Structurally-Informed Deconvolution of Functional Magnetic Resonance Imaging Data

Neural activity occurs in the shape of spatially organized patterns: networks of brain regions activate in synchrony. Many of these functional networks also happen to be strongly structurally connected. We use this information to revisit the fundamental problem of functional magnetic resonance imagi...

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Bibliographic Details
Published in2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) pp. 1545 - 1549
Main Authors Bolton, Thomas A.W., Farouj, Younes, Inan, Mert, Van De Ville, Dimitri
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2019
Subjects
Blood flow
Brain modeling
Correlation
Deconvolution
Functional magnetic resonance imaging
Graph signal processing
Signal processing
Total activation
Wiring
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Summary:Neural activity occurs in the shape of spatially organized patterns: networks of brain regions activate in synchrony. Many of these functional networks also happen to be strongly structurally connected. We use this information to revisit the fundamental problem of functional magnetic resonance imaging (fMRI) data deconvolution. Using tools from graph signal processing (GSP), we extend total activation, a spatiotemporal deconvolution technique, to data defined on graph domains. The resulting approach simultaneously cancels out the effect of the haemodynamics, and promotes spatial patterns that are in harmony with predefined structural wirings. More precisely, we minimize a functional involving one data fidelity and two regularization terms. The first regularizer uses the concept of generalized total variation to promote sparsity in the activity transients domain. The second term controls the overall spatial variation over the graph structure. We demonstrate the relevance of this structurally-driven regularization on synthetic and experimental data.
ISSN:1945-8452
DOI:10.1109/ISBI.2019.8759218