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

Young Beom Lee, Kwangsun Yoo, Jee Hoon Roh, Won Jin Moon, Yong Jeong

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


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 languageEnglish
Pages (from-to)897-913
Number of pages17
JournalBrain Topography
Issue number5
Publication statusPublished - 2019 Sep 30
Externally publishedYes


  • 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


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