Learning Pairwise-Similarity Guided Sparse Functional Connectivity Network for MCI Classification
Year: 2017
Type: conference-paper
Abstract: Learning functional connectivity (FC) network from resting-state function magnetic resonance imaging (RS-fMRI) data via sparse representation (SR) or weighted SR (WSR) has been proved to be promising for the diagnosis of Alzheimer's disease and its prodromal stage, mild cognitive impairment (MCI). However, traditional SR/WSR based approaches learn the representation of each brain region independently, without fully taking into account the possible relationship between brain regions. To remedy this limitation, we propose a novel FC modeling approach by considering two types of possible relationship between different brain regions which are incorporated into SR/WSR approaches in the form of regularization. In this way, the representations of all brain regions can be jointly learned. Furthermore, an efficient alternating optimization algorithm is also developed to solve the resulting model. Experimental results show that our proposed method not only outperforms SR and WSR in the diagnosis of MCI subjects, but also leads to the brain FC network with better modularity structure.
Authors: Xiaobo Chen, Han Zhang, Yu Zhang, Zuoyong Li, Dinggang Shen
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
Awards: Analyzing Large-Scale Neuroimaging Data in Alzheimer's Disease, Assessing Large-scale Brain Connectivities in Mild Cognitive Impairment, Quantifying Brain Abnormality by Multimodality Neuroimage Analysis, Diagnosis of Alzheimer's Disease Using Dynamic High-Order Brain Networks, Longitudinal Mapping of Human Brain Development in the First Years of Life, Imaging Genomics Based Brain Disease Prediction, Development of Robust Brain Measurement Tools Informed by Ultrahigh Field 7T MRI
OpenAlex ID: W2903796292