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The brain response to drug-related cues is an important marker in addiction-medicine. However, the temporal dynamics of this response in repeated exposure to cues are not well known. In an fMRI drug cue-reactivity task, the presence of rapid habituation or sensitization was investigated by modeling time and its interaction with condition (drug>neutral) using an initial discovery-sample. Replication of this temporal response was tested in two other clinical populations all abstinent during their early recovery (treatment). Sixty-five male participants (35.8 ± 8.4 years-old) with methamphetamine use disorder (MUD) were recruited as the discovery-sample from an abstinence-based residential treatment program. A linear mixed effects model was used to identify areas with a time-by-condition interaction in the discovery-sample. Replication of these effects was tested in two other samples (29 female with MUD from a different residential program and 22 male with opioid use disorder from the same residential program as the discovery sample). The second replication sample was re-tested within two weeks. In the discovery-sample, clusters within the VMPFC, amygdala and ventral striatum showed both a main effect of condition and a condition-by-time interaction, indicating a habituating response to drug-related but not neutral cues. The estimates for the main effects and interactions were generally consistent between the discovery and replication-samples across all clusters. The re-test data showed a consistent lack of drug > neutral and habituation response within all selected clusters in the second cue-exposure session. The VMPFC, amygdala and ventral striatum show habituation in response to drug-related cues which is consistent among different clinical populations. This habituated response in the first session of cue-exposure and lack of reactivity in the second session of exposure may be important for informing the development of cue-desensitization interventions. [Display omitted]

Several imaging modalities, including T1-weighted structural imaging, diffusion tensor imaging, and functional MRI can show chronological age related changes. Employing machine learning algorithms, an individual's imaging data can predict their age with reasonable accuracy. While details vary according to modality, the general strategy is to: (1) extract image-related features, (2) build a model on a training set that uses those features to predict an individual's age, (3) validate the model on a test dataset, producing a predicted age for each individual, (4) define the \"Brain Age Gap Estimate\" (BrainAGE) as the difference between an individual's predicted age and his/her chronological age, (5) estimate the relationship between BrainAGE and other variables of interest, and (6) make inferences about those variables and accelerated or delayed brain aging. For example, a group of individuals with overall positive BrainAGE may show signs of accelerated aging in other variables as well. There is inevitably an overestimation of the age of younger individuals and an underestimation of the age of older individuals due to \"regression to the mean.\" The correlation between chronological age and BrainAGE may significantly impact the relationship between BrainAGE and other variables of interest when they are also related to age. In this study, we examine the detectability of variable effects under different assumptions. We use empirical results from two separate datasets [training = 475 healthy volunteers, aged 18-60 years (259 female); testing = 489 participants including people with mood/anxiety, substance use, eating disorders and healthy controls, aged 18-56 years (312 female)] to inform simulation parameter selection. Outcomes in simulated and empirical data strongly support the proposal that models incorporating BrainAGE should include chronological age as a covariate. We propose either including age as a covariate in step 5 of the above framework, or employing a multistep procedure where age is regressed on BrainAGE prior to step 5, producing BrainAGE Residualized (BrainAGER) scores.

Imbalances in approach-avoidance conflict (AAC) decision-making (e.g., sacrificing rewards to avoid negative outcomes) are considered central to multiple psychiatric disorders. We used computational modelling to examine 2 factors that are often not distinguished in descriptive analyses of AAC: decision uncertainty and sensitivity to negative outcomes versus rewards (emotional conflict). A previously validated AAC task was completed by 478 participants, including healthy controls (n = 59), people with substance use disorders (n = 159) and people with depression and/or anxiety disorders who did not have substance use disorders (n = 260). Using an active inference model, we estimated individual-level values for a model parameter that reflected decision uncertainty and another that reflected emotional conflict. We also repeated analyses in a subsample (59 healthy controls, 161 people with depression and/or anxiety disorders, 56 people with substance use disorders) that was propensity-matched for age and general intelligence. The model showed high accuracy (72%). As further validation, parameters correlated with reaction times and self-reported task motivations in expected directions. The emotional conflict parameter further correlated with self-reported anxiety during the task (r = 0.32, p < 0.001), and the decision uncertainty parameter correlated with self-reported difficulty making decisions (r = 0.45, p < 0.001). Compared to healthy controls, people with depression and/or anxiety disorders and people with substance use disorders showed higher decision uncertainty in the propensity-matched sample (t = 2.16, p = 0.03, and t = 2.88, p = 0.005, respectively), with analogous results in the full sample; people with substance use disorders also showed lower emotional conflict in the full sample (t = 3.17, p = 0.002). This study was limited by heterogeneity of the clinical sample and an inability to examine learning. These results suggest that reduced confidence in how to act, rather than increased emotional conflict, may explain maladaptive approach-avoidance behaviours in people with psychiatric disorders.

