Measuring Brain Connectivity via Shape Analysis of fMRI Time Courses and Spectra

Global Diffeomorphic Phase Alignment of Time-Series from Resting-State fMRI Data

We present a novel method for global diffeomorphic phase alignment of time-series data from resting-state functional magnetic resonance imaging (rsfMRI) signals. Additionally, we propose a multidimensional, continuous, invariant functional representation of brain time-series data and solve a general global cost function that brings both the temporal rotations and phase reparameterizations in alignment. We define a family of cost functions for spatiotemporal warping and compare time-series warps across them. This method achieves direct alignment of time-series, allows population analysis by aligning time-series activity across subjects and shows improved global correlation maps, as well as z-scores from independent component analysis (ICA), while showing new information exploited by phase alignment that was not previously recoverable.

Elastic Registration of Single Subject Task Based fMRI Signals

Single subject task-based fMRI analyses generally suffer from low detection sensitivity with parameter estimates from the general linear model (GLM) lying below the significance threshold especially for similar contrasts or conditions. In this paper, we present a shape-based approach for alignment of condition-specific time course activity for single subject task-based fMRI. Our approach extracts signals for each condition from the entire time course, constructs an unbiased average of those signals, and warps each signal to the mean. As the warping is diffeomorphic, non-linear and allows large deformations of time series if required, we term this approach as elastic functional registration. On a single subject level, our method significantly detects more clusters and more activated voxels in relevant subcortical regions in healthy controls.