We considered three well-studied motor adaptation protocols to test the ability of children with autism to acquire internal models of action. Two experiments involved learning to control a novel tool (reach adaptation with a robotic arm), while the third involved learning to compensate for a transformation on the visual input. As evidenced by the after-effects of adaptation, the children with autism improved their performances through formation of predictive internal models, with rates of acquisition and forgetting that were not different from normally developing children.
The ability to adapt voluntary movements to novel conditions introduced by prisms, or to novel tools introduced by robots, is thought to depend on the integrity of the cerebellum. For example, adaptation to distorting prisms (Weiner et al.
), visual transformations (Sanes et al.
) and force fields (Maschke et al.
; Smith and Shadmehr, 2005
), as well as many non-reaching adaptation tasks (for review, see Ito, 2002
), are impaired with cerebellar damage. Other conditions that have been consistently implicated in prism adaptation are posterior parietal damage (Welch and Goldstein, 1972
; Newport and Jackson, 2006
) and schizophrenia (Bigelow et al.
). In contrast, studies on amnesia (Milner et al.
; Shadmehr et al.
), Alzheimer's and Korsakoff's (Weiner et al.
) have not found any effect on adaptation of reaching movements.
However, the role of basal ganglia in learning of such skills has been more controversial. While impairments in motor adaptation have been found in patients in Parkinson's disease (Boller et al.
; Canavan et al.
; Contreras-Vidal and Buch, 2003
) and Huntington's disease (Paulsen et al.
), a number of well-designed studies have failed to see any evidence for an impairment in either of these conditions, especially during initial learning (Weiner et al.
; Stern et al.
; Fernandez-Ruiz et al.
; Marinelli et al.
). Several authors have suggested that this could be explained because some paradigms allow for explicit learning or strategizing, and that in these cases basal ganglia disorders can affect learning (Contreras-Vidal and Buch, 2003
; Fernandez-Ruiz et al.
; Marinelli et al.
). A recent report found normal learning in PD but abnormal consolidation of the motor memories (Marinelli et al.
). Therefore, while there is little doubt that cerebellar disorders generally produce impaired learning in the motor tasks that were studied here, the role of the basal ganglia in the learning process remains poorly understood (Shadmehr and Krakauer, 2008
Given the highly consistent finding from post-mortem studies revealing cerebellar pathology in autism (Williams et al.
; Ritvo et al.
; Bauman and Kemper, 1994
; Bailey et al.
; Fatemi et al.
); it should follow that children with autism would demonstrate impairment in motor adaptation. However, our findings here suggest otherwise. Our data suggest that motor adaptation is normal in children with autism. One possible interpretation is that cerebellar function is still largely intact in autism, despite neuroanatomical changes observed in individuals with the disorder. If this is true, then the deficits in motor function and skill acquisition (i.e. gait, coordination, balance, rhythmicity, motor planning/sequencing, imitation and dyspraxia; for review see Gidley Larson and Mostofsky, 2006
) seen in autism may instead be due to dysfunction within other regions critical for motor/procedural learning (i.e. frontal, parietal, basal ganglia) or abnormalities in connections between these regions.
In particular, parietal regions are critical for the storage and implementation of spatial and temporal representations of movement formulas (Heilman and Gonzalez Rothi, 2003
). These representations are necessary for acquiring and executing novel motor sequences. Children with autism also show impairments in motor control and planning (i.e. impairments in producing correct grip position for picking up an item) (Hughes, 1996
), dysrythmic movements (Jansiewicz et al.
), and functions important to motor/procedural learning for which the frontal lobe and basal ganglia are integral (Rinehart et al.
; Lehericy et al.
; Monchi et al.
; Rinehart et al.
). Findings from neuroimaging studies in individuals with autism provide evidence for abnormalities in these regions (Piven et al.
; Abell et al.
; Carper and Courchesne, 2000
; Carper et al.
; McAlonan et al.
; Hardan et al.
; Carper and Courchesne, 2005
). Further, it has been suggested that autism is associated with an overgrowth of localized cortical connections with undergrowth of more distant connections between cerebral cortical regions and subcortical structures (Herbert et al.
; Happe and Frith, 2006
), with resulting impaired complex information processing (Minshew et al.
) and ‘weak central coherence’ (Shah and Frith, 1993
). Thus, the deficits in motor function and in motor skill acquisition seen in autism may be due to abnormalities in neural connections across a distributed network.
Alternatively, it is possible that the cerebellar lesions found in individuals with autism are reflective of abnormal development and may be of different clinical significance than acquired cerebellar lesions. Studies examining the effect of cerebellar lesions on motor adaptation have thus far focused on humans and macaque monkeys with acquired lesions (Martin et al.
; Baizer et al.