Maladaptive behavior during approach-avoidance conflict (AAC) is common to multiple psychiatric disorders. Using computational modeling, we previously reported that individuals with depression, anxiety, and substance use disorders (DEP/ANX; SUDs) exhibited differences in decision uncertainty and sensitivity to negative outcomes versus reward (emotional conflict) relative to healthy controls (HCs). However, it remains unknown whether these computational parameters and group differences are stable over time. We analyzed 1-year follow-up data from a subset of the same participants ( N  = 325) to assess parameter stability and relationships to other clinical and task measures. We assessed group differences in the entire sample as well as a subset matched for age and IQ across HCs ( N  = 48), SUDs ( N  = 29), and DEP/ANX ( N  = 121). We also assessed 2–3 week reliability in a separate sample of 30 HCs. Emotional conflict and decision uncertainty parameters showed moderate 1-year intra-class correlations (.52 and .46, respectively) and moderate to excellent correlations over the shorter period (.84 and .54, respectively). Similar to previous baseline findings, parameters correlated with multiple response time measures ( p s < .001) and self-reported anxiety ( r  = .30, p  < .001) and decision difficulty ( r  = .44, p  < .001). Linear mixed effects analyses revealed that patients remained higher in decision uncertainty (SUDs, p  = .009) and lower in emotional conflict (SUDs, p  = .004, DEP/ANX, p  = .02) relative to HCs. This computational modelling approach may therefore offer relatively stable markers of transdiagnostic psychopathology.

Background Posttraumatic stress disorder (PTSD) is associated with reduced executive functioning and verbal memory performance, as well as abnormal task‐specific activity in prefrontal cortex (PFC) and anterior cingulate cortices (ACC). The current study examined how PTSD symptoms and neuropsychological performance in combat veterans relates to (1) medial PFC and ACC activity during cognitive inhibition, and (2) task‐independent PFC functional connectivity. Methods Thirty‐nine male combat veterans with varying levels of PTSD symptoms completed the multisource interference task during functional magnetic resonance imaging. Robust regression analyses were used to assess relationships between percent signal change (PSC: incongruent–congruent) and both PTSD severity and neuropsychological performance. Analyses were conducted voxel‐wise and for PSC extracted from medial PFC and ACC regions of interest. Resting‐state scans were available for veterans with PTSD. Regions identified via task‐based analyses were used as seeds for resting‐state connectivity analyses. Results Worse PTSD severity and neuropsychological performance related to less medial PFC and rostral ACC activity during interference processing, driven partly by increased activation to congruent trials. Worse PTSD severity related to reduced functional connectivity between these regions and bilateral, lateral PFC (Brodmann area 10). Worse neuropsychological performance related to reduced functional connectivity between these regions and the inferior frontal gyrus. Conclusions PTSD and associated neuropsychological deficits may result from difficulties regulating medial PFC regions associated with “default mode,” or self‐referential processing. Further clarification of functional coupling deficits between default mode and executive control networks in PTSD may enhance understanding of neuropsychological and emotional symptoms and provide novel treatment targets.

IntroductionAlthough neuroscience has made tremendous progress towards understanding the basic neural circuitry underlying important processes such as attention, memory and emotion, little progress has been made in applying these insights to psychiatric populations to make clinically meaningful treatment predictions. The overall aim of the Tulsa 1000 (T-1000) study is to use the NIMH Research Domain Criteria framework in order to establish a robust and reliable dimensional set of variables that quantifies the positive and negative valence, cognition and arousal domains, including interoception, to generate clinically useful treatment predictions.Methods and analysisThe T-1000 is a naturalistic study that will recruit, assess and longitudinally follow 1000 participants, including healthy controls and treatment-seeking individuals with mood, anxiety, substance use and eating disorders. Each participant will undergo interview, behavioural, biomarker and neuroimaging assessments over the course of 1 year. The study goal is to determine how disorders of affect, substance use and eating behaviour organise across different levels of analysis (molecules, genes, cells, neural circuits, physiology, behaviour and self-report) to predict symptom severity, treatment outcome and long-term prognosis. The data will be used to generate computational models based on Bayesian statistics. The final end point of this multilevel latent variable analysis will be standardised assessments that can be developed into clinical tools to help clinicians predict outcomes and select the best intervention for each individual, thereby reducing the burden of mental disorders, and taking psychiatry a step closer towards personalised medicine.Ethics and disseminationEthical approval was obtained from Western Institutional Review Board screening protocol #20101611. The dissemination plan includes informing health professionals of results for clinical practice, submitting results to journals for peer-reviewed publication, presenting results at national and international conferences and making the dataset available to researchers and mental health professionals.Trial registration number NCT02450240; Pre-results.

