyotrophic lateral sclerosis (ALS)/motor neuron disease (MND)'. The Cochrane Database of Systematic Reviews. 3 (3): CD001447. doi:10.1002/14651858.CD001447.pub3. PMID22419278.
7. ^'FDA approves drug to treat ALS'. U.S. Food and Drug Administration. 5 May 2017. Archived from the original on 8 May 2017.
8. ^ ^abcHardiman O, Al-Chalabi A, Brayne C, Beghi E, van den Berg LH, Chio A, Martin S, Logroscino G, Rooney J (July 2017). 'The changing picture of amyotrophic lateral sclerosis: lessons from European registers'. Journal of Neurology, Neurosurgery, and Psychiatry. 88 (7): 557–63. doi:10.1136/jnnp-2016-314495. PMID28285264.
9. ^ ^ab'Motor neurone disease'. NHS Choices. Archived from the original on 29 December 2014. Retrieved 2 January 2015.
10. ^ ^{abcdefghijklmnopqrstuvwxyzaa}Hardiman O, Al-Chalabi A, Chio A, Corr EM, Logroscino G, Robberecht W, Shaw PJ, Simmons Z, van den Berg LH (October 2017). 'Amyotrophic lateral sclerosis'. Nature Reviews. Disease Primers. 3 (17071): 17071. doi:10.1038/nrdp.2017.71. PMID28980624.
11. ^ ^{abcdefghijklmnopqrstuv}van Es MA, Hardiman O, Chio A, Al-Chalabi A, Pasterkamp RJ, Veldink JH, van den Berg LH (November 2017). 'Amyotrophic lateral sclerosis'. Lancet. 390 (10107): 2084–2098. doi:10.1016/S0140-6736(17)31287-4. PMID28552366.
12. ^ ^abcChiò A, Mora G, Lauria G (February 2017). 'Pain in amyotrophic lateral sclerosis'. The Lancet. Neurology. 16 (2): 144–57. arXiv:1607.02870. doi:10.1016/S1474-4422(16)30358-1. PMID27964824.
13. ^ ^abcWingo TS, Cutler DJ, Yarab N, Kelly CM, Glass JD (2011). 'The heritability of amyotrophic lateral sclerosis in a clinically ascertained United States research registry'. PLOS One. 6 (11): e27985. Bibcode:2011PLoSO..627985W. doi:10.1371/journal.pone.0027985. PMC3222666. PMID22132186.
14. ^ ^{abcdefghijklmn}Soriani M, Desnuelle C (May 2017). 'Care management in amyotrophic lateral sclerosis'. Revue Neurologique. 173 (5): 288–89. doi:10.1016/j.neurol.2017.03.031. PMID28461024.
15. ^ ^abcdefghijkConnolly S, Galvin M, Hardiman O (April 2015). 'End-of-life management in patients with amyotrophic lateral sclerosis'. The Lancet. Neurology. 14 (4): 435–42. doi:10.1016/S1474-4422(14)70221-2. PMID25728958.
16. ^ ^abcdefghiSwinnen B, Robberecht W (November 2014). 'The phenotypic variability of amyotrophic lateral sclerosis'. Nature Reviews. Neurology. 10 (11): 661–70. doi:10.1038/nrneurol.2014.184. PMID25311585.
17. ^ ^abcdefgAl-Chalabi A, Hardiman O (November 2013). 'The epidemiology of ALS: a conspiracy of genes, environment and time'. Nature Reviews. Neurology. 9 (11): 617–28. doi:10.1038/nrneurol.2013.203. PMID24126629.
18. ^ ^abcdefMehta P, Kaye W, Raymond J, Punjabi R, Larson T, Cohen J, Muravov O, Horton K (November 2018). 'Prevalence of Amyotrophic Lateral Sclerosis – United States, 2015'. Morbidity and Mortality Weekly Report. 67 (46): 1285–1289. doi:10.15585/mmwr.mm6746a1. PMC5858037. PMID30462626.
19. ^ ^abcdeRowland LP (March 2001). 'How amyotrophic lateral sclerosis got its name: the clinical-pathologic genius of Jean-Martin Charcot'. Archives of Neurology. 58 (3): 512–15. doi:10.1001/archneur.58.3.512. PMID11255459.
20. ^Kelly, Evelyn B. (2013). Encyclopedia of human genetics and disease. Santa Barbara, CA: Greenwood. pp. 79–80. ISBN978-0-313-38713-5. Archived from the original on 8 September 2017.
21. ^Youngson, David B. Jacoby, Robert M. (2004). Encyclopedia of family health (3rd ed.). Tarrytown, NY: Marshall Cavendish. p. 1256. ISBN978-0-7614-7486-9. Archived from the original on 8 September 2017.
22. ^ ^abcdefghRenton AE, Chiò A, Traynor BJ (January 2014). 'State of play in amyotrophic lateral sclerosis genetics'. Nature Neuroscience. 17 (1): 17–23. doi:10.1038/nn.3584. hdl:2318/156177. PMC4544832. PMID24369373.
23. ^ ^abcdeLutz C (August 2018). 'Mouse models of ALS: Past, present and future'. Brain Research. 1693 (Part A): 1–10. doi:10.1016/j.brainres.2018.03.024. PMID29577886.
24. ^Song P (August 2014). 'The Ice Bucket Challenge: The public sector should get ready to promptly promote the sustained development of a system of medical care for and research into rare diseases'. Intractable & Rare Diseases Research. 3 (3): 94–96. doi:10.5582/irdr.2014.01015. PMC4214244. PMID25364651.
