Processing strategies for time‐course data sets in functional mri of the human brain

Abstract

Image processing strategies for functional magnetic resonance imaging (FMRI) data sets acquired using a gradient-recalled echo-planar imaging sequence are considered. The analysis is carried out using the mathematics of vector spaces. Data sets consisting of N sequential images of the same slice of brain tissue are analyzed in the time-domain and also, after Fourier transformation, in the frequency domain. A technique for thresholding is introduced that uses the shape of the response in a pixel compared with the shape of a reference waveform as the decision criterion. A method is presented to eliminate drifts in data that arise from subject movement. The methods are applied to experimental FMRI data from the motor-cortex and compared with more conventional image-subtraction methods. Several finger motion paradigms are considered in the context of the various image processing strategies. The most effective method for image processing involves thresholding by shape as characterized by the correlation coefficient of the data with respect to a reference waveform followed by formation of a cross-correlation image. Emphasis is placed not only on image formation, but also on the use of signal processing techniques to characterize the temporal response of the brain to the paradigm.

Failure to direct detect magnetic field dephasing corresponding to ERP generation

“…A general linear transform model (GLM) [Friston 1995], which assumes that the fMRI signal possesses linear characteristics with respect to the stimulus and that the temporal noise is white, was used to estimate the response. We first used a correlation template generated by convolving the box-car function of the stimulation paradigm with a canonical fMRI impulse response function described by Friston et al [Friston 1994] to do the crosscorrelation analysis [Bandettini 1993]. Cross-correlation maps were thresholded (P < 0.001) to generate activation maps.…”

fMRI response to spatial working memory in adolescents with comorbid marijuana and alcohol use disorders

“…We first applied a motion-correction algorithm to the time series data (Cox and Jesmanowicz, 1999). Second, we correlated the time series data with a set of reference vectors that represented the block design of the task and accounted for delays in hemodynamic response (Bandettini et al, 1993), while covarying for estimated motion and linear trends. Next, we transformed imaging data to standard coordinates (Lancaster et al, 2000;Talairach and Tournoux, 1988) then resampled the functional data into 3.5 mm 3 voxels.…”

Non-invasive epileptic focus localization using EEG-triggered functional MRI and electromagnetic tomography

“…The exclusion of these sequences resulted in 43 triggered multi-slice MRIs and 43 control images, which were then statistically analyzed. The two sets of 43 images were analyzed using a standard crosscorrelation computation of each individual pixel (Bandettini et al, 1993). Bonferroni correction was used to eliminate false positives derived from multiple comparisons.…”