Modularity-Guided Functional Brain Network Analysis for Early-Stage Dementia Identification

Function brain network (FBN) analysis has shown great potential in identifying brain diseases, such as Alzheimer's disease (AD) and its prodromal stage, namely mild cognitive impairment (MCI). It is essential to identify discriminative and interpretable features from function brain networks, so...

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Veröffentlicht in:Frontiers in neuroscience Jg. 15; S. 720909
Hauptverfasser: Zhang, Yangyang, Jiang, Xiao, Qiao, Lishan, Liu, Mingxia
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Frontiers Media S.A 05.08.2021
Schlagworte:
classification
feature selection
functional brain network
modularity
Neuroscience
signed spectral clustering
ISSN:1662-453X, 1662-4548, 1662-453X
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Zusammenfassung:Function brain network (FBN) analysis has shown great potential in identifying brain diseases, such as Alzheimer's disease (AD) and its prodromal stage, namely mild cognitive impairment (MCI). It is essential to identify discriminative and interpretable features from function brain networks, so as to improve classification performance and help us understand the pathological mechanism of AD-related brain disorders. Previous studies usually extract node statistics or edge weights from FBNs to represent each subject. However, these methods generally ignore the topological structure (such as modularity) of FBNs. To address this issue, we propose a modular-LASSO feature selection (MLFS) framework that can explicitly model the modularity information to identify discriminative and interpretable features from FBNs for automated AD/MCI classification. Specifically, the proposed MLFS method first searches the modular structure of FBNs through a signed spectral clustering algorithm, and then selects discriminative features via a modularity-induced group LASSO method, followed by a support vector machine (SVM) for classification. To evaluate the effectiveness of the proposed method, extensive experiments are performed on 563 resting-state functional MRI scans from the public ADNI database to identify subjects with AD/MCI from normal controls and predict the future progress of MCI subjects. Experimental results demonstrate that our method is superior to previous methods in both tasks of AD/MCI identification and MCI conversion prediction, and also helps discover discriminative brain regions and functional connectivities associated with AD.
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This article was submitted to Brain Imaging Methods, a section of the journal Frontiers in Neuroscience
Edited by: Jun Shi, Shanghai University, China
Reviewed by: Pingkun Yan, Rensselaer Polytechnic Institute, United States; Kuangyu Shi, University of Bern, Switzerland
ISSN:1662-453X
1662-4548
1662-453X
DOI:10.3389/fnins.2021.720909