Learnable Community-Aware Transformer for Brain Connectome Analysis with Token Clustering

Kernekoncepter

A novel token clustering brain transformer-based model (TC-BrainTF) offers improved accuracy in identifying ASD and classifying genders through rigorous testing on ABIDE and HCP datasets.

Resumé

Neuroscientific research reveals the brain's complex network can be organized into distinct functional communities. Traditional models are limited by predefined community clusters and a fixed number of communities. The TC-BrainTF model introduces a token clustering module based on the transformer architecture to cluster ROIs into communities. Results show improved accuracy in identifying ASD and classifying genders. The model allows for dynamic learning of community-specific ROI embeddings without predefined structures, enhancing flexibility and adaptability in deciphering the brain's functional organization.

Statistik

Our model achieves the best performance across AUROC, Accuracy, and Specificity on the ABIDE dataset.
TC-BrainTF surpasses traditional constraints by optimizing prompt tokens through orthogonal loss to enhance intra-community relations.
The addition of orthogonal loss improves classification metrics in terms of AUROC, accuracy, and specificity.

Citater

"Our results demonstrate that our learnable community-aware model TC-BrainTF offers improved accuracy in identifying ASD and classifying genders."
"The TC-BrainTF model introduces a token clustering module based on the transformer architecture to cluster ROIs into communities."

Vigtigste indsigter udtrukket fra

Learnable Community-Aware Transformer for Brain Connectome Analysis with Token Clustering

by Yanting Yang... kl. arxiv.org 03-14-2024

https://arxiv.org/pdf/2403.08203.pdf

Dybere Forespørgsler

How can dynamic clustering improve the understanding of brain connectivity beyond predefined structures

Dynamic clustering in brain connectivity analysis can enhance our understanding beyond predefined structures by allowing the model to adapt and capture the intricate relationships within and between functional communities. Traditional methods often rely on predetermined community clusters, which may not accurately represent the dynamic nature of the brain's organization. By employing dynamic clustering techniques like token clustering in transformer-based models, such as TC-BrainTF, researchers can discover previously unrecognized patterns and gain new insights into how different regions interact within and across communities. This flexibility enables the model to adjust to varying brain network configurations across tasks or individuals, leading to a more comprehensive representation of brain connectivity dynamics.

What potential limitations may arise from applying orthogonal loss in transformer-based models

While applying orthogonal loss in transformer-based models like TC-BrainTF can improve classification metrics such as AUROC, accuracy, and specificity, there are potential limitations that researchers need to consider. One limitation is that overly emphasizing orthogonality between prompt tokens may lead to overfitting or convergence issues during training. Additionally, enforcing strict orthogonality constraints could limit the model's ability to capture subtle variations or nuances in community-specific associations within the data. Moreover, depending on the dataset characteristics and complexity of interactions between ROIs, orthogonal loss may introduce computational overhead or increase training time due to additional optimization requirements.

How can the findings from community-specific associations contribute to advancements in neurological research

The findings from community-specific associations identified by models like TC-BrainTF have significant implications for advancements in neurological research. By uncovering salient clusters associated with specific cognitive functions or disorders like Autism Spectrum Disorder (ASD), researchers can gain deeper insights into how these conditions manifest at a neural level. Understanding these community-specific patterns can aid in identifying biomarkers for neurological disorders, improving diagnostic accuracy, and potentially guiding personalized treatment strategies based on individual brain connectivity profiles. Furthermore, exploring the correlations between functional clusters decoded by advanced neuroimaging techniques and cognitive keywords offers a bridge between neuroscience interpretations and clinical applications, paving the way for targeted interventions tailored to an individual's unique brain network organization.