Linear Models
Research Papers
Real-time fMRI processing with physiological noise correction - Comparison with off-line analysis
BACKGROUND: While applications of real-time functional magnetic resonance imaging (rtfMRI) are growing rapidly, there are still limitations in real-time data processing compared to off-line analysis. NEW METHODS: We developed a proof-of-concept real-time fMRI processing (rtfMRIp) system utilizing a personal computer (PC) with a dedicated graphic processing unit (GPU) to demonstrate that it is now possible to perform intensive whole-brain fMRI data processing in real-time. The rtfMRIp performs slice-timing correction, motion correction, spatial smoothing, signal scaling, and general linear model (GLM) analysis with multiple noise regressors including physiological noise modeled with cardiac (RETROICOR) and respiration volume per time (RVT). RESULTS: The whole-brain data analysis with more than 100,000voxels and more than 250volumes is completed in less than 300ms, much faster than the time required to acquire the fMRI volume. Real-time processing implementation cannot be identical to off-line analysis when time-course information is used, such as in slice-timing correction, signal scaling, and GLM. We verified that reduced slice-timing correction for real-time analysis had comparable output with off-line analysis. The real-time GLM analysis, however, showed over-fitting when the number of sampled volumes was small. COMPARISON WITH EXISTING METHODS: Our system implemented real-time RETROICOR and RVT physiological noise corrections for the first time and it is capable of processing these steps on all available data at a given time, without need for recursive algorithms. CONCLUSIONS: Comprehensive data processing in rtfMRI is possible with a PC, while the number of samples should be considered in real-time GLM.
View Full Paper →Reducing vascular variability of fMRI data across aging populations using a breathholding task
The magnitude and shape of blood oxygen level-dependent (BOLD) responses in functional MRI (fMRI) studies vary across brain regions, subjects, and populations. This variability may be secondary to neural activity or vasculature differences, thus complicating interpretations of BOLD signal changes in fMRI experiments. We compare the BOLD responses to neural activity and a vascular challenge and test a method to dissociate these influences in 26 younger subjects (ages 18-36) and 24 older subjects (ages 51-78). Each subject performed a visuomotor saccade task (a vascular response to neural activity) and a breathholding task (vascular dilation induced by hypercapnia) during separate runs in the same scanning session. For the saccade task, signal magnitude showed a significant decrease with aging in FEF, SEF, and V1, and a delayed time-to-peak with aging in V1. The signal magnitudes from the saccade and hypercapnia tasks showed significant linear regressions within subjects and across individuals and populations. These two tasks had weaker, but sometimes significant linear regressions for time-to-peak and coherence phase measures. The significant magnitude decrease with aging in V1 remained after dividing the saccade task magnitude by the hypercapnia task magnitude, implying that the signal decrease is neural in origin. These findings may lead to a method to identify vascular reactivity-induced differences in the BOLD response across populations and the development of methods to account for the influence of these vasculature differences in a simple, noninvasive manner.
View Full Paper →Estimating alertness from the EEG power spectrum
In tasks requiring sustained attention, human alertness varies on a minute time scale. This can have serious consequences in occupations ranging from air traffic control to monitoring of nuclear power plants. Changes in the electroencephalographic (EEG) power spectrum accompany these fluctuations in the level of alertness, as assessed by measuring simultaneous changes in EEG and performance on an auditory monitoring task. By combining power spectrum estimation, principal component analysis and artificial neural networks, we show that continuous, accurate, noninvasive, and near real-time estimation of an operator's global level of alertness is feasible using EEG measures recorded from as few as two central scalp sites. This demonstration could lead to a practical system for noninvasive monitoring of the cognitive state of human operators in attention-critical settings.
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