Marie Martel
email: mf.martel90@gmail.com

Livio Finos

Salam Bahmad

Eric Koun

Romeo Salemme

Sandrine Sonié

Pierre Fourneret

Christina Schmitz

Alice Catherine Roy

Motor deficits in autism differ from that of developmental coordination disorder

Keywords: Autism Spectrum Disorder, Developmental Coordination Disorder, feedforward control, feedback control, internal models, action representation, kinematics

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

In addition to the typical pattern of impaired communication, social interaction and behavioural stereotypies (DSM-5: American Psychiatric Association, 2013), children with ASD also experience fine and gross motor troubles, clumsiness, and atypical sensorimotor development, with a prevalence of over 50% [START_REF] Bauman | Motor dysfunction in autism[END_REF][START_REF] Downey | Motor activity in children with autism: A review of current literature[END_REF][START_REF] Hirata | Motor Skills and Social Impairments in Children With Autism Spectrum Disorders: A Pilot Study Using the Japanese Version of the Developmental Coordination Disorder Questionnaire (DCDQ-J)[END_REF][START_REF] Miyahara | Brief report: Motor incoordination in children with Asperger syndrome and learning disabilities[END_REF][START_REF] Paquet | Current knowledge on motor disorders in children with autism spectrum disorder (ASD)[END_REF][START_REF] Provost | A comparison of motor delays in young children: Autism spectrum disorder, developmental delay, and developmental concerns[END_REF][START_REF] Whyatt | Motor skills in children aged 7-10 years, diagnosed with autism spectrum disorder[END_REF]. Almost 80% of people with ASD live, indeed, with varying degrees of motor difficulties that impact on their daily functioning [START_REF] Lai | Autism[END_REF]. Meanwhile, Developmental Coordination Disorders (DCD) is another neurodevelopmental condition diagnosed on the basis of impaired motor skills in absence of any intellectual disability nor neurological injury (DSM-5: American Psychiatric Association, 2013). Children with DCD experience general clumsiness, poor balance [START_REF] Geuze | Postural control in children with developmental coordination disorder[END_REF], associated with coordination and handwriting problems which negatively impact their academic and life achievements (Geuze, 2005a;[START_REF] Paquet | Current knowledge on motor disorders in children with autism spectrum disorder (ASD)[END_REF][START_REF] Smits-Engelsman | Fine motor deficiencies in children diagnosed as DCD based on poor grapho-motor ability[END_REF][START_REF] Van Der Linde | Activities of Daily Living in Children With Developmental Coordination Disorder: Performance, Learning, and Participation[END_REF][START_REF] Van Waelvelde | Parameterization of movement execution in children with developmental coordination disorder[END_REF]. A study on a cohort of almost 12000 American families affected by ASD from the SPARK database [START_REF] Bhat | Is Motor Impairment in Autism Spectrum Disorder Distinct From Developmental Coordination Disorder? A Report From the SPARK Study[END_REF] observed that 87% of the sample had motor impairments according to the DCD-Questionnaire (thus qualified as at risk for DCD; [START_REF] Schoemaker | Evaluation of the Developmental Coordination Disorder Questionnaire as a screening instrument[END_REF]. Similarly, over 97% of a group of children with ASD scored below the 16 th percentile on the M-ABC [START_REF] Miller | Shared Features or Co-occurrence? Evaluating Symptoms of Developmental Coordination Disorder in Children and Adolescents with Autism Spectrum Disorder[END_REF]. A diagnosis of ASD and DCD being recognized by the DSM-5 (American Psychiatric Association, 2013), any child with ASD screened as DCD with the typical tests (M-ABC, [START_REF] Henderson | MABC-2: Batterie d'Évaluation du Mouvement chez l'Enfant[END_REF]DCD-Q, Schoemaker et al., 2006) could be diagnosed as ASD+DCD. However, while the symptoms at the behavioural level, as indicated by the evaluation scales, might look similar, it is not clear that they are of the same nature. For instance, gesture to command and imitation of meaningful gestures have been found to distinguish ASD from DCD, even when controlling for social deficits [START_REF] Kilroy | Unique deficit in embodied simulation in autism: An fMRI study comparing autism and developmental coordination disorder[END_REF][START_REF] Kilroy | Motor performance, praxis, and social skills in autism spectrum disorder and developmental coordination disorder[END_REF]. This strongly urges for disentangling the pattern of motor deficits pertaining to ASD from those pertaining to the DCD populations, as they might not be managed in the same way during motor interventions. , Motor control is not a unitary function but instead relies on two modes of control: the feedforward and the feedback. While the latter corrects the movement already launched, the former, also called anticipatory, defines in advance the motor command according to the build-up of a sensorimotor representation of the characteristics of the object, the effector, the relationship between the two, such as the distance from the body. Once the movement starts and somatosensory information is available, it is compared to that predicted by the so-called forward model. Any mismatch will lead to corrective commands, hence feedback control, and an update of the model to improve the action performance (see, for review, [START_REF] Desmurget | Forward modeling allows feedback control for fast reaching movements[END_REF]. In typically developing children, it has been established that almost a decade was necessary to reach adult-like patterns in reach and lifting tasks [START_REF] Forssberg | Development of human precision grip. II. Anticipatory control of isometric forces targeted for object's weight[END_REF][START_REF] Martel | Highs and Lows in Motor Control Development[END_REF][START_REF] Roy | Syntax at hand: Common syntactic structures for actions and language[END_REF][START_REF] Schneiberg | The development of coordination for reach-to-grasp movements in children[END_REF]. Regarding atypical development, the literature has not reached a consensus. ASD individuals are known for experiencing difficulties in executive planning, but some discrepancy persists regarding motor planning abilities and motor execution (see [START_REF] Rinehart | Movement preparation in highfunctioning autism and Asperger disorder: A serial choice reaction time task involving motor reprogramming[END_REF] for preserved motor execution and [START_REF] Forti | Motor planning and control in autism. A kinematic analysis of preschool children[END_REF][START_REF] Glazebrook | How do Individuals with Autism Plan Their Movements[END_REF][START_REF] Glazebrook | The role of vision for online control of manual aiming movements in persons with autism spectrum disorders[END_REF] for impaired movement execution). The complexity of the task sometimes determines the ability of ASD individuals in movement planning. They seem able to plan their movement when the information is directly accessible, however when asked to anticipate a movement on an uncertain target, they fail to adopt an optimal starting position [START_REF] Glazebrook | How do Individuals with Autism Plan Their Movements[END_REF] and display different kinematic signatures than TD children [START_REF] Cavallo | Identifying the signature of prospective motor control in children with autism[END_REF] as if self-generated planning was disrupted in the ASD population. Fabbri-Destro and collaborators (2008) asked children with ASD to reach for an object and to place it in a specific container, that could be either large (easy task) or small (difficult task). Children with ASD failed to modify their reaching movement kinematics as a function of the difficulty, suggesting that they could not take into account the movement goal [START_REF] Fabbri-Destro | Planning actions in autism[END_REF]. This view is supported by patients' inability to perform the end-state comfort bar task [START_REF] Hughes | Brief report: Planning problems in autism at the level of motor control[END_REF]. Indeed a lack of anticipation (feedforward control) has been repetitively observed [START_REF] Bäckström | Motor planning and movement execution during goal-directed sequential manual movements in 6-year-old children with autism spectrum disorder: A kinematic analysis[END_REF][START_REF] Cattaneo | Impairment of actions chains in autism and its possible role in intention understanding[END_REF][START_REF] Hughes | Brief report: Planning problems in autism at the level of motor control[END_REF][START_REF] Mari | The reach-to-grasp movement in children with autism spectrum disorder[END_REF][START_REF] Martineau | Impairment of a cortical event-related desynchronisation during a bimanual load-lifting task in children with autistic disorder[END_REF][START_REF] Rinehart | Movement preparation in highfunctioning autism and Asperger disorder: A serial choice reaction time task involving motor reprogramming[END_REF][START_REF] Schmitz | Motor control and children with autism: Deficit of anticipatory function?[END_REF][START_REF] Stoit | Grasping Motor Impairments in Autism: Not Action Planning but Movement Execution is Deficient[END_REF]. In a reach-to-grasp paradigm, Mari and collaborators (2003) observed a lack of coordination between the reaching and the grasping phases in ASD, especially in those with lower IQ. Yet, recent studies challenged this view reporting a disrupted feedforward and feedback control in ASD individuals [START_REF] Mosconi | Feedforward and feedback motor control abnormalities implicate cerebellar dysfunctions in autism spectrum disorder[END_REF][START_REF] Shafer | Visual and somatosensory feedback mechanisms of precision manual motor control in autism spectrum disorder[END_REF][START_REF] Wang | Individuals with autism spectrum disorder show abnormalities during initial and subsequent phases of precision gripping[END_REF]; see also [START_REF] Ming | Prevalence of motor impairment in autism spectrum disorders[END_REF]Sacrey, Germani, Bryson, & Zwaigenbaum, 2014 for review). Previous studies reported that adding a visual distractor to a task influenced both feedforward and feedback control of typical children, but not of children with ASD [START_REF] Dowd | Do Planning and Visual Integration Difficulties Underpin Motor Dysfunction in Autism? A Kinematic Study of Young Children with Autism[END_REF], highlighting their preference for proprioceptive over visual feedback [START_REF] Lidstone | Moving Toward Understanding Autism: Visual-Motor Integration, Imitation, and Social Skill Development[END_REF]; see also [START_REF] Glazebrook | The role of vision for online control of manual aiming movements in persons with autism spectrum disorders[END_REF] for visuo-proprioceptive integration).

