BrainSync: An Orthogonal Transformation for Synchronization of fMRI Data Across Subjects

Abstract

We describe a method that allows direct comparison of resting fMRI (rfMRI) time series across subjects. For this purpose, we exploit the geometry of the rfMRI signal space to conjecture the existence of an orthogonal transformation that synchronizes fMRI time series across sessions and subjects. The method is based on the observation that rfMRI data exhibit similar connectivity patterns across subjects, as reflected in the pairwise correlations between different brain regions. The orthogonal transformation that performs the synchronization is unique, invertible, efficient to compute, and preserves the connectivity structure of the original data for all subjects. Similarly to image registration, where we spatially align the anatomical brain images, this synchronization of brain signals across a population or within subject across sessions facilitates longitudinal and cross-sectional studies of rfMRI data. The utility of this transformation is illustrated through applications to quantification of fMRI variability across subjects and sessions, joint cortical clustering of a population and comparison of task-related and resting fMRI.

Are you thinking what I'm thinking? Synchronization of resting fMRI time-series across subjects

“…We then describe an orthogonal transformation that makes the rfMRI data from two subjects directly comparable. The BrainSync transform retains the original signal geometry by preserving the pairwise geodesic distances between all pairs of points on the hypersphere while also temporally aligning or synchronizing the two scans (Joshi et al, 2017). This synchronization results in an approximate matching of the time-series at homologous locations across subjects.…”

Automatic Identification of Twin Zygosity in Resting-State Functional MRI

“…The correlation between twins at voxel level computed using CSR features is feed into in a two-layer ANN. Currently, the majority of brain image analysis studies compute correlation directly from resting state fMRI signal (Biswal et al 1997, Peltier et al 2005, Rogers et al 2007, Liang et al 2012, Finn et al 2015, Joshi et al 2017. In contrast, CSR compactly represent fMRI components into frequency components, which provides a superior performance with respect to the classification accuracy.…”