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Ketamine is a dissociative anesthetic that induces a shift in global consciousness states and related brain dynamics. Portable low-density EEG systems could be used to monitor these effects. However, previous evidence is almost null and lacks adequate methods to address global dynamics with a small number of electrodes. This study delves into brain high-order interactions (HOI) to explore the effects of ketamine using portable EEG. In a double-blinded cross-over design, 30 male adults (mean age = 25.57, SD = 3.74) were administered racemic ketamine and compared against saline infusion as a control. Both task-driven (auditory oddball paradigm) and resting-state EEG were recorded. HOI were computed using advanced multivariate information theory tools, allowing us to quantify nonlinear statistical dependencies between all possible electrode combinations. Ketamine induced an increase in redundancy in brain dynamics (copies of the same information that can be retrieved from 3 or more electrodes), most significantly in the alpha frequency band. Redundancy was more evident during resting state, associated with a shift in conscious states towards more dissociative tendencies. Furthermore, in the task-driven context (auditory oddball), the impact of ketamine on redundancy was more significant for predictable (standard stimuli) compared to deviant ones. Finally, associations were observed between ketamine's HOI and experiences of derealization. Ketamine appears to increase redundancy and HOI across psychometric measures, suggesting these effects are correlated with alterations in consciousness towards dissociation. In comparisons with event-related potential (ERP) or standard functional connectivity metrics, HOI represent an innovative method to combine all signal spatial interactions obtained from low-density dry EEG in drug interventions, as it is the only approach that exploits all possible combinations between electrodes. This research emphasizes the potential of complexity measures coupled with portable EEG devices in monitoring shifts in consciousness, especially when paired with low-density configurations, paving the way for better understanding and monitoring of pharmacological-induced changes., (© 2024. The Author(s).)

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. Studies investigating the use of music during ketamine anesthesia are also included in the review because anesthesia and sedation were the early drivers of ketamine use. Studies pertaining to recreational ketamine use were omitted. The review was limited to articles published in the English language but not restricted by publication year. To the best of our knowledge, this scoping review is the first comprehensive exploration of the interplay between music and ketamine/esketamine and offers valuable insights to researchers interested in designing future studies., Competing Interests: Declaration of Competing Interest Dr. Carlos A. Zarate Jr, is listed as a co-inventor on a patent for the use of ketamine in major depression and suicidal ideation; as a co-inventor on a patent for the use of (2 R,6 R)-hydroxynorketamine, (S)-dehydronorketamine, and other stereoisomeric dehydroxylated and hydroxylated metabolites of (R,S)-ketamine metabolites in the treatment of depression and neuropathic pain; and as a co-inventor on a patent application for the use of (2 R,6 R)-hydroxynorketamine and (2 S,6 S)-hydroxynorketamine in the treatment of depression, anxiety, anhedonia, suicidal ideation, and post-traumatic stress disorders. He has assigned his patent rights to the U.S. government but will share a percentage of any royalties that may be received by the government. All other authors have no conflict of interest to disclose, financial or otherwise., (Published by Elsevier Ltd.)

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., Methods: The aim of this study is therefore to investigate functional connectivity as well as functional network organization characteristics using graph-theory measures of the whole brain in male adults with AS compared to healthy controls (HC) (AS: n = 15, age range 21-55 (mean ± sd: 39.5 ± 11.6), HC: n = 15, age range 22-57 [mean ± sd: 33.5 ± 8.5])., Results: No significant differences were found when comparing the region-by-region connectivity at the whole-brain level between the AS group and HC. However, measures of "transitivity," which reflect local information processing and functional segregation, and "assortativity," indicating network resilience, were reduced in the AS group compared to HC. On the other hand, global efficiency, which represents the overall effectiveness and speed of information transfer across the entire brain network, was increased in the AS group., Discussion: Our findings suggest that individuals with AS may have alterations in the organization and functioning of brain networks, which could contribute to the distinctive cognitive and behavioural features associated with this condition. We suggest further research to explore the association between these altered functional patterns in brain networks and specific behavioral traits observed in individuals with AS, which could provide valuable insights into the underlying mechanisms of its symptomatology., Competing Interests: MW is a member of the following advisory boards and gave presentations to the following companies: Bayer AG, Germany; Boehringer Ingelheim, Germany; and Biologische HeilmittelHeel GmbH, Germany, conducted studies with institutional research support from HEEL and Janssen Pharmaceutical Research for a clinical trial (IIT) on ketamine in patients with MDD, unrelated to this investigation, and did not receive any financial compensation from the companies mentioned above. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Javaheripour, Wagner, de la Cruz, Walter, Szycik and Tietze.)

