Spatial-Temporal Dependency Modeling and Network Hub Detection for Functional MRI Analysis via Convolutional-Recurrent Network

Early identification of dementia at the stage of mild cognitive impairment (MCI) is crucial for timely diagnosis and intervention of Alzheimer's disease (AD). Although several pioneering studies have been devoted to automated AD diagnosis based on resting-state functional magnetic resonance ima...

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Veröffentlicht in:IEEE transactions on biomedical engineering Jg. 67; H. 8; S. 2241 - 2252
Hauptverfasser: Wang, Mingliang, Lian, Chunfeng, Yao, Dongren, Zhang, Daoqiang, Liu, Mingxia, Shen, Dinggang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.08.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Alzheimer's disease
Automation
Brain
Brain mapping
Brain modeling
Cognitive ability
Convolution
Dementia
Dementia disorders
Dependence
Diagnosis
Feature extraction
Functional magnetic resonance imaging
hub detection
Hubs
Long short-term memory
Machine learning
Magnetic resonance imaging
Medical imaging
Modelling
Network hubs
neural network
Neural networks
Neurodegenerative diseases
Neuroimaging
Recurrent neural networks
resting-state functional MRI
Sequences
Sliding
Spatial-temporal dependency
Time series
Time series analysis
ISSN:0018-9294, 1558-2531, 1558-2531
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Zusammenfassung:Early identification of dementia at the stage of mild cognitive impairment (MCI) is crucial for timely diagnosis and intervention of Alzheimer's disease (AD). Although several pioneering studies have been devoted to automated AD diagnosis based on resting-state functional magnetic resonance imaging (rs-fMRI), their performance is somewhat limited due to non-effective mining of spatial-temporal dependency. Besides, few of these existing approaches consider the explicit detection and modeling of discriminative brain regions (i.e., network hubs) that are sensitive to AD progression. In this paper, we propose a unique Spatial-Temporal convolutional-recurrent neural Network (STNet) for automated prediction of AD progression and network hub detection from rs-fMRI time series. Our STNet incorporates the spatial-temporal information mining and AD-related hub detection into an end-to-end deep learning model. Specifically, we first partition rs-fMRI time series into a sequence of overlapping sliding windows. A sequence of convolutional components are then designed to capture the local-to-global spatially-dependent patterns within each sliding window, based on which we are able to identify discriminative hubs and characterize their unique contributions to disease diagnosis. A recurrent component with long short-term memory (LSTM) units is further employed to model the whole-brain temporal dependency from the spatially-dependent pattern sequences, thus capturing the temporal dynamics along time. We evaluate the proposed method on 174 subjects with 563 rs-fMRI scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, with results suggesting the effectiveness of our method in both tasks of disease progression prediction and AD-related hub detection.
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ISSN:0018-9294
1558-2531
1558-2531
DOI:10.1109/TBME.2019.2957921