Two OHBM Posters on Persistence Landscape for Brain Connectivity Analysis

We have two OHBM papers that deal with topological analysis of brain connectivity using persistent homology technique whose brain applications have been pioneered first by Moo K. Chung of Univ. Wisconsin. Our first paper applied the idea to EEG signal analysis for depression patients.

• “Topological Analysis of EEG Connectivity Patterns of Depressed Patients using Persistence Landscape” by Jae Jun Yoo, Jae Seung Chang, Moo K. Chung and Jong Chul Ye.

The second paper applied the idea to Parkinson’s disease patients.

• Functional Connectivity Studies of the Default Mode Network in Parkinson’s Disease with Cognitive Impairment” by Maryam Ghahremani, Jong Chul Ye, Yong Jeong.

To allow rigorous statistical analysis for the resulting topological features, a new topological tool called “persistence landscape” was employed. The main advantages of using persistence landscape is that the central limit theorem holds in this domain and we can calculate p-value to test an alternative hypothesis.