Brain-state extraction algorithm based on the state transition (BEST)
A fully automatic dynamic functional brain network analysis in fMRI study
- 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, BEST detects brain-state transition time-points using spatial standard deviation of the brain activity pattern that changes over time
- Using Bayesian information criterion (BIC) to the clustering method to estimate and extract the number of brain-states
Lee, Y-B., Yoo K., Roh, J.H., Moon, W.J., Jeong, Y. (2019), Brain-state extraction algorithm based on the state transition (BEST): a dynamic functional brain network analysis in fMRI study. Brain Topography 32 (5), 897-913
For technical questions and feedback, please send an email to Dr. Lee yblee85(at)ibs.re.kr
Exploring Feature Dimensions to Learn a New Policy in an Uninformed Reinforcement Learning Task
- Exploration of multi-dimensional features in an uninformed reinforcement-learning task.
- Two sets of behavioral experiments (SET1. MRI taken, SET2. behavior only) and fMRI GLM results are included.
- Exploration and transfer learning behavior were examined from SET1 (n=29) and validated from SET2 (n=29).
- Conducted GLM analysis with three model-based parameters (Value, Error, and Cognitive Entropy).
- Single threshold (Small volume correction (SVC) peak-level p<0.05 within combined ROI masks) is used.
Choung, O. H., Lee, S. W., and Jeong, Y. (2017), Exploring Feature Dimensions to Learn a New Policy in an Uninformed Reinforcement Learning Task. Sci. Rep., 7(1): 17676. doi:10.1038/s41598-017-17687-2
For technical questions and feedback, please send an email to Oh-hyeon Choung - iohyeonki(at)gmail.com
Degree-based statistic for group network comparison and center persistency for brain connectivity analysis
- Perform a cluster-wise inference
- Provide a corrected p-value in testing hypothesis of 2 sample t-test or Pearson's correlation analysis
- New definition of a cluster which helps overcoming two innate drawbacks of cluster-wise correction
- Robust parameter settings for spatial specificity & the arbitrariness of an initial cluster forming threshold
- Provide an easily interpretable and statistically reliable result
Yoo, K., Lee, P., Chung, M. K., Sohn, W. S., Chung, S. J., Na, D. L., Ju, D. and Jeong, Y. (2017),
Degree-based statistic and center persistency for brain connectivity analysis. Hum. Brain Mapp., 38: 165–181. doi:10.1002/hbm.23352
For technical questions and feedback, please send an email to Dr. Yoo raybeam(at)kaist.ac.kr
Sparse SPM (SSPM)
Group Sparse-dictionary learning in SPM framework for resting-state functional connectivity MRI analysis
- Coming soon
- Requirement: MATLAB
Lee Y., Lee J., Tak S., Lee K., Na DL., Seo S., Jeong Y., Ye J., The Alzheimer's Disease Neuroimaging Initiative (2016),
Sparse SPM: Group Sparse-dictionay learning in SPM framework for resting-state functional connectivity MRI analysis. Neuroimage 125: 1032-1045. doi: 10.1016/j.neuroimage.2015.10.081
For technical questions and feedback, please send an email to Dr. Lee yblee85(at)kaist.ac.kr