A few points suffice: Efficient large-scale computation of brain voxel-wise functional connectomes from a sparse spatio-temporal point-process

TL;DR

This paper validates a sparse point-process method for efficiently computing voxel-wise functional connectomes from fMRI data, significantly reducing data size and computation time while maintaining accuracy.

Contribution

The authors demonstrate that a sparse point-process representation can replicate standard correlation-based connectomes, enabling large-scale, efficient brain connectivity analysis.

Findings

Method compares well with standard techniques in sleep and aging studies.

Requires only about 1% of original fMRI data.

Enables faster and more scalable brain network analysis.

Abstract

Large efforts are currently under way to systematically map functional connectivity between all pairs of millimeter-scale brain regions using big volumes of neuroimaging data. Functional magnetic resonance imaging (fMRI) can produce these functional connectomes, however, large amounts of data and lengthy computation times add important overhead to this task. Previous work has demonstrated that fMRI data admits a sparse representation in the form a discrete point-process containing sufficient information for the efficient estimation of functional connectivity between all pairs of voxels. In this work we validate this method, by replicating results obtained with standard whole-brain voxel-wise linear correlation matrices in two datasets. In the first one (n=71) we study the changes in node strength (a measure of network centrality) during deep sleep. The second is a large database (n=1147) of subjects in which we look at the age-related reorganization of the voxel-wise network of functional connections. In both cases it is shown that the proposed method compares well with standard techniques, despite requiring of the order of 1 % of the original fMRI time series. Overall, these results demonstrate that the proposed approach allows efficient fMRI data compression and a subsequent reduction of computation times.