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Further Evidence of Complex Motor Dysfunction in Drug Naive Children with Autism Using Automatic Motion Analysis of Gait

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Title: Further Evidence of Complex Motor Dysfunction in Drug Naive Children with Autism Using Automatic Motion Analysis of Gait
Language: English
Authors: Nobile, Maria, Perego, Paolo, Piccinini, Luigi
Source: Autism: The International Journal of Research and Practice. May 2011 15(3):263-283.
Availability: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: http://sagepub.com
Peer Reviewed: Y
Physical Description: PDF
Page Count: 21
Publication Date: 2011
Document Type: Journal Articles
Reports - Research
Descriptors: Evidence, Rehabilitation Programs, Autism, Motion, Children, Disabilities, Minimal Brain Dysfunction, Evaluation, Spatial Ability, Time Perspective, Pervasive Developmental Disorders, Intelligence Quotient, Control Groups, Patients, Comparative Analysis, Kinetics, Early Adolescents
DOI: 10.1177/1362361309356929
ISSN: 1362-3613
Abstract: In order to increase the knowledge of locomotor disturbances in children with autism, and of the mechanism underlying them, the objective of this exploratory study was to reliably and quantitatively evaluate linear gait parameters (spatio-temporal and kinematic parameters), upper body kinematic parameters, walk orientation and smoothness using an automatic motion analyser (ELITE systems) in drug naive children with Autistic Disorder (AD) and healthy controls. The children with AD showed a stiffer gait in which the usual fluidity of walking was lost, trunk postural abnormalities, highly significant difficulties to maintain a straight line and a marked loss of smoothness (increase of jerk index), compared to the healthy controls. As a whole, these data suggest a complex motor dysfunction involving both the cortical and the subcortical area or, maybe, a possible deficit in the integration of sensory-motor information within motor networks (i.e., anomalous connections within the fronto-cerebello-thalamo-frontal network). Although the underlying neural structures involved remain to be better defined, these data may contribute to highlighting the central role of motor impairment in autism and suggest the usefulness of taking into account motor difficulties when developing new diagnostic and rehabilitation programs. (Contains 4 tables and 2 figures.)
Abstractor: As Provided
Number of References: 43
Entry Date: 2011
Accession Number: EJ928320
Database: ERIC
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  Value: <anid>AN0061767201;f9d01may.11;2011Jun23.15:40;v2.2.460</anid> <title id="AN0061767201-1">Further evidence of complex motor dysfunction in drug naïve children with autism using automatic motion analysis of gait </title> <p>263</p> <p>Further evidence of complex motor dysfunction in drug naïve children with autism using automatic motion analysis of gait</p> <p>SAGE Publications, Inc. 201110.1177/1362361309356929</p> <p>© The Author(s), 2011. 2011 The Author(s)</p> <p>MariaNobile Child Psychiatry Unit, The 'Eugenio Medea' Scientific Institute, Bosisio Parini, Italy, maria.nobile@bp.lnf.it</p> <p>PaoloPerego Gait Analysis Laboratory,The 'Eugenio Medea' Scientific Institute, Bosisio Parini, Italy</p> <p>LuigiPiccinini Functional Rehabilitation Unit, The 'Eugenio Medea' Scientific Institute, Bosisio Parini, Italy</p> <p>ElisaMani Child Psychiatry Unit, The 'Eugenio Medea' Scientific Institute, Bosisio Parini, Italy</p> <p>AgneseRossi Child Psychiatry Unit,The 'Eugenio Medea' Scientific Institute, Bosisio Parini, Italy</p> <p>MonicaBellina Child Psychiatry Unit, The 'Eugenio Medea' Scientific Institute, Bosisio Parini, Italy</p> <p>MassimoMolteni Child Psychiatry Unit, The 'Eugenio Medea' Scientific Institute, Bosisio Parini, Italy</p> <p>In order to increase the knowledge of locomotor disturbances in children with autism, and of the mechanism underlying them, the objective of this exploratory study was to reliably and quantitatively evaluate linear gait parameters (spatio-temporal and kinematic parameters), upper body kinematic parameters, walk orientation and smoothness using an automatic motion analyser (ELITE systems) in drug naïve children with Autistic Disorder (AD) and healthy controls. The children with AD showed a stiffer gait in which the usual fluidity of walking was lost, trunk postural abnormalities, highly significant difficulties to maintain a straight line and a marked loss of smoothness (increase of jerk index), compared to the healthy controls. As a whole, these data suggest a complex motor dysfunction involving both the cortical and the subcortical area or, maybe, a possible deficit in the integration of sensory-motor information within motor networks (i.e., anomalous connections within the fronto-cerebello-thalamo-frontal network). Although the underlying neural structures involved remain to be better defined, these data may contribute to highlighting the central role of motor impairment in autism and suggest the usefulness of taking into account motor difficulties when developing new diagnostic and rehabilitation programs.</p> <p>autism gait analysis jerk locomotion system motor dysfunction</p> <p>264 ADDRESS Correspondence should be addressed to: M A R I A N O B I L E ,‘Eugenio Medea’ Scienti c Institute, Child Psychiatry Department, via Don Luigi Monza 20, 23842 Bosisio Parini (LC), Italy. e-mail: maria.nobile@bp.lnf.it</p> <p>Introduction Autism (MIM 209850) is a lifelong neuropsychiatric disorder with onset in early childhood. First described by Leo Kanner (1943), autism is a disorder de ned by impairment in social reciprocity, verbal and nonverbal language, and imaginative play, and by restricted and repetitive behavior. There are also associated symptoms that are not part of this diagnostic constellation but nonetheless appear to be potential neurologically and clinically important elements of this syndrome, as suggested by recent advances in autism research. Disturbance of motor anticipation and coor- dination, postural and gait abnormalities, clumsiness and various degrees of dystonia, bradykinesia and hyperkinesias (Damasio and Maurer, 1978; Green et al., 2002; Kohen-Raz et al., 1992; Minshew et al., 2004; Vernazza- Martin et al., 2005; Vilensky et al. 1981), and recently, subtle neurological signs such as over ow and dysrhythmia (Jansiewicz et al., 2006) have been reported, suggesting a de cit in basic motor control (Ghaziuddin and Butler, 1998; Nayate et al., 2005). Besides the observed dif culties in basic motor skill, numerous studies have also documented dif culties with