Practice-related changes in neural activation patterns investigated via wavelet-based clustering analysis

Objectives: To evaluate brain activation using functional magnetic resonance imaging (fMRI) and specifically, activation changes across time associated with practice-related cognitive control during eye movement tasks.

Experimental design: Participants were engaged in antisaccade performance (generating a glance away from a cue) while fMR images were acquired during two separate test sessions: (1) at pre-test before any exposure to the task and (2) at post-test, after 1 week of daily practice on antisaccades, prosaccades (glancing toward a target), or fixation (maintaining gaze on a target).

Principal observations: The three practice groups were compared across the two test sessions, and analyses were conducted via the application of a model-free clustering technique based on wavelet analysis. This series of procedures was developed to avoid analysis problems inherent in fMRI data and was composed of several steps: detrending, data aggregation, wavelet transform and thresholding, no trend test, principal component analysis (PCA), and K-means clustering. The main clustering algorithm was built in the wavelet domain to account for temporal correlation. We applied a no trend test based on wavelets to significantly reduce the high dimension of the data. We clustered the thresholded wavelet coefficients of the remaining voxels using PCA K-means clustering.

Conclusion: Over the series of analyses, we found that the antisaccade practice group was the only group to show decreased activation from pre-test to post-test in saccadic circuitry, particularly evident in supplementary eye field, frontal eye fields, superior parietal lobe, and cuneus.