Community-Aware Transformer for Autism Prediction in fMRI Connectome (2307.10181v1)

Abstract: Autism spectrum disorder(ASD) is a lifelong neurodevelopmental condition that affects social communication and behavior. Investigating functional magnetic resonance imaging (fMRI)-based brain functional connectome can aid in the understanding and diagnosis of ASD, leading to more effective treatments. The brain is modeled as a network of brain Regions of Interest (ROIs), and ROIs form communities and knowledge of these communities is crucial for ASD diagnosis. On the one hand, Transformer-based models have proven to be highly effective across several tasks, including fMRI connectome analysis to learn useful representations of ROIs. On the other hand, existing transformer-based models treat all ROIs equally and overlook the impact of community-specific associations when learning node embeddings. To fill this gap, we propose a novel method, Com-BrainTF, a hierarchical local-global transformer architecture that learns intra and inter-community aware node embeddings for ASD prediction task. Furthermore, we avoid over-parameterization by sharing the local transformer parameters for different communities but optimize unique learnable prompt tokens for each community. Our model outperforms state-of-the-art (SOTA) architecture on ABIDE dataset and has high interpretability, evident from the attention module. Our code is available at https://github.com/ubc-tea/Com-BrainTF.

• The paper presents a novel community-aware transformer, Com-BrainTF, that integrates local-global hierarchical modeling to boost ASD prediction accuracy.
• It employs structured brain community information to capture intricate inter- and intra-community connectivity patterns, outperforming traditional models.
• The model’s interpretable attention mechanisms identify key biomarkers, paving the way for improved clinical decision-making in neuroimaging.

Community-Aware Transformer for Autism Prediction in fMRI Connectome: A Critical Examination

The paper outlined in the paper "Community-Aware Transformer for Autism Prediction in fMRI Connectome" introduces a novel framework, Com-BrainTF, aimed at improving the diagnostic accuracy for Autism Spectrum Disorder (ASD) using functional Magnetic Resonance Imaging (fMRI) data. The paper identifies a significant limitation in existing transformer-based models—the uniform treatment of all brain Regions of Interest (ROIs) without accounting for inter- and intra-community dynamics—and addresses it by incorporating community-specific knowledge into the model architecture.

The core innovation of Com-BrainTF lies in its hierarchical local-global transformer framework, which efficiently learns community-aware node embeddings. The authors leverage the structured organization of the brain into functional communities, a notion well-established in cognitive neuroscience, to inform the model design. By doing so, the model captures nuanced patterns of connectivity both within and between these communities, a critical aspect considering the altered connectivity patterns often observed in ASD.

The quantitative evaluation on the ABIDE dataset demonstrates Com-BrainTF's superior performance over established models such as BrainNetCNN and FBNETGEN, with the model achieving notable AUROC, accuracy, sensitivity, and specificity scores. The careful avoidance of over-parameterization through parameter sharing in local transformers while maintaining unique community-specific learnable prompt tokens is particularly commendable, as it suggests an effective balance between model complexity and computational efficiency.

Notably, the paper illustrates the high interpretability of the model's attention mechanisms, highlighting key functional networks with pertinent connectivity alterations linked to ASD, such as the Default Mode Network (DMN) and Somatomotor Network (SMN). This aspect not only enhances the model's applicability as a diagnostic tool but also reinforces its utility in uncovering biomarkers for neurological conditions.

The research holds substantive implications for both theoretical and practical domains. Theoretically, it reinforces the importance of considering brain community structures in neuroimaging analyses, potentially encouraging more nuanced models in future studies. Practically, it provides a scalable framework that might extend to other neurological and psychiatric conditions, eventually aiding in the clinical decision-making process.

Looking ahead, the paper suggests intriguing future research directions, including extending Com-BrainTF to different neuroimaging modalities and exploring alternate atlas-based parcellations. Such endeavors could further refine the predictions and extend applicability across various neurodevelopmental and psychiatric disorders.

In summary, this paper presents a methodologically robust and interpretative framework for ASD prediction from fMRI data, effectively demonstrating the integration of community-aware insights into advanced model architectures. It forms a credible step forward in the field of AI-assisted diagnostics, reflecting the potential of AI in advancing personalized medicine through precise, data-driven model interpretations.