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Brain Connect. 2017 Oct;7(8):465-481. doi: 10.1089/brain.2017.0543.

Time-Resolved Resting-State Functional Magnetic Resonance Imaging Analysis: Current Status, Challenges, and New Directions.

Author information

1
1 Department of Biomedical Engineering, Emory University/Georgia Institute of Technology , Atlanta, Georgia .
2
2 Basque Center of Cognition , Brain and Language, San Sebastian, Spain .
3
3 Section on Functional Imaging Methods, NIMH, NIH, Bethesda, Maryland.
4
4 Functional MRI Core Facility, NIMH, NIH, Bethesda, Maryland.
5
5 Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra , Barcelona, Spain .
6
6 Instituci├│ Catalana de la Recerca i Estudis Avan├žats (ICREA) , Barcelona, Spain.
7
7 Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences , Leipzig, Germany .
8
8 School of Psychological Sciences, Monash University , Melbourne, Australia .
9
9 The Mind Research Network, Albuquerque, New Mexico.
10
10 Department of Electrical and Computer Engineering, The University of New Mexico , Albuquerque, New Mexico.

Abstract

Time-resolved analysis of resting-state functional magnetic resonance imaging (rs-fMRI) data allows researchers to extract more information about brain function than traditional functional connectivity analysis, yet a number of challenges in data analysis and interpretation remain. This article briefly summarizes common methods for time-resolved analysis and presents some of the pressing issues and opportunities in the field. From there, the discussion moves to interpretation of the network dynamics observed with rs-fMRI and the role that rs-fMRI can play in elucidating the large-scale organization of brain activity.

KEYWORDS:

dynamic analysis; dynamic connectivity; network dynamics; time-resolved resting-state fMRI

PMID:
28874061
PMCID:
PMC5653134
DOI:
10.1089/brain.2017.0543
[Indexed for MEDLINE]
Free PMC Article

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