Elsevier

NeuroImage

Volume 59, Issue 2, 16 January 2012, Pages 1524-1533
NeuroImage

Do distinct atypical cortical networks process biological motion information in adults with Autism Spectrum Disorders?

https://doi.org/10.1016/j.neuroimage.2011.08.033Get rights and content

Abstract

Whether people with Autism Spectrum Disorders (ASDs) have a specific deficit when processing biological motion has been a topic of much debate. We used psychophysical methods to determine individual behavioural thresholds in a point-light direction discrimination paradigm for a small but carefully matched groups of adults (N = 10 per group) with and without ASDs. These thresholds were used to derive individual stimulus levels in an identical fMRI task, with the purpose of equalising task performance across all participants whilst inside the scanner. The results of this investigation show that despite comparable behavioural performance both inside and outside the scanner, the group with ASDs shows a different pattern of BOLD activation from the TD group in response to the same stimulus levels. Furthermore, connectivity analysis suggests that the main differences between the groups are that the TD group utilise a unitary network with information passing from temporal to parietal regions, whilst the ASD group utilise two distinct networks; one utilising motion sensitive areas and another utilising form selective areas. Furthermore, a temporal-parietal link that is present in the TD group is missing in the ASD group. We tentatively propose that these differences may occur due to early dysfunctional connectivity in the brains of people with ASDs, which to some extent is compensated for by rewiring in high functioning adults.

Highlights

► ASD and TD adults show comparable performance in a simple biological motion task. ► ASD and TD groups activate different brain regions during task. ► ASD and TD groups show different patterns of effective connectivity during task. ► Results suggest that each group uses different brain networks to reach the same goal.

Introduction

Do people with Autism Spectrum Disorders (ASDs) have a specific deficit when processing human movements? This question has been a matter of much debate in recent years and has received mixed answers (see Kaiser and Shiffrar, 2009, Simmons et al., 2009; for reviews). The earliest studies suggested that people with ASDs were impaired at categorising emotions from human actions, but were comparable to Typically Developed (TD) controls in the categorisation of actions Moore et al. (1997)). Subsequent studies, however, suggested that in addition to these problems, there was an underlying difficulty with integrating local motion signals into the global percept of a human (Blake et al., 2003). Since then a number of studies have put forth conflicting evidence on this issue.

Klin et al. (2002) showed that adolescents with autism do not preferentially attend to salient social visual cues and the same group have since found that children as young as 15 months do not preferentially attend to canonical displays of human movement (Klin et al., 2003, Klin et al., 2009). A recent study by Annaz and colleagues has shown that children with ASDs show a flat developmental trajectory in biological motion processing between the ages of 5 and 12, and that by age 12 the ASD group is substantially poorer at discriminating intact from scrambled Point-Light Displays (PLDs) (Annaz et al., 2010). Furthermore, the difficulties experienced by people with ASDs in processing biological motion seem to be independent of coherent motion perception (Koldewyn et al., 2010).

In contrast however, a number of studies have found no specific biological motion processing deficit in adults with ASDs. Hubert et al. (2007) & Parron et al. (2008) found small but non-significant differences between adult TD and ASD groups in tasks involving action categorisation from PLDs. Atkinson (2009) concluded similarly, but suggested that there may be underlying motion processing difficulties that affect emotion categorisation. This study, however, was without a biological motion condition so the influence of local motions on biological motion was left unanswered. Murphy et al. (2009) carried out a rigorously controlled experiment to determine whether adults with ASDs were specifically impaired relative to a TD group at integrating local motion cues into a coherent global percept of a walker. They used a direction discrimination task in which the points on the PLDs of the walkers were either intact or scrambled to new locations, and the walkers were embedded in noise masks consisting of varying densities of moving dots, with half moving from left to right and half moving from right to left. They found that although d' and proportion correct were consistently lower, and reaction times was consistently higher, in the ASD group than in the TD group across experimental levels, these differences did not reach significance. From this, Murphy et al. (2009) concluded that there is no evidence for biological motion processing deficits in people with ASDs. This view is supported by a recent study by Saygin et al. (2010), which found no differences in biological or non-biological motion perception between adults with and without diagnoses of an ASD.

A key point of interest here is that studies using children with ASDs and TD controls tend to find significant differences between the groups (e.g. Blake et al., 2003 Ages 8–10; Klin, et al., 2003 Aged 2; Klin, et al., 2009 Aged 2; Annaz et al., 2010 Ages 5–12), studies which use adults tend to find no differences(e.g. Hubert, et al., 2007 Ages 15–34; Murphy, et al., 2009 Mean Age 26; Atkinson, 2009 Ages 18–58) and those that use an intermediate age range tend to find mixed results(e.g. Moore, et al., 1997 Ages 11–19; Parron, et al., 2008 Ages 7–18). This suggests that there may be an underlying dysfunction in biological motion processing amongst people with ASDs, which, although manifest at a young age, may be hidden by compensatory mechanisms later in life. These mechanisms may be due to adults with ASDs accomplishing the same tasks using different brain regions and pathways, which have adapted during adolescence to incorporate these functions.

