Highcor: A novel data-driven regressor identification method for BOLD fMRI

A data-driven regressor selection technique for reducing physiological noise in BOLD fMRI is presented that capitalizes on additional information contained in the phase of the signal time-course. This method, termed highcor, identifies a set of suspect voxels by selecting based on high temporal correlation between the magnitude and phase components of the time-course. Temporal regressors are generated from principal component analysis of this voxel set. Regressor spectral content is investigated with high temporal resolution datasets, and filtering performance is demonstrated. The technique is benchmarked against compcor, an increasingly popular data driven technique. Highcor was found to select a unique set of physiological noise source voxels, and identification of confound and physiologically related signals was robust even at slow temporal sampling rates. Filtering using regressors derived from compcor and highcor voxels resulted in reductions in overall temporal standard deviation in cortical areas of 16.1% ± 3.1%, and 18.1% ± 3.8%, (mean ± sd.) as measured over 36 BOLD fMRI datasets that featured an anti-saccade task. An approach combining both methods resulted in further reductions of temporal standard deviations by 31.4% ± 3.8%. In these regions, mean temporal SNR values were improved from 38.7 ± 3.4 to 47.7 ± 3.7 (cc), 49.2 ± 2.2 (hc), and 57.8 ± 2.3 (hc + cc). tSNR increases from data-driven filtering translated into some associated improvements in overall detection of task in the sample datasets.