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Abstract #3139

Locating seed automatically in posterior cingulate cortex for resting state fMRI data analysis by using unsupervised machine learning 

Mingyi Li1, Katherine Koenig1, Jian Lin1, and Mark Lowe1
1Imaging Institute, Cleveland Clinic, Cleveland, OH, United States

We developed an automatic pipeline to generate seed clusters and corresponding connectivity maps for rs-fMRI data analysis by using unsupervised machine learning method. It only needed manual participation in the very end to review the candidate seed cluster locations and connectivity maps to make decision. Seeds in our pipeline were determined functionally within large pre-defined ROI which could be derived by using automatic brain segmentation tools like FreeSurfer or image registration. Successful application of the pipeline to locate seeds in PCC of control subjects and patients will be presented in this abstract.

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