performance of skilled motor gestures (Vanvuchelen et al., 2007), suggest- ing the presence of a generalized praxis de cit. The association between dif culties in praxis performance and the de cit in motor coordination has not been fully de ned. Dziuk et al. (2007) reported that basic motor skill performance was a signi cant predictor of praxis performance, but also that dyspraxia could not be completely accounted for by impairment in basic motor skills in autism. From a developmental perspective, these ndings may re ect abnormalities in neural circuits important for the building of internal representation of body schema and acquisition of sensory repre- sentations of movement and/or the motor sequence programs necessary to execute them (Buxbaum et al., 2000). Retrospective analyses of children diagnosed with autism have revealed that these children display abnormal movements even during their rst year of life, before social and linguistic dif culties have emerged (Esposito and Venuti, 2008; Teitelbaum et al., 1998). Infants later diagnosed with autism 265 have been found to display abnormal motor development, such as an ab- normal pattern of righting, a lack of protective re exes when falling, abnormal gait sequencing, delayed development through the stages of walking and abnormal gait positioning (Teitelbaum et al., 1998). Marked asymmetry in crawling and walking movements has also been noted. These abnormalities were attributed to the abnormal retention of primitive re ex, resulting in the maintenance of an immature neural system. In a recent retrospective study, Esposito and Venuti (2008) reported that after six months of independent walking, children with a later diagnosis of Autistic Disorder (AD) revealed problems performing the heel-to-toe pattern, and showed more asymmetric posture of the arm while walking and higher frequencies of anomalies in general movement. Quite surpris- ingly, the biggest abnormalities found in this study were in the way children with AD used their arms. The authors suggest that an abnormal arm-movement pattern could be a means to maintain balance control. These data agree with the idea of an involvement of the cerebellum, given its role in motor coordination and balance control. Furthermore, the data support the idea that movement abnormalities should be viewed as neurological signs rather than delayed milestones, and as a core symptom of autism rather than an additional feature, as suggested by Leary and Hill (1996). On the basis of this hypothesis, and relying on the fact that the motor system can be more easily assessed than other systems that underlie complex cognitive function, movement has been proposed as a possible reliable diagnostic marker of autism and a useful tool to understand the nature of the motor system dysfunction and, consequently, the abnormalities in neural circuitry underlying autism (for a review see Nayate et al., 2005). In the last few years, movement has also come to be considered as a possible precursor sign of autism (Esposito and Venuti, 2008). In spite of the belief that the assessment of the locomotion system provides information that is relatively immune to experimental in uence, not all studies lead to consistent results and support the contention that motor impairments are an essential feature of the autism phenotype. In particular, studies based on the clinical ratings of gross and ne motor dysfunction, coordination and balance, performed both on home video- tape and in clinical settings, did not nd any differences between children with Autism Spectrum Disorder (ASD) and children with general develop- mental delays (Ozonoff et al., 2008; Provost et al., 2007; Rogers et al., 2003; Rogers et al., 2008), children with speci c language disorders (Noterdaeme at al., 2002), and children with learning disabilities (Miyahara et al., 1997). Quantitative assessment of locomotion and postural control, employing measures and tasks that are more sensitive to subtle gait abnormalities (i.e., kinematic analysis of gait), was suggested as a useful tool to detect subtle 266 signs of motor system dysfunction in autism (Nayate et al., 2005), and to investigate possible neural correlates of motor system dysfunction. Further- more, gait analysis takes into consideration different features integrated into the same locomotor act, namely: basic motor skills assessed by gait parameters; parameters related to equilibrium, body orientation and postural control; and parameters related to motor planning of a goal-directed behavior, assessed as the ability to de ne the best trajectory to reach the goal. Recent advances have enabled the faster acquisition of kinematic data without the use of encumbering attachments and wires trailing from the subjects. Improved computer integration and software mean that kinetic data are more automatically obtained, and a modern-day gait laboratory can now, in fact, allow for the routine assessment of gait in standard settings (Kerrigan, 1998). Early studies of locomotion in ASD (for a review see Nayate et al., 2005) were essentially based on the qualitative analysis of gait to look into possible neural substrates of motor dysfunction. They reported con icting results suggesting the involvement of different structures, in that the frontostriatal motor system, the cerebellar motor system, and the prefrontal and parietal cortex connected with movement planning, were each suggested as the possible underlying neural circuitry. Vilensky et al. (1981) revealed that affected children showed reduced stride lengths, increased stance times, increased hip- exion at toe-off, decreased knee extension and ankle dorsi ection at ground contact. The authors suggested that gait in children with AD could resemble those of Parkinsonian patients and may be the result of a speci c dysfunction of the motor system involving the basal ganglia. In contrast, Hallett et al. (1993) showed that velocity of gait, step length, cadence, step width, stance time and vertical ground reaction forces were normal in ve adult patients affected by AD. The only signi cant abnor- mality was the decreased range of motion of the ankle. Since the velocity of gait and step length were in the average range, it was proposed that the de cit was not of the Parkinsonian type but rather cerebellar in nature. Vernazza-Martin et al. (2005), using kinematic analysis of gait (i.e., ELITE system, the same automatic motion analyzer available at the Gait Analysis Laboratory of our Institute), investigated basic gait parameters, equilibrium and body orientation parameters and walk orientation towards an experimenter-imposed goal in nine children with AD and six healthy