Detecting cortical activities from fMRI time-course data using the music algorithm with forward and backward covariance averaging
✍ Scribed by Kensuke Sekihara; Hideaki Koizumi
- Publisher
- John Wiley and Sons
- Year
- 1996
- Tongue
- English
- Weight
- 730 KB
- Volume
- 35
- Category
- Article
- ISSN
- 0740-3194
No coin nor oath required. For personal study only.
✦ Synopsis
Abstract
A method is proposed for processing time‐course fMRI data taken wKh successlve single‐shot echo‐planar imaging. The proposed method uses a two‐dimensional version of the multiple signal classification (MUSIC) algorithm and the technique called covariance averaging, both of which were developed in the field of sensor‐array processing. The proposed method consists of four steps: calculate the averaged data covariance matrix, determine the number of activities using this covariance matrix, estimate the locations of the activities, and estimate their time evolution curves. Computer simulation resutts showed that a nearly fourfold improvement in the spatial resolution can be attained due to the method's super‐resolution capability.