TY - JOUR
T1 - Brain-State Extraction Algorithm Based on the State Transition (BEST)
T2 - A Dynamic Functional Brain Network Analysis in fMRI Study
AU - Lee, Young Beom
AU - Yoo, Kwangsun
AU - Roh, Jee Hoon
AU - Moon, Won Jin
AU - Jeong, Yong
N1 - Funding Information:
Acknowledgements This study was supported by grant HI14C2768 from the Korea Health Technology Research and Development Project through the Korea Health Industry Development Institute, funded by the Ministry of Health &Welfare, Republic of Korea.
Publisher Copyright:
© 2019, Springer Science+Business Media, LLC, part of Springer Nature.
PY - 2019/9/30
Y1 - 2019/9/30
N2 - 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.
AB - 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.
KW - Bayesian information criterion
KW - Brain-state
KW - Functional MRI
KW - Number of components
KW - Spatial standard deviation
KW - Transition time-point
UR - http://www.scopus.com/inward/record.url?scp=85067083553&partnerID=8YFLogxK
U2 - 10.1007/s10548-019-00719-7
DO - 10.1007/s10548-019-00719-7
M3 - Article
C2 - 31161473
AN - SCOPUS:85067083553
VL - 32
SP - 897
EP - 913
JO - Brain Topography
JF - Brain Topography
SN - 0896-0267
IS - 5
ER -