controls aged 4 to 6 years. Each child was asked to walk to the door of a playhouse located approximately 5 meters from their starting position. The achievement of the experimenter-imposed goal was measured by two parameters: the mean distance between the child and the door and the mean orientation of the trajectory to the door. Results underlined that the 267 main components affected in children with AD during locomotion were not at the level of movement programming (gait parameters or balance control) but at the level of movement planning, therefore affecting the orientation towards the goal of action and the de nition of trajectory. These data suggest that the affected anatomical structures could be the prefrontal and parietal cortex, and other structures linked to this associative cortex. Cognitive level of functioning and its possible contribution to impairment of movement planning and programming were not investigated. Such con icting results might be at least partially accounted for by two important factors, namely different characteristics of the included subjects based on different inclusion-exclusion criteria (broad range IQ and age, patients on medication not excluded) and different assessment methods. As in the case of other motor parameters investigated, for example postural stability or the reach to grasp movement, some reported gait abnormalities could be features of developmental disorder in general, rather than a speci c feature of autism per se (Mari et al., 2003; Minshew et al., 2004; Ozonoff et al., 2008). As regards the use of different assessment methods, it should be noted that subtle abnormalities in muscle tone, motor controls, praxis and postural control are often dif cult to measure reliably with qualitative analysis, even with expert clinical examination. Two recent works, however, (Rinehart et al., 2006a; 2006b) have used both quantitative and qualitative analysis of gait to investigate motor func- tion in two groups of non–mentally retarded children with AD. The rst group with AD, aged 4 to 6 years, showed greater dif culty walking along a stride line and greater stride-length and stride-time variability than the control group, and, on qualitative analysis, the children with AD proved to be uncoordinated and lacking in motor smoothness, with postural abnor- malities in the head and trunk. In the same way, the second group with AD, aged 6 to 14 years, showed signi cantly greater stride length variability than the healthy controls and on qualitative analysis, abnormal arm postur- ing, and lack in motor smoothness. The authors underlined the stability of abnormal gait features across developmental periods and suggested the involvement of both cerebellar and fronto-striatal basal ganglia regions. The main aim of the present study has been to gather additional and reliable data on the locomotion system of non–mentally retarded and drug- naïve children with AD both in order to con rm (or otherwise) the presence of motor alterations, besides social and linguistic disturbances, as an essen- tial feature of the autistic syndrome, and also to further investigate the nature of the motor system dysfunction underlying autism. To achieve this aim we used the more reliable quantitative analysis not only to evaluate basic spatio-temporal and kinematic gait parameters but also to investigate other motor features like stiffness, coordination, smoothness 268 and posture, suggested to be potential markers of autistic disorders by many authors and by DSM-IV-TR, too (American Psychiatric Association; 2001; see page 71). Besides spatio-temporal gait parameters used by many inves- tigators, we used kinematic gait parameters to better describe the motion of each body segment both in linear and angular terms of displacement, velocity and acceleration in space with respect to time. Body posture and the ability of children to walk in a straight line were also evaluated by quan- titative analysis using upper body kinematic parameters and walk orienta- tion parameters, respectively. The quantitative evaluation of the smoothness of movement was performed by the measurement of the normalized jerk cost, that is, changes of acceleration (Hogan, 1984; Wolpert et al., 1995). Several studies have demonstrated that poor movement control, as in, for example, children and patients with neurological diseases, is associated with high jerk cost (Contreras-Vidal and Buch, 2 0 0 3 ; Yan et al., 2000). Accord- ing to the theory of minimum jerk it can be argued that voluntary move- ments are generally planned to be as smooth as possible within the physical limitations of the system and in consideration of other performance goals. A thorough, global analysis of these parameters provides useful data on basic motor skills and on motor control strategies involved in movement coordination, smoothness and posture. To explore the relationship between basic motor skills and motor control strategies and the social, communi- cative and behavioral impairments that de ne autism, we investigated the possible correlation between these parameters and Autism Diagnostic Inter- view–Revised (ADI-R; Lord et al., 1994) scores (Social, Communication and Repetitive Behavior). It was hypothesized that, for children with AD, poorer basic motor skills and motor control strategies would correlate with higher ADI-R scores. To summarize, in order to increase the knowledge of locomotor distur- bances in children with autism, and of the mechanism underlying them, the objective of this exploratory study was to reliably and quantitatively evaluate linear gait parameters (spatio-temporal and kinematic parameters), upper body kinematic parameters, walk orientation and smoothness using an automatic motion analyser (ELITE systems) in drug naïve children with AD and healthy controls. Method Participants This study consisted of 16 children with AD (12 male and 4 female; mean age 10.56 ± 2.50 years; range: 6 to 14 years) and 16 healthy controls (12 male and 4 female; mean age 9.99 ± 2.28 years; range: 6 to 14 years). 