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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