25. ^'8B60 Motor neuron disease'. ICD-11 for Mortality and Morbidity Statistics. World Health Organization. Retrieved 24 January 2019.
26. ^ ^abcJawdat O, Statland JM, Barohn RJ, Katz JS, Dimachkie MM (November 2015). 'Amyotrophic Lateral Sclerosis Regional Variants (Brachial Amyotrophic Diplegia, Leg Amyotrophic Diplegia, and Isolated Bulbar Amyotrophic Lateral Sclerosis)'. Neurologic Clinics. 33 (4): 775–85. doi:10.1016/j.ncl.2015.07.003. PMC4629514. PMID26515621.
27. ^ ^abcdefGrad LI, Rouleau GA, Ravits J, Cashman NR (August 2017). 'Clinical Spectrum of Amyotrophic Lateral Sclerosis (ALS)'. Cold Spring Harbor Perspectives in Medicine. 7 (8): 1–16. doi:10.1101/cshperspect.a024117. PMID28003278.
28. ^ ^abcdChiò A, Calvo A, Moglia C, Mazzini L, Mora G (July 2011). 'Phenotypic heterogeneity of amyotrophic lateral sclerosis: a population based study'. Journal of Neurology, Neurosurgery, and Psychiatry. 82 (7): 740–46. doi:10.1136/jnnp.2010.235952. PMID21402743.
29. ^ ^abcdeAl-Chalabi A, Hardiman O, Kiernan MC, Chiò A, Rix-Brooks B, van den Berg LH (October 2016). 'Amyotrophic lateral sclerosis: moving towards a new classification system'. The Lancet. Neurology. 15 (11): 1182–94. doi:10.1016/S1474-4422(16)30199-5. PMID27647646.
30. ^Teoh HL, Carey K, Sampaio H, Mowat D, Roscioli T, Farrar M (2017). 'Inherited Paediatric Motor Neuron Disorders: Beyond Spinal Muscular Atrophy'. Neural Plasticity. 2017: 1–22. doi:10.1155/2017/6509493. PMC5467325. PMID28634552.
31. ^ ^abTard C, Defebvre L, Moreau C, Devos D, Danel-Brunaud V (May 2017). 'Clinical features of amyotrophic lateral sclerosis and their prognostic value'. Revue Neurologique. 173 (5): 263–72. doi:10.1016/j.neurol.2017.03.029. PMID28477850.
32. ^Gautier G, Verschueren A, Monnier A, Attarian S, Salort-Campana E, Pouget J (August 2010). 'ALS with respiratory onset: Clinical features and effects of non-invasive ventilation on the prognosis'. Amyotrophic Lateral Sclerosis. 11 (4): 379–82. doi:10.3109/17482960903426543. PMID20001486.
33. ^Lui AJ, Byl NN (June 2009). 'A Systematic Review of the Effect of Moderate Intensity Exercise on Function and Disease Progression in Amyotrophic Lateral Sclerosis'. Journal of Neurologic Physical Therapy. 33 (2): 68–87. doi:10.1097/NPT.0b013e31819912d0. PMID19556916.
34. ^McCluskey L, Vandriel S, Elman L, Van Deerlin VM, Powers J, Boller A, et al. (15 October 2014). 'ALS-Plus syndrome: Non-pyramidal features in a large ALS cohort'. Journal of the Neurological Sciences. 345 (1–2): 118–24. doi:10.1016/j.jns.2014.07.022. PMC4177937. PMID25086858.
35. ^ ^abcdefghijBrown RH, Al-Chalabi A (13 July 2017). 'Amyotrophic Lateral Sclerosis'. The New England Journal of Medicine. 377 (2): 162–172. doi:10.1056/NEJMra1603471. PMID28700839.
36. ^ ^abcMartin S, Al Khleifat A, Al-Chalabi A (2017). 'What causes amyotrophic lateral sclerosis?'. F1000Research. 6: 371. doi:10.12688/f1000research.10476.1. PMC5373425. PMID28408982.
37. ^Raaphorst J, Beeldman E, De Visser M, De Haan RJ, Schmand B (October 2012). 'A systematic review of behavioural changes in motor neuron disease'. Amyotrophic Lateral Sclerosis. 13 (6): 493–501. doi:10.3109/17482968.2012.656652. PMID22424127.
38. ^ ^abBeeldman E, Raaphorst J, Klein Twennaar M, de Visser M, Schmand BA, de Haan RJ (June 2016). 'The cognitive profile of ALS: a systematic review and meta-analysis update'. Journal of Neurology, Neurosurgery, and Psychiatry. 87 (6): 611–19. doi:10.1136/jnnp-2015-310734. PMID26283685.
39. ^ ^ab'Motor neurone disease – Symptoms'(Page last reviewed: Jan 15, 2015). NHS Choices. Archived from the original on 19 September 2016. Retrieved 18 September 2016.
40. ^Gordon PH, Miller RG, Moore DH (September 2004). 'ALSFRS-R'. Amyotrophic Lateral Sclerosis and Other Motor Neuron Disorders. 5 Suppl 1: 90–93. doi:10.1080/17434470410019906. PMID15512883.
41. ^Creemers H, Grupstra H, Nollet F, van den Berg LH, Beelen A (June 2015). 'Prognostic factors for the course of functional status of patients with ALS: a systematic review'. Journal of Neurology. 262 (6): 1407–23. doi:10.1007/s00415-014-7564-8. PMID25385051.