269 The children with AD were consecutively recruited from the Child Psychiatry Unit of our Institute over an 18-month period. To be included in the study the children with AD had to have a clinical diagnosis of AD according to DSM-IV-TR criteria (American Psychiatric Association, 2001); the clinical diagnosis of AD at admission was con rmed independently by direct observation using the Autism Diagnostic Observation Scale (ADOS, Lord et al., 1989) and by the standardized ADI-R.The healthy controls were recruited from the normal population among relatives or friends of workers at our Institute. Inclusion criteria for all the participants were ages 6 to 14 years, Full- Scale IQ of 70 or higher, over 1 meter tall and able to walk independently without any orthosis or assistive devices. Exclusion criteria for the children with AD were associated neurologic, genetic, infectious or metabolic dis- order, or a seizure disorder, or present or past use of any psychoactive drugs. Exclusion criteria for the healthy controls were evidence of birth or developmental abnormalities, acquired brain injury, a current or past history of psychiatric or neurological disorder, a medical disorder with implications for the CNS, any other medical disorder requiring medication either at regular intervals or at the time of the assessment. In other words, all participants included in this study were drug-naïve, to rule out the possible confounding effects of psychoactive drugs on movement (Dinca et al., 2005; Jansiewicz et al., 2006). The cognitive functioning of the children with AD was assessed using an age-appropriate Wechsler Intelligence Scale (WISC-III-R). Intellectual functioning of the healthy controls was evaluated using a short form of the Wechsler Intelligence Scale, including two Verbal (information and vocab- ulary) and two Performance (picture completion and block design) sub- tests. This short form is reported to be a reliable estimate of full scale IQ score (Sattler, 1992). Characteristics of both patients and controls are reported in Table 1. They did not differ signi cantly in gender, age, body weight, body height, and in Performance IQ. Not surprisingly the group with AD had a slight but signi cantly lower Full-Scale IQ than the controls (p = .01) and a signi cantly lower Verbal IQ (p < .001). The parents of all the children signed a written consent for participa- tion in the study, as approved by the Ethical Committee of our Institute. Procedure All the tests were conducted in the Gait Analysis Laboratory of the Insti- tute. The analysis of the gait cycle was achieved by 8 infrared cameras using the optoelectronic technique with passive markers, Elite System (Bts ® 270 Table 1 Summary of participants’ characteristics AD = Autistic Disorder; ADI-R = Autism Diagnostic Interview–Revised. Bioengineering, Milan, Italy). The sample rate of the acquisition was 100 Hz. In order to evaluate the kinematic parameters the markers were posi- tioned on the patient’s body according to the Davis protocol (Davis et al., 1991) allowing us to obtain a 3D reconstruction of the gait (see Figure 1). Synchronized frontal and sagittal video recordings were acquired too. The body segments were de ned as planes crossing three markers. In particular the pelvis was de ned by markers positioned on the two superior iliac spines and on the sacrum; the shoulder by markers on both the acromions and on C7; the thighs by markers on the hip joints, on the femoral condyles and centrally on the outside thighs; the legs by markers on the tibial condyles, on the malleolus and centrally on the external side of the tibias; the feet by markers on the external malleolus, on the V metatarsal joints and on the back of the heels. After collecting some anthropometric information (knee and ankle diameter, body height, body weight, height of the pelvis and distance between the anterior iliac spines), a rigid frame was computed for each sample frame starting from raw data (Davis et al., 1991). Eulero’s angles were computed in order to obtain ex-extension, ab-adduction and intra-extra rotation of every joint (hip, knee, and ankle). All the graphs were normal- ized as a percentage of the gait cycle, in order to compare different trials. The participants were instructed to walk barefoot at their own natural speed along a 10 meter walkway. They were asked to start from a pre- de ned point and to reach and go over another pre-de ned point directly in front of them. These two points were highlighted with coloured tape on 271</p> <p>Figure 1 3D reconstruction of human body and indexed joints according to Davis’ protocol. Coronal and sagittal view. the ground. Three successfully completed trials were collected for each patient and then fully analyzed quantitatively. A trial was considered com- pleted when the participant reached and went over the end point. No signi cant difference in the number or location of the successful trials in respect to the whole experiment was found between the two groups. For every participant, every parameter was calculated as the mean of the values obtained in the three trials considered. The analysis of raw data was carried out by Matlab (Mathworks ®) applying a 8 Hz II order low pass Butter- worth lter (Roithner et al., 2000). Parameters In order to quantify differences in gait strategies as reliably as possible, some indices from kinematic data, upper body posture, walk orientation and smoothness of movement were computed in a single gait cycle. 272 The obtained outcome parameters were as follows: 1. Spatio-temporal parameters (see Figure 2): Stance period (as a percent- age of the gait cycle, which begins with initial contact and ends at toe- off of the same limb); Double Support period (as a percentage of the gait cycle, when both the feet were in contact with the ground); Normalized Mean Velocity (mean velocity of progression normalized for body height;); Step Width (medio-lateral distance between the two feet during double support); Normalized Stride Length (longitudinal distance from one foot strike to the next one of the same foot, normal- ized for body height); Stride Time (duration between two contacts of the same foot); Cadence (number of steps per time unit). 2. Kinematic gait parameters: Flex-extension angles of the hip (H), knee (K) and ankle (A) joints: value of the angle for the above mentioned joints at the initial contact (IC) of a gait cycle (HIC, KIC, AIC); value of the angle for the same joints at the toe-off (TO) of a gait cycle (JTO); Range of Motion (ROM) for the same joints, calculated as the maximum minus the minimum values during a gait cycle (HROM, KROM, AROM); Mean Foot Progression (MFP), mean angle between the orien- tation of the foot and the straight walkway. Increased MFP means a lateral search of stability: the higher the MFP, the wider the support space obtained. Figure 2 The normal gait cycle (reprinted with permission from Human Walking by Inman V.T. et al., 1981,Williams & Wilkins, Baltimore, London, p. 26) 273 3. Upper body kinematic parameters: Range of motion of pelvis oscilla- tion, calculated as maximum minus minimum values of the pelvis angles in the frontal plane (pelvic obliquity), in the sagittal plane (pelvic tilt) and in the horizontal plane (pelvic rotation); range of motion of shoulder oscillation, calculated as maximum minus minimum values of the shoulder angles in the frontal plane (shoulder obliquity) and in the horizontal plane (shoulder rotation). 