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Abstract #2437

Selecting parcellation schemes for regional cortical thickness estimations using a machine learning approach

Hsin-Yu Chen1, Chia-Min Chen1, Teng-Yi Huang1, and Tzu-Chao Chuang2

1Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan, 2Department of Electrical Engineering, National Sun Yat-sen University, Taiwan

In this study, we present a systematic approach to derive effective MR biomarkers of cerebral cortical thickness using machine learning methods and a large-scale database. Three neuroanatomical parcellation schemes for assessing region cortical thickness were compared. The results supported using the Desikan–Killiany atlas1 of FreeSurfer produced robust results of age and gender predictions in normal subjects.

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