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Nat Commun. 2018 Apr 11;9(1):1399. doi: 10.1038/s41467-018-03664-4.

Towards a new approach to reveal dynamical organization of the brain using topological data analysis.

Author information

1
Department of Psychiatry & Behavioral Sciences, Stanford University, 401 Quarry Rd, St 1356, Stanford, CA, 94305, USA. saggar@stanford.edu.
2
Department of Psychological & Brain Sciences & Network Science Institute, Indiana University, 1101 East 10th Street, Bloomington, IN, 47405, USA.
3
Section on Functional Imaging Methods, National Institute of Mental Health, NIH, Building 10, Room 1D80, 10 Center Dr. MSC 1148, Bethesda, MD, 20892, USA.
4
Functional MRI Core Facility, National Institute of Mental Health, Building 10, Room 1D80B, 10 Center Dr. MSC 1148, Bethesda, MD, 20892, USA.
5
Department of Mathematics, Stanford University, 383L, Third Floor, Building 380, Stanford, CA, 94305, USA.
6
Ayasdi, Inc, 4400 Bohannon Drive, Suite 200, Menlo Park, CA, 94025, USA.
7
Department of Radiology, Stanford University, Lucas Center P-074, Stanford, CA, 94305, USA.
8
Department of Psychiatry & Behavioral Sciences, Stanford University, 401 Quarry Rd, St 1356, Stanford, CA, 94305, USA.
9
Department of Pediatrics, Stanford University, 725 Welch Road, Palo Alto, CA, 94304, USA.

Abstract

Little is known about how our brains dynamically adapt for efficient functioning. Most previous work has focused on analyzing changes in co-fluctuations between a set of brain regions over several temporal segments of the data. We argue that by collapsing data in space or time, we stand to lose useful information about the brain's dynamical organization. Here we use Topological Data Analysis to reveal the overall organization of whole-brain activity maps at a single-participant level-as an interactive representation-without arbitrarily collapsing data in space or time. Using existing multitask fMRI datasets, with the known ground truth about the timing of transitions from one task-block to next, our approach tracks both within- and between-task transitions at a much faster time scale (~4-9 s) than before. The individual differences in the revealed dynamical organization predict task performance. In summary, our approach distills complex brain dynamics into interactive and behaviorally relevant representations.

PMID:
29643350
PMCID:
PMC5895632
DOI:
10.1038/s41467-018-03664-4
[Indexed for MEDLINE]
Free PMC Article

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