Neuroscientists have long known that brain function is inherently variable. 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 that reflects development, cognitive adaptability, flexibility, and performance. In the present thesis, we examine how brain signal variability (measured with a modified BOLD time series standard deviation (SDBOLD)) relates to human aging and cognitive performance in a series of studies. In Study 1, we examined brain variability during fixation baseline periods. We found that not only was the SDBOLD pattern robust, its unique age-predictive power was more than five times that of meanBOLD (a common measure of BOLD activity), yet revealed a spatial pattern virtually orthogonal to meanBOLD. Contrary to typical conceptions of age-related neural noise, young adults exhibited greater brain variability overall. In Study 2, we found that younger, faster, and more consistent performers exhibited significantly higher brain variability across three cognitive tasks, and showed greater variability-based regional differentiation compared to older, poorer performing adults. SDBOLD and meanBOLD spatial patterns were again orthogonal across brain measures. Study 3 demonstrated experimental condition-based modulations in SDBOLD. SDBOLD was an effective discriminator between internal (lower variability) and external (higher variability) cognitive demands, particularly in younger, high performing adults. Finally, to gauge the extent that brain variability can be incrementally manipulated within a single cognitive domain, Study 4 examined parametric modulations in SDBOLD on a face processing task in a young-only sample. Results indicated that SDBOLD can be robustly manipulated through experimental control, and that this manipulation linearly follows performance trends across conditions. These studies help establish the age- and performance-relevance of BOLD variability. We thus argue that the precise nature of relations between aging, cognition, and brain function is incompletely characterized by using mean-based brain measures exclusively.

Neuroscientists have long known that brain function is inherently variable. 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 that reflects development, cognitive adaptability, flexibility, and performance. In the present thesis, we examine how brain signal variability (measured with a modified BOLD time series standard deviation (SDBOLD)) relates to human aging and cognitive performance in a series of studies. In Study 1, we examined brain variability during fixation baseline periods. We found that not only was the SDBOLD pattern robust, its unique age-predictive power was more than five times that of meanBOLD (a common measure of BOLD activity), yet revealed a spatial pattern virtually orthogonal to meanBOLD. Contrary to typical conceptions of age-related neural noise, young adults exhibited greater brain variability overall. In Study 2, we found that younger, faster, and more consistent performers exhibited significantly higher brain variability across three cognitive tasks, and showed greater variability-based regional differentiation compared to older, poorer performing adults. SDBOLD and meanBOLD spatial patterns were again orthogonal across brain measures. Study 3 demonstrated experimental condition-based modulations in SDBOLD. SDBOLD was an effective discriminator between internal (lower variability) and external (higher variability) cognitive demands, particularly in younger, high performing adults. Finally, to gauge the extent that brain variability can be incrementally manipulated within a single cognitive domain, Study 4 examined parametric modulations in SDBOLD on a face processing task in a young-only sample. Results indicated that SDBOLD can be robustly manipulated through experimental control, and that this manipulation linearly follows performance trends across conditions. These studies help establish the age- and performance-relevance of BOLD variability. We thus argue that the precise nature of relations between aging, cognition, and brain function is incompletely characterized by using mean-based brain measures exclusively.

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Numerous studies demonstrate that moment-to-moment neural variability is behaviorally relevant and beneficial for tasks and behaviors requiring cognitive flexibility. However, it remains unclear whether the positive effect of neural variability also holds for cognitive persistence. Moreover, different brain variability measures have been used in previous studies, yet comparisons between them are lacking. In the current study, we examined the association between resting-state BOLD signal variability and two metacontrol policies (i.e., persistence vs. flexibility). Brain variability was estimated from resting-state fMRI (rsfMRI) data using two different approaches (i.e., Standard Deviation (SD), and Mean Square Successive Difference (MSSD)) and metacontrol biases were assessed by three metacontrol-sensitive tasks. Results showed that brain variability measured by SD and MSSD was highly positively related. Critically, higher variability measured by MSSD in the attention network, parietal and frontal network, frontal and ACC network, parietal and motor network, and higher variability measured by SD in the parietal and motor network, parietal and frontal network were associated with reduced persistence (or greater flexibility) of metacontrol (i.e., larger Stroop effect or worse RAT performance). These results show that the beneficial effect of brain signal variability on cognitive control depends on the metacontrol states involved. Our study highlights the importance of temporal variability of rsfMRI activity in understanding the neural underpinnings of cognitive control.