A novel joint HCPMMP method for automatically classifying Alzheimer's and different stage MCI patients.
A novel joint HCPMMP method for automatically classifying Alzheimer's and different stage MCI patients.
Behav Brain Res. 2019 Mar 02;:
Authors: Sheng J, Wang B, Zhang Q, Liu Q, Ma Y, Liu W, Shao M, Chen B
Abstract
A 360-area surface-based cortical parcellation was recently generated using multimodal data in a group average of 210 healthy young adults from the Human Connectome Project (HCP). In order to automatically and accurately identify mild cognitive impairment (MCI) at its two levels (early MCI and late MCI), Alzheimer's disease (AD) and healthy control (HC), a novel joint HCP MMP method was first proposed to delineate the cortical architecture and function connectivity in a group of non healthy adults. The proposed method was applied to register a dataset of 96 resting-state functional connectomes from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to Connectivity Informatics Technology Initiative (CIFTI) space and parcellated brain into human connectome project multi-modal parcellation (HCPMMP) with 360 areas. Various network features in each node of the connectivity network were considered as the candidate features for classification.The fine-grained multi-modal based on HCP-MMP combined with machine learning in identification for EMCI, LMCI, AD and HC. Applying various network features, including strength, betweenness centrality, clustering coefficient, local efficiency, eigenvector centrality, etc, we trained and te...
Source: Behavioural Brain Research - Category: Neurology Authors: Sheng J, Wang B, Zhang Q, Liu Q, Ma Y, Liu W, Shao M, Chen B Tags: Behav Brain Res Source Type: research