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Abstract #3988

Seed-based resting state fMRI data analysis pipeline by using unsupervised machine learning

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

We have developed an automatic pipeline to generate seed clusters and corresponding maps for rs-fMRI data analysis by using unsupervised machine learning method. It only needs manual participation in the end to review the candidate seed cluster locations and connectivity maps to make decision. In contrast to common anatomical seed scheme which usually consists of a small neighborhood surrounding a single voxel, seeds in our pipeline were determined functionally within large pre-defined ROI which can be derived by using automatic brain segmentation tools like FreeSurfer. The pipeline was tested successfully on rs-fMRI studies with accompanied task-based fMRI involving motor cortex.

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