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. We hypothesized that ketamine alters the modulation of the startle reflex during processing of unpleasant and pleasant stimuli and weakens the resting-state functional connectivity (rsFC) within the modulatory pathway, namely between the centromedial nucleus of the amygdala and nucleus reticularis pontis caudalis. In a randomized, double-blind, placebo-controlled, cross-over study, thirty-two healthy male participants underwent ultra-high field resting-state functional magnetic resonance imaging at 7 T before and 24 h after placebo and S-ketamine infusions. Participants completed the AMSR task at baseline and one day after each infusion. In contrast to our hypothesis, ketamine infusion did not impact startle potentiation during processing of unpleasant stimuli but resulted in diminished startle attenuation during processing of pleasant stimuli. This diminishment significantly correlated with end-of-infusion plasma levels of ketamine and norketamine. Furthermore, ketamine induced a decrease in rsFC within the modulatory startle reflex pathway. The results of this first study on the effect of ketamine on the AMSR suggest that ketamine might attenuate the motivational significance of pleasant stimuli in healthy participants one day after infusion., (© 2023. Springer Nature Limited.)

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. We compared the patterns of brain functional dynamics in a large sample of 314 patients with MDD (65.9% female; age (mean ± standard deviation): 35.9 ± 13.4) and 498 healthy controls (59.4% female; age: 34.0 ± 12.8). The HMM model was used to explain variations in rs-fMRI functional connectivity and averaged functional activity across the whole-brain by using a set of six unique recurring states. This study compared the proportion of time spent in each state and the average duration of visits to each state to assess stability between different groups. Compared to healthy controls, patients with MDD showed significantly higher proportional time spent and temporal stability in a state characterized by weak functional connectivity within and between all brain networks and relatively strong averaged functional activity of regions located in the somatosensory motor (SMN), salience (SN), and dorsal attention (DAN) networks. Both proportional time spent and temporal stability of this brain state was significantly associated with depression severity. Healthy controls, in contrast to the MDD group, showed proportional time spent and temporal stability in a state with relatively strong functional connectivity within and between all brain networks but weak averaged functional activity across the whole brain. These findings suggest that disrupted brain functional synchrony across time is present in MDD and associated with current depression severity., (© 2023. The Author(s).)

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. We obtained resting-state functional magnetic resonance imaging data from the REST-meta-MDD (N = 2338) and PsyMRI (N = 1039) consortia. Classification of functional connectivity matrices was done using support vector machines (SVM) and graph convolutional neural networks (GCN), and performance was evaluated using 5-fold cross-validation. Features were visualized using GCN-Explainer, an ablation study and univariate t-testing. The results showed a mean classification accuracy of 61% for MDD versus controls. Mean accuracy for classifying (non-)medicated subgroups was 62%. Sex classification accuracy was substantially better across datasets (73-81%). Visualization of the results showed that classifications were driven by stronger thalamic connections in both datasets, while nearly all other connections were weaker with small univariate effect sizes. These results suggest that whole brain resting-state connectivity is a reliable though poor biomarker for MDD, presumably due to disease heterogeneity as further supported by the higher accuracy for sex classification using the same methods. Deep learning revealed thalamic hyperconnectivity as a prominent neurophysiological signature of depression in both multicenter studies, which may guide the development of biomarkers in future studies., (© 2023. The Author(s).)