4. Walk orientation (WO) calculated on the horizontal component of the data obtained from the C7 marker and the walkway (Z direction) : The starting point was calculated as the rst encountered in ection point on the horizontal projection on C7 series, and the end point was the target position. The walkway line was tracked from the starting point to the end point. The walk orientation was calculated as the maximum absolute distance in relation to the walkway line. This is considered an indicator of an individual’s ability to maintain a straight line when walking and to choose the best trajectory to the goal, with a higher value showing higher deviation from a straight-line walking pattern. (A smaller value is indicative of less lateral veering, i.e., walking in a straighter line.) 5. Smoothness of movement was evaluated using the normalized Jerk- Cost (Romero et al., 2003). The jerk value was calculated using the algorithm described by Teuling et al. (1997) on the hip, knee and ankle joints, from the starting to the end point.Three seconds (out of a whole trial) were considered, therefore three hundred samples were analyzed. An 8 Hz low pass second order Butterworth lter was added before the jerk calculation in order to obtain a value insensitive to the instrumen- tal noise (Roithner et al., 2000). Normalized Jerk-Cost is a unit-free value as it is normalized for both the amplitude and the duration of movement (Teulings et al., 1997); it can be easily compared between different gaits, independently of stride length or velocity of the gait. Statistical Analysis Simple Factorial Analysis of coVariance (ANcOVA) was used to compare gait parameters (independent variables) between the children with AD and the healthy controls. To control for possible Full-Scale IQ (FSIQ) confounding effects on the tests, FSIQ was included as a covariate in all between-group comparisons. A level of signi cance of 0.05 was adopted throughout the data analysis. Results are expressed as mean +/– standard deviation. Because this study was exploratory, there was no correction for multiple comparisons and the results should be considered as descriptive. Concern- ing some gait parameters, for example stance period, normalized stride length, stride time, and all the gait kinematic parameters and jerk, an initial 274 comparison was made between the right and the left limb. When no statis- tical difference was found between the right and the left limb, the data from both sides were pooled for between-group comparisons. Spearman’s Rho was used to examine correlations between ADI-R scores (Social, Communication and Repetitive Behavior) and gait parameters. Results Spatio–temporal parameters (see Table 2) Stance period, double support period, normalized mean velocity, step width, normalized stride length, stride time and cadence were analyzed by ANcOVA. None of the spatio-temporal parameters were signi cantly differ- ent between groups except a signi cantly shorter stride length (p = .01) and a signi cantly wider step width (p = .03) in the children with AD. Normalized mean velocity was also – but not signi cantly – reduced in the children with AD (p = . 0 6) . There were no signi cant correlations between FSIQ or ADI-R scores and spatio-temporal parameters. These data suggest a pathological gait, where patients tend to augment their walking stability, that is, the velocity is usually decreased, the distance between the feet (step width) may be widened and the stride length is frequently shortened. Table 2 Spatio-temporal gait parameters in the children with AD and the healthy controls Note. All parameters were analyzed by ANcOVA with FSIQ as covariate. AD = Autistic Disorder; ANcOVA = Analysis of covariance; FSIQ = Full-Scale IQ. Kinematic gait parameters (see Table 3) ANcOVA showed a signi cant reduction of hip and knee range of motion (ROM) in the children with AD (F = 4.77, p = .04 and F = 8.90, p < .01, respectively). Ankle ROM was also – but not signi cantly – reduced in patients with AD (p > .05). Furthermore, at the toe-off, the group with AD 275 Table 3 Kinematic gait parameters in the children with AD and the healthy controls</p> <p>Note. All parameters were analyzed by ANcOVA with FSIQ as covariate. AD = Autistic Disorder; ANcOVA = Analysis of covariance; FSIQ = Full-Scale IQ. HIC: Hip angle at Initial Contact; HTO: Hip angle at Toe-Off; HROM: Hip Range of Motion; KIC: Knee angle at Initial Contact; KTO: Knee angle at Toe-Off; KROM: Knee Range of Motion; AIC: Ankle angle at Initial Contact; ATO: Ankle angle at Toe-Off; AROM: Ankle Range of Motion; MFP: Mean Foot Progression. showed a signi cant reduction of ankle plantar exion (ATO, F = 6.63, p = .01), and a reduced knee ex-extension angle (KTO, F = 6.09, p = .02). There was a signi cant correlation between knee ROM and FSIQ (R = 0.51, p < .01). ANcOVA revealed no differences between the children with autism and the healthy controls for the other parameters and no other signi cant correlation between FISQ or ADI-R scores and the kinematic gait para- meters. These data show a global reduction of range of motion (ROM) for all considered joints (hip, knee and ankle) for the whole gait cycle, and, at the toe-off, a smaller ankle plantar- exion and a more exed knee. This reduction may be due to an increased rigidity of the walking pattern. Upper body kinematic parameters and walk orientation (see Table 4) Shoulder obliquity was signi cantly higher in the children with AD (F = 4.73, p = .04); pelvic obliquity and pelvic rotation also showed a slight, but not signi cant, reduction (p = .07 and p = .10); pelvic tilt and shoulder rotation showed no signi cant difference between the two groups. These data suggest a good stabilization of the hip and also a good equilibrium of 276 Table 4 Upper body kinematic parameters and walk orientation in the children with AD and the healthy controls Note. All parameters were analyzed by ANcOVA with FSIQ as covariate. AD = Autistic Disorder; ANcOVA = Analysis of covariance; FSIQ = Full-Scale IQ. posture maintenance while walking (i.e., lack of differences at the pelvis level), while the higher angular dispersion of the shoulder in the frontal plane in the children with AD could be linked with some problems in body orientation while walking. The mean absolute angle of Walk Orientation was signi cantly higher in the group with AD (F = 15.279, p = .001). These data indicate that the ability to maintain a straightforward trajectory appeared markedly disturbed