Stress engendered by stereotype threatening situations may facilitate encoding of negative, stereotype confirming feedback received during a performance among women in science, technology, engineering and mathematics (STEM). It is unclear, however, whether this process is comprised of the same neurophysiological mechanisms evident in any emotional memory encoding context, or if this encoding bias directly undermines positive self-perceptions in the stigmatized domain. A total of 160 men and women completed a math test that provided veridical positive and negative feedback, a memory test for feedback, and math self-enhancing and valuing measures in a stereotype threatening or neutral context while continuous electroencephalography activity and startle probe responses to positive and negative feedback was recorded. Indexing amygdala activity to feedback via startle responses and emotional memory network connectivity elicited during accurate recognition of positive and negative feedback via graph analyses, only stereotype threatened women encoded negative feedback better when they exhibited increased amygdala activity and emotional memory network connectivity in response to said feedback. Emotional memory biases, in turn, predicted decreases in women’s self-enhancing, math valuing and performance. Findings provide an emotional memory encoding-based mechanism for well-established findings indicating that women have more negative math self-perceptions compared with men regardless of actual performance.

Previous research suggests that people show increased self-referential processing when they provide criticism to others, and that this self-referential processing can have negative effects on interpersonal perceptions and behavior. The current research hypothesized that adopting a self-distanced perspective (i.e. thinking about a situation from a non-first person point of view), as compared with a typical self-immersed perspective (i.e. thinking about a situation from a first-person point of view), would reduce self-referential processing during the provision of criticism, and in turn improve interpersonal perceptions and behavior. We tested this hypothesis in an interracial context since research suggests that self-referential processing plays a role in damaging interracial relations. White participants prepared for mentorship from a self-immersed or self-distanced perspective. They then conveyed negative and positive evaluations to a Black mentee while electroencephalogram (EEG) was recorded. Source analysis revealed that priming a self-distanced (vs self-immersed) perspective predicted decreased activity in regions linked to self-referential processing (medial prefrontal cortex; MPFC) when providing negative evaluations. This decreased MPFC activity during negative evaluations, in turn, predicted verbal feedback that was perceived to be more positive, warm and helpful. Results suggest that self-distancing can improve interpersonal perceptions and behavior by decreasing self-referential processing during the provision of criticism.

Neuromarkers from whole-brain functional connectivity reveal the cognitive recovery scheme for overt hepatic encephalopathy after liver transplantation

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.

Mapping human behaviors to brain activity has become a key focus in modern cognitive neuroscience. As methods such as functional MRI (fMRI) advance cognitive scientists show an increasing interest in investigating neural activity in terms of functional connectivity and brain networks, rather than activation in a single brain region. Due to the noisy nature of neural activity, determining how behaviors are associated with specific neural signals is not well-established. Previous research has suggested graph theory techniques as a solution. Graph theory provides an opportunity to interpret human behaviors in terms of the topological organization of brain network architecture. Graph theory-based approaches, however, only scratch the surface of what neural connections relate to human behavior. Recently, the development of data-driven methods, e.g., machine learning and deep learning approaches, provide a new perspective to study the relationship between brain networks and human behaviors across the whole brain, expanding upon past literatures. In this review, we sought to revisit these data-driven approaches to facilitate our understanding of neural mechanisms and build models of human behaviors. We start with the popular graph theory approach and then discuss other data-driven approaches such as connectome-based predictive modeling, multivariate pattern analysis, network dynamic modeling, and deep learning techniques that quantify meaningful networks and connectivity related to cognition and behaviors. Importantly, for each topic, we discuss the pros and cons of the methods in addition to providing examples using our own data for each technique to describe how these methods can be applied to real-world neuroimaging data.

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Extensive research has established a relationship between individual differences in brain activity in a resting state and individual differences in behavior. Conversely, when individuals are engaged in various tasks, certain task-evoked reorganization occurs in brain functional connectivity, which can consequently influence individuals’ performance as well. Here, we show that resting state and task-dependent state brain patterns interact as a function of contexts engendering stress. Findings revealed that when the resting state connectome was examined during performance, the relationship between connectome strength and performance only remained for participants under stress (who also performed worse than all other groups on the math task), suggesting that stress preserved brain patterns indicative of underperformance whereas non-stressed individuals spontaneously transitioned out of these patterns. Results imply that stress may impede the reorganization of a functional network in task-evoked brain states. This hypothesis was subsequently verified using graph theory measurements on a functional network, independent of behavior. For participants under stress, the functional network showed less topological alterations compared to non-stressed individuals during the transition from resting state to task-evoked state. Implications are discussed for network dynamics as a function of context.

Summary Individuals constantly encounter feedback from others and process this feedback in various ways to maintain positive situational state self-esteem in relation to semantic-based or trait self-esteem. Individuals may utilize episodic or semantic-driven processes that modulate feedback in two different ways to maintain general self-esteem levels. To date, it is unclear how these processes work while individuals receive social feedback to modulate state self-esteem. Utilizing neural regions associated with semantic self-oriented and basic encoding processes (medial prefrontal cortex (mPFC) and posterior cingulate cortex (PCC), respectively), in addition to time-frequency and Granger causality analyses to assess mPFC and PCC interactions, this study examined how the encoding of social feedback modulated individuals' (N = 45) post-task state self-esteem in relation to their trait self-esteem. Findings highlight the dynamic interplay between mPFC and PCC that modulate state self-esteem in relation to trait self-esteem, to maintain high self-esteem in general in the moment and over time.