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Proceedings Paper

Identifying HIV associated neurocognitive disorder using large-scale Granger causality analysis on resting-state functional MRI
Author(s): Adora M. DSouza; Anas Z. Abidin; Lutz Leistritz; Axel Wismüller
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Paper Abstract

We investigate the applicability of large-scale Granger Causality (lsGC) for extracting a measure of multivariate information flow between pairs of regional brain activities from resting-state functional MRI (fMRI) and test the effectiveness of these measures for predicting a disease state. Such pairwise multivariate measures of interaction provide high-dimensional representations of connectivity profiles for each subject and are used in a machine learning task to distinguish between healthy controls and individuals presenting with symptoms of HIV Associated Neurocognitive Disorder (HAND). Cognitive impairment in several domains can occur as a result of HIV infection of the central nervous system. The current paradigm for assessing such impairment is through neuropsychological testing. With fMRI data analysis, we aim at non-invasively capturing differences in brain connectivity patterns between healthy subjects and subjects presenting with symptoms of HAND. To classify the extracted interaction patterns among brain regions, we use a prototype-based learning algorithm called Generalized Matrix Learning Vector Quantization (GMLVQ). Our approach to characterize connectivity using lsGC followed by GMLVQ for subsequent classification yields good prediction results with an accuracy of 87% and an area under the ROC curve (AUC) of up to 0.90. We obtain a statistically significant improvement (p<0.01) over a conventional Granger causality approach (accuracy = 0.76, AUC = 0.74). High accuracy and AUC values using our multivariate method to connectivity analysis suggests that our approach is able to better capture changes in interaction patterns between different brain regions when compared to conventional Granger causality analysis known from the literature.

Paper Details

Date Published: 24 February 2017
PDF: 9 pages
Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 101330M (24 February 2017); doi: 10.1117/12.2254690
Show Author Affiliations
Adora M. DSouza, Univ. of Rochester (United States)
Anas Z. Abidin, Univ. of Rochester (United States)
Lutz Leistritz, Friedrich-Schiller-Univ. Jena (Germany)
Axel Wismüller, Univ. of Rochester (United States)
Ludwig Maximilian Univ. (Germany)

Published in SPIE Proceedings Vol. 10133:
Medical Imaging 2017: Image Processing
Martin A. Styner; Elsa D. Angelini, Editor(s)

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