mlsanityData to 14 Oct 2026

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

arXiv 2405.00077 ↗First posted 30 Apr 2024cs.LG eess.SPcited by 12

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Abstract (arXiv)

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.

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Builds on 26 of its 43 references that are in the map. First posted on arXiv 30 Apr 2024.

Builds on

26 of 43 references.

References · 26

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14 more
  1. 2022FBNETGEN: Task-aware GNN-based fMRI Analysis via Functional Brain Network Generationcited by 67
  2. 2020Learning Continuous System Dynamics from Irregularly-Sampled Partial Observationscited by 61
  3. 2022Interpretable Graph Neural Networks for Connectome-Based Brain Disorder Analysiscited by 59
  4. 2016A probabilistic model for the numerical solution of initial value problemscited by 32
  5. 2023Generalizing Graph ODE for Learning Complex System Dynamics across Environmentscited by 23
  6. 2019Polynomial Matrix Completion for Missing Data Imputation and Transductive Learningcited by 15
  7. 2022Learning Task-Aware Effective Brain Connectivity for fMRI Analysis with Graph Neural Networkscited by 13
  8. 2021Edges in Brain Networks: Contributions to Models of Structure and Functioncited by 9
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  14. 2022Whole Brain Network Dynamics of Epileptic Seizures at Single Cell Resolutioncited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 5; 2026: 7.

12 papers.

  1. 2025Graph ODEs and Beyond: A Comprehensive Survey on Integrating Differential Equations with Graph Neural Networkscited by 14
  2. 2025Simulator and Experience Enhanced Diffusion Model for Comprehensive ECG Generationcited by 8
  3. 2025FD-Bench: A Modular and Fair Benchmark for Data-driven Fluid Simulationcited by 5
  4. 2026ODEBrain: Continuous-Time EEG Graph for Modeling Dynamic Brain Networkscited by 3
  5. 2025NeuroTree: Hierarchical Functional Brain Pathway Decoding for Mental Health Disorderscited by 1
  6. 2025Spatio-Temporal Graph Deep Learning with Stochastic Differential Equations for Uncovering Alzheimer's Disease Progressioncited by 1
  7. 2026GeoDynamics: A Geometric State-Space Neural Network for Understanding Brain Dynamics on Riemannian Manifoldscited by 1
  8. 2026SDE-Driven Spatio-Temporal Hypergraph Neural Networks for Irregular Longitudinal fMRI Connectome Modeling in Alzheimer's Diseasecited by 0
  9. 2026Integrating Mechanistic and Data-Driven Models for Neurological Disorders through Differentiable Programmingcited by 0
  10. 2026DevelopmentODE: Structured Neural ODEs for Early Brain Development Dynamics Across a Decadecited by 0
  11. 2026FAST-Brain: A Flow-Aligned Spatio-Temporal Surrogate Brain Modelcited by 0
  12. 2026GRFBrain: Graph-Structured Rectified Flows for EEG Dynamic Modelingcited by 0