in the children with AD. There were no signi- cant correlations between FSIQ or ADI-R scores and the above-mentioned parameters. Smoothness of movement The Normalized Jerk-Cost (J-C) values measured at knee and ankle were signi cantly higher in the children with AD (knee J-C: mean 386.07, SD 140.39; ankle J-C: mean 647.46, SD 243.26) than in the healthy controls (knee J-C: mean 277.4, SD 59.4; ankle J-C: mean 421.38, SD 108.42; F = 8.65, p < .01 and F = 13.12, p < .001, respectively). The J-C values at hip were slightly but not signi cantly augmented in the children with AD (mean 138.24, SD 54.14 vs mean 107.03, SD 41.6; F = 2.59, p = .08). None of these parameters was correlated with FSIQ or with ADI-R scores. Con- sistently with previous descriptive data suggesting reduced smoothness, jerk-cost analysis revealed a signi cant loss of smoothness in two (hip and knee) of the three joints considered. Discussion The main objective of the present study was to con rm the presence of motor alterations as an essential feature of autistic syndrome, and also to 277 further investigate the nature of motor system dysfunction and the neural circuitry underlying. It was hypothesized that a thorough quantitative analysis, not only of linear gait parameters but also of parameters regarding upper body kinematic, walk orientation and smoothness, could be useful in gathering additional and reliable information. To better achieve this aim we included children who have never been treated by any psychoactive drug in order to control the impact of medication side effects upon inves- tigated movements. Our data showed an impaired performance of the children with AD on a wide variety of motor parameters (including upper body kinematic para- meters, walk orientation, and smoothness) con rming the presence of basic and complex motor dysfunction in Autistic syndrome. Collected spatio-temporal gait data suggest a smaller stride length, wider step width and a trend to a slight reduction of velocity. These data suggest a pathological gait, where patients tend to augment their own walking stabil- ity; that is, the velocity is usually decreased, the distance between the feet (step width) may be widened and the stride length is frequently shortened. Kinematic gait data suggest a global reduction of range of motion (ROM) for all considered joints (hip, knee and ankle) for the whole gait cycle, and, at the toe-off, a smaller ankle plantar- exion and a more exed knee. No toe-walking was observed but there was a reduction of hip, knee and ankle joint ROM which may be due to an increased rigidity of the walking pattern. Upper body kinematic data suggest a good stabilization of the hip and also a good equilibrium of posture maintenance while walking (i.e., lack of differences at the pelvis level), while the higher angular dispersion of the shoulder in the frontal plane in the children with AD could be linked with some problems in body orientation while walking. As a whole, these data suggest the presence of a stiffer gait in which the usual uidity of walking is lost. The range of motion at the hips and knees is reduced, the base is wider and the stride is shorter; postural abnor- malities are also present. This type of pathological gait could re ect the presence of compensatory strategies to maintain balance control. These abnormalities of gait and posture are very similar to motor impairment described in patients with Parkinson’s and may be the result of a speci c dysfunction of the motor system involving the fronto-striatal basal ganglia region. On the other hand, walk orientation data suggest a highly signi cant difference between the children with AD and the healthy controls in the ability to maintain a straight line while walking. These data suggest the presence of a veering gate resembling some characteristics of ataxic gait and could implicate the involvement of cerebellum or other regions like brain- stem and thalamus, or, maybe, reveal a de cit in sensory motor integration. 278 Consistently with previous descriptive data, jerk-cost analysis revealed a signi cant loss of smoothness in two (hip and knee) of the three joints considered. The mechanism responsible for increased jerk values remains to be fully de ned. According to Iarocci and McDonald (2006) jerky move- ments might be a re ection of motor control strategies (on-line control of movement) dominated by a feedback rather than a feed-forward mode of control. This could be a consequence of a de cit in the integration of propioceptive and visual feed-back information and results in continuous feedback corrections. Thus autistic children adopt a strategy of motor control which enables them to react to environmental stimuli, rather than to predict them. A possible explanation of the use of an ‘on-line’ control of movement strategy could lie in an impairment in the building of internal representation of the body schema, and of representations linked to external parameters; this impairment may be due to a disorder in the processing and integration of sensory-motor information. From a developmental perspective, these de cits could also be seen as an inability in complete automatization of a learned motor sequence. Our data altogether suggest the presence of a pervasive impairment in movements involving not only basic motor skills (linear gait parameters) but also motor control strategies based on processing and integration of sensory-motor information. These results are largely in agreement with qualitative and quantitative data reported by Rinehart et al. (2006a; 2006b) in a same age-range group of non mentally retarded children with AD and in a group of younger newly diagnosed children with AD, thus con rming the presence of abnormal gait features and, especially, of dif culties walking along a stride line, abnormal trunk posture and lack in motor smoothness. They are also in agreement with data suggesting a de cit in postural control and the extensive use of compensatory strategies to maintain balance control both in children with AD (Kohen-Raz et al., 1992; Minshew et al., 2004) and in toddlers later diagnosed as autistic (Esposito and Venuti, 2008) and, especially, with data suggesting a de cit of postural anticipation func- tion in children with AD (Schmitz et al., 2003). These results are also consistent with very recent neuroimaging data suggesting not a localized de cit but a diffusely decreased connectivity across the motor execution network and a pattern of decreased cerebellar activation and increased premotor activation during motor task perfor- mance (Mostofsky et al., 2009). The observed association between Knee-ROM and FSIQ is consistent