Learnable Community-Aware Transformer for Brain Connectome Analysis with
  Token Clustering
Yanting Yang Beidi Zhao Zhuohao Ni Yize Zhao Xiaoxiao Li
Neuroscientific research has revealed that the complex brain network can be
organized into distinct functional communities, each characterized by a
cohesive group of regions of interest (ROIs) with strong interconnections.
These communities play a crucial role in comprehending the functional
organization of the brain and its implications for neurological conditions,
including Autism Spectrum Disorder (ASD) and biological differences, such as in
gender. Traditional models have been constrained by the necessity of predefined
community clusters, limiting their flexibility and adaptability in deciphering
the brain's functional organization. Furthermore, these models were restricted
by a fixed number of communities, hindering their ability to accurately
represent the brain's dynamic nature. In this study, we present a token
clustering brain transformer-based model ($\texttt{TC-BrainTF}$) for joint
community clustering and classification. Our approach proposes a novel token
clustering (TC) module based on the transformer architecture, which utilizes
learnable prompt tokens with orthogonal loss where each ROI embedding is
projected onto the prompt embedding space, effectively clustering ROIs into
communities and reducing the dimensions of the node representation via merging
with communities. Our results demonstrate that our learnable community-aware
model $\texttt{TC-BrainTF}$ offers improved accuracy in identifying ASD and
classifying genders through rigorous testing on ABIDE and HCP datasets.
Additionally, the qualitative analysis on $\texttt{TC-BrainTF}$ has
demonstrated the effectiveness of the designed TC module and its relevance to
neuroscience interpretations.