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Multi-echo fMRI, particularly the multi-echo independent component analysis (ME-ICA) algorithm, has previously proven useful for increasing the sensitivity and reducing false positives for functional MRI (fMRI) based resting state connectivity studies. Less is known about its efficacy for task-based fMRI, especially at the single subject level. This work, which focuses exclusively on individual subject results, compares ME-ICA to single-echo fMRI and a voxel-wise T2(⁎) weighted combination of multi-echo data for task-based fMRI under the following scenarios: cardiac-gated block designs, constant repetition time (TR) block designs, and constant TR rapid event-related designs. Performance is evaluated primarily in terms of sensitivity (i.e., activation extent, activation magnitude, percent detected trials and effect size estimates) using five different tasks expected to evoke neuronal activity in a distributed set of regions. The ME-ICA algorithm significantly outperformed all other evaluated processing alternatives in all scenarios. Largest improvements were observed for the cardiac-gated dataset, where ME-ICA was able to reliably detect and remove non-neural T1 signal fluctuations caused by non-constant repetition times. Although ME-ICA also outperformed the other options in terms of percent detection of individual trials for rapid event-related experiments, only 46% of all events were detected after ME-ICA; suggesting additional improvements in sensitivity are required to reliably detect individual short event occurrences. We conclude the manuscript with a detailed evaluation of ME-ICA outcomes and a discussion of how the ME-ICA algorithm could be further improved. Overall, our results suggest that ME-ICA constitutes a versatile, powerful approach for advanced denoising of task-based fMRI, not just resting-state data., (Published by Elsevier Inc.)

The first two decades of brain research using fMRI have been dominated by studies that measure signal changes in response to a presented task. A rapidly increasing number of studies are showing that consistent activation maps appear by assessment of signal correlations during time periods in which the subjects were not directed to perform any specific task (i.e. "resting state correlations"). Even though neural interactions can happen on much shorter time scales, most "resting state" studies assess these temporal correlations over a period of about 5 to 10 min. Here we investigate how these temporal correlations change on a shorter time scale. We examine changes in brain correlations to the posterior cingulate cortex (PCC) across a 10-minute scan. We show: (1) fMRI correlations fluctuate over time, (2) these fluctuations can be periodic, and (3) correlations between the PCC and other brain regions fluctuate at distinct frequencies. While the precise frequencies of correlation fluctuations vary across subjects and runs, it is still possible to parse brain regions and combinations of brain regions based on fluctuation frequency differences. To evaluate the potential biological significance of these empirical observations, we then use synthetic time series data with identical amplitude spectra, but randomized phase to show that similar effects can still appear even if the timing relationships between voxels are randomized. This implies that observed correlation fluctuations could occur between regions with distinct amplitude spectra, whether or not there are dynamic changes in neural connectivity between such regions. As more studies of brain connectivity dynamics appear, particularly studies using correlation as a key metric, it is vital to better distinguish true neural connectivity dynamics from connectivity fluctuations that are inherently part of this method. Our results also highlight the rich information in the power spectra of fMRI data that can be used to parse brain regions., (Published by Elsevier Inc.)