Abstract
Alterations in interregional neural connectivity have been suggested as a signature of the pathobiology of autism. There have been many reports of functional and anatomical connectivity being altered while individuals with autism are engaged in complex cognitive and social tasks. Although disrupted instantaneous correlation between cortical regions observed from functional MRI is considered to be an explanatory model for autism, the causal influence of a brain area on another (effective connectivity) is a vital link missing in these studies. The current study focuses on addressing this in an fMRI study of Theory-of-Mind (ToM) in 15 high-functioning adolescents and adults with autism and 15 typically developing control participants. Participants viewed a series of comic strip vignettes in the MRI scanner and were asked to choose the most logical end to the story from three alternatives, separately for trials involving physical and intentional causality. The mean time series, extracted from 18 activated regions of interest, were processed using a multivariate autoregressive model (MVAR) to obtain the causality matrices for each of the 30 participants. These causal connectivity weights, along with assessment scores, functional connectivity values, and fractional anisotropy obtained from DTI data for each participant, were submitted to a recursive cluster elimination based support vector machine classifier to determine the accuracy with which the classifier can predict a novel participant's group membership (autism or control). We found a maximum classification accuracy of 95.9% with 19 features which had the highest discriminative ability between the groups. All of the 19 features were effective connectivity paths, indicating that causal information may be critical in discriminating between autism and control groups. These effective connectivity paths were also found to be significantly greater in controls as compared to ASD participants and consisted predominantly of outputs from the fusiform face area and middle temporal gyrus indicating impaired connectivity in ASD participants, particularly in the social brain areas. These findings collectively point toward the fact that alterations in causal connectivity in the brain in ASD could serve as a potential non-invasive neuroimaging signature for autism.
Highlights
A biological origin for autism spectrum disorders (ASD) had been proposed even in the earliest published accounts of the disorder (Kanner, 1943; Asperger, 1944)
These path weights were the most discriminative features among all the different metrics used in classification; (2) Effective connectivity paths most important for classification were significantly reduced (p < 0.05) in ASD participants compared to typical control participants; and (3) The paths that were among the top ranked features in the classification analysis were found to be negatively correlated with the Autism Spectrum Quotient (AQ) and positively correlated with the Reading the Mind in the Eyes (RME) test scores
The goals of this study were: (1) to investigate effective connectivity among brain areas during intentional causal attribution in ASD and (2) to utilize machine learning techniques to classify participants based on effective connectivity weights from this study, and behavior assessment scores, functional connectivity, and fractional anisotropy obtained from diffusion tensor imaging (DTI) data from our previous study (Kana et al, 2012)
Summary
A biological origin for autism spectrum disorders (ASD) had been proposed even in the earliest published accounts of the disorder (Kanner, 1943; Asperger, 1944). Brain imaging techniques in the last decade, functional and structural MRI, have pointed to disrupted cortical connectivity as a defining neural feature of ASD (Kana et al, 2011; Just et al, 2012). Diffusion tensor imaging (DTI) studies have reported disruptions in anatomical connectivity in ASD (Barnea-Goraly et al, 2004, 2010; Alexander et al, 2007; Keller et al, 2007; Jou et al, 2011; see Travers et al, 2012 for a review). There is converging evidence for connection abnormalities, the neural connectivity model of ASD is based primarily on functional connectivity, with some contributing evidence from white matter integrity. While the insights gained from these models are valuable, functional connectivity is a method for assessing zero-lag correlations, and does not provide insight into the time-lagged relationships and direction of such causal influence
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