42. ^Castrillo-Viguera C, Grasso DL, Simpson E, Shefner J, Cudkowicz ME (2010). 'Clinical significance in the change of decline in ALSFRS-R'. Amyotrophic Lateral Sclerosis (Journal Article). 11 (1–2): 178–80. doi:10.3109/17482960903093710. PMID19634063.
43. ^Sabatelli M, Madia F, Conte A, Luigetti M, Zollino M, Mancuso I, Lo Monaco M, Lippi G, Tonali P (September 2008). 'Natural history of young-adult amyotrophic lateral sclerosis'. Neurology. 71 (12): 876–81. doi:10.1212/01.wnl.0000312378.94737.45. PMID18596241.
44. ^Paganoni S, Deng J, Jaffa M, Cudkowicz ME, Wills AM (July 2011). 'Body mass index, not dyslipidemia, is an independent predictor of survival in amyotrophic lateral sclerosis'. Muscle & Nerve. 44 (1): 20–24. doi:10.1002/mus.22114. PMC4441750. PMID21607987. Lay summary – Massachusetts General Hospital (11 May 2011).
45. ^'Stephen Hawking serves as role model for ALS patients'. CNN. 20 April 2009. Archived from the original on 15 August 2016.
46. ^ ^abVajda A, McLaughlin RL, Heverin M, Thorpe O, Abrahams S, Al-Chalabi A, Hardiman O (March 2017). 'Genetic testing in ALS: A survey of current practices'. Neurology. 88 (10): 991–999. doi:10.1212/WNL.0000000000003686. PMC5333513. PMID28159885.
47. ^Byrne S, Walsh C, Lynch C, Bede P, Elamin M, Kenna K, McLaughlin R, Hardiman O (June 2011). 'Rate of familial amyotrophic lateral sclerosis: a systematic review and meta-analysis'. Journal of Neurology, Neurosurgery, and Psychiatry. 82 (6): 623–7. doi:10.1136/jnnp.2010.224501. PMID21047878.
48. ^ ^abcHe J, Mangelsdorf M, Fan D, Bartlett P, Brown MA (December 2015). 'Amyotrophic Lateral Sclerosis Genetic Studies: From Genome-wide Association Mapping to Genome Sequencing'. The Neuroscientist. 21 (6): 599–615. doi:10.1177/1073858414555404. PMID25378359.
49. ^ ^abCorcia P, Couratier P, Blasco H, Andres CR, Beltran S, Meininger V, Vourc'h P (May 2017). 'Genetics of amyotrophic lateral sclerosis'. Revue Neurologique. 173 (5): 254–262. doi:10.1016/j.neurol.2017.03.030. PMID28449881.
50. ^Chia R, Chiò A, Traynor BJ (January 2018). 'Novel genes associated with amyotrophic lateral sclerosis: diagnostic and clinical implications'. The Lancet. Neurology. 17 (1): 94–102. doi:10.1016/S1474-4422(17)30401-5. PMC5901717. PMID29154141.
51. ^Zou ZY, Liu CY, Che CH, Huang HP (January 2016). 'Toward precision medicine in amyotrophic lateral sclerosis'. Annals of Translational Medicine. 4 (2): 27. doi:10.3978/j.issn.2305-5839.2016.01.16. PMC4731596. PMID26889480.
52. ^Sontheimer, Harald (2015). Diseases of the Nervous System. Academic Press. p. 170. ISBN978-0-12-800403-6. Archived from the original on 8 September 2017. Retrieved 2 May 2015.
53. ^Couratier P, Corcia P, Lautrette G, Nicol M, Marin B (May 2017). 'ALS and frontotemporal dementia belong to a common disease spectrum'. Revue Neurologique. 173 (5): 273–279. doi:10.1016/j.neurol.2017.04.001. PMID28449882.
54. ^ ^abNguyen HP, Van Broeckhoven C, van der Zee J (June 2018). 'ALS Genes in the Genomic Era and their Implications for FTD'. Trends in Genetics. 34 (6): 404–423. doi:10.1016/j.tig.2018.03.001. PMID29605155.
55. ^ ^abBeard JD, Kamel F (1 January 2015). 'Military service, deployments, and exposures in relation to amyotrophic lateral sclerosis etiology and survival'. Epidemiologic Reviews. 37 (1): 55–70. doi:10.1093/epirev/mxu001. PMC4325667. PMID25365170.
56. ^Belbasis L, Bellou V, Evangelou E (March 2016). 'Environmental Risk Factors and Amyotrophic Lateral Sclerosis: An Umbrella Review and Critical Assessment of Current Evidence from Systematic Reviews and Meta-Analyses of Observational Studies'. Neuroepidemiology. 46 (2): 96–105. doi:10.1159/000443146. PMID26731747.
57. ^Beard JD, Steege AL, Ju J, Lu J, Luckhaupt SE, Schubauer-Berigan MK (July 2017). 'Mortality from Amyotrophic Lateral Sclerosis and Parkinson's Disease Among Different Occupation Groups - United States, 1985-2011'. MMWR. Morbidity and Mortality Weekly Report. 66 (27): 718–722. doi:10.15585/mmwr.mm6627a2. PMC5687590. PMID28704346. Archived from the original on 22 December 2017.