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. Eighteen individuals with MA dependence and 21 healthy controls were investigated using Stroop task, fMRI, and an impulsivity questionnaire. Overall, patients were found to have significantly poorer accuracy on the Stroop task and higher self-rated impulsivity. Comparing brain activations during the task, decreased activation in the dorsolateral prefrontal cortex (DLPFC), anterior midcingulate cortex (aMCC), and dorsal striatum was observed in individuals with MA dependence, compared to healthy controls. Altered fMRI signal in DLPFC and aMCC significantly correlated with impaired behavioral task performance in individuals with MA dependence. Furthermore, significantly lower and pronounced brain activations in the MA group were additionally detected in several sensory cortical regions, i.e., in the visual, auditory, and somatosensory cortices. The results of the current study provide evidence for the negative impact of chronic crystal meth consumption on the proper functioning of the fronto-cingulate and striatal brain regions, presumably underlying the often-observed deficits in executive functions in individuals with MA use disorder. As a new finding, we also revealed abnormal activation in several sensory brain regions, suggesting the negative effect of MA use on the proper neural activity of these regions. This blunted activation could be the cause of the observed deficits in executive functions and the associated altered brain activation in higher-level brain networks.

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. A medium-size significant correlation was found [ES: 0.52 (CI: 0.47-0.57)] with moderating effects of the COVID-19 pandemic and military service as causes of trauma. The prevalence of insomnia in PTSD/PTSS was 63% [CI: 45%-78%] and was moderated by the cause of trauma as well as the PTSD/PTSS assessment scale. The findings from this meta-analysis highlight the importance of screening and managing insomnia in PTSD patients., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

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.psymri.com ). After applying strict exclusion criteria, the final sample consisted of 606 MDD patients (age: 35.8 ± 11.9 y.o.; females: 60.7%) and 476 healthy participants (age: 33.3 ± 11.0 y.o.; females: 56.7%). We found significant relative hypoconnectivity within somatosensory motor (SMN), salience (SN) networks and between SMN, SN, dorsal attention (DAN), and visual (VN) networks in MDD patients. No significant differences were detected within the default mode (DMN) and frontoparietal networks (FPN). In addition, alterations in network organization were observed in terms of significantly lower network segregation of SMN in MDD patients. Although medicated patients showed significantly lower FC within DMN, FPN, and SN than unmedicated patients, there were no differences between medicated and unmedicated groups in terms of network organization in SMN. We conclude that the network organization of cortical networks, involved in processing of sensory information, might be a more stable neuroimaging marker for MDD than previously assumed alterations in higher-order neural networks like DMN and FPN., (© 2021. The Author(s).)

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. However, it should be considered that the results of meta-analyses could also be divergent due to heterogeneity in search strategy, selection criteria, imaging modalities, behavioral tasks, number of experiments, data organization methods, and statistical analysis with different multiple comparison thresholds. Following an introduction to the problem and the concepts of quantitative summaries of neuroimaging findings, we propose practical recommendations for clinicians and researchers for conducting transparent and methodologically sound neuroimaging meta-analyses. This should help to consolidate the search for convergent regional brain abnormality in neuropsychiatric disorders., (© 2019 Wiley Periodicals, Inc.)

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. Based on the preferred reporting for systematic reviews and meta-analyses statement, we searched the PubMed database and performed reference tracking and finally retrieved 31 eligible functional neuroimaging studies. Then, we applied activation estimation likelihood meta-analysis and found reduced activity mainly in the right intraparietal sulcus and superior parietal lobule. The functional decoding analysis using the BrainMap database indicated that this region is mostly related to visuospatial perception, memory and reasoning. The significant co-activation of this region using the BrainMap database were found in the left superior parietal lobule, intraparietal sulcus, bilateral occipital cortex, left fusiform gyrus and thalamus. This region also connected with the superior parietal lobule, intraparietal sulcus, insula, inferior frontal gyrus, precentral, occipital and cerebellum through resting-state functional connectivity in healthy subjects. Taken together, our findings highlight the role of superior parietal cortex in SD., (Copyright © 2019 Elsevier Ltd. All rights reserved.)