The nature of motor impairment in DCD is also debated. Jover and collaborators (2010) observed a lack of effectiveness of feedforward control in a paradigm where posturo-motor coordination was at stake. When considering voluntary movements, children with DCD appeared able to program their movement accurately in easy end-state-comfort tasks, while they displayed difficulties in complex tasks [START_REF] Adams | Testing predictive control of movement in children with developmental coordination disorder using converging operations[END_REF][START_REF] Noten | Mild impairments of motor imagery skills in children with DCD[END_REF][START_REF] Smyth | Planning and execution of action in children with and without developmental coordination disorder[END_REF]. In visually-guided reaching tasks, their initial direction error has been found unaffected attesting of preserved feedforward control, while the path length could be longer in children with DCD than in TD children suggesting that they reached for a longer than necessary distance, which could highlight deficits in integrating visual information and online control [START_REF] Grohs | Unimanual and bimanual motor performance in children with developmental coordination disorder (DCD) provide evidence for underlying motor control deficits[END_REF]. Regarding the feedback control, children with DCD were found impaired in Rapid Online Control (ROC) through double step paradigms (Hyde & Wilson, 2011a[START_REF] Hyde | Dissecting online control in Developmental Coordination Disorder: A kinematic analysis of double-step reaching[END_REF]; but see [START_REF] Adams | Testing predictive control of movement in children with developmental coordination disorder using converging operations[END_REF] for absence of ROC deficit). This impaired feedback control is generally attributed to a difficulty in integrating error signals between what is predicted and what is actually felt [START_REF] Kagerer | Visuomotor adaptation in children with developmental coordination disorder[END_REF][START_REF] Kagerer | Abrupt, but not gradual visuomotor distortion facilitates adaptation in children with developmental coordination disorder[END_REF]; see for reviews [START_REF] Adams | Compromised motor control in children with DCD: A deficit in the internal model?-A systematic review[END_REF][START_REF] Gomez | Developmental coordination disorder: Core sensori-motor deficits, neurobiology and etiology[END_REF][START_REF] Smits-Engelsman | Noise, variability, and motor performance in developmental coordination disorder[END_REF][START_REF] Zwicker | Developmental coordination disorder: A review and update[END_REF]. Children with DCD would have a deficit in their internal modelling function and reduced visuo-motor processing (e.g. [START_REF] Fuelscher | Reduced motor imagery efficiency is associated with online control difficulties in children with probable developmental coordination disorder[END_REF][START_REF] Reynolds | Motor imagery ability and internal representation of movement in children with probable developmental coordination disorder[END_REF]for review, Steenbergen et al., 2020).

Finally, some authors support the hypothesis of a maturational delay as compared to age-matched typical children rather than structural impairment [START_REF] Hyde | Impaired online control in children with developmental coordination disorder reflects developmental immaturity[END_REF][START_REF] Li | Kinesthetic deficit in children with developmental coordination disorder[END_REF][START_REF] Zoia | Performance of gesture in children with and without DCD: Effects of sensory input modalities[END_REF]. Recently, using a tool-use paradigm we probed body representation plasticity and found a deficit in children with DCD and adolescents thus favoring the internal model deficit hypothesis [START_REF] Martel | Body schema plasticity is altered in Developmental Coordination Disorder[END_REF].

From this short review, it seems that DCD and ASD motor impairments share some characteristics but might have distinct profiles, which was confirmed by several reviews focusing on the differences and similarities between the two populations [START_REF] Caçola | Behavioral comparisons in Autism Spectrum Disorder and Developmental Coordination Disorder: A systematic literature review[END_REF][START_REF] Kilroy | A Review of Functional and Structural Neurobiology of the Action Observation Network in Autism Spectrum Disorder and Developmental Coordination Disorder[END_REF][START_REF] Piek | Sensory-motor deficits in children with developmental coordination disorder, attention deficit hyperactivity disorder and autistic disorder[END_REF]). However, very few studies directly compared DCD and ASD groups of children on motor tasks. To date, most of them focused on action imitation or observation and found both similarities and differences in performance [START_REF] Dewey | Motor and gestural performance in children with autism spectrum disorders, developmental coordination disorder, and/or attention deficit hyperactivity disorder[END_REF][START_REF] Gauthier | Behavioral Own-Body-Transformations in Children and Adolescents With Typical Development, Autism Spectrum Disorder, and Developmental Coordination Disorder[END_REF][START_REF] Green | The severity and nature of motor impairment in Asperger's syndrome: A comparison with specific developmental disorder of motor function[END_REF][START_REF] Paquet | Nature of motor impairments in autism spectrum disorder: A comparison with developmental coordination disorder[END_REF][START_REF] Xavier | Toward a motor signature in autism: Studies from human-machine interaction[END_REF]. In a small sample of children, Miller and collaborators (2019) observed that children with DCD and ASD differed in their speed profile in a dynamic postural control task.

Studies investigating voluntary movements in children with DCD or ASD, without taxing visuospatial performance or imitation skills are scarce and call for homogeneous inclusion criteria. Some studies considered the dual diagnosis DCD + ASD when children with ASD scored below the 15 th percentile at the M-ABC, thus showing motor impairments [START_REF] Caeyenberghs | Neural signature of developmental coordination disorder in the structural connectome independent of comorbid autism[END_REF]. Others compared ASD and DCD groups with different levels of motor impairments [START_REF] Dewey | Motor and gestural performance in children with autism spectrum disorders, developmental coordination disorder, and/or attention deficit hyperactivity disorder[END_REF][START_REF] Gauthier | Behavioral Own-Body-Transformations in Children and Adolescents With Typical Development, Autism Spectrum Disorder, and Developmental Coordination Disorder[END_REF][START_REF] Green | The severity and nature of motor impairment in Asperger's syndrome: A comparison with specific developmental disorder of motor function[END_REF][START_REF] Paquet | Nature of motor impairments in autism spectrum disorder: A comparison with developmental coordination disorder[END_REF] and some did not consider the level of motor impairment in their groups [START_REF] Miller | Children with Autism Spectrum Disorder, Developmental Coordination Disorder, and typical development differ in characteristics of dynamic postural control: A preliminary study[END_REF][START_REF] Xavier | Toward a motor signature in autism: Studies from human-machine interaction[END_REF]. Here we sought to identify the fingerprint of the ASD and DCD motor impairment, to this aim children motor deficits were matched for their severity (using a gold-standard test).

We chose an ecological paradigm that allowed investigating both feedforward and feedback mechanisms in one single task [START_REF] Martel | Highs and Lows in Motor Control Development[END_REF][START_REF] Roy | Syntax at hand: Common syntactic structures for actions and language[END_REF], without highly taxing the visual system. The task consisted in reaching for an object and displacing it to a different location. We manipulated a non-visible property of the object namely the object weight, and the knowledge the child had about it before starting the movement. When the weight was known, the reaching phase allowed investigating feedforward control as the reaching phase could be adapted to the weight of the object. Adults and children older than 7-8 years old [START_REF] Martel | Highs and Lows in Motor Control Development[END_REF][START_REF] Roy | Syntax at hand: Common syntactic structures for actions and language[END_REF] reach faster for a heavy object, in order to compensate for the consequences of the upcoming weight. In kinematics, reaching faster translates in peaks of acceleration or velocity showing an increased amplitude and /or an anticipated latency. Once the object in hand (during the Displace phase), a major weight would typically slow down the movement as evidenced by delayed peaks of smaller amplitudes. Based on our above review of the literature, we hypothesized feedforward control to be preserved in children with DCD, that is a reaching phase adapted to the weight of the upcoming object as observed in TD peers. On the contrary, we hypothesized that children with ASD would fail using a proper anticipatory control feedforward control and would display a very similar profile (namely undifferentiated peak latencies and amplitudes) whether they were reaching for a light or a heavy weight. Complementarily, when the weight was unknown, the displacing phase allowed investigating feedback control. Indeed, in this case, the participants had no means to anticipate the weight of the object, and thus had to wait until they receive somatosensory feedback once the object in hand to adapt their movement to the weight. Adults and children around 11-12 years old have an optimal feedback control and are able to erase the consequences of the weight before the end of the movement [START_REF] Roy | Syntax at hand: Common syntactic structures for actions and language[END_REF]. Younger children display consequences of a heavy weight on their movement for the entire displacing phase time-course [START_REF] Martel | Highs and Lows in Motor Control Development[END_REF]. In light with previous work, we hypothesized that children with DCD would be impaired in feedback control and would display a different performance when compared to their TD peers in the Displace phase when the object weight was unknown. On the contrary, children with ASD should perform similarly to TD children given that our task does not require a high level of visual processing.

