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Disease prediction based on functional connectomes using a scalable and spatially-informed support vector machine
2014
NeuroImage
Substantial evidence indicates that major psychiatric disorders are associated with distributed neural dysconnectivity, leading to strong interest in using neuroimaging methods to accurately predict disorder status. In this work, we are specifically interested in a multivariate approach that uses features derived from whole-brain resting state functional connectomes. However, functional connectomes reside in a high dimensional space, which complicates model interpretation and introduces
doi:10.1016/j.neuroimage.2014.03.067
pmid:24704268
pmcid:PMC4072532
fatcat:n35ycrdfrrhn5p2ml7cp4s7q3u