Abstract
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.
Original language | English |
---|---|
Pages (from-to) | 897-913 |
Number of pages | 17 |
Journal | Brain Topography |
Volume | 32 |
Issue number | 5 |
DOIs | |
Publication status | Published - 2019 Sept 30 |
Externally published | Yes |
Bibliographical note
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.
Keywords
- Bayesian information criterion
- Brain-state
- Functional MRI
- Number of components
- Spatial standard deviation
- Transition time-point
ASJC Scopus subject areas
- Anatomy
- Radiological and Ultrasound Technology
- Radiology Nuclear Medicine and imaging
- Neurology
- Clinical Neurology