Brain Signal Variability Differentially Affects Cognitive Flexibility and Cognitive Stability

Abstract

Recent research yielded the intriguing conclusion that, in healthy adults, higher levels of variability in neuronal processes are beneficial for cognitive functioning. Beneficial effects of variability in neuronal processing can also be inferred from neurocomputational theories of working memory, albeit this holds only for tasks requiring cognitive flexibility. However, cognitive stability, i.e., the ability to maintain a task goal in the face of irrelevant distractors, should suffer under high levels of brain signal variability. To directly test this prediction, we studied both behavioral and brain signal variability during cognitive flexibility (i.e., task switching) and cognitive stability (i.e., distractor inhibition) in a sample of healthy human subjects and developed an efficient and easy-to-implement analysis approach to assess BOLD-signal variability in event-related fMRI task paradigms. Results show a general positive effect of neural variability on task performance as assessed by accuracy measures. However, higher levels of BOLD-signal variability in the left inferior frontal junction area result in reduced error rate costs during task switching and thus facilitate cognitive flexibility. In contrast, variability in the same area has a detrimental effect on cognitive stability, as shown in a negative effect of variability on response time costs during distractor inhibition. This pattern was mirrored at the behavioral level, with higher behavioral variability predicting better task switching but worse distractor inhibition performance. Our data extend previous results on brain signal variability by showing a differential effect of brain signal variability that depends on task context, in line with predictions from computational theories.

BOLD and EEG Signal Variability at Rest Differently Relate to Aging in the Human Brain

“…These studies primarily measured the blood oxygen level dependent (BOLD) signal using fMRI. For example, it has been demonstrated that the variance of the task-evoked BOLD response was differentially related to aging as well as cognitive performance (Armbruster-Genc et al, 2016;Garrett et al, 2013a). Similarly, spontaneous signal variability in resting state fMRI (rsfMRI) has been associated with age (Grady and Garrett, 2018;Nomi et al, 2017), emotional state (state anxiety; Labrenz et al, 2018), and mental or neural disorders such as stroke (Kielar et al, 2016), Attention Deficit Hyperactivity Disorder (Nomi et al, 2018) or 22q11.2 deletion syndrome (Zöller et al, 2017).…”

Default mode contributions to automated information processing

“…However, the successful pursuit of goals for adaptive purposes also requires a level of cognitive stability or maintenance (4). In fact, a considerable portion of our daily lives comprises learned, automatic, reflexive or habitual behaviors under specific contexts in stable environments, as opposed to the controlled and effortful processes commonly scrutinized in experimental settings (5).…”

Multiscale energy reallocation during low‐frequency steady‐state brain response

“…In fact, the BSV is primarily increased at resonant frequencies while decreased at nonresonant frequencies, indicating frequency‐specific energy reallocation. The frequency effect challenges the monotonous relationship between BSV and cognitive performance as previously suggested (Garrett et al, ), arguing that the brain can flexibly reallocate energy among different functional subsystems (Armbruster‐Genç, Ueltzhöffer, & Fiebach, ).…”