1. A comparison of network definitions for detecting sex differences in brain connectivity using Support Vector Machines
- Author
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George W. Hafzalla, Neda Jahanshad, Margaret J. Wright, Joshua Faskowitz, Katie L. McMahon, Anjanibhargavi Ragothaman, Paul M. Thompson, Meredith N. Braskie, Gautam Prasad, and Greig I. de Zubicaray
- Subjects
0301 basic medicine ,Normalization (statistics) ,Connectomics ,Computer science ,business.industry ,Human brain ,Structural connectome ,Machine learning ,computer.software_genre ,Article ,Support vector machine ,03 medical and health sciences ,030104 developmental biology ,0302 clinical medicine ,medicine.anatomical_structure ,Connectome ,medicine ,Artificial intelligence ,business ,Classifier (UML) ,computer ,030217 neurology & neurosurgery - Abstract
Human brain connectomics is a rapidly evolving area of research, using various methods to define connections or interactions between pairs of regions. Here we evaluate how the choice of (1) regions of interest, (2) definitions of a connection, and (3) normalization of connection weights to total brain connectivity and region size, affect our calculation of the structural connectome. Sex differences in the structural connectome have been established previously. We study how choices in reconstruction of the connectome affect our ability to classify subjects by sex using a support vector machine (SVM) classifier. The use of cluster-based regions led to higher accuracy in sex classification, compared to atlas-based regions. Sex classification was more accurate when based on finer cortical partitions and when using dilations of regions of interest prior to computing brain networks.
- Published
- 2017
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