Journal article
Brain Density Clustering Analysis: A New Approach to Brain Functional Dynamics
Frontiers in neuroscience, Vol.15, pp.621716-621716
2021
DOI: 10.3389/fnins.2021.621716
PMCID: PMC8076753
PMID: 33927587
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.
Details
- Title: Subtitle
- Brain Density Clustering Analysis: A New Approach to Brain Functional Dynamics
- Creators
- Ashkan Faghiri - The Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia Institute of Technology, Georgia State University, Emory UniversityEswar Damaraju - The Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia Institute of Technology, Georgia State University, Emory UniversityAysenil Belger - Department of Psychiatry, The University of North Carolina, Chapel HillJudith M Ford - Department of Psychiatry, University of California, San FranciscoDaniel Mathalon - Department of Psychiatry, University of California, San FranciscoSarah McEwen - Department of Psychiatry and Biobehavioral Sciences, University of California, Los AngelesBryon Mueller - Department of Psychiatry, University of MinnesotaGodfrey Pearlson - School of Medicine, Yale UniversityAdrian Preda - Department of Psychiatry and Human Behavior, University of California, IrvineJessica A Turner - Department of Psychology, Georgia State UniversityJatin G Vaidya - Department of Psychiatry, The University of IowaTheodorus Van Erp - Department of Psychiatry and Human Behavior, University of California, IrvineVince D Calhoun - The Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia Institute of Technology, Georgia State University, Emory University
- Resource Type
- Journal article
- Publication Details
- Frontiers in neuroscience, Vol.15, pp.621716-621716
- DOI
- 10.3389/fnins.2021.621716
- PMID
- 33927587
- PMCID
- PMC8076753
- NLM abbreviation
- Front Neurosci
- ISSN
- 1662-4548
- eISSN
- 1662-453X
- Publisher
- Frontiers Media S.A
- Grant note
- R01EB020407; P20GM103472 / National Institutes of Health NIH 1 U24 RR021992; NIH 1 U24 RR025736-01 / National Center for Research Resources
- Language
- English
- Date published
- 2021
- Academic Unit
- Psychiatry; Iowa Neuroscience Institute; University College Courses
- Record Identifier
- 9984070809202771
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