Refined measure of functional connectomes for improved identifiability and prediction

• Type of Material: Article
• Authors: Biao Cai, Tulane University; Gemeng Zhang, Tulane University; Wenxing Hu, Tulane University; Aiying Zhang, Tulane University; Pascal Zille, Tulane University; Yipu Zhang, Chang'an University; Julia M Stephen, Emory University; Tony W Wilson, University of Nebraska Medical Center (UNMC); Vince Calhoun, Emory University; Yu‐Ping Wang, Tulane University
• Language: English
• Date: 2019-07-29
• Publisher: WILEY
• Publication Version: Final Published Version
• Copyright Statement: © 2019 Wiley Periodicals, Inc.
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1002/hbm.24741
• Title of Journal or Parent Work: HUMAN BRAIN MAPPING
• Volume: 40
• Issue: 16
• Start Page: 4843
• End Page: 4858
• Grant/Funding Information: National Institutes of Health, Grant/Award Numbers: R01 MH104680, P20 GM109068, R01 MH103220, R01 MH107354; National Science Foundation, Grant/Award Number: 1539067
• Keywords: CONNECTIVITY; individual identification; Radiology, Nuclear Medicine & Medical Imaging; BRAIN NETWORKS; Science & Technology; cognitive behavior prediction; Neurosciences & Neurology; refined connectomes; Life Sciences & Biomedicine; VARIABILITY; Neurosciences; sparse dictionary learning model; DEFAULT MODE; functional connectivity; Neuroimaging
• Research Categories: Engineering, Biomedical; Engineering, Electronics and Electrical

Abstract

Brain functional connectome analysis is commonly based on population-wise inference. However, in this way precious information provided at the individual subject level may be overlooked. Recently, several studies have shown that individual differences contribute strongly to the functional connectivity patterns. In particular, functional connectomes have been proven to offer a fingerprint measure, which can reliably identify a given individual from a pool of participants. In this work, we propose to refine the standard measure of individual functional connectomes using dictionary learning. More specifically, we rely on the assumption that each functional connectivity is dominated by stable group and individual factors. By subtracting population-wise contributions from connectivity patterns facilitated by dictionary representation, intersubject variability should be increased within the group. We validate our approach using several types of analyses. For example, we observe that refined connectivity profiles significantly increase subject-specific identifiability across functional magnetic resonance imaging (fMRI) session combinations. Besides, refined connectomes can also improve the prediction power for cognitive behaviors. In accordance with results from the literature, we find that individual distinctiveness is closely linked with differences in neurocognitive activity within the brain. In summary, our results indicate that individual connectivity analysis benefits from the group-wise inferences and refined connectomes are indeed desirable for brain mapping.