Robust automated computational approach for classifying frontotemporal neurodegeneration: Multimodal/multicenter neuroimaging.

ABSTRACT:

Introduction

Timely diagnosis of behavioral variant frontotemporal dementia (bvFTD) remains challenging because it depends on clinical expertise and potentially ambiguous diagnostic guidelines. Recent recommendations highlight the role of multimodal neuroimaging and machine learning methods as complementary tools to address this problem.

Methods

We developed an automatic, cross-center, multimodal computational approach for robust classification of patients with bvFTD and healthy controls. We analyzed structural magnetic resonance imaging and resting-state functional connectivity from 44 patients with bvFTD and 60 healthy controls (across three imaging centers with different acquisition protocols) using a fully automated processing pipeline, including site normalization, nati

SUBMITTER: Donnelly-Kehoe PA 

PROVIDER: S-EPMC6719282 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature