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Proceedings Paper

Classification of attention-deficit/hyperactivity disorder from resting-state functional MRI with mutual connectivity analysis
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Paper Abstract

Previous studies have shown that functional brain connectivity in the Attention-Deficit/Hyperactivity Disorder (ADHD) shows signs of atypical or delayed development. Here, we investigate the use of a nonlinear brain connectivity estimator, namely Mutual Connectivity Analysis with Local Models (MCA-LM), which estimates nonlinear interdependence of time-series pairs in terms of local cross-predictability. As a reference method, we compare MCA-LM performance with cross-correlation, which has been widely used in the functional MRI (fMRI) literature. Pairwise measures like MCA-LM and cross-correlation provide a high-dimensional representation of brain connectivity profiles and are used as features for disease identification from fMRI data. Therefore, a feature selection step is implemented by using Kendall’s Tau rank correlation coefficient for dimensionality reduction. Finally, a Support Vector Machine (SVM) is used for classifying between subjects with ADHD and healthy controls in a Multi-Voxel Pattern Analysis (MVPA) approach on a subset of 176 subjects from the ADHD- 200 data repository. Using 100 different training/test separations and evaluating a wide range of numbers of selected features, we obtain a mean Area Under receiver operating Curve (AUC) range of [0.65,0.70] and a mean accuracy range of [0.6,0.67] for MCA-LM, which outperforms cross-correlation, which yields a mean AUC range of [0.6,0.64] and a mean accuracy range of [0.57,0.59]. Our results suggest that MCA-LM as a nonlinear measure is better suited at extracting relevant information from fMRI time-series data than the current clinical standard of cross-correlation, and may thus provide valuable contributions to the development of novel imaging biomarkers for ADHD.

Paper Details

Date Published: 28 February 2020
PDF: 8 pages
Proc. SPIE 11317, Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging, 1131703 (28 February 2020); doi: 10.1117/12.2549997
Show Author Affiliations
Seyed Saman Saboksayr, Univ. of Rochester (United States)
Adora M. DSouza, Univ. of Rochester (United States)
John J. Foxe, Univ. of Rochester (United States)
Axel Wismüller, Univ. of Rochester (United States)
Ludwig Maximilian Univ. (Germany)

Published in SPIE Proceedings Vol. 11317:
Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging
Andrzej Krol; Barjor S. Gimi, Editor(s)

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