Temporal dynamics of affective scene processing in the healthy adult human brain.

Understanding how the brain distinguishes emotional from neutral scenes is crucial for advancing brain-computer interfaces, enabling real-time emotion detection for faster, more effective responses, and improving treatments for emotional disorders like depression and anxiety. However, inconsistent research findings have arisen from differences in study settings, such as variations in the time windows, brain regions, and emotion categories examined across studies. This review sought to compile the existing literature on the timing at which the adult brain differentiates basic affective from neutral scenes in less than one second, as previous studies have consistently shown that the brain can begin recognizing emotions within just a few milliseconds.

Exploring the impact of music on response to ketamine/esketamine: A scoping review.

Music and ketamine are both known to affect therapeutic outcomes, but few studies have investigated their co-administration. This scoping review describes the existing literature on the joint use of music and ketamine-or esketamine (the S(+) enantiomer of ketamine)-in humans. The review considers that extant studies have explored the intersection of ketamine/esketamine and music in healthy volunteers and in patients of various age groups, at different dosages, through different treatment processes, and have varied the sequence of playing music relative to ketamine/esketamine administration.

Altered brain network organization in adults with Asperger's syndrome: decreased connectome transitivity and assortativity with increased global efficiency.

Introduction: Autism spectrum disorder (ASD) is a neurodevelopmental disorder that persists into adulthood with both social and cognitive disturbances. Asperger's syndrome (AS) was a distinguished subcategory of autism in the DSM-IV-TR defined by specific symptoms including difficulties in social interactions, inflexible thinking patterns, and repetitive behaviour without any delay in language or cognitive development. Studying the functional brain organization of individuals with these specific symptoms may help to better understand Autism spectrum symptoms.

The effect of ketamine on affective modulation of the startle reflex and its resting-state brain correlates.

Ketamine is a rapid-acting antidepressant that also influences neural reactivity to affective stimuli. However, the effect of ketamine on behavioral affective reactivity is yet to be elucidated. The affect-modulated startle reflex paradigm (AMSR) allows examining the valence-specific aspects of behavioral affective reactivity.

Altered brain dynamic in major depressive disorder: state and trait features.

Temporal neural synchrony disruption can be linked to a variety of symptoms of major depressive disorder (MDD), including mood rigidity and the inability to break the cycle of negative emotion or attention biases. This might imply that altered dynamic neural synchrony may play a role in the persistence and exacerbation of MDD symptoms. Our study aimed to investigate the changes in whole-brain dynamic patterns of the brain functional connectivity and activity related to depression using the hidden Markov model (HMM) on resting-state functional magnetic resonance imaging (rs-fMRI) data.

Neural Correlates of Impaired Cognitive Control in Individuals with Methamphetamine Dependence: An fMRI Study.

Impaired cognitive and behavioral control has often been observed in people who use methamphetamine (MA). However, a comprehensive understanding of the neural substrates underlying these impairments is still lacking. The goal of the present study was to study the neural correlates of impaired cognitive control in individuals with MA dependence according to DSM-IV criteria.

Functional connectivity signatures of major depressive disorder: machine learning analysis of two multicenter neuroimaging studies.

The promise of machine learning has fueled the hope for developing diagnostic tools for psychiatry. Initial studies showed high accuracy for the identification of major depressive disorder (MDD) with resting-state connectivity, but progress has been hampered by the absence of large datasets. Here we used regular machine learning and advanced deep learning algorithms to differentiate patients with MDD from healthy controls and identify neurophysiological signatures of depression in two of the largest resting-state datasets for MDD.

Insomnia and post-traumatic stress disorder: A meta-analysis on interrelated association (n = 57,618) and prevalence (n = 573,665).

Posttraumatic stress disorder (PTSD) is a common mental disorder, which is strongly associated with insomnia, yet their epidemiological overlap is poorly understood. To determine the convergent quantitative magnitude of their relationship, PubMed, EMBASE, Scopus, Web of Science, PubPsych, and PsycINFO were searched to identify studies that either reported the correlation or frequency of insomnia symptoms in PTSD and posttraumatic stress symptoms (PTSS), or both. Out of 3714 records, 75 studies met selection criteria and aggregate effect size (ES) estimates were generated for the correlations (K=44, comprising 57,618 subjects) and frequencies (K=33, comprising 573,665 subjects with PTSD/PTSS) of insomnia symptoms in PTSD/PTSS.

Altered resting-state functional connectome in major depressive disorder: a mega-analysis from the PsyMRI consortium.

Major depressive disorder (MDD) is associated with abnormal neural circuitry. It can be measured by assessing functional connectivity (FC) at resting-state functional MRI, that may help identifying neural markers of MDD and provide further efficient diagnosis and monitor treatment outcomes. The main aim of the present study is to investigate, in an unbiased way, functional alterations in patients with MDD using a large multi-center dataset from the PsyMRI consortium including 1546 participants from 19 centers ( www.

Practical recommendations to conduct a neuroimaging meta-analysis for neuropsychiatric disorders.

Over the past decades, neuroimaging has become widely used to investigate structural and functional brain abnormality in neuropsychiatric disorders. The results of individual neuroimaging studies, however, are frequently inconsistent due to small and heterogeneous samples, analytical flexibility, and publication bias toward positive findings. To consolidate the emergent findings toward clinically useful insight, meta-analyses have been developed to integrate the results of studies and identify areas that are consistently involved in pathophysiology of particular neuropsychiatric disorders.

Functional brain alterations in acute sleep deprivation: An activation likelihood estimation meta-analysis.

Sleep deprivation (SD) is a common problem in modern societies, which leads to cognitive dysfunctions including attention lapses, impaired working memory, hindering decision making, impaired emotional processing, and motor vehicle accidents. Numerous neuroimaging studies have investigated the neural correlates of SD, but these studies have reported inconsistent results. Thus, we aimed to identify convergent patterns of abnormal brain functions due to acute SD.