Material and methods

Participants

Using the same paradigm in typically developing children [START_REF] Martel | Highs and Lows in Motor Control Development[END_REF] we reported consistent and large effects with groups of 10 participants. To account for variability in atypical populations we increased the sample size in each group up to 14 children. We recruited 3 groups of 14 participants aged between 7 and 12 years old: children with ASD (with no official diagnosis of DCD; 2 lefthanded; 2 girls; mean age ± SD = 10 years ± 1 year 5 month), children with DCD (3 left-handed; 6 girls; mean age ± SD = 10 years 7 months ± 1 year 4 month), and a control group of TD children (1 left-handed; 4 girls; mean age ± SD = 10 years 5 months ± 1 year 6 months), all matched for age (F(2,39) = .624; p = .541). All of the children were schooled in their age class in typical school system. IQ was determined according to the standardized procedures of the fourth version of the Wechsler intelligence scale for children (WISC-4) (Wechsler, 2003). None of the children had any cognitive disabilities (IQ score > 70); note that some data is missing for a few participants as they dropped out before having their IQ test. Both atypical groups (available data 11/14 for the ASD, 12/14 for the DCD) had a similar verbal (VCI) but not perceptive (PRI) IQ, with the ASD group scoring better at PRI (VCI ± SD for DCD and ASD groups respectively: 106 ± 10; 113 ± 18; p = .131; PRI ± SD for DCD and ASD groups respectively: 91 ± 15; 103 ± 14; p = .037). While a score at the WISC-4 was already available for some, most of the TD children underwent a shorter IQ test providing us with a global score. Crucially, we paired the two atypical groups in terms of motor skills (see below), to investigate the nature of impairments associated with qualitatively equivalent motor impairment in each disorder. Participants scores are summed up in Table 1.

The ASD group was referred to us by the national reference centre for ASD of Lyon. Note that children with ASD were included for another study which required an fMRI and a MEG. They were thus screened by a psychiatrist who ensured they could undergo this second study, which excluded comorbidities such as ADHD, anxiety, and mood disorders. There was no other community involvement in this work. All children were diagnosed with ASD by a trained team of specialists, following the DSM-5 (American Psychiatric Association, 2013; IQ, ADI-ADOS). Importantly, they had no official diagnosis of DCD. They were also screened on the French adaptation of the Movement ABC [START_REF] Henderson | Movement assessment battery for children[END_REF][START_REF] Soppelsa | Manuel de la Batterie d'Evaluation du Mouvement chez l'Enfant[END_REF] and we used this score to match both the ASD and DCD groups in terms of M-ABC mean score (DCD: 22.8 ± 6.3; ASD: 18.5 ± 8.9; t(26) = -1,50; p = .147). Ten out of fourteen scored under the 5 th percentile, one scored below the 15 th percentile but under the 5 th percentile for manual dexterity subcategory, and the last two children were above the 15 th cut-off. Thus, despite not being diagnosed as DCD, most of the children with ASD exhibited serious motor difficulties.

The children with DCD were referred to our lab by parents' associations as well as by the health network Dys/10 (specialized in the care of developmental disorders, from diagnosis with a multidisciplinary team to follow-ups with schools). All children were diagnosed DCD according to the DSM-5 (American Psychiatric Association, 2013), and accordingly had no neurological condition nor intellectual disability. Children with DCD were additionally screened on the Movement ABC by a trained physical therapist, as part of the inclusion criteria. To be included in the study, children had to score below the 5 th percentile (12 children) or below the 15 th percentile but under the 5 th percentile for manual dexterity (2 children). Comorbidity of other troubles (e.g. dyslexia, ADHD…) was not an excluding criteria, except for Developmental Language Disorder (DLD) as we previously showed specific motor disorders on the task we used in this population [START_REF] Roy | Syntax at hand: Common syntactic structures for actions and language[END_REF].

TD children were recruited from neighbouring schools and had no known neurodevelopmental disorders; they were not the siblings of any included children with developmental disorders. Each child scored above the 15 th percentile on the M-ABC (mean score ± SD = 2.8 ± 1.5). Their mean score significantly differed from the two other groups (F(2,39) = 38.7; p ˂ .001; TD vs DCD: p < .001; TD vs ASD: p < .001).

All parents and children gave written informed consent to participate in the study and were naïve to its purpose. The local ethics committee (CPP Sud-Est II) approved this study. After completion of the tasks, children received a board game.

Table 1. Individual score for the typically developing (TD), Developmental Coordination Disorder (DCD) and Autism Spectrum Disorder (ASD) groups. The scores on manual dexterity, ball skill and balance are the three subtests of the M-ABC. The scores ≤ 5 th percentile are in bold, and those ≤ 15 th percentile in italic. The scores for the VCI (Verbal Comprehension Index), PRI (Perceptual Reasoning Index), WMI (Working Memory Index) and PSI (Processing speed Index) are subtests of IQ test. Missing data for IQ score indicated that we lost contact with the families before we could have them get an IQ test.

ID

Apparatus

Children were comfortably seated in a quiet room, in front of a table. A bottle was located at 25cm aligned with the participants' midline. A predefined position was marked at 15cm in the frontal axis of the target object (on the right for right-handed and on the left for left-handed children). The objects were two visually identical white opaque bottles (250mL), one termed hereafter "light" (50g) and the other termed hereafter "heavy" (500g). Prior to the experiment, participants were asked to familiarize themselves and verbally characterize the perceived difference between the two target objects. All the participants manipulated the same weight to allow direct comparisons.

Performance measures were derived from infrared markers displacement via an optoelectronic highresolution motion-tracking device (Optotrak 3020; Northern Digital Inc; see below).

Procedure

Participants started each trial with their dominant hand resting in a pinch grip position on a starting point placed on the edge of the table, in front of their body midline. Upon hearing an acoustic Go signal, children had to reach and grasp the object with their dominant hand at a comfortable speed and displace it to the predefined position. At the end of each trial, participants were back to the starting position, their finger in a pinch grip position, waiting for the next trial. To ensure a uniformly sized grasp surface across participants, children were required to grasp the object by its blue cap (40mm).

Training trials with both weights were provided until the child moved naturally without being confused by the markers. The non-dominant hand was kept under the table, on the ipsilateral thigh.

We assessed two conditions in block, with a short break in between:

 Known object weight: it was proposed first and allowed for feedforward control assessment.

Each child had to perform 15 trials in a row with an object and then 15 trials with the other, the weight being counterbalanced between participants. The block design ensured the child could use her optimal strategy based on stabilized internal representation.

 Unknown object weight: it allowed for feedback control assessment after lifting the object.

Here, light and heavy objects were pseudo-randomly presented (n = 15 each). The experimenter's manipulation was concealed and it was emphasized to the child that she should not try to guess the object weight.

The order of the two conditions (known and then unknown) was kept the same for all children, as we wanted to ensure that children had enough experience with the two bottles and their different weights before performing the Unknown object weight condition. The performed action consisted of two different phases: the Reach phase (before contact with the object) and the Displace phase (lifting and displacing the object). When known, the object weight representation could shape the Reach phase before any physical contact with the object thus opening a window on the feedforward control efficiency. When unknown, the object weight was physically perceived in the Displace phase thus testifying on the feedback processing efficiency [START_REF] Martel | Highs and Lows in Motor Control Development[END_REF].

Movement recordings and data processing

The positions of 6 active infrared markers were recorded during the movement. Markers were positioned on the wrist at the styloid process of the radius, on the nail of the thumb and on the nail of the index, as well as on the experimental table to reconstruct the movement in a 3D referential (sampling rate at 300 Hz; spatial resolution: 0.1 mm). Two additional markers were placed on the target objects to measure the height at which they were lifted. Data pre-processing consisted in applying a second-order Butterworth dual pass filter (cutoff frequency: 10Hz), and then kinematics parameters were analysed with a custom Matlab software developed in the ImpAct Team (CRNL -INSERM U1028 -CNRS UMR5292). The kinematics values were extracted semi-automatically, each trial was manually checked.

The extracted kinematic variables were the following:

-Reach phase (8 parameters): numbers are indicated on Figure 1 As described earlier, carrying a heavy object slows down the movement (i.e. longer latencies and smaller amplitudes) in the Displace phase. When the weight is not anticipated in the Reach phase, the consequences of the weight are maximized in the Displace phase. This translates into earlier and higher peaks for an unknown light object, which does not slow down the movement, and later and smaller peaks for an unknown heavy object which slows the movement to a great extent. Optimal feedback control aims at reducing these differences, which might be completely compensated by the end of the Displace phase (Height parameter). Atypical feedback control would be visible in larger differences between light and heavy weight compared to a TD group, or through a larger number of parameters impacted in the Displace phase.

