Dynamical Change of Signal Complexity in the Brain During Inhibitory Control Processes

Abstract

Abstract:The ability to inhibit impulses and withdraw certain responses are essential for human's survival in a fast-changing environment. These processes happen fast, in a complex manner, and require our brain to make a fast adaptation to inhibit the impulsive response. The present study employs multiscale entropy (MSE) to analyzing electroencephalography (EEG) signals acquired alongside a behavioral stop-signal task to theoretically quantify the complexity (indicating adaptability and efficiency) of neural systems to investigate the dynamical change of complexity in the brain during the processes of inhibitory control. We found that the complexity of EEG signals was higher for successful than unsuccessful inhibition in the stage of peri-stimulus, but not in the pre-stimulus time window. In addition, we found that the dynamical change in the brain from pre-stimulus to peri-stimulus stage for inhibitory control is a process of decreasing complexity. We demonstrated both by sensor-level and source-level MSE that the processes of losing complexity is temporally slower and spatially restricted for successful inhibition, and is temporally quicker and spatially extensive for unsuccessful inhibition.

Altered EEG Signal Complexity Induced by Hand Proximity: A Multiscale Entropy Approach

“…However, some studies using clinical applications have suggested the parameters of m = 1 or 2 and r = 0.1 to 0.25 to provide a high validity for sample entropy in EEG signals (e.g., Escudero et al, 2006 ; Takahashi et al, 2009 ; Yang et al, 2013 ). With these suggested parameters we have also obtained good results in the past when analyzing EEG signals in the context of cognitive tasks similar to the current study ( Wang et al, 2014 ; Huang et al, 2015 ). Specifically, in this study the pattern length, m , was set to 1 (i.e., one data point was used for pattern matching).…”

“…For example, using a stop-signal task that is designed to induce inhibitory control mechanism, studies have shown that people who are better able to suppress a motor response tend to show higher EEG complexity in MSE analysis. This is true in between-subject studies ( Huang et al, 2015 ), as well as within-subject studies where the same participants’ EEG signals are measured pre- and post-intervention ( Liang et al, 2014 ). In a VSTM study, Wang et al (2014) also showed that physically active elderly adults had higher EEG signal complexity compared to their sedentary counterparts.…”

“…Specifically, in this study the pattern length, m , was set to 1 (i.e., one data point was used for pattern matching). The similarity criterion, r , was set to 0.30, meaning that data points were considered to be indistinguishable if the absolute amplitude difference between them was ≤30% of the time series standard deviation ( Huang et al, 2015 ). Data preprocessing was performed using SPM8 (Statistical Parametric Mapping) and custom MATLAB (MathWorks) scripts 1 .…”

“…Indeed, in the medical field, MSE has already been applied to the analysis of EEG signals for two decades, and has been shown to be sensitive to the EEG differences between healthy and epileptic children ( El Sayed Hussein Jomaa et al, 2019 ), healthy from MCI and Alzheimer’s disease patients ( Morabito et al, 2012 ), and even between awake, light, and deep anesthesia ( Li et al, 2010 ). In cognitive domains, previous research has also been able to use MSE to differentiate the EEG signals between good and poor performance in visuospatial working memory ( Wang et al, 2014 ) and cognitive control ( Liang et al, 2014 ; Huang et al, 2015 ), especially at higher time scales (e.g., 10–20 time scales). Notably, Wang et al (2014) were able to show that, in the context of VSTM, physically active elderly adults performed better than inactive controls, and such behavioral distinction was also observable in MSE analysis at higher time scales.…”

The critical role of phase difference in theta oscillation between bilateral parietal cortices for visuospatial working memory

“…This is based on the assumption that (1) high-performers already possess an optimally amount of variability, or complexity 55 , in their endogenous neural signals, and (2) theta tACS with fixed phase envelope is too monotonous and thus eliminates such intrinsic signal variability. Indeed, it has been reported that high-performers in cognitive tasks tend to exhibit more complex neural signals 56 (via entropy) than low-performers 57 , and using tACS to boost phase-locking may sometimes be detrimental to WM performance 58 . Looking closer at our results, we speculate that perhaps the optimal phase difference for our task may be somewhere between 0° and 90°, hence why a tACS phase difference of 0° was able to pull low-performers closer to the ballpark and improve their VWM, but a tACS phase of 180° could impair high-performers already-accurate phase relationship more so than the 0° condition.…”