A scoping review of electroencephalographic (EEG) markers for tracking neurophysiological changes and predicting outcomes in substance use disorder treatment.

Substance use disorders (SUDs) constitute a growing global health crisis, yet many limitations and challenges exist in SUD treatment research, including the lack of objective brain-based markers for tracking treatment outcomes. Electroencephalography (EEG) is a neurophysiological technique for measuring brain activity, and although much is known about EEG activity in acute and chronic substance use, knowledge regarding EEG in relation to abstinence and treatment outcomes is sparse. We performed a scoping review of longitudinal and pre-post treatment EEG studies that explored putative changes in brain function associated with abstinence and/or treatment in individuals with SUD. Following PRISMA guidelines, we identified studies published between January 2000 and March 2022 from online databases. Search keywords included EEG, addictive substances (e.g., alcohol, cocaine, methamphetamine), and treatment related terms (e.g., abstinence, relapse). Selected studies used EEG at least at one time point as a predictor of abstinence or other treatment-related outcomes; or examined pre- vs. post-SUD intervention (brain stimulation, pharmacological, behavioral) EEG effects. Studies were also rated on the risk of bias and quality using validated instruments. Forty-four studies met the inclusion criteria. More consistent findings included lower oddball P3 and higher resting beta at baseline predicting negative outcomes, and abstinence-mediated longitudinal decrease in cue-elicited P3 amplitude and resting beta power. Other findings included abstinence or treatment-related changes in late positive potential (LPP) and N2 amplitudes, as well as in delta and theta power. Existing studies were heterogeneous and limited in terms of specific substances of interest, brief times for follow-ups, and inconsistent or sparse results. Encouragingly, in this limited but maturing literature, many studies demonstrated partial associations of EEG markers with abstinence, treatment outcomes, or pre-post treatment-effects. Studies were generally of good quality in terms of risk of bias. More EEG studies are warranted to better understand abstinence- or treatment-mediated neural changes or to predict SUD treatment outcomes. Future research can benefit from prospective large-sample cohorts and the use of standardized methods such as task batteries. EEG markers elucidating the temporal dynamics of changes in brain function related to abstinence and/or treatment may enable evidence-based planning for more effective and targeted treatments, potentially pre-empting relapse or minimizing negative lifespan effects of SUD.

How stereotype threat affects the brain dynamics of creative thinking in female students.

When people are placed in a situation where they are at risk of substantiating a negative stereotype about their social group (a scenario termed stereotype threat), the extra pressure to avoid this outcome can undermine their performance. Substantial and consistent gender disparities in STEM fields leave women vulnerable to stereotype threat, including the stereotype that women are not as good at generating creative and innovative ideas as men. We tested whether female students' creative thinking is affected by a stereotype threat by measuring power in the alpha frequency band (8-12Hz oscillations) that has been associated with better creative thinking outcomes. Counter to expectations that a stereotype threat would reduce alpha power associated with creative thinking, analyses showed increased alpha power following the introduction of the stereotype threat. This outcome suggests that women may have attempted to increase their internal attention during the task in order to disprove the stereotype. Behaviorally, this effort did not lead to changes in creative performance, suggesting that the stereotype threat decoupled alpha power from creative thinking outcomes. These results support a growing school of thought in the neuroscience of creativity literature that the alpha power often seen in conjunction with creative behavior is not necessarily related to the creativity processes themselves, but rather might be part of a larger network modulating the distribution of attentional resources more broadly.

A systematic data-driven approach to analyze sensor-level EEG connectivity: Identifying robust phase-synchronized network components using surface Laplacian with spectral-spatial PCA.

Although conventional averaging across predefined frequency bands reduces the complexity of EEG functional connectivity (FC), it obscures the identification of resting-state brain networks (RSN) and impedes accurate estimation of FC reliability. Extending prior work, we combined scalp current source density (CSD; spherical spline surface Laplacian) and spectral-spatial PCA to identify FC components. Phase-based FC was estimated via debiased-weighted phase-locking index from CSD-transformed resting EEGs (71 sensors, 8 min, eyes open/closed, 35 healthy adults, 1-week retest). Spectral PCA extracted six robust alpha and theta components (86.6% variance). Subsequent spatial PCA for each spectral component revealed seven robust regionally focused (posterior, central, and frontal) and long-range (posterior-anterior) alpha components (peaks at 8, 10, and 13 Hz) and a midfrontal theta (6 Hz) component, accounting for 37.0% of FC variance. These spatial FC components were consistent with well-known networks (e.g., default mode, visual, and sensorimotor), and four were sensitive to eyes open/closed conditions. Most FC components had good-to-excellent internal consistency (odd/even epochs, eyes open/closed) and test-retest reliability (ICCs ≥ .8). Moreover, the FC component structure was generally present in subsamples (session × odd/even epoch, or smaller subgroups [n = 7-10]), as indicated by high similarity of component loadings across PCA solutions. Apart from systematically reducing FC dimensionality, our approach avoids arbitrary thresholds and allows quantification of meaningful and reliable network components that may prove to be of high relevance for basic and clinical research applications.

Human attribute concepts: relative ubiquity across twelve mutually isolated languages.

It has been unclear which human-attribute concepts are most universal across languages. To identify common-denominator concepts, we used dictionaries for 12 mutually isolated languages-Maasai, Supyire Senoufo, Khoekhoe, Afar, Mara Chin, Hmong, Wik-Mungkan, Enga, Fijian, Inuktitut, Hopi, and Kuna-representing diverse cultural characteristics and language families, from multiple continents. A composite list of every person-descriptive term in each lexicon was closely examined to determine the content (in terms of English translation) most ubiquitous across languages. Study 1 identified 28 single-word concepts used to describe persons in all 12 languages, as well as 41 additional terms found in 11 of 12. Results indicated that attribute concepts related to morality and competence appear to be as cross-culturally ubiquitous as basic-emotion concepts. Formulations of universal-attribute concepts from Osgood and Wierzbicka were well-supported. Study 2 compared lexically based personality models on the relative ubiquity of key associated terms, finding that 1- and 2-dimensional models draw on markedly more ubiquitous terms than do 5- or 6-factor models. We suggest that ubiquitous attributes reflect common cultural as well as common biological processes.