Main parameters as recorded from a single trial on the wrist are illustrated in Figure 1.

[insert Figure 1.]

Statistical analysis

Similarly to our previous studies [START_REF] Martel | Highs and Lows in Motor Control Development[END_REF][START_REF] Roy | Syntax at hand: Common syntactic structures for actions and language[END_REF], we performed the statistical analysis only on the last eight trials per weight and condition to guaranty a more stable representation.

Analyses were performed with "R" (R Core Team, 2018) and missing values were ignored. We performed mixed model analyses on participants' individual trials for each kinematic parameter. In terms of interpretation, linear mixed models are similar to ANOVAs, but more adequate for our study as they account for relatively small sample size with (relatively high) variability [START_REF] Boisgontier | The anova to mixed model transition[END_REF]. We used the "lme4" [START_REF] Bates | Fitting Linear Mixed-Effects Models Using lme4[END_REF] and "lmerTest" [START_REF] Kuznetsova | lmerTest Package: Tests in Linear Mixed Effects Models[END_REF] packages. For each model, we opted for the maximal random structure [START_REF] Barr | Random effects structure for confirmatory hypothesis testing: Keep it maximal[END_REF] that still allowed the model to converge.

We ran three different types of models, investigating three different aspect of motor control:

-Movement execution (i.e. Reach phase with no object in hand, and no possible anticipation):

we compared the performance of each group, neglecting the weight factor, in the Reach phase of the unknown condition only. We used mixed models to assess the effect of Group (TD/DCD/ASD; fixed effect), while taking into account participants' variability (random intercept). This analysis allowed to investigate movement execution when not influenced by internal representations of the weight (since the weight is unknown) nor the weight itself (since the object is not in hand).

-Feedforward and feedback modes of control: we examined the effects of Knowledge (Known/Unknown), Weight (Light/Heavy), Group (TD/DCD/ASD), and their interactions (fixed effects), as well as the intra and inter-individual variability of weight and knowledge effect (random slopes) in all the conditions. We particularly focused on triple interactions

Weight × Group × Knowledge between the TD and any of the other group, in each phase of the movement (Reach phase for feedforward control; Displace phase for feedback control). In short, here to investigate feedforward control we compared the three groups during the Reach phase to determine the presence of weight effects when it was known (and the absence of any weight effects when it was unknown). To investigate feedback control, we compared the three groups during the Displace phase of an unknown weight to evaluate whether the weight consequences would be reduced over time.

-Internal sensorimotor representation of the weight: we focused on any group which showed impaired feedforward control (we hypothesised it would concern only the ASD group), and modelled the effects of Weight (Light/Heavy) and Knowledge (Known/Unknown)

and their interaction (fixed effects), as well as the intra and inter-individual variability of weight and knowledge effect (random slopes; maximal random effects for each parameter).

We particularly focused on the interaction Weight × Knowledge. This analysis specifically allowed to investigate whether despite an absent feedforward control (no modulation of the Reach phase for a known object weight), feedback control once the object in hand would benefit of knowing the weight in advance, attesting of the building of a correct sensorimotor representation of the weight.

Results

Movement execution (Reach phase -Unknown weight)

As a global index of movement execution, we examined the differences between groups when reaching for an unknown object weight. By examining movement without any object in hand, we assessed the characteristics of simple motor execution for each group.

During the Reach phase, the latencies and amplitudes of acceleration, velocity, and deceleration of the wrist (parameters 1-3) never differed between the TD group and the ASD group (all p > .28), neither did the latency and amplitude of the maximum finger aperture (all p > .29, parameter 4). This suggests that both groups displayed a similar kinematic pattern when reaching for an object of unknown weight, indicating a preserved movement execution in ASD. The DCD group was slower than the TD group, displaying delayed wrist (velocity latency: t = 2.09, p = .043; deceleration latency: t = 2.43, p = .020, parameters 2 and 3 respectively) and finger aperture latencies (maximum finger aperture latency: t = 2.96, p = .006, parameter 4), as well as reduced amplitudes (acceleration: t = -2.10, p = .042; deceleration: t = 2.19, p = .035; parameters 1 and 3 respectively). The DCD group tended to be slower than the ASD group on several parameters (p < .09 on 4/8 parameters), however the results reached significance only for the maximum finger aperture latency (t = 3.17, p = .003; see Table S1, parameter 4). In sum, the DCD group was specifically atypically slow in their movement execution, even with no constraints to slow them (i.e. no object in hand).

Feedforward and feedback modes of control

Reach phase and feedforward strategy

When the object weight was unknown, as expected, the latencies and amplitudes of the acceleration, velocity, and deceleration of the wrist, as well as the latency and amplitude of the maximum finger aperture during the Reach phase did not significantly differ as a function of object weight (parameters 1-4: all p > .430 for the TD group; all p > .328 for the DCD group; all p > .143 for the ASD group; velocity amplitude: SE = 29.4, t = -2.26, p = .024). Conversely, the TD and DCD groups never differed from each other on their latencies and amplitudes of wrist acceleration, velocity, and deceleration, neither on their latency and amplitude of maximum finger aperture (parameters 1-4: all p > .380; Figure 2; see Table S3 for all the means and statistic).

Specifically, the ASD group did not significantly modulate their latencies and amplitudes of wrist acceleration, velocity and deceleration, neither their latency and amplitude of maximum finger aperture during the Reach phase as a function of object weight (parameters 1-4: all p > .126). By contrast, TD and DCD groups anticipated the heavy object by increasing either their velocity (parameter 2; DCD: SE = 17.4, t = 2.57, p = .012; Figure 2A andC) or their deceleration (parameter 3;

TD: SE = 179, t = -1.99, p = .050; Figure 2B andD). In addition, TD children exhibited a smaller finger aperture (parameter 4; SE = 1.51, t = 2.11, p = .040) when reaching for the heavy object (see Table S2 for all the means and statistic). Importantly, this pattern was consistent among most children in each group, as can be seen in Figure 2C and D: TD and DCD groups displayed a positive (for the velocity) or negative (for the deceleration) difference, indicating an increased amplitude for a heavy known weight, while children with ASD did not consistently modulate their peaks. This pattern of increasing amplitudes for a heavy weight is consistent with what has been reported previously and indicates efficient feedforward control [START_REF] Martel | Highs and Lows in Motor Control Development[END_REF][START_REF] Roy | Syntax at hand: Common syntactic structures for actions and language[END_REF], compensating in advance the consequences of the heavy object on the movement.

In sum, these results point towards a similar feedforward mode of control for the TD and DCD groups, reaching faster for a heavy object, while children with ASD did not modulate their movement according to the object to reach. This pattern was consistent among most children, showing homogeneity in the different profiles.

[insert Figure 2.]

Displace phase and online control

Once in hand, a heavy object slows down the movement (longer latencies and smaller amplitudes for parameters 5,6,7), especially in the unknown condition when the reach phase could not anticipate the effect of object weight. Efficiency of feedback control is indicated by the number of parameters altered by the weight disparity (the more efficient, the least parameters from the end of the movement), and by the quantitative difference between light and heavy objects (the smaller, the better the feedback control).

In the unknown condition, as the weight could not be anticipated, its effect was observable in each of the groups, with reduced amplitudes and longer latencies for the heavy weight compared to the light one (see Table S4). However, significant triple interactions Group × Weight × Knowledge underlined the peculiar behaviour of children with DCD with respect to TD children (parameter 5; acceleration amplitude: SE = 281, t = -2.86, p = .004; Figure 3A) and to children with ASD (parameter 6 and 8 respectively; velocity amplitude: SE = 21.2, t = 2.76, p = .006; height: SE = 5.95, t = 3.36, p < .001;

Figure 3B). Children with DCD were less impacted by an unknown light weight on their Displace phase as compared to both TD and children with ASD: when the weight could not be anticipated, both groups got carried away by the unexpected light object, displaying higher amplitudes while the DCD group was less affected. TD and ASD groups in turn did not differ among them, displaying the same difference in amplitudes and latencies between an unknown light and heavy object (see Table S3 for all the means and statistic). Such reduced difference in the DCD group could in theory highlight a better feedback control than both the other groups, but this is highly unlikely. Indeed, we showed earlier that children with DCD were slower during their Reach Phase: with a slower movement, there is more time to update and react to an unexpected light weight, explaining the reduced amplitudes compared to TD children and children with ASD. Importantly, this pattern was consistent among most children in each group, as can be seen in Figure 3C [insert Figure 3.]