). The findings reveal that motor adaptation relies on cerebellar mechanisms to learn motor patterns through trial and error (Lang and Bastian, 1999
). However, there is little known about the effect of cerebellar lesions occurring early in brain development. Given the developmental context of autism, compensatory mechanisms may exist leading to normal adaptation.
Adaptation of movement is a basic function central to successful performance of simple tasks necessary for survival. More specifically, humans are constantly adjusting their internal model to account for the effects of the external environment (i.e. moving while holding and object, weight of object, etc.), as well as internal changes (i.e. fatigue, growth, etc.). For instance, in order to reach out, grab a hold of food, and bring it to one's mouth to eat, the internal model must constantly be adjusting for the distance of the food from the body, the type of grip required to grasp the food, the weight of the food, the movement trajectory of the arm from the table to the mouth, the width of the mouth, etc. Given that motor adaptation may be critical for human development and survival, in the face of cerebellar lesions occurring early in development, adaptation may be preserved at the expense of other cerebellar functions.
In an fMRI study of attention and simple motor function in individuals with autism, Allen and Courchesne (2003
) reported that cerebellar activation in the autism group, compared to a typically developing control group, spread from the areas normally associated with simple motor tasks (paleocerebellum ipsilateral to movement) to include regions of the cerebellum not associated with simple motor tasks (contralateral and posterior cerebellum). The authors posited that an ‘early loss of Purkinje neurons might cause more primitive functions normally subserved by paleocerebellar regions to be displaced into the neocerebellum at the cost of tissue that subserves cognitive function’ (p. 271). As such, a loss of Purkinje cells in early developing brains of children with autism may result in preferential sparing of motor adaptation, which is central to survival, resulting in less availability of cerebellar resources necessary for other motor and non-motor functions.
Along these lines it may also be possible that children with autism rely on explicit, declarative mechanisms to guide more procedurally based motor adaptation and learning. Parents of children with HFA and Asperger's syndrome commonly report above-average ability to memorize scripted information (Gidley Larson and Mostofsky, 2006
) and published studies suggest that individuals with autism show both impaired procedural learning (Mostofsky et al.
) and excessive reliance on explicit/declarative learning when acquiring predictive knowledge (Klinger and Dawson, 2001
; Walenski et al.
). In the reach adaptation task that we considered here, performance improvements rely not only on the implicit memory systems, but are also aided by the declarative system (Hwang et al.
). Although the influence of the declarative system is thought to be small, it is possible that in children with autism it plays a more prominent role. Because generalization patterns exhibited by the declarative contributions are distinct from the implicit system (Malfait and Ostry, 2004
), future experiments may be able to test whether the robust performance exhibited by the autistic children is due to their declarative system.
Given the central role of adaptation to survival, it is possible that for individuals with autism, abnormalities in the cerebellum and other areas critical to motor learning necessitate recruitment of circuits involved in declarative learning. Manipulations to adaptation paradigms, such as gradual perturbation that minimizes explicit awareness, would help to examining this hypothesis. Techniques that examine neural activity associated with these functions (e.g. fMRI) would also be useful. Specifically fMRI would help to determine whether individuals with HFA demonstrate compensated use of cerebellar regions or employ different brain regions, such as the basal ganglia, in motor adaptation.
A limitation of the current and previously published studies on motor adaptation (Mostofsky et al.
) in autism is that examination focused on uni-manual upper-limb adaptation. Gait abnormalities are often reported in autism (DeMyer et al.
; Vilensky et al.
; Rinehart et al.
), in contrast to upper limb movements, gait adaptation relies on more medial cerebellar regions. Examination of gait adaptation may provide further insight into the neurological basis of autism. A second limitation of this study is that while the acquisition of internal models of sensorimotor adaptation appears to be normal in children with autism, it is unclear whether there would be any form of generalization of these newly acquired internal models on future performance. Thus, further research examining performance over days and/or weeks is warranted in order to determine consolidation as well as generalization towards the performance of future movements.
Lastly, it is possible that in the various tests of motor learning, performance of the HFA children was comparable to healthy controls because our sample size was too small to detect significant differences. To quantify the likelihood of this, we performed a power analysis. When a two-sided t-test for independent samples was used to compare the control and autism groups’ final LI at a significance level of 5%, our sample size had 80% power to detect a difference in learning index of 0.15 (1.3 SD) for the force field task, and of 0.20 (1.3 SD) for the visual rotation task. The same analysis was done for the prism after-effects, and it showed that our sample size had 80% power to detect a 16 cm (1.0 SD) difference. The ability to detect differences in the mean of two groups that are within 1.5 SD of each other indicates that our experiment had power to detect reasonably small differences in performance. Therefore, it is unlikely that our findings represent a type II error.
In summary, children with autism demonstrated normal motor adaptation in a number of tasks that required acquisition of an internal model. These findings are consistent with previous findings of normal adaptation (Mostofsky et al.
) and are despite the overwhelming evidence of cerebellar pathology in individuals with autism.