Tantalising evidence comes from two recent fMRI studies which have suggested that although behavioural performance appears equivalent in biological motion tasks, the underlying neural processes may be different in adults with ASDs and TD controls. Freitag et al. (2008) found that when viewing displays of intact and scrambled PLDs the TD groups showed higher fMRI signals for the intact displays in the Middle Temporal Gyrus (MTG), the posterior Superior Temporal Sulcus (STSp), the Fusiform Gyrus (FG), the Inferior Parietal Lobule (IPL), the Intra-Parietal Sulcus (IPS), the Post-Central Gyrus (PstCG) and the Superior Frontal Gyrus (SFG). In contrast, the ASD group showed markedly sparse activation of regions and no differences in activation for the two types of stimuli in the STSp, FG, IPL , or IPS, which are all regions that are implicated in biological motion processing (Beauchamp et al., 2003, Grafton et al., 1996, Grèzes et al., 2001, Grossman et al., 2000, Howard et al., 1996, Puce et al., 1998); Saygin et al., 2004, Vaina et al., 2001. Freitag et al. (2008) claim that there are two possible explanations of their finding: the first being that people with ASDs have difficulty in higher-order motion processing and the second being that people with ASDs have difficulties in integrating complex motion information in the associative cortex. Herrington et al. (2007) used a direction discrimination task to investigate processing of biological motion in people with ASDs. Participants were asked to say whether they thought PLDs of intact or scrambled PLDs were moving to the left or the right whilst in an fMRI scanner. Like Freitag et al. (2008), they found no significant differences in behavioural performance between the two groups, but significantly different patterns of neural activation to the intact PLDs versus fixation. In addition to those regions found to be activated differently in people with ASDs by Freitag et al. (2008), such as the STS, inferior parietal regions, the precentral gyrus (preCG) and the FG, Herrington et al. (2007) also found that the Inferior Temporal Gyrus (ITG), Middle Occipital Gyrus (MOG) and the Angular Gyrus (AG) were less activated to these stimuli in the ASD group.

Here we combine a novel technique for individually determining biological motion coherence thresholds with standard fMRI contrast techniques and Granger Causality Mapping (GCM), to elucidate differences in the brain networks utilised by a group of adults with ASDs and an age- and IQ-matched control group. Participants were asked to report whether PLDs of human walkers were walking to the left or to the right. The PLDs contained a varying ratio of “intact” and “scrambled” points, totalling 15 in every stimulus. Higher ratios of intact to scrambled points increased the amount of structural cues available for direction discrimination. Each participant performed the psychophysical task outside the scanner and their individual 50% and 84% correct thresholds were calculated. The participants then viewed stimuli containing the ratio of intact to scrambled ratio that equated to their own thresholds in an fMRI scanner, essentially equating performance across participants inside the scanner. We determined for each group those regions that showed a greater BOLD response to the 84% correct stimuli than the 50% correct stimuli and used these as seeds in a GCM analysis, mapping the influence to and from each of the seed regions. The results support the findings of equivalent behavioural performance in processing biological motion using different underlying brain regions. In addition to previous work, we found that whereas the TD group used a network of regions suggesting integration of form and motion information from the ITG to parietal regions, the ASD group used a group of regions confined to the occipital and temporal lobe, including MT + and the FG. Furthermore, these two regions seemed to form the starting points of two distinct networks in the ASD group, suggesting a lack of integration in form and motion cues as hypothesised by Freitag et al. (2008).

Section snippets

Participants

Ten high-functioning adults with Autism Spectrum Disorders (aged between 18 and 38) and ten age- and IQ-matched control participants (aged between 19 and 37) took part in the study. All participants in the clinical group had a confirmed diagnosis of having an autism spectrum disorder according to DSM-IV criteria from a qualified clinician, using either the Autism Diagnostic Interview (4 participants) (Lord et al., 1994), or the Diagnostic Interview for Social and Communication Disorders (6

Results

The results of the psychophysical experiment showed that there were no significant differences between the group with ASDs and the TD control group in terms of the number of “intact” points that were required to reach either the 50% or 84% correct thresholds (50% correct - t(18) =  0.79, p = 0.44, d = 0.35; 84% correct - t(18) =  1.87, p = 0.08, d = 0.83), or any of the parameters from the fits (β - t(18) =  0.58, p = 0.57, d = 0.26; γ - t(18) = 0.08, p = 0.94, d = 0.04; λ - t(18) =  0.85, p = 0.41, d = 0.38). It should be

Discussion

These results suggest that adults with ASDs are able to achieve comparable behavioural performance in tasks involving processing configural information from human movements, but utilising substantially different brain networks from TD individuals. Unlike in previous studies, the differential activations cannot be explained away in terms of potential threshold performance differences between the two groups (Simmons et al., 2009). Furthermore, we have provided evidence that in TD individuals the