Internal sensorimotor representation of weight in the ASD group

In the group with ASD, mixed models indicated no interaction between Weight and Knowledge on any of the parameters of the Reach phase (see Table 2). This is consistent with the absence of feedforward control described above. Noteworthy, interactions were present on most of the parameters of the Displace phase: children with ASD performed better in the Displace phase when the weight was previously known, despite not having anticipated the weight in the Reach phase. The cost of the weight (difference between light and heavy) in the Displace phase was reduced for a known weight compared to an unknown one, suggesting that they were able to take the weight information in consideration after all (see Figure 4 for an illustration with the velocity amplitude).

Discussion

This study aimed at determining the nature and specificity of motor control impairments in Autism Spectrum Disorders as compared to Developmental Coordination Disorders. In order to clearly distinguish between feedforward and feedback modes of control, we manipulated a non-visible characteristic of object in a reach to displace paradigm. Feedforward control was quantifiable in the known weight condition during the Reach phase that is before any haptic feedback. Conversely feedback processing was assessed during the Displace phase that is after the contact with the object.

Our findings highlight a preserved feedforward control, but an impaired movement execution in children with DCD, in sharp contrast children with ASD displayed a preserved feedback, online control but an impaired feedforward control, i.e. they failed to anticipate the object's weight during the Reach phase. Interestingly, we were able to state that they were nevertheless able to build a representation of the weight.

An impaired motor execution in DCD but not in ASD patients

Interestingly, children with ASD exhibited an intact motor execution. While we did not replicate the findings of several authors who observed atypical slowness in ASD [START_REF] Forti | Motor planning and control in autism. A kinematic analysis of preschool children[END_REF][START_REF] Glazebrook | How do Individuals with Autism Plan Their Movements[END_REF][START_REF] Glazebrook | The role of vision for online control of manual aiming movements in persons with autism spectrum disorders[END_REF], our results are instead close to those of Rodgers and colleagues, who found no increase in movement time in young children with ASD grasping an object (Rodgers et al, 2019).

Their study also revealed, in older children with ASD, that atypical movement slowness emerges with increased task complexity. Here the reach-to-grasp task did not present any difficulty for them, which could explain the absence of such slowness. Importantly, in the present study participants with ASD had an IQ within normal range and indeed Mari and colleagues reported a link between IQ and motor coordination, low IQ being more impaired [START_REF] Mari | The reach-to-grasp movement in children with autism spectrum disorder[END_REF]. In the DCD group, on the contrary, most of the movement parameters were delayed, and the amplitudes reduced; such an atypical slowness has been reported in the literature [START_REF] Ameratunga | Goal-directed upper limb movements by children with and without DCD: A window into perceptuo-motor dysfunction?[END_REF][START_REF] Biancotto | Neuromotor deficits in developmental coordination disorder: Evidence from a reach-to-grasp task[END_REF][START_REF] Maruff | Abnormalities of imaged motor sequences in children with developmental coordination disorder[END_REF][START_REF] Plumb | Online corrections in children with and without DCD[END_REF]. Having controlled for IQ and motor impairment severity, our findings show unambiguously that this atypical slowness characterize the motor execution of children with DCD.

Opposite pattern of feedforward control between ASD and DCD

As previously reported on the same paradigm [START_REF] Martel | Highs and Lows in Motor Control Development[END_REF][START_REF] Roy | Syntax at hand: Common syntactic structures for actions and language[END_REF], the typically developing group used an anticipatory strategy to avoid or minimize the weight effects of the object.

They modulated their Reach phase increasing their amplitudes before the grasp of the heavy object.

This strategy of speeding up the Reach phase is meant to increase load force, hence preventing the lengthening of the Displace phase [START_REF] Johansson | Coordinated isometric muscle commands adequately and erroneously programmed for the weight during lifting task with precision grip[END_REF]). An efficient feedforward control may result in a total disappearance of the weight effects in the Displace phase of the movement, as shown in healthy adults and 11 years old children in our paradigm [START_REF] Roy | Syntax at hand: Common syntactic structures for actions and language[END_REF]. Children up to 10 years old display a not fully mature feedforward control in lifting tasks [START_REF] Forssberg | Development of human precision grip. I: Basic coordination of force[END_REF][START_REF] Forssberg | Development of human precision grip. II. Anticipatory control of isometric forces targeted for object's weight[END_REF][START_REF] Kuhtz-Buschbeck | Development of prehension movements in children: A kinematic study[END_REF], 1999;[START_REF] Martel | Highs and Lows in Motor Control Development[END_REF][START_REF] Olivier | Age-related differences in the reaching and grasping coordination in children: Unimanual and bimanual tasks[END_REF][START_REF] Paré | Developmental changes in prehension during childhood[END_REF]. Hence, as our group was constituted of children from age 8 to 12 years old, the Displace phase protraction was still observable. Importantly, children with DCD exhibited a similar strategy, when knowing the weight, they went faster prior to contact with the heavy object, increasing their peaks amplitudes. This testified of their preserved ability to use feedforward control [START_REF] Adams | Testing predictive control of movement in children with developmental coordination disorder using converging operations[END_REF][START_REF] Noten | Mild impairments of motor imagery skills in children with DCD[END_REF][START_REF] Smyth | Visual information and the control of reaching in children: A comparison between children with and without developmental coordination disorder[END_REF] and witnessed the accuracy of their object representations in the case of ecological and easy planning tasks. Yet, this preserved feedforward control in voluntary movements contrasts with the immaturity found in postural control [START_REF] Jover | Anticipatory postural adjustments in a bimanual load-lifting task in children with developmental coordination disorder[END_REF]. This discrepancy may find an explanation in terms of time constraints. Indeed, children with DCD displayed an atypical slowness in our study, and postural anticipation relies on a timely muscular inhibition. As pointed out by Jover and colleagues (Jover et al., 2010, p. 854), the "precise mastering of timing parameter is one of the key factors in the development of anticipatory postural adjustments during childhood". Our findings nicely extend the results reported by Jover and co-workers in postural control highlighting that individuals with DCD may not suffer from an impaired anticipatory control per se but rather present a marked deficit when time matters [START_REF] Nobusako | Deficits in Visuo-Motor Temporal Integration Impacts Manual Dexterity in Probable Developmental Coordination Disorder[END_REF]. Nevertheless, our study suggests that children with DCD are able to accurately build internal representations, which are correctly turned into a motor command similarly as TD individuals.

By contrast, children with ASD showed no trace of feedforward control, as they did not modulate any parameters in the Reach phase when the weight was known. This recalls results in postural control, in which children with ASD from 6 to 10 years old exhibited an absence of feedforward control [START_REF] Martineau | Impairment of a cortical event-related desynchronisation during a bimanual load-lifting task in children with autistic disorder[END_REF][START_REF] Schmitz | Motor control and children with autism: Deficit of anticipatory function?[END_REF], while anticipatory adjustments have been observed in the same task as early as age 4 in typical population [START_REF] Schmitz | Building anticipatory postural adjustment during childhood: A kinematic and electromyographic analysis of unloading in children from 4 to 8 years of age. Experimental Brain Research[END_REF]. This study thus confirms the presence of a feedforward deficit in ASD (see also [START_REF] Foster | Getting Off to a Shaky Start: Specificity in Planning and Feedforward Control During Sensorimotor Learning in Autism Spectrum Disorder[END_REF]. Notably, despite a similar Reach phase in the known and unknown weight condition, we observed a more efficient Displace phase when the weight was known, indicating that children with ASD were able to build a weight representation. As soon as they had the object in hand, they were able to integrate the somatosensory feedback, and could only then benefit from their weight representation, which enabled them a better control during the Displace phase. It would seem that they can use this somatosensory information when it is immediately available to them, but the maintenance of this information in memory, in the form of a representation used to scale the kinematics in advance, seems to be lacking. Altogether, these results are in line with recent theories of impaired predictive processes in ASD (see Haker et al., 2016 for review). Under the general hypothesis that the brain entails an internal model of its environment, which forms and updates predictions, a variety of mechanistic explanations have been provided to account for differences in prediction processes in ASD. Among those, imprecise internal models, or priors, have been put forward to account for their altered perception [START_REF] Pellicano | When the world becomes 'too real': A Bayesian explanation of autistic perception[END_REF]. In the context of action prediction, this would also imply that people with ASD would favour the use of proprioceptive information over reliance on internal models.

Integrity of feedback control in ASD

Remarkably the ASD group displayed an online control similar to that observed in the TD group. This echoed back to the preferential use of feedback control over feedforward control in postural tasks [START_REF] Schmitz | Motor control and children with autism: Deficit of anticipatory function?[END_REF][START_REF] Somogyi | Visual feedback increases postural stability in children with autism spectrum disorder[END_REF]. Our results tend to converge with the literature arguing for a preserved feedback control in children with ASD, as observed in adaptation tasks [START_REF] Foster | Getting Off to a Shaky Start: Specificity in Planning and Feedforward Control During Sensorimotor Learning in Autism Spectrum Disorder[END_REF][START_REF] Gidley Larson | Acquisition of internal models of motor tasks in children with autism[END_REF][START_REF] Masterton | Proprioceptive versus visual control in autistic children[END_REF]. Given that online control skills have been shown to predict the abilities on the M-ABC test [START_REF] Blanchard | Online Control of Prehension Predicts Performance on a Standardized Motor Assessment Test in 8-to 12-Year-Old Children[END_REF], we cannot totally rule out the fact that some children with ASD with more severe motor impairments might show difficulties with feedback control. The variability of motor abilities present in individuals with autism, and the relatively small size of our sample, call for caution in generalising the preservation of this online control. However, TD children and children with ASD seemed to have similar patterns of feedback control despite their drastically M-ABC scores, which would rather argue against this possibility.

