Large-scale extended Granger causality for classification of marijuana users from functional MRI

M. Ali Vosoughi; Axel Wismüller

doi:10.1117/12.2582310

15 February 2021 Large-scale extended Granger causality for classification of marijuana users from functional MRI

M. Ali Vosoughi, Axel Wismüller

Proceedings Volume 11600, Medical Imaging 2021: Biomedical Applications in Molecular, Structural, and Functional Imaging; 116000D (2021) https://doi.org/10.1117/12.2582310
Event: SPIE Medical Imaging, 2021, Online Only

Abstract

It has been shown in the literature that marijuana use is associated with changes in brain network connectivity. We investigate whether large-scale Extended Granger Causality (lsXGC) can capture such changes using restingstate fMRI. This method combines dimension reduction with source time-series augmentation and uses predictive time-series modeling for estimating directed causal relationships among fMRI time-series. It is a multivariate approach, since it is capable of identifying the interdependence of time-series in the presence of all other timeseries of the underlying dynamic system. Here, we investigate whether this model can serve as a biomarker for classifying marijuana users from typical controls using 126 adult subjects with a childhood diagnosis of ADHD from the Addiction Connectome Preprocessed Initiative (ACPI) database. We use brain connections estimated by lsXGC as features for classification. After feature extraction, we perform feature selection by Kendall’s-tau rank correlation coefficient followed by classification using a support vector machine. 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. Within a cross-validation scheme of 100 different training/test (90%/10%) data splits, we obtain a mean accuracy range of [0.714, 0.985] and a mean Area Under the receiver operating characteristic Curve (AUC) range of [0.779, 0.999] across all tested numbers of features for lsXGC, which is significantly better than results obtained with cross-correlation, namely mean accuracy of [0.728, 0.912] and mean AUC of [0.825, 0.969]. Our results suggest the applicability of lsXGC as a potential biomarker for marijuana use.

Conference Presentation

Citation Download Citation

M. Ali Vosoughi and Axel Wismüller "Large-scale extended Granger causality for classification of marijuana users from functional MRI", Proc. SPIE 11600, Medical Imaging 2021: Biomedical Applications in Molecular, Structural, and Functional Imaging, 116000D (15 February 2021); https://doi.org/10.1117/12.2582310

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