Conclusion

Our results suggest that TD individuals use a network of regions consistent with form and motion integration models of biological motion processing. However, individuals with ASDs use distinct networks based around form and motion sensitive areas, suggesting that these cues may be processed independently. We propose that a key component of typical human motion processing is the link between temporal and parietal regions. Consequently, disruption of this network, potentially from early white

Acknowledgments

We thank all of the generous individuals who have devoted time to reading and commenting on the draft versions of this paper, and in particular the reviewers who offered invaluable comments and suggestions. Grant support for the authors has been gratefully received from the ESRC/MRC (“Social Interactions: A Cognitive Neurosciences Approach” (RES-060-25-0010)) (D.S., F.P.), and Autism Speaks (“The processing of biological motion patterns in adults with high-functioning autism” No. 1428) (D.S.,

References (81)

  • J.L. Matson et al.

    Diagnosing high incidence autism spectrum disorders in adults

    Res. Autism Spectr Disord.

    (2009)
  • L.S. McKay et al.

    Contribution of configural information in a direction discrimination task: evidence using a novel masking paradigm

    Vision Res.

    (2009)
  • P. Murphy et al.

    No evidence for impaired perception of biological motion in adults with autistic spectrum disorders

    Neuropsychologia

    (2009)
  • L.M. Oberman et al.

    EEG evidence for mirror neuron dysfunction in autism spectrum disorders

    Cogn. Brain Res.

    (2005)
  • M.V. Peelen et al.

    Patterns of fMRI activity dissociate overlapping functional brain areas that respond to biological motion

    Neuron

    (2006)
  • K.A. Pelphrey et al.

    Brain activation evoked by perception of gaze shifts: the influence of context

    Neuropsychologia

    (2003)
  • A. Roebroeck et al.

    Mapping directed influence over the brain using Granger causality and fMRI

    NeuroImage

    (2005)
  • M.B. Schippers et al.

    The effect of intra- and inter-subject variability of hemodynamic responses on group level Granger causality analyses

    Neuroimage

    (2011)
  • D.R. Simmons et al.

    Vision in autism spectrum disorders

    Vision Res.

    (2009)
  • M.E. Villalobos et al.

    Reduced functional connectivity between V1 and inferior frontal cortex associated with visuomotor performance in autism

    NeuroImage

    (2005)
  • T.D. Wager et al.

    Optimization of experimental design in fMRI: a general framework using a genetic algorithm

    NeuroImage

    (2003)
  • J.H.G. Williams et al.

    Neural mechanisms of imitation and ‘mirror neuron’ functioning in autistic spectrum disorder

    Neuropsychologia

    (2006)
  • D. Annaz et al.

    Development of motion processing in children with autism

    Dev. Science

    (2010)
  • L. Barnett et al.

    Granger causality and transfer entropy are equivalent for Gaussian variables

    Phys. Rev. Lett.

    (2009)
  • S. Baron-Cohen et al.

    Autism: a window onto the development of the social and analytic brain

    Ann. Rev. Neurosci.

    (2005)
  • M.S. Beauchamp et al.

    FMRI responses to video and point-light displays of moving humans and manipulable objects

    J. Cogn. Neurosci.

    (2003)
  • M.K. Belmonte et al.

    Autism and abnormal development of brain connectivity

    J. Neurosci.

    (2004)
  • R. Blake et al.

    Visual recognition of biological motion is impaired in children with autism

    Psychol. Sci.

    (2003)
  • D.H. Brainard

    The psychophysics toolbox

    Spat. Vis.

    (1997)
  • M.F. Casanova et al.

    Minicolumnar abnomalities in Autism

    Acta Neuropathol.

    (2006)
  • E. Courchesne et al.

    Unusual brain growth patterns in early life in patients with autistic disorder: an MRI study

    Neurology

    (2001)
  • P.E. Downing et al.

    A cortical area selective for visual processing of the human body

    Science

    (2001)
  • P.E. Downing et al.

    The role of the extrastriate body area in action perception

    Soc. Neurosci.

    (2006)
  • P.E. Downing et al.

    Domain Specificity in Visual Cortex

    Cereb. Cortex

    (2006)
  • F. Faul et al.

    G*Power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences

    Behav. Res. Methods

    (2007)
  • F. Faul et al.

    Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses

    Behav. Res. Methods

    (2009)
  • C. Frith

    What do imaging studies tell us about the neural basis of autism?

    Novartis Found Symp.

    (2003)
  • M.A. Giese et al.

    Neural mechanisms for the recognition of biological movements

    Nat. Rev. Neurosci.

    (2003)
  • R. Goebel et al.

    Analysis of functional image analysis contest (FIAC) data with Brainvoyager QX: from single-subject to cortically aligned group general linear model analysis and self-organizing group independent component analysis

    Hum. Brain Mapp.

    (2006)
  • S.T. Grafton et al.

    Localization of grasp representations in humans by PET: 2. Observation compared with imagination

    Exp. Brain Res.

    (1996)
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