Deconvolution filtering: temporal smoothing revisited.
Inferences made from analysis of BOLD data regarding neural processes are potentially confounded by multiple competing sources: cardiac and respiratory signals, thermal effects, scanner drift, and motion-induced signal intensity changes. To address this problem, we propose deconvolution filtering, a process of systematically deconvolving and reconvolving the BOLD signal via the hemodynamic response function such that the resultant signal is composed of maximally likely neural and neurovascular signals. To test the validity of this approach, we compared the accuracy of BOLD signal variants (i.e., unfiltered, deconvolution filtered, band-pass filtered, and optimized band-pass filtered BOLD signals) in identifying useful properties of highly confounded, simulated BOLD data: (1) reconstructing the true, unconfounded BOLD signal, (2) correlation with the true, unconfounded BOLD signal, and (3) reconstructing the true functional connectivity of a three-node neural system. We also tested this approach by detecting task activation in BOLD data recorded from healthy adolescent girls (control) during an emotion processing task. Results for the estimation of functional connectivity of simulated BOLD data demonstrated that analysis (via standard estimation methods) using deconvolution filtered BOLD data achieved superior performance to analysis performed using unfiltered BOLD data and was statistically similar to well-tuned band-pass filtered BOLD data. Contrary to band-pass filtering, however, deconvolution filtering is built upon physiological arguments and has the potential, at low TR, to match the performance of an optimal band-pass filter. The results from task estimation on real BOLD data suggest that deconvolution filtering provides superior or equivalent detection of task activations relative to comparable analyses on unfiltered signals and also provides decreased variance over the estimate. In turn, these results suggest that standard preprocessing of the BOLD signal ignores significant sources of noise that can be effectively removed without damaging the underlying signal.
Department of Computer Science, University of Arkansas at Little Rock (UALR), Little Rock, AR 72204, USA. Electronic address: kabush@ualr.edu.
Keith
K
Bush
Brain Imaging Research Center, University of Arkansas for Medical Sciences (UAMS), Little Rock, AR 72205, USA.
Josh
J
Cisler
D000293:Adolescent; D000465:Algorithms; D001931:Brain Mapping / Q000379:methods*; D002648:Child; D003198:Computer Simulation; D004644:Emotions; D005260:Female; D005583:Fourier Analysis; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010775:Photic Stimulation; D011930:Reaction Time
BOLD signal; Deconvolution; Filtering; Functional connectivity; Imaging analysis; fMRI