Learning Dynamic Graph Representation of Brain Connectome with Spatio-Temporal Attention (2105.13495v2)

Abstract: Functional connectivity (FC) between regions of the brain can be assessed by the degree of temporal correlation measured with functional neuroimaging modalities. Based on the fact that these connectivities build a network, graph-based approaches for analyzing the brain connectome have provided insights into the functions of the human brain. The development of graph neural networks (GNNs) capable of learning representation from graph structured data has led to increased interest in learning the graph representation of the brain connectome. Although recent attempts to apply GNN to the FC network have shown promising results, there is still a common limitation that they usually do not incorporate the dynamic characteristics of the FC network which fluctuates over time. In addition, a few studies that have attempted to use dynamic FC as an input for the GNN reported a reduction in performance compared to static FC methods, and did not provide temporal explainability. Here, we propose STAGIN, a method for learning dynamic graph representation of the brain connectome with spatio-temporal attention. Specifically, a temporal sequence of brain graphs is input to the STAGIN to obtain the dynamic graph representation, while novel READOUT functions and the Transformer encoder provide spatial and temporal explainability with attention, respectively. Experiments on the HCP-Rest and the HCP-Task datasets demonstrate exceptional performance of our proposed method. Analysis of the spatio-temporal attention also provide concurrent interpretation with the neuroscientific knowledge, which further validates our method. Code is available at https://github.com/egyptdj/stagin

• The paper presents STAGIN, a novel spatio-temporal attention graph network that dynamically models brain connectivity from neuroimaging data.
• It introduces dynamic graph construction by integrating GRU-derived temporal features with spatial one-hot encodings to capture brain network fluctuations.
• Attention mechanisms like GARO, SERO, and a Transformer encoder yield impressive results, achieving over 88% accuracy in gender classification and 99% in task decoding.

Learning Dynamic Graph Representation of Brain Connectome with Spatio-Temporal Attention

The paper presents an advanced approach to understanding the dynamic nature of brain connectivity using graph representations that incorporate both spatial and temporal dimensions. Entitled "Learning Dynamic Graph Representation of Brain Connectome with Spatio-Temporal Attention", the research introduces a method called Spatio-Temporal Attention Graph Isomorphism Network (STAGIN). This paper addresses the limitations of current graph neural network (GNN)-based methods that typically apply static analysis to functional connectivity (FC) networks, ignoring their inherent dynamic properties.

Core Contributions

The primary contribution of the paper is the development of STAGIN, which integrates spatial and temporal attention mechanisms into the graph representation learning process for functional neuroimaging data. The authors highlight several technical advancements:

1. Dynamic Graph Construction: The authors propose a dynamic graph model by concatenating temporal information derived through Gated Recurrent Units (GRU) with conventional spatial one-hot encodings of nodes. This addresses previous representations that failed to encapsulate temporal fluctuations within the brain network.
2. Attention-Based READOUT: Two novel attention mechanisms—the Graph-Attention READOUT (GARO) and Squeeze-Excitation READOUT (SERO)—are introduced for improving graph representation through attention-based node pooling. This method contrasts with existing methods that often utilize static pooling mechanisms.
3. Temporal Attention with Transformer Encoder: The integration of a Transformer encoder facilitates the model’s ability to account for temporal dynamics over sequences of graph representations, enabling better temporal interpretability.
4. Orthogonal Regularization: To enhance the expressivity of node features transformed into graph-level representations, the approach involves an orthogonal regularization strategy that prevents overlap in the basis spanned by node features.

Experimental Validation

Two primary datasets from the Human Connectome Project are utilized: HCP-Rest and HCP-Task. The effectiveness of STAGIN is demonstrated through substantial performance improvement in classifying gender from resting-state fMRI and decoding task types from task-based fMRI datasets. The results reported include an accuracy of over 88% for gender classification and 99% for task decoding, outperforming existing GNN models.

Implications and Future Work

The implications of this research are multifaceted:

• Improved Model Accuracy: By capturing dynamic functional states, STAGIN models support more accurate phenotype predictions.
• Enhanced Interpretability: The use of spatio-temporal attention mechanisms offers concurrent neuroscientific interpretability, linking model decisions to known brain connectivity phenomena.
• Potential Biomedical Applications: The proposed method holds promise for developing biomarkers linked to psychiatric or neurological conditions by revealing patterns over time rather than static snapshots.

However, the authors also acknowledge potential negative impacts, mainly ethical considerations concerning privacy and misuse of such predictive models. This sensitivity to ethical concerns underlines the importance of considering the broader implications of technologically advanced methods.

Moving forward, the authors suggest refinement in the identification of critical nodes in spatial attention and further exploration into adaptive pooling techniques that can enhance model flexibility without oversimplification. Extending this approach to other types of neuroimaging data or integrating additional behavioral data could deepen understanding of brain function and broaden the utility of GNNs in neuroscientific research.

In conclusion, the paper signifies an important step in the evolution of graph-based analysis in neuroscience, providing a sophisticated tool for dissecting the intricate dynamics of brain networks.