with data suggesting a reduction of joint range of motion in individuals with mental retardation (Angelopoulou et al., 1999). However, there still remained a signi cant effect of diagnosis on knee ROM after covarying for 279 FSIQ; this nding suggests that knee stiffness in the group with AD cannot be entirely accounted for by developmental disorder in general. The level of impairment in basic motor skills and motor control strat- egies did not correlate with social, communicative and behavioral impair- ments as assessed by ADI-R. This suggests that the motor parameters investigated may not be a sensitive index for the typology of autistic symptoms investigated by ADI-R, at least in our sample. Another possible explanation for this negative nding could be the relative narrow range of ADI-R scores among children with AD and without mental retardation, and the consequent low statistical power to detect a signi cant correla- tion. It could be useful to extend future investigation of motor parameters to children with low- and high functioning Autism Spectrum Disorders (including Asperger’s Disorders) and to administer ADI-R to healthy controls, too. Our results should be regarded with some limitations in mind. Firstly, we did not use any other instruments or clinical examination to assess basic motor skills or praxis; thus it is not possible to evaluate the correlation between impairment in gait and posture and other basic motor skill per- formance or praxis performance. The only investigated motor correlations were between gait parameters and ADI-R restricted, repetitive behavior scores and there were no statistically signi cant correlations, thus suggest- ing that these parameters may not be a sensitive index for motor behavior as assessed by ADI-R. Further studies investigating the potential use of motion analysis in predicting the presence of speci c motor alteration in children with AD could be useful. Secondly, the sample size was quite small, although well selected in that all patients and controls were drug naïve. Thirdly, quantitative gait analysis is a broad and distal probe of the functionality of the neural system. Future studies, however, using standard- ized quantitative analysis of gait and posture (including upper body kine- matic data, walk orientation, and jerk-cost index) and brain imaging techniques will provide precise correlations between motor impairments and underpinning neural circuitry. Examination of correlation between parameters related to both basic motor skills and motor control strategies and anatomic magnetic resonance imaging (MRI) measurements of brain structures could shed light on the brain circuitries involved and indicate to which extent they contribute to phenotypic features of autism. A dysfunc- tion of fronto-striatal basal ganglia region could determine spatio-temporal parameter impairments, and a dysfunction of the cerebellum or of the brainstem and thalamus circuitries could determine the ability to maintain a straight line while walking. Alternatively, abnormalities of the white matter and connectivity could determine the presence of a complex motor dysfunction extending through all the examined motor parameters.</p> <p>280 In conclusion, using an entirely automatic motion analysis system we revealed the presence of a stiffer gait, in which the usual uidity of walking is lost, of trunk postural abnormalities and of highly signi cant dif culties to maintain a straight line and a signi cant loss of smoothness (increase of jerk index) in a sample of drug-naïve children with AD. Our data altogether suggest the presence of a pervasive impairment in movements involving not only basic motor skills (linear gait parameters) but also motor control strategies based on the processing and integration of sensory-motor infor- mation. Thus, the use of a thorough quantitative analysis of gait revealed not only the involvement of both the fronto-striatal basal ganglia region and the cerebellar region, but also a possible de cit in the integration of sensory- motor information within motor networks (i.e., anomalous connections within the fronto-cerebello-thalamo-frontal network). Future studies based on both neuroimaging and careful analysis of motor de cits could help to better understand the neural underpinning of the motor system, a system critical for the development and achievement of more complex behavior necessary for social and communicative development. Although the underlying neural structures involved remain to be demonstrated, these data may contribute to highlight the central role of motor impairment in children with autism and suggest the usefulness to take into account motor dif culties when developing new diagnostic and rehabilitation programs. Acknowledgements This study was supported by Grant R.C.2005–2006 from the Italian Ministry of Health.</p> <p>References American Psychiatric Association (2001) Diagnostic and Statistical Manual of Mental Disorders, 4th Edition,Text Revision (DSM-IV-TR). Washington, DC: Author. Angelopoulou, N., Tsimaras, V., Christoulas, K., & Mandroukas, K. (1999) ‘Measurement of Range of Motion in Individuals with Mental Retardation and with or without Down Syndrome’, Perceptual and Motor Skills 89: 550-6. Buxbaum, L.J., Giovannetti, T., & Libon, D. ( 2000) ‘The Role of the Dynamic Body Schema in Praxis: Evidence from Primary Progressive Apraxia’, Brain and Cognition 44: 166-191. 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( 2008) ‘Analysis of Toddler’s Gait After Six Months of Independent Walking to Identify Autism: A Preliminary Study’, Perceptual and Motor Skills 106: 259-269. Ghaziuddin, M., & Butler, E. ( 1998) ‘Clumsiness in Autism and Asperger Syndrome: A Further Report’, Journal of Intellectual Disability Research 42: 43-48. Green, D., Baird, G., Barnett, A.L., Henderson, L., Huber, J., & Henderson, S.E. (2002) ‘The Severity and Nature of Motor Impairment in Asperger’s Syndrome: A Comparison with Specific Developmental Disorder of Motor Function’, Journal of Child Psychology and Psychiatry 43: 655-668. Hallett, M., Lebiedowska, M.K., Thomas, S.L., Stanhope, S.J., Denckla, M.B., & Rumsey, J. ( 1993) ‘Locomotion of Autistic Adults’, Archives of Neurology 50: 1304-1308. Hogan, N. ( 1984) ‘An Organizing Principle for a Class of Voluntary Movements’, Journal of Neuroscience 4: 2745-54. Iarocci, G., & McDonald, J. ( 2006) ‘Sensory Integration and the Perceptual Experience of Persons with Autism’, Journal of Autism and Developmental Disorders 36: 77-90. Jansiewicz, E.M., Goldberg, M.C., Newschaffer, C.J., Denckla, M.B., Landa, R., & Mostofsky, S.H. (2006) ‘Motor Signs Distinguish Children with High Functioning