BrainODE: Dynamic Brain Signal Analysis via Graph-Aided Neural Ordinary Differential Equations

• URL: http://arxiv.org/abs/2405.00077v1
• Date: Tue, 30 Apr 2024 10:53:30 GMT
• Title: BrainODE: Dynamic Brain Signal Analysis via Graph-Aided Neural Ordinary Differential Equations
• Authors: Kaiqiao Han, Yi Yang, Zijie Huang, Xuan Kan, Yang Yang, Ying Guo, Lifang He, Liang Zhan, Yizhou Sun, Wei Wang, Carl Yang,
• Abstract summary: 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.
• Score: 67.79256149583108
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Brain network analysis is vital for understanding the neural interactions regarding brain structures and functions, and identifying potential biomarkers for clinical phenotypes. However, widely used brain signals such as Blood Oxygen Level Dependent (BOLD) time series generated from functional Magnetic Resonance Imaging (fMRI) often manifest three challenges: (1) missing values, (2) irregular samples, and (3) sampling misalignment, due to instrumental limitations, impacting downstream brain network analysis and clinical outcome predictions. In this work, we propose a novel model called BrainODE to achieve continuous modeling of dynamic brain signals using Ordinary Differential Equations (ODE). By learning latent initial values and neural ODE functions from irregular time series, BrainODE effectively reconstructs brain signals at any time point, mitigating the aforementioned three data challenges of brain signals altogether. Comprehensive experimental results on real-world neuroimaging datasets demonstrate the superior performance of BrainODE and its capability of addressing the three data challenges.

Related papers

• Brain3D: Generating 3D Objects from fMRI [76.41771117405973]
  We design a novel 3D object representation learning method, Brain3D, that takes as input the fMRI data of a subject. We show that our model captures the distinct functionalities of each region of human vision system. Preliminary evaluations indicate that Brain3D can successfully identify the disordered brain regions in simulated scenarios.
  arXiv  Detail & Related papers  (2024-05-24T06:06:11Z)
• Interpretable Spatio-Temporal Embedding for Brain Structural-Effective Network with Ordinary Differential Equation [56.34634121544929]
  In this study, we first construct the brain-effective network via the dynamic causal model. We then introduce an interpretable graph learning framework termed Spatio-Temporal Embedding ODE (STE-ODE) This framework incorporates specifically designed directed node embedding layers, aiming at capturing the dynamic interplay between structural and effective networks.
  arXiv  Detail & Related papers  (2024-05-21T20:37:07Z)
• MindBridge: A Cross-Subject Brain Decoding Framework [60.58552697067837]
  Brain decoding aims to reconstruct stimuli from acquired brain signals. Currently, brain decoding is confined to a per-subject-per-model paradigm. We present MindBridge, that achieves cross-subject brain decoding by employing only one model.
  arXiv  Detail & Related papers  (2024-04-11T15:46:42Z)
• BrainWave: A Brain Signal Foundation Model for Clinical Applications [21.624743680602744]
  We present BrainWave, the first foundation model for both invasive and non-invasive neural recordings. BrainWave pretrained on more than 40,000 hours of electrical brain recordings (13.79 TB of data) from approximately 16,000 individuals. Our analysis show that BrainWave outperforms all other competing models and consistently achieves state-of-the-art performance in the diagnosis and identification of neurological disorders.
  arXiv  Detail & Related papers  (2024-02-15T16:04:11Z)
• Leveraging Brain Modularity Prior for Interpretable Representation Learning of fMRI [38.236414924531196]
  Resting-state functional magnetic resonance imaging (rs-fMRI) can reflect spontaneous neural activities in brain. Previous studies propose to extract fMRI representations through diverse machine/deep learning methods for subsequent analysis. This paper proposes a Brain Modularity-constrained dynamic Representation learning (BMR) framework for interpretable fMRI analysis.
  arXiv  Detail & Related papers  (2023-06-24T23:45:47Z)
• 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)
• Neural Language Models are not Born Equal to Fit Brain Data, but Training Helps [75.84770193489639]
  We examine the impact of test loss, training corpus and model architecture on the prediction of functional Magnetic Resonance Imaging timecourses of participants listening to an audiobook. We find that untrained versions of each model already explain significant amount of signal in the brain by capturing similarity in brain responses across identical words. We suggest good practices for future studies aiming at explaining the human language system using neural language models.
  arXiv  Detail & Related papers  (2022-07-07T15:37:17Z)
• Functional2Structural: Cross-Modality Brain Networks Representation Learning [55.24969686433101]
  Graph mining on brain networks may facilitate the discovery of novel biomarkers for clinical phenotypes and neurodegenerative diseases. We propose a novel graph learning framework, known as Deep Signed Brain Networks (DSBN), with a signed graph encoder. We validate our framework on clinical phenotype and neurodegenerative disease prediction tasks using two independent, publicly available datasets.
  arXiv  Detail & Related papers  (2022-05-06T03:45:36Z)
• Ranking of Communities in Multiplex Spatiotemporal Models of Brain Dynamics [0.0]
  We propose an interpretation of neural HMMs as multiplex brain state graph models we term Hidden Markov Graph Models (HMs) This interpretation allows for dynamic brain activity to be analysed using the full repertoire of network analysis techniques. We produce a new tool for determining important communities of brain regions using a random walk-based procedure.
  arXiv  Detail & Related papers  (2022-03-17T12:14:09Z)

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.