Children with DCD were less affected by the unexpected light object than the other two groups of children who lifted the object too high when it was unexpectedly light, resulting in large amplitudes.

Rather than a better online control in children with DCD compared to the ASD and TD groups, these results should be interpreted as the reflect of the marked slowness characterizing DCD movements. It is unlikely that children with DCD used slowness as a strategy: indeed, slowness was observed in each phase of the movement, whether children had the object in hand or not, and whether they knew its weight or not. Such a slowness strategy has on the contrary been observed in children with ASD to gain sensory feedback in a postural task [START_REF] Schmitz | Motor control and children with autism: Deficit of anticipatory function?[END_REF]. Thus, we could not confirm our prediction that children with DCD show atypical feedback control. Nevertheless the atypical sensory processing [START_REF] Elbasan | Sensory integration and activities of daily living in children with developmental coordination disorder[END_REF][START_REF] Ringold | Sensory Modulation in Children with Developmental Coordination Disorder Compared to Autism Spectrum Disorder and Typically Developing Children[END_REF][START_REF] Schoemaker | Perceptual skills of children with developmental coordination disorder[END_REF] observed in children with DCD could be a factor affecting feedback control.

Clinical implications of this work

Distinct pattern of motor impairment found in our sample of children with ASD or DCD despite similar scoring on the standard M-ABC assessment highlights the fact that this clinical test makes a quantitative assessment of motor skills, but not a qualitative one. Accordingly, machine learning approaches to classify diagnosis as ASD or DCD failed to use motor features from the M-ABC and other standard tests as a criterion to differentiate ASD and DCD [START_REF] Harrison | Motor and sensory features successfully decode autism spectrum disorder and combine with the original RDoC framework to boost diagnostic classification[END_REF]). Yet, using the M-ABC score as a criterion to group children in terms of having DCD, ASD or ASD + DCD (i.e. ASD diagnosis with impaired motor skills), Caeyenberghs and collaborators observed low overlap between DCD and ASD in terms of structural brain networks' topological properties [START_REF] Caeyenberghs | Neural signature of developmental coordination disorder in the structural connectome independent of comorbid autism[END_REF]. Abnormalities would be disorder-specific, and the comorbid DCD + ASD would depict a unique neural signature with topological changes that were more widespread than in only DCD. In sum, current standard tests do not always inform clinicians on the nature of impairments but give a necessary overview of life-impacting motor difficulties in each child so that they can benefit from support and rehabilitation.

Our results suggest that children with ASD would benefit from rehabilitation aiming at improving their feedforward control, maybe through the support of their preserved representations and use of sensory information. This could for instance be done through tasks that would encourage children to use a feedforward strategy, but that would be motivating and engaging enough so they could benefit from practice. Complementarily, because children with ASD were able to build a correct representation of the weight, and to use it once the object in hand, verbalisation and conscientisation could be beneficial. In our paradigm, this would translate by having them take the heavy object in hand, and explain to them that the object is heavy so they need more strength to carry it compared to the light one, and so by going faster when reaching for it, the movement would be easier. Verbalising the consequences of a non-anticipated movement would achieve a similar goal, to explain that they would have struggled less if they had anticipated the movement beforehand.

Conclusion

In sum, our work has highlighted that despite similar impairments level as evaluated by the M-ABC, children with ASD and DCD exhibit different patterns of motor deficits in a simple ecological reaching paradigm that does not tax the visual system. Children with ASD would lack feedforward control, in line with a more general hypothesis of impaired predictive processes, while children with DCD would be comparable to age-matched TD peers. Children with ASD would instead have a preserved online control and motor execution, while the picture was blurrier for those with DCD.

Notably, so far our paradigm allowed to differentiate the motor disorders of populations with DCD, ASD and DLD [START_REF] Roy | Syntax at hand: Common syntactic structures for actions and language[END_REF]. Finally, our study also indicates that while it is important to rehabilitate the motor skills of a large majority of children with ASD, it is equally important to identify their strengths (such as the use of feedback control) and weaknesses (such as the use of feedforward control) in order to adapt and target motor skills training, which is clearly different from that of children with DCD. Despite no modulation of the kinematics according to the weight in the Reach phase, children with ASD controlled the object better once in hand when its weight was previously known, as shown by a significant interaction. This suggests that they were nonetheless able to build a correct weight representation and use it, but only when they received feedback confirming their predictions (object in hand). Bar graphs illustrate mean values for each group ± sem.

  when possible o (1) Latency and amplitude of the maximum wrist acceleration o (2) Latency and amplitude of the maximum wrist velocity o (3) Latency and amplitude of the maximum wrist deceleration o (4) Latency and amplitude of the maximum finger apertureCarrying a heavy object slows down the movement (i.e. longer latencies and smaller amplitudes) in the Displace phase. Thus, a typical feedforward control would consist in anticipating this speed reduction by going faster before grasping the object, in order to minimize the consequences of the weight once the object is in hand. This translates into shorter latencies and higher amplitudes in the Reach phase. Note that as the amplitude of the deceleration is negative, higher amplitude for this parameter means more negative. Anticipation might also be visible on the maximum finger aperture during the movement and its latency, participants opening their fingers less, and earlier for a heavy known weight. Any of the 8 parameters of the Reach phase could be modified, but a similar number of parameters modified in each group along the same direction speak for a consistent and trustworthy pattern. Conversely, the absence of modulation, or an opposite one (namely faster reaching for the light object) according to the known weight in the Reach phase would indicate atypical feedforward control. -Displace phase (7 parameters): o (5) Latency and amplitude of the maximum wrist acceleration o (6) Latency and amplitude of the maximum wrist velocity o (7) Latency and amplitude of the maximum wrist deceleration o (8) Height at which the object was lifted. This is chronologically the last parameter of the movement.

Figure 2 ,

 2 Figure 2, dashed bars; see TableS2for all the means and statistic). This comparison does not inform

  and D: most children displayed a positive acceleration or height difference, indicating that they were a bit carried away by a light unknown weight. Children with DCD seem overall the least affected, as shown by the reduced differences when compared to children with ASD and TD children. Overall, our results point to a general slowness in children with DCD associated with a reduced effect of the object weight in the Displace phase when unknown. Conversely, both TD and ASD groups displayed a preserved online control and were able to adapt efficiently to the unknown weight of the object. This pattern was consistent among most children, showing homogeneity in the different profiles.

Figure 1 .

 1 Figure 1. Kinematic data of a single trial from a TD child, separated in a Reach and a Displace phase. Circled peaks are extracted for the wrist profile analyses, both in amplitudes (m/s or m/s²) and in latencies (ms). Two parameters are missing in the figure because they are extracted from different markers namely parameter 4, the finger aperture and parameter 8 referring to the object height when lifted. Numbers are referred to in the methods and results.

Figure 2 .

 2 Figure 2. A/Triple interactions Group × Weight × Knowledge for the maximal wrist velocity amplitude in the Reach phase of the movement. B/Triple interactions Group × Weight × Knowledge for the maximal wrist deceleration amplitude in the Reach phase of the movement.When they knew the weight, both TD children and children with DCD modulated their Reach phase by going faster (increased amplitudes) to anticipate the consequences of the weight once the object in hand. This suggests efficient feedforward control in both groups. Conversely, children with ASD did not display a marked anticipation, with no difference between a light and a heavy weight indicating that they were unable of using feedforward control. Bar graphs illustrate mean values for each group ± sem. C/Individual performance ordered by size for the velocity amplitude in the known condition. Positive values indicate that the amplitude increased for a heavy weight (efficient feedforward control), while negative values indicate reduced amplitude. Most of the children in the TD and DCD groups displayed increased amplitudes for a heavy weight (8/14 in each group), while children with ASD barely modulated their reach (only 1/14 correctly modulated their reaching amplitude). D/Individual performance ordered by size for the deceleration amplitude in the known condition. Negative values indicate that the amplitude increased for a heavy weight (efficient feedforward control), while positive values indicate reduced amplitude. Most of the children in the TD and DCD groups displayed increased amplitudes for a heavy weight (respectively 6/14 and 8/14), while the pattern was more random for children with ASD (only 4/14 modulated their deceleration amplitude in the right direction).

Figure 3 .

