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- 2017
Predicting behavioral variant frontotemporal dementia with pattern classification in multi-center structural MRI dataDOI: 10.1016/j.nicl.2017.02.001 Keywords: bvFTD, behavioral variant frontotemporal dementia, FTLD, frontotemporal lobar degeneration, FEW, family wise error, GMD, gray matter density, MNI, Montreal Neurological Institute, MPRAGE, magnetization-prepared rapid gradient echo, MRI, magnetic resonance imaging, SVM, support vector machine, VBM, voxel based morphometry Atrophy, Behavioral variant frontotemporal dementia, Diagnostic criteria, Frontotemporal lobar degeneration, MRI, Pattern classification Abstract: Frontotemporal lobar degeneration (FTLD) is a common cause of early onset dementia. Behavioral variant frontotemporal dementia (bvFTD), its most common subtype, is characterized by deep alterations in behavior and personality. In 2011, new diagnostic criteria were suggested that incorporate imaging criteria into diagnostic algorithms. The study aimed at validating the potential of imaging criteria to individually predict diagnosis with machine learning algorithms
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