Autism and Asperger’s Syndrome from Controls’, Journal of Autism and Developmental Disorders 36: 613-621. Kanner, L. ( 1943) ‘Autistic Disturbances of Affective Contact’ , Nervous Child 2: 217-250. Kerrigan, D.C. ( 1998) ‘Introduction/Prologue’, in J.A. DeLisa (ed), Gait Analysis in the Science of Rehabilitation. Baltimore, MD: Department of Veteran Affairs-Veterans Health Administration. Kohen-Raz, R., Volkmar, F.R., & Cohen, D.J. ( 1992) ‘Postural Control in Children with Autism’ , Journal of Autism and Developmental Disorders 22: 419-432. Leary, M.R., & Hill, D.A. ( 1996) ‘Moving On: Autism and Movement Disturbance’ , Mental Retardation 34: 39-53. Lord, C., Rutter, M., Goode, S., Heemsbergen, J., Jordan, H., Mawhood, L., & Schopler, E. ( 1989) ‘Autism Diagnostic Observation Schedule: A Standardized Observation of Communicative and Social Behavior’, Journal of Autism and Developmental Disorders 19: 185-212. Lord, C., Rutter, M., & Le Couteur, A. (1994) ‘Autism Diagnostic Interview-Revised: A Revised Version of a Diagnostic Interview for Caregivers of Individuals with Possible Pervasive Developmental Disorders’, Journal of Autism and Developmental Disorders 24: 659-85. Mari, M., Castiello, U., Marks, D., Marraffa, C., & Prior, M. ( 2003) ‘The Reach-to-Grasp Movement in Children with Autism Spectrum Disorder’, Philosophical Transaction of the Royal Society of London B Biological Science 358: 393-403. Minshew, N.J., Sung, K., Jones, B.L., & Furman, J.M. ( 2004) ‘Underdevelopment of the Postural Control System in Autism’, Neurology 63: 2056-61. Miyahara, M., Tsujii, M., Hori, M., Nakanishi, K., Kageyama, H., & Sugiyama, T. ( 1997) ‘Brief Report: Motor Incoordination in Children with Asperger Syndrome and Learning Disabilities’, Journal of Autism and Developmental Disorders 27: 595-603. 282 Mostofsky, S.H., Powell, S.K., Simmonds, D.J., Goldberg, M.C., Caffo, B., & Pekar, J.J. ( 2009) ‘Decreased Connectivity and Cerebellar Activity in Autism during Motor Task Performance’, Brain 132: 2413-25.Nayate, A., Bradshaw, J.L., & Rinehart , N.J. (2005) ‘Autism and Asperger’s Disorder: Are They Movement Disorders Involving the Cerebellum and/or Basal Ganglia?’ Brain Research Bulletin 67: 327-334. Noterdaeme, M., Mildenberger, K., Minow, F., & Amorosa, H. ( 2002) ‘Evaluation of Neuromotor Deficits in Children with Autism and Children with a Specific Speech and Language Disorder’, European Child and Adolescent Psychiatry 11: 219-25. Ozonoff, S., Young, G.S., Goldring, S., Greiss-Hess, L., Herrera, A.M., Steele, J., Macari, S., Hepburn, S., & Rogers, S.J. ( 2008) ‘Gross Motor Development, Movement Abnormalities, and Early Identification of Autism’, Journal of Autism and Developmental Disorder 38: 644-56. Provost, B., Lopez, B.R., & Heimerl, S. ( 2007) ‘A Comparison of Motor Delays in Young Children: Autism Spectrum Disorder, Developmental Delay, and Developmental Concerns’ , Journal of Autism and Developmental Disorder 37: 321-8. Rogers, S.J., Hepburn, S.J., Stackhouse, T., & Wehner, E. ( 2003) ‘Imitation Performance in Toddlers with Autism and Those with Other Developmental Disorders’, Journal of Child Psychology and Psychiatry 44: 763-781. Rogers, S.J., Young, G.S., Cook, I., Giolzetti, A., & Ozonoff, S. ( 2008) ‘Deferred and Immediate Imitation in Regressive and Early Onset Autism’, Journal of Child Psychology and Psychiatry 49: 449-457. Roithner, R., Schwameder, H., & Muller, E. ( 2000) ‘Determination of Optimal Filter Parameters For Filtering Kinematic Walking Data Using Butterworth Low Pass Filter’ , ISBS 2000 PROCEEDINGS. Romero, D.H., Van Gemmert, A.W., Adler, C.H., Bekkering, H., & Stelmach, G.E. (2003) ‘Altered Aiming Movements in Parkinson’s Disease Patients and Elderly Adults as a Function of Delays in Movement Onset’, Experimental Brain Research 151: 249-261. Rinehart, N.J., Tonge, B.J., Bradshaw, J.L., Iansek, R., Enticott P.G., & McGinley, J. ( 2006a) ‘Gait Function in High-Functioning Autism and Asperger’s Disorder: Evidence for Basal-Ganglia and Cerebellar Involvement? ’ European Child and Adolescent Psychiatry 15: 256-64. Rinehart, N.J., Tonge, B.J., Iansek, R., McGinley, J., Brereton, A.V., Enticott, P.G., & Bradshaw, J.L. (2006b) ‘Gait Function in Newly Diagnosed Children with Autism: Cerebellar and Basal Ganglia Related Motor Disorder’, Developmental Medicine and Child Neurology 48: 819-824. Sattler, J.M. ( 1992) Assessment of Children-Revised and Updated Third Edition. San Diego, CA: Jerome M. Sattler . Schmitz, C., Martineau, J., Barthélémy, C., & Assaiante, C. 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( 1981) ‘Gait Disturbances in Patients with Autistic Behavior: A Preliminary Study’, Archives of Neurology 38: 646-649. Wolpert, D.M., Ghahramani, Z., & Jordan, M.I. ( 1995) ‘Are Arm Trajectories Planned in Kinematic or Dynamic Coordinates? An Adaptation Study’, Experimental Brain Research 103: 460-70. Yan, J.H., Thomas, J.R., Stelmach, G.E., & Thomas, K.T. ( 2000) ‘Developmental Features of Rapid Aiming Arm Movement Across the Lifespan’, Journal of Motor Behavior 32: 121-40.</p> <aug> <p>By Maria Nobile; Paolo Perego; Luigi Piccinini; Elisa Mani; Agnese Rossi; Monica Bellina and Massimo Molteni</p> </aug>
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  Data: In order to increase the knowledge of locomotor disturbances in children with autism, and of the mechanism underlying them, the objective of this exploratory study was to reliably and quantitatively evaluate linear gait parameters (spatio-temporal and kinematic parameters), upper body kinematic parameters, walk orientation and smoothness using an automatic motion analyser (ELITE systems) in drug naive children with Autistic Disorder (AD) and healthy controls. The children with AD showed a stiffer gait in which the usual fluidity of walking was lost, trunk postural abnormalities, highly significant difficulties to maintain a straight line and a marked loss of smoothness (increase of jerk index), compared to the healthy controls. As a whole, these data suggest a complex motor dysfunction involving both the cortical and the subcortical area or, maybe, a possible deficit in the integration of sensory-motor information within motor networks (i.e., anomalous connections within the fronto-cerebello-thalamo-frontal network). Although the underlying neural structures involved remain to be better defined, these data may contribute to highlighting the central role of motor impairment in autism and suggest the usefulness of taking into account motor difficulties when developing new diagnostic and rehabilitation programs. (Contains 4 tables and 2 figures.)
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