TSP-GNN: a novel neuropsychiatric disorder classification framework based on task-specific prior knowledge and graph neural network

Abstract

Neuropsychiatric disorder (ND) is often accompanied by abnormal functional connectivity (FC) patterns in specific task contexts. The distinctive task-specific FC patterns can provide valuable features for ND classification models using deep learning. However, most previous studies rely solely on the whole-brain FC matrix without considering the prior knowledge of task-specific FC patterns. Insight by the decoding studies on brain-behavior relationship, we develop TSP-GNN, which extracts task-specific prior (TSP) connectome patterns and employs graph neural network (GNN) for disease classification. TSP-GNN was validated using publicly available datasets. Our results demonstrate that different ND types show distinct task-specific connectivity patterns. Compared with the whole-brain node characteristics, utilizing task-specific nodes enhances the accuracy of ND classification. TSP-GNN comprises the first attempt to incorporate prior task-specific connectome patterns and the power of deep learning. This study elucidates the association between brain dysfunction and specific cognitive processes, offering valuable insights into the cognitive mechanism of neuropsychiatric disease.

Keywords: brain decoding; functional connectivity; graph neural network; neuropsychiatric disorders; task-specific prior knowledge.

Figure 1

The architecture overview of the proposed TSP-GNN framework which combines task-specific patterns for disease diagnosis. The top half of the framework decodes task patterns based on cognitive performance, while the bottom extracts task-specific functional connectome and graph theoretical measures from various disease datasets. Subsequently, phenotypic information is integrated to construct the population-based graph neural network to achieve disease classification.

Figure 2

Results of permutation tests on task-state and resting-state fMRI decoding. The green histograms illustrate the correlation values’ distribution between the predicted task performances and those obtained from 10,000 permutation tests. The red line marks the correlation from the predictions of the original Elastic-Net model to the actual outcomes, clearly showing that the permutation test outcomes systematically register below the baseline correlation.

Figure 3

FCs with the best task performance prediction capability. The nodes and edges of the brain network are created by averaging the FC strength of a particular task across all people, and the strength determines the node size and edge thickness. Connections within a module are depicted using the same color as the module in which it is situated, whereas gray lines represent inter-module connections.

Figure 4

The distribution of functional brain networks associated with edges differs across decoding modes of task states. DAN, dorsal attention network; DMN, default mode network; FPN, frontoparietal network; LIM, limbic network; SMN, somatomotor network; SUB, subcortical network; VAN, ventral attention network; VIS, visual network.

Figure 5

Visualizing the distribution set of nodes involved in the optimal task combination: (A) for ADHD dataset, (B) for ABIDE dataset. The findings indicate that the decoded results (node distribution) are relatively independent, with a low proportion of nodes belonging to the intersection of multiple tasks.

Figure 6

On the ADHD (A) and ABIDE (B) datasets, the classification performance of the TSP-GNN framework was compared to that of various machine learning and deep learning methods. The task priors used were combinations of the best four, five, and six combinations described in section 3.3. M_R_S_W stands for a task combination of motor, relational processing, social cognitive, and working memory tasks, and the remaining acronyms are similar.