Resting-state BOLD signal variability is associated with individual differences in metacontrol

Abstract

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.

Figure 1

Statistics of mean RT in the Stroop task and inter-correlations between behavioral assessments. (a) Mean reaction time (RT) in (corresponding) incongruent condition was larger than RT in (corresponding) congruent condition; (b) inter-correlation between the size of Stroop effect, RT-CV of Stroop task, RAT scores, AUT flexibility scores and AUT fluency scores. *p < 0.05, ***p < 0.001.

Figure 2

Spatial maps (Z-threshold > 1.0, in the left panel) and time series (in the right panel) for selected independent components of the mean for all participants.

Figure 3

The correlation between the size of Stroop effect and brain variability of the attention network (i.e., IC8) was close to significance. The higher the brain variability of IC8 estimated by MSSD, the larger the size of Stroop effect.

Figure 4

RAT performance was significantly (or, in the case of e, close to significantly) negatively correlated with brain variability of the parietal and motor network (i.e., IC3), parietal and frontal network (i.e., IC6), frontal and ACC network (i.e., IC9). Brain variability was calculated using SD in (a,b); brain variability was measured by MSSD in (c–e).

Figure 5

Examples for conditions and design of the color-word matching Stroop task. For the upper two examples, the correct answer would be “YES,” for the lower two examples, the correct answer would be “NO.”