Functional connectivity (FC) patterns in functional MRI exhibit dynamic behavior on the scale of seconds, with rich spatiotemporal structure and limited sets of whole-brain, quasi-stable FC configurations (FC states) recurring across time and subjects. Based on previous evidence linking various aspects of cognition to grouplevel, minute-to-minute FC changes in localized connections, we hypothesized that whole-brain FC states may reflect the global, orchestrated dynamics of cognitive processing on the scale of seconds. To test this hypothesis, subjects were continuously scanned as they engaged in and transitioned between mental states dictated by tasks. FC states computed within windows as short as 22.5 s permitted robust tracking of cognition in single subjects with near perfect accuracy. Accuracy dropped markedly for subjects with the lowest task performance. Spatially restricting FC information decreased accuracy at short time scales, emphasizing the distributed nature of whole-brain FC dynamics, beyond univariate magnitude changes, as valuable markers of cognition.fMRI | connectivity dynamics | functional connectivity states | cognitive states | classification R esting state functional MRI (rs-fMRI) focuses on spatial patterns of blood oxygenation level dependent (BOLD) signal cofluctuations recorded in the absence of externally driven tasks or stimulation. These patterns, known as functional connectivity (FC) patterns, are usually computed on the basis of an entire scan (often >6 min). Their cognitive significance (1) and long term reproducibility (2) are well established, and preliminary data suggest that they have potential clinical value (3). However, recent studies have shown that FC patterns are highly dynamic at shorter temporal scales (4) (i.e., tens of seconds), adding yet another challenge to developing fMRI-based protocols with sufficient single-subject specificity and sensitivity to inform clinical decisions.FC patterns computed with 1-to 2-min portions of a scan can vary substantially around a mean FC pattern obtained using complete 6-to 20-min scans. This dynamic behavior has been observed in awake and sleeping humans (5-8), as well as in anesthetized animals (9, 10). Several studies involving simultaneous fMRI and electrophysiological recordings have suggested that FC dynamics may be driven by neurophysiological sources rather than noise (6,11,12). Furthermore, FC dynamics exhibit rich spatiotemporal structure. Connections between higher order cognitive regions are more variable than those between primary sensorymotor regions (13-15), and a limited set of whole-brain, quasistable FC configurations-known as FC states-reliably recur both within and across subjects at rest (13,16).Given that cognition is supported by highly dynamic brain processes, it has been hypothesized that FC states may reflect changes in ongoing cognitive states during rest (13). Initial task-based studies have been able to differentiate between a limited set of mental tasks (17, 18) and arousal levels (19) on the basis of l...

Resting state functional MRI (rsfMRI) connectivity patterns are not temporally stable, but fluctuate in time at scales shorter than most common rest scan durations (5–10 min). Consequently, connectivity patterns for two different portions of the same scan can differ drastically. To better characterize this temporal variability and understand how it is spatially distributed across the brain, we scanned subjects continuously for 60 min, at a temporal resolution of 1 s, while they rested inside the scanner. We then computed connectivity matrices between functionally-defined regions of interest for non-overlapping 1 min windows, and classified connections according to their strength, polarity, and variability. We found that the most stable connections correspond primarily to inter-hemispheric connections between left/right homologous ROIs. However, only 32% of all within-network connections were classified as most stable. This shows that resting state networks have some long-term stability, but confirms the flexible configuration of these networks, particularly those related to higher order cognitive functions. The most variable connections correspond primarily to inter-hemispheric, across-network connections between non-homologous regions in occipital and frontal cortex. Finally we found a series of connections with negative average correlation, but further analyses revealed that such average negative correlations may be related to the removal of CSF signals during pre-processing. Using the same dataset, we also evaluated how similarity of within-subject whole-brain connectivity matrices changes as a function of window duration (used here as a proxy for scan duration). Our results suggest scanning for a minimum of 10 min to optimize within-subject reproducibility of connectivity patterns across the entire brain, rather than a few predefined networks.

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.