Beyond the Snapshot: Brain Tokenized Graph Transformer for Longitudinal
Brain Functional Connectome Embedding
- URL: http://arxiv.org/abs/2307.00858v2
- Date: Thu, 13 Jul 2023 03:29:05 GMT
- Title: Beyond the Snapshot: Brain Tokenized Graph Transformer for Longitudinal
Brain Functional Connectome Embedding
- Authors: Zijian Dong, Yilei Wu, Yu Xiao, Joanna Su Xian Chong, Yueming Jin,
Juan Helen Zhou
- Abstract summary: Brain functional connectome (FC)-based Graph Neural Networks (GNN) have emerged as a valuable tool for the diagnosis and prognosis of neurodegenerative diseases such as Alzheimer's disease (AD)
However, these models are tailored for brain FC at a single time point instead of characterizing FC trajectory.
In this work, we proposed the first interpretable framework for brain FC trajectory embedding with application to neurodegenerative disease diagnosis and prognosis.
- Score: 4.7719542185589585
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Under the framework of network-based neurodegeneration, brain functional
connectome (FC)-based Graph Neural Networks (GNN) have emerged as a valuable
tool for the diagnosis and prognosis of neurodegenerative diseases such as
Alzheimer's disease (AD). However, these models are tailored for brain FC at a
single time point instead of characterizing FC trajectory. Discerning how FC
evolves with disease progression, particularly at the predementia stages such
as cognitively normal individuals with amyloid deposition or individuals with
mild cognitive impairment (MCI), is crucial for delineating disease spreading
patterns and developing effective strategies to slow down or even halt disease
advancement. In this work, we proposed the first interpretable framework for
brain FC trajectory embedding with application to neurodegenerative disease
diagnosis and prognosis, namely Brain Tokenized Graph Transformer (Brain
TokenGT). It consists of two modules: 1) Graph Invariant and Variant Embedding
(GIVE) for generation of node and spatio-temporal edge embeddings, which were
tokenized for downstream processing; 2) Brain Informed Graph Transformer
Readout (BIGTR) which augments previous tokens with trainable type identifiers
and non-trainable node identifiers and feeds them into a standard transformer
encoder to readout. We conducted extensive experiments on two public
longitudinal fMRI datasets of the AD continuum for three tasks, including
differentiating MCI from controls, predicting dementia conversion in MCI, and
classification of amyloid positive or negative cognitively normal individuals.
Based on brain FC trajectory, the proposed Brain TokenGT approach outperformed
all the other benchmark models and at the same time provided excellent
interpretability. The code is available at
https://github.com/ZijianD/Brain-TokenGT.git
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