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Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

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Manifold learning for fMRI time-varying functional connectivity.

Javier Gonzalez-Castillo¹, Isabel S Fernandez¹, Ka Chun Lam²

¹Section on Functional Imaging Methods, National Institute of Mental Health, Bethesda, MD, United States.

Frontiers in Human Neuroscience

|July 27, 2023

Summary

Manifold learning techniques (MLTs) can reduce the dimensionality of time-varying functional connectivity (tvFC) data. While effective for labeled data, MLTs face challenges with unlabeled resting-state tvFC analysis.

Keywords:

Laplacian Eigenmaps (LE)T-SNE Uniform Manifold Approximation and Projection (UMAP)data visualization fMRI manifold learning time-varying functional connectivity

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Area of Science:

Neuroimaging
Computational Neuroscience
Data Science

Background:

Whole-brain functional connectivity (FC) measured with fMRI evolves over time across various scales.
Exploring time-varying FC (tvFC) is challenging due to its high dimensionality.
Low-dimensional representations are sought to retain important aspects of tvFC data.

Purpose of the Study:

To investigate the utility of manifold learning techniques (MLTs) for reducing tvFC data dimensionality.
To estimate the intrinsic dimension (ID) of tvFC data manifolds.
To evaluate the performance and robustness of state-of-the-art MLTs (LE, T-SNE, UMAP) for tvFC analysis.

Main Methods:

Discussion of theoretical underpinnings for tvFC data lying on low-dimensional manifolds.
Estimation of the intrinsic dimension (ID) of tvFC data.
Empirical evaluation of Laplacian Eigenmaps (LEs), T-distributed Stochastic Neighbor Embedding (T-SNE), and Uniform Manifold Approximation and Projection (UMAP) on tvFC data.

Main Results:

tvFC data exhibits an intrinsic dimension ranging from 4 to 26, varying between rest and task states.
UMAP and T-SNE effectively captured concurrent subject identity and task information, while LE captured only one.
Significant variability in embedding quality was observed across MLTs and hyperparameters; heuristics for selection are provided.

Conclusions:

MLTs can generate meaningful, low-dimensional representations of tvFC data, useful for labeled datasets.
The intrinsic dimension of tvFC data is relatively low, supporting the use of MLTs.
Application of MLTs to unlabeled resting-state tvFC data remains challenging, necessitating careful consideration of feature normalization and temporal autocorrelation.