Seed-Based Connectivity Analysis in Functional MRI

摘要

Functional MRI (fMRI) methods are presented for utilizing a magnetic resonance tomograph to map connectivity between brain areas in the resting state in real-time without the use of regression of confounding signal changes. They encompass: (a) iterative computation of the sliding window correlation between the signal time courses in a seed region and each voxel of an fMRI image series, (b) Fisher Z-transformation of each correlation map, (c) computation of a running mean and a running standard deviation of the Z-maps across a second sliding window to produce a series of meta mean maps and a series of meta standard deviation maps, and (d) thresholding of the meta maps. This methodology can be combined with regression of confounding signals within the sliding window. It is also applicable to task-based real-time fMRI, if the location of at least one task-activated voxel is known.

主权项

1. A method for the evaluation of resting state functional MRI (fMRI) data from nuclear magnetic resonance tomographs that measures the correlation between a seed region signal time series and the signal time series in a plurality of voxels in the fMRI data comprising the steps of
performing fMRI measurements to create a series of fMRI data with N time points using a sampling interval Δt that is equal to or shorter than the Nyquist sampling interval 1/(2f) required for sampling a periodic resting state signal with frequency f, wherein f is the lowest frequency of interest in the resting state signal spectrum; preprocessing of fMRI data using the steps of motion correction, slice time correction, spatial normalization into the space of a standardized brain atlas, spatial smoothing and time domain low pass filtering; extraction of the signal time course in a seed region; computation of the sliding window correlation between the signal time courses in said seed region and in a plurality of voxels in said fMRI data, utilizing K