58. ^Sutedja NA, Fischer K, Veldink JH, van der Heijden GJ, Kromhout H, Heederik D, Huisman MH, Wokke JJ, van den Berg LH (2009). 'What we truly know about occupation as a risk factor for ALS: a critical and systematic review'. Amyotrophic Lateral Sclerosis. 10 (5–6): 295–301. doi:10.3109/17482960802430799. PMID19922116.
59. ^Ingre C, Roos PM, Piehl F, Kamel F, Fang F (2015). 'Risk factors for amyotrophic lateral sclerosis'. Clinical Epidemiology. 7: 181–93. doi:10.2147/CLEP.S37505. PMC4334292. PMID25709501.
60. ^Kamel F, Umbach DM, Bedlack RS, Richards M, Watson M, Alavanja MC, Blair A, Hoppin JA, Schmidt S, Sandler DP (June 2012). 'Pesticide exposure and amyotrophic lateral sclerosis'. Neurotoxicology. 33 (3): 457–62. doi:10.1016/j.neuro.2012.04.001. PMC3358481. PMID22521219.
61. ^Bozzoni V, Pansarasa O, Diamanti L, Nosari G, Cereda C, Ceroni M (2016). 'Amyotrophic lateral sclerosis and environmental factors'. Functional Neurology. 31 (1): 7–19. PMC4819821. PMID27027889.
62. ^Malek AM, Barchowsky A, Bowser R, Youk A, Talbott EO (August 2012). 'Pesticide exposure as a risk factor for amyotrophic lateral sclerosis: a meta-analysis of epidemiological studies: pesticide exposure as a risk factor for ALS'. Environmental Research. 117: 112–9. Bibcode:2012ER..117.112M. doi:10.1016/j.envres.2012.06.007. PMID22819005.
63. ^ ^abGardner RC, Yaffe K (May 2015). 'Epidemiology of mild traumatic brain injury and neurodegenerative disease'. Molecular and Cellular Neurosciences. 66 (Pt B): 75–80. doi:10.1016/j.mcn.2015.03.001. PMC4461453. PMID25748121.
64. ^Watanabe Y, Watanabe T (October 2017). 'Meta-analytic evaluation of the association between head injury and risk of amyotrophic lateral sclerosis'. European Journal of Epidemiology. 32 (10): 867–879. doi:10.1007/s10654-017-0327-y. PMID29080013.
65. ^Veldink JH, Kalmijn S, Groeneveld GJ, Titulaer MJ, Wokke JH, van den Berg LH (January 2005). 'Physical activity and the association with sporadic ALS'. Neurology. 64 (2): 241–5. doi:10.1212/01.WNL.0000149513.82332.5C. PMID15668420.
66. ^Armon C (November 2007). 'Sports and trauma in amyotrophic lateral sclerosis revisited'. Journal of the Neurological Sciences. 262 (1–2): 45–53. doi:10.1016/j.jns.2007.06.021. PMID17681549.
67. ^ ^abHarwood CA, McDermott CJ, Shaw PJ (August 2009). 'Physical activity as an exogenous risk factor in motor neuron disease (MND): a review of the evidence'. Amyotrophic Lateral Sclerosis. 10 (4): 191–204. doi:10.1080/17482960802549739. PMID19263258.
68. ^Hamidou B, Couratier P, Besançon C, Nicol M, Preux PM, Marin B (July 2014). 'Epidemiological evidence that physical activity is not a risk factor for ALS'. European Journal of Epidemiology. 29 (7): 459–75. doi:10.1007/s10654-014-9923-2. PMID24986107.
69. ^Lacorte E, Ferrigno L, Leoncini E, Corbo M, Boccia S, Vanacore N (July 2016). 'Physical activity, and physical activity related to sports, leisure and occupational activity as risk factors for ALS: A systematic review'. Neuroscience and Biobehavioral Reviews. 66: 61–79. doi:10.1016/j.neubiorev.2016.04.007. PMID27108217.
70. ^Couratier P, Corcia P, Lautrette G, Nicol M, Preux P, Marin B (January 2016). 'Epidemiology of amyotrophic lateral sclerosis: A review of literature'. Neuroepidemiology. 172 (1): 37–45. doi:10.1016/j.neurol.2015.11.002. PMID26727307.
71. ^Tharmaratnam T, Iskandar M, Tabobondung T, Tobbia I, Gopee-Ramanan P, Tabobondung T (19 June 2018). 'Chronic Traumatic Encephalopathy in Professional American Football Players: Where Are We Now?'. Frontiers in Neurology. 19 (9): 445. doi:10.3389/fneur.2018.00445. PMC6018081. PMID29971037.
72. ^Gardner A, Iverson G, McCrory P (January 2014). 'Chronic traumatic encephalopathy in sport: a systematic review'. British Journal of Sports Medicine. 48 (2): 84–90. doi:10.1136/bjsports-2013-092646. PMID23803602.
73. ^Belson, Ken (22 April 2015). 'Judge Approves Deal in N.F.L. Concussion Suit'. New York Times. Retrieved 10 August 2018.
74. ^'Kevin Turner, N.F.L. Player Who Later Fought the League, Dies at 46'. The New York Times. 24 March 2016. Archived from the original on 28 March 2016. Retrieved 27 March 2016.
75. ^Armon C (November 2009). 'Smoking may be considered an established risk factor for sporadic ALS'. Neurology. 73 (20): 1693–8. doi:10.1212/WNL.0b013e3181c1df48. PMC2788806. PMID19917993.
