Brain Density Clustering Analysis: A New Approach to Brain Functional Dynamics

• Type of Material: Article
• Authors: Ashkan Faghiri, Emory University; Eswar Damaraju, Emory University; Aysenil Belger, University of North Carolina; Judith M Ford, University of California San Francisco; Daniel Mathalon, University of California San Francisco; Sarah McEwen, University of California Los Angeles; Bryon Mueller, Univ Minnesota; Godfrey Pearlson, Yale University; Adrian Preda, University of California Irvine; Jessica A Turner, Georgia State University; Jatin G Vaidya, University of Iowa; Theodorus Van Erp, University of California Irvine; Vince Calhoun, Emory University
• Language: English
• Date: 2021-04-13
• Publisher: FRONTIERS MEDIA SA
• Publication Version: Final Published Version
• Copyright Statement: © 2021 Faghiri, Damaraju, Belger, Ford, Mathalon, McEwen, Mueller, Pearlson, Preda, Turner, Vaidya, Van Erp and Calhoun.
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.3389/fnins.2021.621716
• Title of Journal or Parent Work: FRONTIERS IN NEUROSCIENCE
• Volume: 15
• Start Page: 621716
• End Page: 621716
• Grant/Funding Information: Data collection was supported by the National Institutes of Health (NIH) grant numbers: 1U24RR021992 and NIH1U24RR025736. Data analysis was supported by the following NIH grants (R01EB020407, R01MH118695, RF1AG063153, and R01 MH-58262) and VA (I01 CX0004971, and Senior Research Career Scientist).
• Keywords: density clustering; Science & Technology; FREQUENCY; Neurosciences; PHASE; functional magnetic resonance imaging; resting state– Neurosciences & Neurology; SCHIZOPHRENIA; independent component analyses; fMRI; Life Sciences & Biomedicine; CONNECTIVITY; brain dynamics; NETWORK; RESTING-STATE FMRI; 1ST-EPISODE
• Research Categories: Health Sciences, Mental Health; Engineering, Electronics and Electrical; Psychology, Behavioral

Abstract

Background: A number of studies in recent years have explored whole-brain dynamic connectivity using pairwise approaches. There has been less focus on trying to analyze brain dynamics in higher dimensions over time. Methods: We introduce a new approach that analyzes time series trajectories to identify high traffic nodes in a high dimensional space. First, functional magnetic resonance imaging (fMRI) data are decomposed using spatial ICA to a set of maps and their associated time series. Next, density is calculated for each time point and high-density points are clustered to identify a small set of high traffic nodes. We validated our method using simulations and then implemented it on a real data set. Results: We present a novel approach that captures dynamics within a high dimensional space and also does not use any windowing in contrast to many existing approaches. The approach enables one to characterize and study the time series in a potentially high dimensional space, rather than looking at each component pair separately. Our results show that schizophrenia patients have a lower dynamism compared to healthy controls. In addition, we find patients spend more time in nodes associated with the default mode network and less time in components strongly correlated with auditory and sensorimotor regions. Interestingly, we also found that subjects oscillate between state pairs that show opposite spatial maps, suggesting an oscillatory pattern. Conclusion: Our proposed method provides a novel approach to analyze the data in its native high dimensional space and can possibly provide new information that is undetectable using other methods.