DeepGraphDMD: Interpretable Spatio-Temporal Decomposition of Non-linear
Functional Brain Network Dynamics
- URL: http://arxiv.org/abs/2306.03088v2
- Date: Thu, 7 Dec 2023 16:32:57 GMT
- Title: DeepGraphDMD: Interpretable Spatio-Temporal Decomposition of Non-linear
Functional Brain Network Dynamics
- Authors: Md Asadullah Turja, Martin Styner and Guorong Wu
- Abstract summary: We develop a generalized version of the GraphDMD algorithm -- DeepGraphDMD -- applicable to arbitrary non-linear graph dynamical systems.
DeepGraphDMD is an autoencoder-based deep learning model that learns Koopman eigenfunctions for graph data.
We show the effectiveness of our method in both simulated data and the resting-state fMRI data.
- Score: 4.127479316753882
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Functional brain dynamics is supported by parallel and overlapping functional
network modes that are associated with specific neural circuits. Decomposing
these network modes from fMRI data and finding their temporal characteristics
is challenging due to their time-varying nature and the non-linearity of the
functional dynamics. Dynamic Mode Decomposition (DMD) algorithms have been
quite popular for solving this decomposition problem in recent years. In this
work, we apply GraphDMD -- an extension of the DMD for network data -- to
extract the dynamic network modes and their temporal characteristics from the
fMRI time series in an interpretable manner. GraphDMD, however, regards the
underlying system as a linear dynamical system that is sub-optimal for
extracting the network modes from non-linear functional data. In this work, we
develop a generalized version of the GraphDMD algorithm -- DeepGraphDMD --
applicable to arbitrary non-linear graph dynamical systems. DeepGraphDMD is an
autoencoder-based deep learning model that learns Koopman eigenfunctions for
graph data and embeds the non-linear graph dynamics into a latent linear space.
We show the effectiveness of our method in both simulated data and the HCP
resting-state fMRI data. In the HCP data, DeepGraphDMD provides novel insights
into cognitive brain functions by discovering two major network modes related
to fluid and crystallized intelligence.
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