 3 Figure 3. A/Triple interactions Group × Weight × Knowledge for the maximal wrist acceleration amplitude in the Displace phase. B/Triple interactions Group × Weight × Knowledge for the maximal height of the bottle in the Displace phase of the movement. Children with DCD displayed different behaviour from the TD and ASD groups, with less difference between a light and a heavy object. While this would in theory indicate a better feedback control for children with DCD, these results were likely to be due to their slowness, which hid any potential impairment. TD children and children with ASD display similar patterns of performance indicating a preserved feedback control in ASD. Bar graphs illustrate mean values for each group ± sem. C/Individual performance ordered by size for the acceleration amplitude in the unknown condition. Positive values indicate that the amplitude increased for a light weight, sign of a poorer online control. All the children were carried away by the light unknown weight, with most of the children with DCD displaying reduced weight cost. D/Individual performance ordered by size for the height to which the object was raised in the unknown condition. Positive values indicate that the light unknown weight was raised higher, as seen in most of the children. Children with DCD displayed overall reduced weight cost.

Figure 4 .

 4 Figure 4. Illustration of interactions Weight × Knowledge on the velocity amplitude of the Reach phase (object not in hand) and the Displace phase (object in hand) when the weight was known or unknown by the children with ASD.Despite no modulation of the kinematics according to the weight in the Reach phase, children with ASD controlled the object better once in hand when its weight was previously known, as shown by a significant interaction. This suggests that they were nonetheless able to build a correct weight representation and use it, but only when they received

Age Manual Ball Balance Total M-ABC VCI PRI WMI PSI TD group

	TD14 12.0	1.5	0.5	1	3		Total IQ = 104
					DCD group
	DCD01 12.3	11.5	8	1	20.5	103	84		78
	DCD02 12.2	8.5	3.5	12	24	122 128	97	96
	DCD03 10.7	4.5	1.5	10	16	118 114		88
	DCD04 10.1	15	8	7.5	30.5	99	86	82	86
	DCD05 10.9	11.5	1	9	21.5	116	96	82	86
	DCD06 9.9	10.5	9	8	27.5	114	84	88	81
	DCD07 9.5	12	3	1.5	16.5	NA NA	NA	NA
	DCD08 10.4	6	10	7	23	94	81		103
	DCD09 8.8	11.5	7	14	32.5	NA NA	NA	NA
	DCD10 8.9	14	6	10	30	106	90	88	86
	DCD11 12.3	15	8	5	28	110	94	94	73
	DCD12 9.0	14	0	0	14	96	79	70	71
	DCD13 10.9	10	0	3	13	98	88	97	106
	DCD14 12.3	14.5	1	7	22.5	90	73	94	78
					ASD group
	ASD01 7.9	5.5	2	7	14.5	96	90	97	93
	ASD02 10.1	4.5	8	4	16.5	116	92	73	64
	ASD03 12.3	10.5	10	9	29.5	NA NA	NA	NA
	ASD04 11.7	4	4	1	9	135	96		103
	ASD05 8.5	5.5	9.5	2.5	17.5	130 104		88
	ASD06 9.6	0	4	0	4	130 114		93
	ASD07 9.3	10	10	10.5	30.5	110	99	94	55
	ASD08 9.6	13	0	6.5	19.5	NA NA	NA	NA
	ASD09 11.2	10	8	7	25	NA NA	NA	NA
	ASD10 9.08	3	0	12.5	15.5	84	114	82	76
	ASD11 8.1	0.5	3	8.5	12	122 124		76
	ASD12 11.4	12.5	10	8	30.5	84	82		69
	ASD13 11.1	13	8	6	27	118	94	97	71
	ASD14 10.2	1.5	3	3	7.5	114 124	76	116
	TD01	8.6	0	1	0	1		Total IQ = 129
	TD02 12.1	0	1.5	1	2.5		Total IQ = 120
	TD03 11.1	3	0	0	3	130 102	112	127
	TD04 12.1	1	1	1	3		Total IQ = 122
	TD05 10.6	2.5	0	0	2.5	110	99	103	90
	TD06	9.1	2	0	0	2		Total IQ = 118
	TD07	9.2	2.5	2	0	4.5	128 126	103	109
	TD08	8.1	2.5	0	0	2.5	99	114	112	100
	TD09 11.9	3	3	0	6	120 107	109	100
	TD10 11.7	0	2.5	2	4.5		Total IQ = 111
	TD11	8.1	0	0	0	0		Total IQ = 107
	TD12	9.9	1	1	0	2		NA
	TD13 11.0	2	0	0	2		Total IQ = 95

Table 2 . Interaction Weight × Knowledge of all the parameters from the Reach phase and the Displace phase in the group with ASD

 2 . P values are in bold when significant. SE = Standard Deviation of the estimates. Numbers in bracket refer to Figure 1 and to numbers in the Methods.

	Reach Phase	Weight × Knowledge	Displace Phase	Weight × Knowledge
		SE	t	p		SE	t	p
	(1) Acceleration Latency	15.8	-0.53 .597 (5) Acceleration Latency	56.7	-2.09	.037
	(1) Acceleration Amplitude	370	-1.51 .132 (5) Acceleration Amplitude	238	4.79 < .001
	(2) Velocity Latency	17.1	.624	.533 (6) Velocity Latency	57.3	-1.81	.071
	(2) Velocity Amplitude	24.4	-1.44 .152 (6) Velocity Amplitude	19.2	5.57 < .001

Acknowledgements

We wish to thank all the participants and their families, M.-C. Thiollier and M. Vernet from the Health Network Dys/10 as well as the association 123 Dys for their help in recruiting the DCD population, H.

This research has received the support from the ANR Samenta (ASD-BARN 01502) and the ANR Developmental Tool Mastery. MM was supported by a grant from the French Ministry of Higher Education and Research.

Data availability

All data and code used in this study will be available on the website of the Open Science Framework (OSF) once it is accepted for publication.


E Javon

C Gatel

A Masseboeuf

E Snoeck

Electron Holography study of the local magnetic switching process in MTJs

We present an electron holography experiment enabling the local and quantitative study of magnetic properties in magnetic tunnel junction. The junction was fully characterized during the switching process and each possible magnetic configuration was highlighted with magnetic induction maps. No magnetic coupling was found between the two layers. We plot a local hysteresis loop that was compared with magnetometry measurement at the macroscopic scale confirming the validity of the local method.

Introduction

Different techniques may be used for the local study of magnetic properties in nanosystems like spin polarised scanning tunnelling microscopy, magnetic force microscopy or Lorentz microscopy. Electron holography (EH) allows a unique combination of the high spatial resolution of transmission electron microscopy (TEM) with the capability to quantitatively analyse local magnetic configurations [1,[START_REF] Heumann | True Single Domain and Configuration-Assisted of Submicron Permalloy Dots observed by Electron Holography[END_REF]. These EH capacities are particularly useful to record local magnetic map during switching processes and to plot the corresponding local hysteresis loop.

We used EH to investigate the magnetic configurations of an epitaxial Au/Co/Fe/MgO/FeV/MgO(001) magnetic tunnel junction (MTJ) grown by molecular beam epitaxy.

This MTJ exhibits a giant tunnel magneto-resistance (TMR) of about 200% at RT [START_REF] Bonell | MgO Epitaxial Magnetic Tunnel Jonctions using FeV electrodes[END_REF]. The TMR properties appear when the magnetic configuration of the two FeV and Fe/Co magnetic layers switches from parallel to antiparallel arrangement. The aim of this article is to locally map the magnetic configurations of the MTJ during the magnetization reversal and to reconstruct the hysteresis loop from quantitative information obtained by EH.

Experiments

The study has been performed using a FEI Tecnai F-20 microscope (200 kV) equipped with a field emission gun and a Cs corrector. Holograms were acquired with the specimen in a field free region in the TEM column by turning off the main objective lens and using the first transfer lens of the Cs corrector like an objective lens [START_REF] Snoeck | Proceeding IMC Sapporo[END_REF]. Thin samples were prepared in cross-sectional geometry using the usual mechanical polishing then precision ion polishing method.

The in-situ magnetization was done by switching on the main objective lens with a slight excitation current in order to create a magnetic field of µ 0 H= 0.33 T along the optic axis of the microscope without distorting the resulting image. The sample was then progressively tilted in order to apply a controlled in plane magnetic field parallel to the layers. The axial component of the applied field has no influence on the reversal process because of the shape anisotropy of the sample and therefore can be neglected in our experiment. The coercive fields of the [Fe/Co] hard and FeV soft ferromagnetic layers are 18 mT and 2 mT respectively. Tilting from -13° to 13° corresponds to the application of an in plane magnetic field between ± 45 mT on the sample, which is thus sufficient to achieve the magnetic reversal processes of the whole MTJ and the investigation of the whole hysteresis loop. Electron holograms were then recorded with a varying in-plane magnetic field.