76. ^Alonso A, Logroscino G, Hernán MA (November 2010). 'Smoking and the risk of amyotrophic lateral sclerosis: a systematic review and meta-analysis'. Journal of Neurology, Neurosurgery, and Psychiatry. 81 (11): 1249–52. doi:10.1136/jnnp.2009.180232. PMID20639382.
77. ^Wang H, O'Reilly ÉJ, Weisskopf MG, Logroscino G, McCullough ML, Thun MJ, Schatzkin A, Kolonel LN, Ascherio A (February 2011). 'Smoking and risk of amyotrophic lateral sclerosis: a pooled analysis of 5 prospective cohorts'. Archives of Neurology. 68 (2): 207–13. doi:10.1001/archneurol.2010.367. PMC3319086. PMID21320987.
78. ^Robberecht W, Philips T (April 2013). 'The changing scene of amyotrophic lateral sclerosis'. Nature Reviews. Neuroscience. 14 (4): 248–64. doi:10.1038/nrn3430. PMID23463272.
79. ^ ^abcdePhilip Van Damme P, Robberecht W, Van Den Bosch L (May 2017). 'Modelling amyotrophic lateral sclerosis: progress and possibilities'. Disease Models and Mechanisms. 10 (5): 537–49. doi:10.1242/dmm.029058. PMC5451175. PMID28468939.
80. ^Xu Z, Henderson RD, David M, McCombe PA (2016). 'Neurofilaments as Biomarkers for Amyotrophic Lateral Sclerosis: A Systematic Review and Meta-Analysis'. PLOS One. 11 (10): e0164625. Bibcode:2016PLoSO.1164625X. doi:10.1371/journal.pone.0164625. PMC5061412. PMID27732645.
81. ^Vu LT, Bowser R (January 2017). 'Fluid-Based Biomarkers for Amyotrophic Lateral Sclerosis'. Neurotherapeutics. 14 (1): 119–134. doi:10.1007/s13311-016-0503-x. PMC5233638. PMID27933485.
82. ^ ^abde Carvalho M, Dengler R, Eisen A, England JD, Kaji R, Kimura J, et al. (March 2008). 'Electrodiagnostic criteria for diagnosis of ALS'. Clinical Neurophysiology. 119 (3): 497–503. doi:10.1016/j.clinph.2007.09.143. PMID18164242.
83. ^Costa J, Swash M, de Carvalho M (November 2012). 'Awaji Criteria for the Diagnosis of Amyotrophic Lateral Sclerosis: A Systematic Review'. Archives of Neurology. 69 (11): 1410–1416. doi:10.1001/archneurol.2012.254. PMID22892641.
84. ^Belsh JM (March 2000). 'ALS diagnostic criteria of El Escorial Revisited: do they meet the needs of clinicians as well as researchers?'. Amyotrophic Lateral Sclerosis and Other Motor Neuron Disorders. 1 (Supplement 1): S57–S60. PMID11464928.
85. ^Silani V, Messina S, Poletti B, Morelli C, Doretti A, Ticozzi N, Maderna L (March 2011). 'The diagnosis of Amyotrophic lateral sclerosis in 2010'. Archives Italiennes de Biologie. 149 (1): 5–27. doi:10.4449/aib.v149i1.1260. PMID21412713.
86. ^'Lambert-Eaton Myasthenic Syndrome (LEMS)'. Misc.medscape.com. Archived from the original on 14 May 2013. Retrieved 18 April 2013.
87. ^'LEMS.com, Lambert-Eaton Myasthenic Syndrome: About'. Lems.com. Archived from the original on 20 January 2013. Retrieved 18 April 2013.
88. ^Mills KR (November 2010). 'Characteristics of fasciculations in amyotrophic lateral sclerosis and the benign fasciculation syndrome'. Brain. 133 (11): 3458–69. doi:10.1093/brain/awq290. PMID20959307.
89. ^Eisen A (2002). 'Amyotrophic lateral sclerosis: A review'. BCMJ. 44 (7): 362–366. Archived from the original on 21 June 2013. Retrieved 21 August 2014.
90. ^Davenport RJ, Swingler RJ, Chancellor AM, Warlow CP (February 1996). 'Avoiding false positive diagnoses of motor neuron disease: lessons from the Scottish Motor Neuron Disease Register'. Journal of Neurology, Neurosurgery, and Psychiatry. 60 (2): 147–51. doi:10.1136/jnnp.60.2.147. PMC1073793. PMID8708642.
91. ^Chieia MA, Oliveira AS, Silva HC, Gabbai AA (December 2010). 'Amyotrophic lateral sclerosis: considerations on diagnostic criteria'. Arquivos de Neuro-Psiquiatria. 68 (6): 837–42. doi:10.1590/S0004-282X2010000600002. PMID21243238.
92. ^Al-Asmi A, Nandhagopal R, Jacob PC, Gujjar A (February 2012). 'Misdiagnosis of Myasthenia Gravis and Subsequent Clinical Implication: A case report and review of literature'. Sultan Qaboos University Medical Journal. 12 (1): 103–8. doi:10.12816/0003095. PMC3286704. PMID22375266.
93. ^ ^abcAbe K, Masashi A, Tsuji S, Itoyama Y, Sobue G, Togo M, et al. (July 2017). 'Safety and efficacy of edaravone in well defined patients with amyotrophic lateral sclerosis: a randomised, double-blind, placebo-controlled trial'. The Lancet. Neurology. 16 (7): 505–12. doi:10.1016/S1474-4422(17)30115-1. PMID28522181.
