On the Role of Situational Stressors in the Disruption of Global Neural Network Stability during Problem Solving

Abstract

When individuals are placed in stressful situations, they are likely to exhibit deficits in cognitive capacity over and above situational demands. Despite this, individuals may still persevere and ultimately succeed in these situations. Little is known, however, about neural network properties that instantiate success or failure in both neutral and stressful situations, particularly with respect to regions integral for problem-solving processes that are necessary for optimal performance on more complex tasks. In this study, we outline how hidden Markov modeling based on multivoxel pattern analysis can be used to quantify unique brain states underlying complex network interactions that yield either successful or unsuccessful problem solving in more neutral or stressful situations. We provide evidence that brain network stability and states underlying synchronous interactions in regions integral for problem-solving processes are key predictors of whether individuals succeed or fail in stressful situations. Findings also suggested that individuals utilize discriminate neural patterns in successfully solving problems in stressful or neutral situations. Findings overall highlight how hidden Markov modeling can provide myriad possibilities for quantifying and better understanding the role of global network interactions in the problem-solving process and how the said interactions predict success or failure in different contexts.

Stereotype-based stressors facilitate emotional memory neural network connectivity and encoding of negative information to degrade math self-perceptions among women

“…Thus, when accounting for non-linear effects, these findings reveal a more complex, nuanced stress response to negative feedback among women in SBS contexts where the stress response is more intense at the beginning of the threatening task, plateaus more quickly, but then rebounds toward the end of the task. These findings are consistent with recent examinations of performance differences among DMT women that indicate performance decrements are more pronounced at the beginning as opposed to the middle and end of a math task, especially when using a math task like the one used in this study, which is much longer than math tasks traditionally used in the ST literature (Liu et al , 2017). …”

Fight or flight: The role of context on biased intergroup shooting behaviors.

“…Epochs were baseline corrected by subtracting the average value of EEG 100 msec prestimuli from the entire epoch. EEG artifacts were removed via FASTER protocol (Fully Automated Statistical Thresholding for EEG artifact Rejection; Liu et al, 2017; Nolan et al, 2010).…”

Behavioral Studies Using Large-Scale Brain Networks – Methods and Validations

“…On the other hand, methods such as EEG and MEG, which have a much higher temporal resolution in comparison to fMRI, are able to be used to estimate brain dynamics by incorporating not only cross-region temporal synchronization but also cross-region phase synchronization ( Chang and Glover, 2010 ; Yaesoubi et al, 2015 ; Demirtaş et al, 2016 ). Cross-region and phase temporal synchronization are achieved by time-frequency analysis using short-time Fourier transformation coherence (STFT; Liu et al, 2017 ) or wavelet transformation coherence ( Chiu et al, 2011 ).…”

“…In brain region-based analyses, a brain state is defined using the most significantly powered region or component, i.e., a single brain region or co-activated brain regions, that explains the largest variance across all the regions ( Anderson et al, 2014 ). For example, in an executive function study ( Liu et al, 2017 ), one of the brain states characterized by dominant power in fronto-polar cortex was defined as the state responsible for solving problems. This was because the fronto-polar region is thought to highly correlate with cognitive processes such as reasoning and working memory ( Klingberg et al, 1997 ; Salazar et al, 2012 ; Darki and Klingberg, 2015 ).…”

“…The following five metrics are commonly assessed in brain states analyses: (1) frequency or proportional occupancy, measured as the proportion of all windows classified as instances of particular states, and computed separately for each state, (2) mean dwell time or mean lifetime, measured as the average number of consecutive windows classified as instances of the same state, (3) inter-transition interval, measured as the number of consecutive instances before a transition to the same state, (4) the number of transitions or the number of states, measured as the number of state transitions across certain conditions/individuals, which may represent the stability of whole-brain dynamics (more states means less stable and fewer states means more stable) ( Liu et al, 2017 ), and (5) state transition probability, measured as the likelihood that a brain state at time instance t remained within the same brain state in the previous time t-1; this can be used to determine the state transition paths. For example, Taghia et al (2018) indicated that the brain state that dominates the high-load working-memory condition does not suddenly shift to the state that dominates the fixation condition without first accessing a state associated with an intermediate cognitive demand.…”