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

Related papers

• Brain-Aware Readout Layers in GNNs: Advancing Alzheimer's early Detection and Neuroimaging [1.074960192271861]
  This study introduces a novel brain-aware readout layer (BA readout layer) for Graph Neural Networks (GNNs) By clustering brain regions based on functional connectivity and node embedding, this layer improves the GNN's capability to capture complex brain network characteristics. Our results show that GNNs with the BA readout layer significantly outperform traditional models in predicting the Preclinical Alzheimer's Cognitive Composite (PACC) score.
  arXiv  Detail & Related papers  (2024-10-03T05:04:45Z)
• Knowledge-Guided Prompt Learning for Lifespan Brain MR Image Segmentation [53.70131202548981]
  We present a two-step segmentation framework employing Knowledge-Guided Prompt Learning (KGPL) for brain MRI. Specifically, we first pre-train segmentation models on large-scale datasets with sub-optimal labels. The introduction of knowledge-wise prompts captures semantic relationships between anatomical variability and biological processes.
  arXiv  Detail & Related papers  (2024-07-31T04:32:43Z)
• BrainODE: Dynamic Brain Signal Analysis via Graph-Aided Neural Ordinary Differential Equations [67.79256149583108]
  We propose a novel model called BrainODE to achieve continuous modeling of dynamic brain signals. By learning latent initial values and neural ODE functions from irregular time series, BrainODE effectively reconstructs brain signals at any time point.
  arXiv  Detail & Related papers  (2024-04-30T10:53:30Z)
• GCAN: Generative Counterfactual Attention-guided Network for Explainable Cognitive Decline Diagnostics based on fMRI Functional Connectivity [1.3426127666129704]
  Diagnosis of mild cognitive impairment (MCI) and subjective cognitive decline (SCD) from fMRI functional connectivity (FC) has gained popularity. Most FC-based diagnostic models are black boxes lacking casual reasoning so they contribute little to the knowledge about FC-based neural biomarkers of cognitive decline. We propose a generative counterfactual attention-guided network (GCAN), which introduces counterfactual reasoning to recognize cognitive decline-related brain regions.
  arXiv  Detail & Related papers  (2024-03-04T06:24:24Z)
• Brain Diffuser: An End-to-End Brain Image to Brain Network Pipeline [54.93591298333767]
  Brain diffuser is a diffusion based end-to-end brain network generative model. It exploits more structural connectivity features and disease-related information by analyzing disparities in structural brain networks across subjects. For the case of Alzheimer's disease, the proposed model performs better than the results from existing toolkits on the Alzheimer's Disease Neuroimaging Initiative database.
  arXiv  Detail & Related papers  (2023-03-11T14:04:58Z)
• DBGDGM: Dynamic Brain Graph Deep Generative Model [63.23390833353625]
  Graphs are a natural representation of brain activity derived from functional magnetic imaging (fMRI) data. It is well known that clusters of anatomical brain regions, known as functional connectivity networks (FCNs), encode temporal relationships which can serve as useful biomarkers for understanding brain function and dysfunction. Previous works, however, ignore the temporal dynamics of the brain and focus on static graphs. We propose a dynamic brain graph deep generative model (DBGDGM) which simultaneously clusters brain regions into temporally evolving communities and learns dynamic unsupervised node embeddings.
  arXiv  Detail & Related papers  (2023-01-26T20:45:30Z)
• BrainFormer: A Hybrid CNN-Transformer Model for Brain fMRI Data Classification [31.83866719445596]
  BrainFormer is a general hybrid Transformer architecture for brain disease classification with single fMRI volume. BrainFormer is constructed by modeling the local cues within each voxel with 3D convolutions. We evaluate BrainFormer on five independently acquired datasets including ABIDE, ADNI, MPILMBB, ADHD-200 and ECHO.
  arXiv  Detail & Related papers  (2022-08-05T07:54:10Z)
• Context-Aware Refinement Network Incorporating Structural Connectivity Prior for Brain Midline Delineation [50.868845400939314]
  We propose a context-aware refinement network (CAR-Net) to refine and integrate the feature pyramid representation generated by the UNet. For keeping the structural connectivity of the brain midline, we introduce a novel connectivity regular loss. The proposed method requires fewer parameters and outperforms three state-of-the-art methods in terms of four evaluation metrics.
  arXiv  Detail & Related papers  (2020-07-10T14:01:20Z)
• A Graph Gaussian Embedding Method for Predicting Alzheimer's Disease Progression with MEG Brain Networks [59.15734147867412]
  Characterizing the subtle changes of functional brain networks associated with Alzheimer's disease (AD) is important for early diagnosis and prediction of disease progression. We developed a new deep learning method, termed multiple graph Gaussian embedding model (MG2G) We used MG2G to detect the intrinsic latent dimensionality of MEG brain networks, predict the progression of patients with mild cognitive impairment (MCI) to AD, and identify brain regions with network alterations related to MCI.
  arXiv  Detail & Related papers  (2020-05-08T02:29:24Z)
• Learning Multi-resolution Graph Edge Embedding for Discovering Brain Network Dysfunction in Neurological Disorders [10.12649945620901]
  We propose Multi-resolution Edge Network (MENET) to detect disease-specific connectomic benchmarks. MENET accurately predicts diagnostic labels and identifies brain connectivities highly associated with neurological disorders.
  arXiv  Detail & Related papers  (2019-12-03T03:46:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.