Dissociating the role of dACC and dlPFC for emotion appraisal and mood regulation using cathodal tDCS

Does a single session of transcranial direct current stimulation enhance both physical and psychological performance in national- or international-level athletes? A systematic review

Some studies showed that a single session of transcranial direct current stimulation (tDCS) has the potential of modulating motor performance in healthy and athletes. To our knowledge, previously published systematic reviews have neither comprehensively investigated the effects of tDCS on athletic performance in both physical and psychological parameters nor investigated the effects of tDCS on high-level athletes. We examined all available research testing a single session of tDCS on strength, endurance, sport-specific performance, emotional states and cognitive performance for better application in competition and pre-competition trainings of national- or international-level athletes. A systematic search was conducted in PubMed, Web of Science, EBSCO, Embase, and Scopus up until to June 2023. Studies were eligible when participants had sports experience at a minimum of state and national level competitions, underwent a single session of tDCS without additional interventions, and received either sham tDCS or no interventions in the control groups. A total of 20 experimental studies (224 participants) were included from 18 articles. The results showed that a single tDCS session improved both physical and psychological parameters in 12 out of the 18 studies. Of these, six refer to the application of tDCS on the motor system (motor cortex, premotor cortex, cerebellum), five on dorsolateral prefrontal cortex and two on temporal cortex. The most sensitive to tDCS are strength, endurance, and emotional states, improved in 67%, 75%, and 75% of studies, respectively. Less than half of the studies showed improvement in sport-specific tasks (40%) and cognitive performance (33%). We suggest that tDCS is an effective tool that can be applied to competition and pre-competition training to improve athletic performance in national- or international-level athletes. Further research would explore various parameters (type of sports, brain regions, stimulation protocol, athlete level, and test tasks) and neural mechanistic studies in improving efficacy of tDCS interventions. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022326989, identifier CRD42022326989.

Intracranial stimulation and EEG feature analysis reveal affective salience network specialization

Emotion is represented in limbic and prefrontal brain areas, herein termed the affective salience network (ASN). Within the ASN, there are substantial unknowns about how valence and emotional intensity are processed-specifically, which nodes are associated with affective bias (a phenomenon in which participants interpret emotions in a manner consistent with their own mood). A recently developed feature detection approach ('specparam') was used to select dominant spectral features from human intracranial electrophysiological data, revealing affective specialization within specific nodes of the ASN. Spectral analysis of dominant features at the channel level suggests that dorsal anterior cingulate (dACC), anterior insula and ventral-medial prefrontal cortex (vmPFC) are sensitive to valence and intensity, while the amygdala is primarily sensitive to intensity. Akaike information criterion model comparisons corroborated the spectral analysis findings, suggesting all four nodes are more sensitive to intensity compared to valence. The data also revealed that activity in dACC and vmPFC were predictive of the extent of affective bias in the ratings of facial expressions-a proxy measure of instantaneous mood. To examine causality of the dACC in affective experience, 130 Hz continuous stimulation was applied to dACC while patients viewed and rated emotional faces. Faces were rated significantly happier during stimulation, even after accounting for differences in baseline ratings. Together the data suggest a causal role for dACC during the processing of external affective stimuli.

Primary headache disorders: From pathophysiology to neurostimulation therapies

Primary headache disorders including migraine, cluster headache, and tension-type headache are among the most common disabling diseases worldwide. The unclear pathogenesis of primary headache disorders has led to high rates of misdiagnosis and limited available treatment options. In this review, we have summarized the pathophysiological factors for a better understanding of primary headache disorders. Advances in functional neuroimaging, genetics, neurophysiology have indicated that cortical hyperexcitability, regional brain dysfunction, central sensitization and neuroplasticity changes play vital roles in the development of primary headache disorders. Moreover, we have also discussed a series of neurostimulation approaches with their stimulation mechanism, safety and efficacy for prevention and treatment of primary headache disorders. Noninvasive or implantable neurostimulation techniques show great promise for treating refractory primary headache disorders.

Anxious Brains: A Combined Data Fusion Machine Learning Approach to Predict Trait Anxiety from Morphometric Features

Trait anxiety relates to the steady propensity to experience and report negative emotions and thoughts such as fear and worries across different situations, along with a stable perception of the environment as characterized by threatening stimuli. Previous studies have tried to investigate neuroanatomical features related to anxiety mostly using univariate analyses and thus giving rise to contrasting results. The aim of this study is to build a predictive model of individual differences in trait anxiety from brain morphometric features, by taking advantage of a combined data fusion machine learning approach to allow generalization to new cases. Additionally, we aimed to perform a network analysis to test the hypothesis that anxiety-related networks have a central role in modulating other networks not strictly associated with anxiety. Finally, we wanted to test the hypothesis that trait anxiety was associated with specific cognitive emotion regulation strategies, and whether anxiety may decrease with ageing. Structural brain images of 158 participants were first decomposed into independent covarying gray and white matter networks with a data fusion unsupervised machine learning approach (Parallel ICA). Then, supervised machine learning (decision tree) and backward regression were used to extract and test the generalizability of a predictive model of trait anxiety. Two covarying gray and white matter independent networks successfully predicted trait anxiety. The first network included mainly parietal and temporal regions such as the postcentral gyrus, the precuneus, and the middle and superior temporal gyrus, while the second network included frontal and parietal regions such as the superior and middle temporal gyrus, the anterior cingulate, and the precuneus. We also found that trait anxiety was positively associated with catastrophizing, rumination, other- and self-blame, and negatively associated with positive refocusing and reappraisal. Moreover, trait anxiety was negatively associated with age. This paper provides new insights regarding the prediction of individual differences in trait anxiety from brain and psychological features and can pave the way for future diagnostic predictive models of anxiety.