EH is an interferometric technique which gives access to the phase shift of an electron wave that has interacted with magnetic and electrostatic potentials. This phase shift is directly related to the electro-magnetic fields in the sample as described by the Aharonov-Bohm effect: [1]. In that one dimensional expression, "x" is lying perpendicular to the electron beam (in our experiment "x" is in the plane of the sample perpendicular to the Co/Fe/MgO/FeV interfaces), and "z" is parallel to the electron beam. C E is an electron energy related constant and V is the electrostatic potential produced by the sample. "B y " is the magnetic induction component perpendicular to both "x" and "z". The electrostatic contribution to the phase shift is directly proportional to the mean inner potential (MIP) of the sample and to its thickness if the MIP is constant along the electron wave path (as in our case). In order to isolate the magnetic component of the phase shift we acquired two holograms obtained with magnetization saturated in two opposite directions. We then have extracted a reference MIP phase image from holograms recorded at +13° and -13° which was then used to extract the magnetic contribution for each tilt angle:

z)dxdz (x, B e z)dz V(x, C = y E     ΔΦ
  réf y mag MIP e dxdz B       ) ( ) ( ) (    



Results and discussion

Two examples of the phase images corresponding to the magnetic contribution to the phase shift are displayed in Figure 1 (a). The map of the magnetic induction components (B x and B y ) in the two layers can be obtained calculating the derivatives: t . ) (

y mag B x     and t . ) ( x mag B y    
(t being the sample thickness) and are reported in Figure 1 (b) and (c). They indicate that the magnetic induction is only running parallel to the layers (B x = 0) and that for +9° tilt the magnetizations in the two Fe/Co and FeV layers are parallel while, for -1° tilt, there are antiparallel. We thus can get the magnetic configuration change of the MTJ for each step of the switching process.

In order to obtain quantitative results we have calculated the values of relative magnetization for each magnetic configuration. The same area has been selected in each "B y " map and the values of all pixels of the "B y " components have been summed to get: t

I   pixels y B
. This sum is related to the magnetic induction along "y" and to the sample thickness. We then can use the MIP profile to get the shape of the sample thickness. It indicates an expected wedge shape of the sample along "x" with a linear slope of 0.95. The total intensity "I" does not give information about the real magnetization but enable us to compare the global magnetization of the junction for all the steps during the switching process. The evolution of "I" as a function of the tilt (i.e. the applied field) has been superimposed on the same plot than the hysteresis loop measured on the macroscopic sample by VSM (vibrating sample magnetometer). These two plots are reported in Figure 2. Experimental

Conclusion

The agreement between measurements extracted from holography study and VSM shows that electron holography is a powerful tool to investigate the magnetic configurations of magnetic nanomaterials and nanosystems like MTJs. Hysteresis loop along any directions can be reconstructed from a series of maps and give quantitative values on the magnetic properties as coercive field. The main advantage of the technique is to couple the measurement and the image showing the magnetic configuration. This technique brings local information which can not be deduced from a simple hysteresis loop.

  points from EH measurements match fairly well the VSM hysteresis loop. The coercive fields of the soft (FeV) and hard (Fe/Co) layers measured by EH agree with the ones measured by VSM. No dipolar coupling between the layers that could occur due to the thin sample preparation for TEM and EH studies are evidenced. Furthermore, the local configuration at -1° tilt (i.e. 3.5 mT) shows the appearance of dark contrast within the FeV layer corresponding to zero magnetisation (antiparallel configuration within the layer along the optical axis) or a magnetization oriented parallel to the optic axis after the switching started. It corresponds to the reversal of magnetic domain leading to the full reversal of the soft magnetic layer.

Figure 1 :

 1 Figure 1: (a) Magnetic phase contribution to the phase shift. (b) "x" derivative of the magnetic phase images mapping the "y" components of the induction. (c) "y" derivative of the magnetic phase images mapping the "x" components of the induction.

Figure 2 :

 2 Figure 2: Superimposition of the normalized magnetization M/MS as a function of the applied magnetic field obtained by VSM (blue dots) and EH (pink dots). Corresponding vectorial map of the magnetization are given for 3 values of the tilt i.e. of the applied magnetic field (Inset top right: the color scale showing the direction and intensity of the magnetic induction)

Acknowledgement

The authors acknowledge financial support from the European Union under the Framework 6 program under a contract for an Integrated Infrastructure Initiative. Reference 026019 ESTEEM and from the French ANR program "Spinchat" N°: BLAN07-1_ 188976. F. Bonell and S. Andrieu are acknowledged for providing the MTJ samples.


Arianna Piccialli

Ann Carine Vandaele

Yannick Willame

Anni Määttänen

Loïc Trompet

Justin Erwin

Frank Daerden

Lori Neary

Shohei Aoki

Sébastien Viscardy

Ozone in the Martian atmosphere observed by TGO/NOMAD-UVIS solar occultations

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Ozone in the Martian atmosphere observed by TGO/NOMAD-UVIS solar occultations Introduction: The NOMAD-UVIS instrument on board the ExoMars Trace Gas Orbiter has been investigating the Martian atmosphere with the occultation technique since April 2018 [1]. In the solar occultation mode, it is mainly devoted to studying the climatology of ozone and aerosols content [2,3,4].

We analyzed almost two Mars Years of ozone vertical distributions acquired at the day-night terminator, corresponding to more than 8300 solar occultations, acquired between April 2018 (MY 34, L =163°) and November 2021 (MY 36, L =132°).

Retrieval method(s):

As in the work of [5], the NOMAD-UVIS ozone retrievals proved more difficult than expected due to the presence of spurious detections of ozone caused by instrumental effects, high dust content, and very low values of ozone. This led us to compare the results from three different retrieval approaches:

• an onion peeling method (OP);

• a full occultation Optimal Estimation Method (FOEM), and

• a direct onion peeling method (DOP).

The OP method is similar to that used for Mars and Venus stellar occultations [6,7]. The FOEM and DOP approaches are based on ASIMUT-ALVL, the BIRA-IASB radiative code [8,9].

The main challenge was to find reliable criteria to exclude spurious detections of O , and we finally adopted two criteria for filtering: i) a detection limit, and ii) the Δχ criterion. Both criteria exclude spurious O values especially near the perihelion, where based on the simulations from a general circulation model, we do expect very low values of ozone.

Comparison of filtering methods between UVIS and SPICAM:

We compared the results of filtering with SPICAM-UVIS observations. The SPICAM team applied very similar criteria for filtering their data to the ones implemented here [5]. Even if the two instruments observed during different Martian Years, the agreement on the filtered O retrievals is very good, and both filtering approaches lead to very similar results (see Figure 1). Impact of gradients at the Martian terminator: Rapid variations in species concentration at the terminator have the potential to cause asymmetries in the species distributions along the line of sight (LOS) of a solar occultation experiment. Ozone, in particular, displays steep gradients across the terminator of Mars due to photolysis [12]. Nowadays, most of the retrieval algorithms for solar and stellar occultations rely on the assumption of a spherically symmetrical atmosphere. However, photochemically induced variations near sunrise/sunset conditions need to be taken into account in the retrieval process in order to prevent inaccuracies.

We investigated the impact of gradients along the LOS for the retrieval of ozone under sunrise/sunset conditions. We used the diurnal variations in the ozone concentration obtained from photochemical model calculations together with an adapted radiative transfer code.

References

Figure 1 :

 1 Figure 1: An example of the effect of the selection criteria for SPICAM-UV and NOMAD-UVIS obser-vations for a latitude band (-60°;-30°). Left panels show the ozone vertical distribution before applying the filtering; right panels shows O profiles after the selection criteria.

Figure 2 :

 2 Figure 2: O (cm ) vs. H O (cm ) vertical profiles measured simultaneously by NOMAD-UVIS at high latitudes in the Northern

  Piccialli, A., Vandaele, A. C., Willame, Y., Määttänen, A., Trompet, L., Erwin, J., Daerden, F., Neary, L., Aoki, S., Viscardy, S., Thomas, I., Depiesse, C., Ristic, B., Mason, J., Patel, M., Wolff, M., Khayat, A., Bellucci, G., and Lopez-Moreno, J. J.: Ozone in the Martian atmosphere observed by TGO/NOMAD-UVIS solar occultations, Europlanet Science Congress 2022, Granada, Spain, 18-23 Sep 2022, EPSC2022-1027, https://doi.org/10.5194/epsc2022-1027, 2022.

	: [1] Vandaele et al. (2015), PSS. [2] Patel et al. (2021), JGR (Planets). [3] Khayat et al. (2021), JGR (Planets). [4] Neefs,
	E., et al. (2015) Applied Optics. [5] Määttänen et al. Icarus, in review. [6] Quémerais et al. (2006), JGR (Planets). [7] Piccialli et al.
	(2015), PSS. [8] Vandaele et al., (2008), JGR (Planets). [9] Piccialli et al. (2021), Icarus. [10] Aoki et al. (2019), JGR (Planets). [11]
	Aoki et al., (2021) EPSC. [12] Lefèvre, et al. (2008), Nature.
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