BrainODE: Dynamic Brain Signal Analysis via Graph-Aided Neural Ordinary Differential Equations (2405.00077v1)

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.

• The paper introduces BrainODE, which uses Neural ODEs to continuously reconstruct fMRI BOLD signals while addressing missing data, irregular sampling, and misalignment.
• It integrates convolutional networks and self-attention for precise latent state learning and dual-graph encoding to capture spatial and temporal brain interactions.
• Experimental results show significant improvements in clinical prediction accuracy, with up to 27.4% AUC increase on real neuroimaging datasets.

Dynamic Brain Signal Analysis Using BrainODE

The paper introduces BrainODE, a compelling framework for the continuous modeling of dynamic brain signals, especially focusing on Blood Oxygen Level Dependent (BOLD) time series derived from functional Magnetic Resonance Imaging (fMRI). The model primarily aims to address three significant challenges present in neuroimaging data: missing values, irregular sampling, and sampling misalignment. These challenges pose substantial hindrance in accurate brain network analysis and the prediction of clinical outcomes.

BrainODE leverages Neural Ordinary Differential Equations (ODEs) to process dynamic brain signals continuously. By capturing latent initial states and mapping neural ODE functions from these signals, it can reconstruct brain signals at any desired point in time. This approach efficiently alleviates the triple threats posed by missing data, irregular samples, and misalignment. The paper illuminates on how traditional methods like polynomial interpolation and discrete computations of Pearson correlations fail to accurately model the intricate correlations among ROIs in dynamic brain networks.

The methodology underlying BrainODE encompasses several critical steps:

1. Brain Latent State Learning: By employing a combination of CNNs and self-attention mechanisms, BrainODE effectively captures short-term brain activity while maintaining long-term dependencies in the BOLD signals. This duality ensures that both local interactions and overarching patterns in the brain data are accounted for without losing the spatial context of ROIs.
2. Graph-Based Encoding of Spatial and Temporal Relations: The model utilizes two distinct graphs — spatial relationships informed by anatomical proximities and temporal interactions derived from the BOLD dynamics. This dual-graph approach ensures that BrainODE considers both the temporal dynamics secured in latent initial states and spatial relations, providing a more robust representation of brain activities.
3. Continuous Representation with ODE: Utilizing a generative model based on ODEs facilitates decoding continuous time series data, offering a refined structure to the BOLD signals and an enhanced insight toward subsequent clinical predictions.

Empirical validation includes robust experimental results using real-world neuroimaging datasets, ABIDE and ABCD. When compared to existing approaches, BrainODE achieves a notable increase (avg. 27.4% in AUC for ABIDE and 15.6% for ABCD) in classification accuracies for clinical predictions. This underscores its efficacy in enhancing the quality of brain signal modeling beyond existing interpolation and recurrent methods.

The introduction of BrainODE provisions a significant advancement in how dynamic brain signals can be handled methodologically. The ability of BrainODE to predict clinical outcomes highlights its potential impact in neurological diagnostics and therapeutic strategies. This approach not only provides superior data preprocessing but could also imply deeper understanding in neuroscientific research, particularly in disorders like Autism Spectrum Disorder (ASD).

Going forward, there are several potential frontiers for the continued development of BrainODE. Future research might explore advanced dynamic models of temporal graphs or delve into integration with structural connectivity data from Diffusion Tensor Imaging (DTI). Additionally, there is room to adapt BrainODE for more comprehensive examination across various types of brain signals beyond fMRI, potentially paving the way for unprecedented strides in personalizing clinical interventions.

In summary, BrainODE represents a significant step in the preprocessing of neuroimaging data, advancing brain network analyses and addressing critical gaps in neuro-clinical applications. The results advocate for its use in ethically responsible academic research, with a caveat to safeguard against any non-academic exploitation due to its potent applicability in interfacing with clinical outcomes.