Abstract #2949

Manifold Learning and Dimensionality Estimation for the Human Functional Connectome

Javier Gonzalez-Castillo¹, Isabel Fernandez¹, Daniel A Handwerker¹, Ka-Chun Lam², Francisco Pereira², and Peter A Bandettini^1,3

¹Section on Functional Imaging Methods, NIMH, Bethesda, MD, United States, ²Machine Learning Team, NIMH, Bethesda, MD, United States, ³FMRI Core Facility, NIMH, Bethesda, MD, United States

Synopsis

Keywords: Machine Learning/Artificial Intelligence, fMRI, Manifold Learning, TSNE, UMAP, Laplacian Eigenmaps, Intrinsic Dimension

The exploration of time-varying aspects of the human functional connectome (FC) is challenging because the high dimensionality of connectivity matrices precludes direct visual inspection in a meaningful manner. Dimensionality reduction helps circumvent this problem, yet effective application requires a-priori knowledge of the intrinsic dimension of the data and careful selection of algorithmic hyperparameters. Here, we first estimate the intrinsic dimension of FC data. Next, we use data with known cognitive state changes to evaluate the effectiveness of Laplacian Eigenmaps, T-SNE and UMAP to generate informative low dimensional representations of time-varying FC data for explorative and predictive purposes.

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