Multisite functional connectivity MRI classification of autism: ABIDE results

Abstract

Background: Systematic differences in functional connectivity MRI metrics have been consistently observed in autism, with predominantly decreased cortico-cortical connectivity. Previous attempts at single subject classification in high-functioning autism using whole brain point-to-point functional connectivity have yielded about 80% accurate classification of autism vs. control subjects across a wide age range. We attempted to replicate the method and results using the Autism Brain Imaging Data Exchange (ABIDE) including resting state fMRI data obtained from 964 subjects and 16 separate international sites.Methods: For each of 964 subjects, we obtained pairwise functional connectivity measurements from a lattice of 7266 regions of interest covering the gray matter (26.4 million “connections”) after preprocessing that included motion and slice timing correction, coregistration to an anatomic image, normalization to standard space, and voxelwise removal by regression of motion parameters, soft tissue, CSF, and white matter signals. Connections were grouped into multiple bins, and a leave-one-out classifier was evaluated on connections comprising each set of bins. Age, age-squared, gender, handedness, and site were included as covariates for the classifier.Results: Classification accuracy significantly outperformed chance but was much lower for multisite prediction than for previous single site results. As high as 60% accuracy was obtained for whole brain classification, with the best accuracy from connections involving regions of the default mode network, parahippocampaland fusiform gyri, insula, Wernicke Area, and intraparietal sulcus. The classifier score was related to symptom severity, social function, daily living skills, and verbal IQ. Classification accuracy was significantly higher for sites with longer BOLD imaging times.Conclusions: Multisite functional connectivity classification of autism outperformed chance using a simple leave-one-out classifier, but exhibited poorer accuracy than for single site results. Attempts to use multisite classifiers will likely require improved classification algorithms, longer BOLD imaging times, and standardized acquisition parameters for possible future clinical utility.

Predicting symptom severity in autism spectrum disorder based on cortical thickness measures in agglomerative data

“…Indeed, previous studies using the ABIDE data have shown that acquisition site has significant effects on basic image properties (Nielsen et al, 2013;Castrillon et al, 2014). This further exacerbates the problem of identification of core neuroanatomical abnormalities in this extremely heterogeneous data.…”

“…The great majority of ASD studies have focused on identifying group differences between typically developing individuals and those with ASD, or conversely, training classifiers to distinguish between these groups (Ecker et al, 2010;Nielsen et al, 2013;Wang et al, 2015). But, perhaps the largest source of heterogeneity is associated with the severity of the disorder.…”

Resting-state functional connectivity predicts longitudinal change in autistic traits and adaptive functioning in autism

“…Diagnostic Observation Schedule (21)(22)(23), the Autism Diagnostic Interview-Revised (20), and the Social Responsiveness Scale (SRS) (19,22,24). As such, brain measures may explain additional variance in behavioral outcomes.…”

“…Functional connectivity strength in individuals with ASD has been found to predict an ASD diagnosis (19)(20)(21) and to correlate with many aspects of cognition and behavior that also predict outcome, including IQ (21,22), and ASD symptomatology using the Autism Significance Few individuals diagnosed with autism spectrum disorder (ASD) go on to achieve high levels of independence or what are considered "very good" outcomes. As such, there is a need to identify predictors of outcomes to improve treatment and services for these individuals.…”

Functional SPECT neuroimaging using machine learning algorithms distinguishes autism spectrum disorder from healthy subjects

“…Pattern classification methods demonstrate a high predictive ability for the detection of ASD and have been performed across a variety of imaging modalities. These include structural MRI [11][12][13], intrinsic functional connectivity MRI (fcMRI) [14][15][16], diffusion tensor imaging (DTI) [17,18] and electroencephalography (EEG) [19,20]. Reported ASD abnormalities have been identified in the cerebellum [21] and cerebellar vermis [22], anterior cingulate gyrus [23], amygdala [24][25][26], hippocampus [24,27,28], and areas of the frontal [29][30][31], temporal [29,32] parietal lobes [30], caudate and putamen [29,33].…”