94. ^ ^abcdYeo CJ, Simmons Z (May 2018). 'Discussing edaravone with the ALS patient: an ethical framework from a U.S. perspective'. Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration. 19 (3–4): 167–72. doi:10.1080/21678421.2018.1425455. PMID29334251.
95. ^ ^abcdOrrell RW (2010). 'Motor neuron disease: systematic reviews of treatment for ALS and SMA'. British Medical Bulletin. 93: 145–59. doi:10.1093/bmb/ldp049. PMID20015852.
96. ^ ^abRadunovic A, Annane D, Rafiq M, Brassington R, Mustfa N (6 October 2017). 'Mechanical ventilation for amyotrophic lateral sclerosis/motor neuron disease'. The Cochrane Database of Systematic Reviews. 10 (10): CD004427. doi:10.1002/14651858.CD004427.pub4. PMID28982219.
97. ^ ^abcAhmed R, Newcombe R, Piper A, Lewis S, Yee B, Kiernan M, Grunstein R (April 2016). 'Sleep disorders and respiratory function in amyotrophic lateral sclerosis'. Sleep Medicine Reviews. 26: 33–42. doi:10.1016/j.smrv.2015.05.007. PMID26166297.
98. ^ ^abEisen A, Krieger C (November 2013). 'Ethical considerations in the management of amyotrophic lateral sclerosis'. Progress in Neurobiology. 110: 45–53. doi:10.1016/j.pneurobio.2013.05.001. PMID23735671.
99. ^ ^abLewis M, Rushanan S (2007). 'The role of physical therapy and occupational therapy in the treatment of amyotrophic lateral sclerosis'. NeuroRehabilitation. 22 (6): 451–61. PMID18198431.
100. ^ ^abcde'Amyotrophic Lateral Sclerosis (ALS)'. American Speech-Language-Hearing Association, Rockville, MD. Archived from the original on 2 August 2012. Retrieved 30 November 2016.
101. ^ ^abcArbesman M, Sheard K (2014). 'Systematic review of the effectiveness of occupational therapy-related interventions for people with amyotrophic lateral sclerosis'. The American Journal of Occupational Therapy. 68 (1): 20–26. doi:10.5014/ajot.2014.008649. PMID24367951.
102. ^ ^abKatzberg HD, Benatar M (January 2011). 'Enteral tube feeding for amyotrophic lateral sclerosis/motor neuron disease'. The Cochrane Database of Systematic Reviews (1): CD004030. doi:10.1002/14651858.CD004030.pub3. PMID21249659.
103. ^ ^abcAndersen PM, Abrahams S, Borasio GD, de Carvalho M, Chio A, Van Damme P, et al. (March 2012). 'EFNS guidelines on the Clinical Management of Amyotrophic Lateral Sclerosis (MALS) – revised report of an EFNS task force'. European Journal of Neurology. 19 (3): 360–75. doi:10.1111/j.1468-1331.2011.03501.x. PMID21914052.
104. ^Carlesi C, Pasquali L, Piazza S, Lo Gerfo A, Caldarazzo Ienco E, Alessi R, Fornai F, Siciliano G (March 2011). 'Strategies for clinical approach to neurodegeneration in Amyotrophic lateral sclerosis'. Archives Italiennes de Biologie. 149 (1): 151–67. doi:10.4449/aib.v149i1.1267. PMID21412722.
105. ^Hardiman O, van den Berg LH (July 2017). 'Edaravone: a new treatment for ALS on the horizon?'. The Lancet. Neurology. 16 (7): 490–91. doi:10.1016/S1474-4422(17)30163-1. PMID28522180.
106. ^ ^abcdefgDorst J, Ludolph A, Huebers A (9 October 2017). 'Disease-modifying and symptomatic treatment of amyotrophic lateral sclerosis'. Therapeutic Advances in Neurological Disorders. 11: 1756285617734734. doi:10.1177/1756285617734734. PMC5784546. PMID29399045.
107. ^ ^abTakei K, Watanabe K, Yuki S, Akimoto M, Sakata T, Palumbo J (October 2017). 'Edaravone and its clinical development for amyotrophic lateral sclerosis'. Amyotrophic Lateral Sclerosis. 18 (sup 1): 5–10. doi:10.1080/21678421.2017.1353101. PMID28872907.
108. ^Baldinger R, Katzberg HD, Weber M (April 2012). 'Treatment for cramps in amyotrophic lateral sclerosis/motor neuron disease'. The Cochrane Database of Systematic Reviews (4): CD004157. doi:10.1002/14651858.CD004157.pub2. PMID22513921.
109. ^ ^abPaganoni S, Karam C, Joyce N, Bedlack R, Carter G (2015). 'Comprehensive rehabilitative care across the spectrum of amyotrophic lateral sclerosis'. NeuroRehabilitation. 37 (1): 53–68. doi:10.3233/NRE-151240. PMC5223769. PMID26409693.
110. ^Danel-Brunaud V, Touzet L, Chevalier L, Moreau C, Devos D, Vandoolaeghe S, Lefebvre L (May 2017). 'Ethical considerations and palliative care in patients with amyotrophic lateral sclerosis: A review'. Revue Neurologique. 173 (5): 300–07. doi:10.1016/j.neurol.2017.03.032. PMID28479121.
