Prof. Paul Geha Profile

Prof. Paul Geha

at Univ of Rochester

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Publications (1)

Proceedings Article | 16 March 2020 Presentation + Paper

Large-scale Extended Granger Causality (lsXGC) for classification of Autism Spectrum Disorder from resting-state functional MRI

Axel Wismüller, John Foxe, Paul Geha, Seyed Saman Saboksayr

Proceedings Volume 11314, 113141Y (2020) https://doi.org/10.1117/12.2550027

KEYWORDS: Functional magnetic resonance imaging, Brain, Magnetic resonance imaging, Feature selection, Principal component analysis, Neuroscience, Dimension reduction

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It has been shown in the literature that Autism Spectrum Disorder (ASD) is associated with changes in brain network connectivity. Therefore, we investigate, if it is possible to capture any significant difference between brain connections of healthy subjects and ASD patients using resting-state fMRI time-series. To this end, we have developed large-scale Extended Granger Causality (lsXGC), which combines dimension reduction with source time-series augmentation and uses predictive time-series modeling for estimating directed causal relationships among resting-state fMRI time-series. This method is a multivariate approach, since it is capable of identifying the influence of each time-series on any other time-series in the presence of all other time-series of the underlying dynamic system. Here, we investigate whether this model can serve as a biomarker for classifying ASD patients from typical controls using a subset of 59 subjects of the Autism Brain Imaging Data Exchange II (ABIDE II) data repository. In this study, we use brain connections as features for classification and estimate them by lsXGC. As a reference method, we compare our results with cross-correlation, which is typically used in the literature as a standard measure of functional connectivity. After feature extraction, we perform feature selection by Kendall’s Tau rank correlation coefficient followed by classification using a Support Vector Machine (SVM). In order to evaluate the diagnostic accuracy of lsXGC, we compare its classification performance with cross-correlation. Within a cross-validation scheme of 100 different training/test data splits, we obtain a mean accuracy range of [0.7,0.81] and a mean Area Under the Receiver Operator Characteristic Curve (AUC) range of [0.78,0.85] across all tested numbers of features for lsXGC, which is significantly better than results obtained with cross-correlation namely mean accuracy of [0.57,0.61] and mean AUC of [0.54,0.59], which clearly demonstrates the applicability of lsXGC as a potential biomarker for ASD.

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