286

Resources

Data Sharing

Exploring Feature Dimensions to Learn a New Policy in an Uninformed Reinforcement Learning Task

Features

  • 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.

Download

References

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

Contact

For technical questions and feedback, please send an email to Oh-hyeon Choung - iohyeonki(at)gmail.com

Degree-based Statistics

Degree-based statistic for group network comparison and center persistency for brain connectivity analysis

Features

  • 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 

Download

References

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

Contact

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

Features

  • Coming soon

Download

  • Requirement: MATLAB 

References

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 

Contact

For technical questions and feedback, please send an email to Dr. Lee yblee85(at)kaist.ac.kr