Core Concepts
The proposed ContrastPool method utilizes a contrastive dual-attention mechanism to extract group-specific information from brain networks, and applies a differentiable graph pooling approach to generate effective and domain-explainable features for disease classification.
Abstract
The paper presents a novel method called ContrastPool for classifying brain networks constructed from functional magnetic resonance imaging (fMRI) data. The key contributions are:
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ContrastPool employs a contrastive dual-attention block that adaptively assigns weights to each region of interest (ROI) and each subject, performs aggregation within groups, and makes contrast across groups to obtain a contrast graph. This contrast graph is then used to guide the message passing in brain network representation learning.
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The proposed differentiable graph pooling method, ContrastPool, generates brain network representations that are effective for disease classification tasks. It leverages the contrast graph to identify the most discriminative ROIs and subjects.
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Experiments on 5 resting-state fMRI brain network datasets spanning 3 diseases (Parkinson's, Alzheimer's, and Autism) demonstrate the superiority of ContrastPool over state-of-the-art baselines.
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A case study confirms that the patterns extracted by ContrastPool match the domain knowledge in neuroscience literature, providing direct and interesting insights into the underlying brain mechanisms of the studied diseases.
Stats
The paper uses 5 resting-state fMRI brain network datasets:
Taowu dataset for Parkinson's disease, with 40 subjects (20 controls, 20 Parkinson's)
PPMI dataset for Parkinson's disease, with 209 subjects (15 controls, 14 SWEDD, 67 prodromal, 113 Parkinson's)
Neurocon dataset for Parkinson's disease, with 41 subjects (15 controls, 26 Parkinson's)
ADNI dataset for Alzheimer's disease, with 1326 subjects (819 cognitive normal, 72 significant memory concern, 102 late MCI, 89 early MCI, 179 MCI, 65 Alzheimer's)
ABIDE dataset for Autism, with 989 subjects (534 typical controls, 455 Autism Spectrum Disorder)
Quotes
"Functional magnetic resonance imaging (fMRI) is a commonly used technique to measure neural activation. Its application has been particularly important in identifying underlying neurodegenerative conditions such as Parkinson's, Alzheimer's, and Autism."
"Recent analysis of fMRI data models the brain as a graph and extracts features by graph neural networks (GNNs). However, the unique characteristics of fMRI data require a special design of GNN. Tailoring GNN to generate effective and domain-explainable features remains challenging."