111. ^Checkoway H, Lundin JI, Kelada SN (2011). 'Chapter 22: Neurodegenerative diseases'. In Rothman N, Hainaut P, Schulte P, Smith M, Boffetta P, Perera F (eds.). Molecular Epidemiology: Principles and Practices. International Agency for Research on Cancer. pp. 408–409. ISBN978-9283221630.
112. ^Chiò A, Logroscino G, Traynor BJ, Collins J, Simeone JC, Goldstein LA, White LA (2013). 'Global Epidemiology of Amyotrophic Lateral Sclerosis: A Systematic Review of the Published Literature'. Neuroepidemiology. 41 (2): 118–30. doi:10.1159/000351153. PMC4049265. PMID23860588.
113. ^ ^abArthur KC, Calvo A, Price TR, Geiger JT, Chiò A, Traynor BJ (11 August 2016). 'Projected increase in amyotrophic lateral sclerosis from 2015 to 2040'. Nature Communications. 7 (12408): 1–6. doi:10.1038/ncomms12408. PMC4987527. PMID27510634.
114. ^ ^abLuna J, Logroscino G, Couratier P, Marin B (May 2017). 'Current issues in ALS epidemiology: Variation of ALS occurrence between populations and physical activity as a risk factor'. Revue Neurologique. 173 (5): 244–53. doi:10.1016/j.neurol.2017.03.035. PMID28477849.
115. ^Zou ZY, Zhou ZR, Che CH, Liu CY, He RL, Huang HP (July 2017). 'Genetic epidemiology of amyotrophic lateral sclerosis: a systematic review and meta-analysis'. Journal of Neurology, Neurosurgery, and Psychiatry. 88 (77): 540–49. doi:10.1136/jnnp-2016-315018. PMID28057713.
116. ^Visser J, de Jong JM, de Visser M (February 2008). 'The history of progressive muscular atrophy: syndrome or disease?'. Neurology. 70 (9): 723–27. doi:10.1212/01.wnl.0000302187.20239.93. PMID18299524.
117. ^Wijesekera LC, Mathers S, Talman P, Galtrey C, Parkinson MH, Ganesalingam J, Willey E, Ampong MA, Ellis CM, Shaw CE, Al-Chalabi A, Leigh PN (March 2009). 'Natural history and clinical features of the flail arm and flail leg ALS variants'. Neurology. 72 (12): 1087–94. doi:10.1212/01.wnl.0000345041.83406.a2. PMC2821838. PMID19307543.
118. ^Lee SE (December 2011). 'Guam dementia syndrome revisited in 2011'. Current Opinion in Neurology. 24 (6): 517–24. doi:10.1097/WCO.0b013e32834cd50a. PMID21986681.
119. ^Brooks BR, Sanjak M, Ringel S, England J, Brinkmann J, Pestronk A, et al. (February 1996). 'The Amyotrophic Lateral Sclerosis Functional Rating Scale: Assessment of Activities of Daily Living in Patients with Amyotrophic Lateral Sclerosis'. Archives of Neurology. 53 (2): 141–7. PMID8639063.
120. ^Cedarbaum JM, Stambler N, Malta E, Fuller C, Hilt D, Thurmond B, Nakanishi A (October 1999). 'The ALSFRS-R: a revised ALS functional rating scale that incorporates assessments of respiratory function'. Journal of the Neurological Sciences. 162 (1–2): 13–21. PMID10540002.
121. ^Wilbourn AJ (October 1998). 'Clinical neurophysiology in the diagnosis of amyotrophic lateral sclerosis: The Lambert and the El Escorial criteria'. Journal of the Neurological Sciences. 160 (Supplement 1): S25–29. PMID9851644.
122. ^Brooks BR (July 1994). 'El Escorial World Federation of Neurology Criteria for the Diagnosis of Amyotrophic Lateral Sclerosis'. Journal of the Neurological Sciences. 124 (Supplement): 96–107. PMID7807156.
123. ^Brooks BR, Miller RG, Swash M, Munsat TL (December 2000). 'El Escorial revisited: Revised criteria for the diagnosis of amyotrophic lateral sclerosis'. Amyotrophic Lateral Sclerosis and Other Motor Neuron Disorders. 1 (5): 293–9. doi:10.1080/146608200300079536. PMID11464847.
124. ^ ^abTeive HA, Lima PM, Germiniani FM, Munhoz RP (May 2016). 'What's in a name? Problems, facts and controversies regarding neurological eponyms'. Arquivos de Neuro-Psiquiatria. 74 (5): 423–25. doi:10.1590/0004-282X20160040. PMID27191240.
125. ^ ^ab'What is ALS?'. The ALS Association. Archived from the original on 21 December 2018. Retrieved 23 December 2018.
126. ^'ALS: Amyotrophic Lateral Sclerosis'. Muscular Dystrophy Association. Archived from the original on 6 August 2018. Retrieved 23 December 2018.
127. ^Goetz CG (March 2000). 'Amyotrophic lateral sclerosis: early contributions of Jean-Martin Charcot'. Muscle & Nerve. 23 (3): 336–43. doi:10.1002/(SICI)1097-4598(200003)23:3<336::AID-MUS4>3.0.CO;2-L. PMID10679709.
128. ^Gordon PH (2006). 'Chapter 1: History of ALS'. In Mitsumoto H, Przedborski S, Gordon PH (eds.). Amyotrophic Lateral Sclerosis. CRC Press. p. 9. ISBN978-0824729240.
