Author
Listed:
- Manish Saggar
(Stanford University)
- Olaf Sporns
(Indiana University)
- Javier Gonzalez-Castillo
(National Institute of Mental Health, NIH)
- Peter A. Bandettini
(National Institute of Mental Health, NIH
National Institute of Mental Health)
- Gunnar Carlsson
(Stanford University
Ayasdi, Inc)
- Gary Glover
(Stanford University)
- Allan L. Reiss
(Stanford University
Stanford University
Stanford University)
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.
Suggested Citation
Manish Saggar & Olaf Sporns & Javier Gonzalez-Castillo & Peter A. Bandettini & Gunnar Carlsson & Gary Glover & Allan L. Reiss, 2018.
"Towards a new approach to reveal dynamical organization of the brain using topological data analysis,"
Nature Communications, Nature, vol. 9(1), pages 1-14, December.
Handle:
RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-03664-4
DOI: 10.1038/s41467-018-03664-4
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