Neuroscientists have long observed that brain activity is naturally variable from moment-to-moment, but neuroimaging research has largely ignored the potential importance of this phenomenon. An emerging research focus on within-person brain signal variability is providing novel insights, and offering highly predictive, complementary, and even orthogonal views of brain function in relation to human lifespan development, cognitive performance, and various clinical conditions. As a result, brain signal variability is evolving as a bona fide signal of interest, and should no longer be dismissed as meaningless noise when mapping the human brain.

Functional magnetic resonance imaging (fMRI) research often attributes blood oxygen level-dependent (BOLD) signal variance to measurement-related confounds. However, what is typically considered "noise" variance in data may be a vital feature of brain function. We examined fMRI signal variability during fixation baseline periods, and then compared SD-and mean-based spatial patterns and their relations with chronological age (20 -85 years). We found that not only was the SD-based pattern robust, it differed greatly, both spatially and statistically, from the mean-based pattern. Notably, the unique age-predictive power of the SD-based pattern was more than five times that of the mean-based pattern. This reliable SD-based pattern of activity highlights an important "signal" within what is often considered measurement-related "noise." We suggest that examination of BOLD signal variability may reveal a host of novel brain-related effects not previously considered in neuroimaging research.

New work suggests that blood oxygen level-dependent (BOLD) signal variability can be a much more powerful index of human age than mean activation, and that older brains are actually less variable than younger brains. However, little is known of how BOLD variability and task performance may relate. In the current study, we examined BOLD variability in relation to age, and reaction time speed and consistency in healthy younger (20 -30 years) and older (56 -85 years) adults on three cognitive tasks (perceptual matching, attentional cueing, and delayed match-to-sample). Results indicated that younger, faster, and more consistent performers exhibited significantly higher brain variability across tasks, and showed greater variability-based regional differentiation compared with older, poorerperforming adults. Also, when we compared brain variability-and typical mean-based effects, the respective spatial patterns were essentially orthogonal across brain measures, and any regions that did overlap were largely opposite in directionality of effect. These findings help establish the functional basis of BOLD variability, and further support the statistical and spatial differentiation between BOLD variability and BOLD mean. We thus argue that the precise nature of relations between aging, cognition, and brain function is underappreciated by using mean-based brain measures exclusively.

Increasing evidence suggests that brain variability plays a number of important functional roles for neural systems. However, the relationship between brain variability and changing cognitive demands remains understudied. In the current study, we demonstrate experimental condition-based modulation in brain variability using functional magnetic resonance imaging (fMRI). Within a sample of younger and older healthy adults, we found that BOLD variability was an effective discriminator between fixation and external cognitive demand. Across a number of regions, brain variability increased broadly on task compared to fixation, particularly in younger and faster performing adults. Conversely, older and slower performing adults exhibited fewer changes in brain variability within and across experimental conditions and brain regions, indicating a reduction in variability-based neural specificity. Increases in brain variability on task may represent a more complex neural system capable of greater dynamic range between brain states, as well as an enhanced ability to efficiently process varying and unexpected external stimuli. The current results help establish the developmental and performance correlates of state-to-state brain variability-based transitions, and offer a new line of inquiry in the study of rest vs. task modes in the human brain. Keywords brain variability; default mode; fMRI; noise Increasing evidence suggests that brain variability is a useful attribute for neural systems, perhaps reflecting greater network complexity, connectivity, increased dynamic range, heightened signal detection thresholds, and superior cognitive processing (Li et al.