129. ^Gordon PH (October 2013). 'Amyotrophic Lateral Sclerosis: An update for 2013 Clinical Features, Pathophysiology, Management and Therapeutic Trials'. Aging and Disease. 4 (5): 295–310. doi:10.14336/AD.2013.0400295. PMC3794725. PMID24124634.
130. ^ ^abHuynh W, Simon NG, Grosskreutz J, Turner MR, Vucic S, Kiernan MC (July 2016). 'Assessment of the upper motor neuron in amyotrophic lateral sclerosis'. Clinical Neurophysiology. 127 (7): 2643–2660. doi:10.1016/j.clinph.2016.04.025. PMID27291884.
131. ^'George Bush delivers possibly the best ALS ice bucket challenge yet'. Independent. Archived from the original on 21 August 2014. Retrieved 20 August 2014.
132. ^'Ice Bucket Challenge funds gene discovery in ALS (MND) research'. BBC News. 27 July 2016. Archived from the original on 28 July 2016. Retrieved 27 July 2016.
133. ^Alexander, Ella. 'Ice Bucket Challenge: Lady Gaga, Justin Bieber, G-Dragon and Oprah – the most entertaining reactions so far'.
134. ^'Ice Bucket Challenge funds discovery of gene linked to ALS'. Archived from the original on 27 July 2016. Retrieved 27 July 2016.
135. ^Kenna KP, van Doormaal PT, Dekker AM, Ticozzi N, Kenna BJ, Diekstra FP, et al. (September 2016). 'NEK1 variants confer susceptibility to amyotrophic lateral sclerosis'. Nature Genetics. 48 (9): 1037–42. doi:10.1038/ng.3626. PMC5560030. PMID27455347.
136. ^ ^abcdeMoujalled D, White AR (March 2016). 'Advances in the Development of Disease-Modifying Treatments for Amyotrophic Lateral Sclerosis'. CNS Drugs. 30 (3): 227–43. doi:10.1007/s40263-016-0317-8. PMID26895253.
137. ^Picher-Martel V, Valdmanis PN, Gould PV, Julien JP, Dupré N (July 2016). 'From animal models to human disease: a genetic approach for personalized medicine in ALS'. Acta Neuropathologica Communications. 11 (4): 1–29. doi:10.1186/s40478-016-0340-5. PMC4940869. PMID27400686.
138. ^ ^abMitsumoto H, Brooks BR, Silani V (November 2014). 'Clinical trials in amyotrophic lateral sclerosis: why so many negative trials and how can trials be improved?'. The Lancet. Neurology. 13 (11): 1127–38. doi:10.1016/S1474-4422(14)70129-2. PMID25316019.
139. ^Fang J, Zhou M, Yang M, Zhu C, He L (May 2013). 'Repetitive transcranial magnetic stimulation for the treatment of amyotrophic lateral sclerosis or motor neuron disease'. The Cochrane Database of Systematic Reviews (5): CD008554. doi:10.1002/14651858.CD008554.pub3. PMID23728676.
140. ^Chen KS, Sakowski SA, Feldman EL (March 2016). 'Intraspinal stem cell transplantation for amyotrophic lateral sclerosis'. Annals of Neurology. 79 (3): 342–53. doi:10.1002/ana.24584. PMC4789073. PMID26696091.
141. ^Abdul Wahid SF, Law ZK, Ismail NA, Azman Ali R, Lai NM (November 2016). 'Cell-based therapies for amyotrophic lateral sclerosis/motor neuron disease'. The Cochrane Database of Systematic Reviews. 11: CD011742. doi:10.1002/14651858.CD011742.pub2. PMID27822919.
142. ^'Public summary of opinion on orphan designation Masitinib mesilate for treatment of amyotrophic lateral sclerosis'(PDF). EMA. European Medicines Agency, Committee for Orphan Medicinal Products. 22 September 2016. Archived(PDF) from the original on 6 November 2016. Retrieved 6 November 2016.
143. ^Bartus RT, Bétourné A, Basile A, Peterson BL, Glass J, Boulis NM (January 2016). 'β2-Adrenoceptor agonists as novel, safe and potentially effective therapies for Amyotrophic lateral sclerosis (ALS)'. Neurobiology of Disease. 85: 11–24. doi:10.1016/j.nbd.2015.10.006. PMID26459114.
144. ^Bräuer S, Zimyanin V, Hermann A (April 2018). 'Prion-like properties of disease-relevant proteins in amyotrophic lateral sclerosis'. Journal of Neural Transmission. 125 (4): 591–613. doi:10.1007/s00702-018-1851-y. PMID29417336.
145. ^Lau DH, Hartopp N, Welsh NJ, Mueller S, Glennon EB, Mórotz GM, Annibali A, Gomez-Suaga P, Stoica R, Paillusson S, Miller CC (February 2018). 'Disruption of ER-mitochondria signalling in fronto-temporal dementia and related amyotrophic lateral sclerosis'. Cell Death & Disease. 9 (3): 327. doi:10.1038/s41419-017-0022-7. PMC5832427. PMID29491392.
146. ^Neumann M (October 2013). 'Frontotemporal lobar degeneration and amyotrophic lateral sclerosis: molecular similarities and differences'. Revue Neurologique. 169 (10): 793–98. doi:10.1016/j.neurol.2013.07.019. PMID24011641.

External links[edit]

• Amyotrophic lateral sclerosis at Curlie