Brain-State Extraction Algorithm Based on the State Transition (BEST): A Dynamic Functional Brain Network Analysis in fMRI Study

Spatial pattern of the brain network changes dynamically. This change is closely linked to the brain-state transition, which vary depending on a dynamic stream of thoughts. To date, many dynamic methods have been developed for decoding brain-states. However, most of them only consider changes over t...

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Vydáno v:Brain topography Ročník 32; číslo 5; s. 897 - 913
Hlavní autoři: Lee, Young-Beom, Yoo, Kwangsun, Roh, Jee Hoon, Moon, Won-Jin, Jeong, Yong
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.09.2019
Springer Nature B.V
Témata:
Algorithms
Bayes Theorem
Bayesian analysis
Biomedical and Life Sciences
Biomedicine
Brain - physiology
Brain mapping
Brain Mapping - methods
Cluster Analysis
Distributed processing
Functional magnetic resonance imaging
Humans
Logic programming
Magnetic Resonance Imaging
Neural networks
Neural Pathways
Neurology
Neurosciences
Original Paper
Psychiatry
Spatial standard deviation
Functional MRI
Transition time-point
Brain-state
Bayesian information criterion
Number of components
ISSN:0896-0267, 1573-6792, 1573-6792
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Shrnutí:Spatial pattern of the brain network changes dynamically. This change is closely linked to the brain-state transition, which vary depending on a dynamic stream of thoughts. To date, many dynamic methods have been developed for decoding brain-states. However, most of them only consider changes over time, not the brain-state transition itself. Here, we propose a novel dynamic functional connectivity analysis method, brain-state extraction algorithm based on state transition (BEST), which constructs connectivity matrices from the duration of brain-states and decodes the proper number of brain-states in a data-driven way. To set the duration of each brain-state, we detected brain-state transition time-points using spatial standard deviation of the brain activity pattern that changes over time. Furthermore, we also used Bayesian information criterion to the clustering method to estimate and extract the number of brain-states. Through validations, it was proved that BEST could find brain-state transition time-points and could estimate the proper number of brain-states without any a priori knowledge. It has also shown that BEST can be applied to resting state fMRI data and provide stable and consistent results.
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ISSN:0896-0267
1573-6792
1573-6792
DOI:10.1007/s10548-019-00719-7