Electroencephalography (EEG) measures the brain's electrophysiological spatio-temporal activities with high temporal resolution. Multichannel and broadband analysis of EEG signals is referred to as EEG microstates (EEG-ms) and can characterize such dynamic neuronal activity. EEG-ms have gained much attention due to the increasing evidence of their association with mental activities and large-scale brain networks identified by functional magnetic resonance imaging (fMRI). Spatially independent EEG-ms are quasi-stationary topographies (e.g., stable, lasting a few dozen milliseconds) typically classified into four canonical classes (microstates A through D). They can be identified by clustering EEG signals around EEG global field power (GFP) maxima points. We examined the EEG-ms properties and the dynamics of cohorts of mood and anxiety (MA) disorders subjects ( = 61) and healthy controls (HCs; = 52). In both groups, we found four distinct classes of EEG-ms (A through D), which did not differ among cohorts. This suggests a lack of significant structural cortical abnormalities among cohorts, which would otherwise affect the EEG-ms topographies. However, both cohorts' brain network dynamics significantly varied, as reflected in EEG-ms properties. Compared to HC, the MA cohort features a lower transition probability between EEG-ms B and D and higher transition probability from A to D and from B to C, with a trend towards significance in the average duration of microstate C. Furthermore, we harnessed a recently introduced theoretical approach to analyze the temporal dependencies in EEG-ms. The results revealed that the transition matrices of MA group exhibit higher symmetrical and stationarity properties as compared to HC ones. In addition, we found an elevation in the temporal dependencies among microstates, especially in microstate B for the MA group. The determined alteration in EEG-ms temporal dependencies among the cohorts suggests that brain abnormalities in mood and anxiety disorders reflect aberrant neural dynamics and a temporal dwelling among ceratin brain states (i.e., mood and anxiety disorders subjects have a less dynamicity in switching between different brain states).

We have previously reported activation in reward, salience and executive control regions during functional MRI (fMRI) using an approach–avoidance conflict (AAC) decision-making task with healthy adults. Further investigations into how anxiety and depressive disorders relate to differences in neural responses during AAC can inform their understanding and treatment. We tested the hypothesis that people with anxiety or depression have altered neural activation during AAC. We compared 118 treatment-seeking adults with anxiety or depression and 58 healthy adults using linear mixed-effects models to examine group-level differences in neural activation (fMRI) during AAC decision-making. Correlational analyses examined relationships between behavioural and neural measures. Adults with anxiety or depression had greater striatal engagement when reacting to affective stimuli (p = 0.008, d = 0.31) regardless of valence, and weaker striatal engagement during reward feedback (p = 0.046, d = −0.27) regardless of the presence of monetary reward. They also had blunted amygdala activity during decision-making (p = 0.023, d = −0.32) regardless of the presence of conflict. Across groups, approach behaviour during conflict decision-making was inversely correlated with striatal activation during affective stimuli (p < 0.001, r = −0.28) and positively related to striatal activation during reward feedback (p < 0.001, r = 0.27). Our transdiagnostic approach did not allow for comparisons between specific anxiety disorders, and our cross-sectional approach did not allow for causal inference. Anxiety and depression were associated with altered neural responses to AAC. Findings were consistent with the role of the striatum in action selection and reward responsivity, and they point toward striatal reactivity as a future treatment target. Blunting of amygdala activity in anxiety or depression may indicate a compensatory response to inhibit affective salience and maintain approach.

Functional magnetic resonance imaging studies frequently use emotional face processing tasks to probe neural circuitry related to psychiatric disorders and treatments with an emphasis on regions within the salience network (e.g., amygdala). Findings across previous test-retest reliability studies of emotional face processing have shown high variability, potentially due to differences in data analytic approaches. The present study comprehensively examined the test-retest reliability of an emotional faces task utilizing multiple approaches to region of interest (ROI) analysis and by examining voxel-wise reliability across the entire brain for both neural activation and functional connectivity. Analyses included 42 healthy adult participants who completed an fMRI scan concurrent with an emotional faces task on two separate days with an average of 25.52 days between scans. Intraclass correlation coefficients (ICCs) were calculated for the ‘FACES-SHAPES’ and ‘FACES’ (compared to implicit baseline) contrasts across the following: anatomical ROIs identified from a publicly available brain atlas (i.e., Brainnetome), functional ROIs consisting of 5-mm spheres centered on peak voxels from a publicly available meta-analytic database (i.e., Neurosynth), and whole-brain, voxel-wise analysis. Whole-brain, voxel-wise analyses of functional connectivity were also conducted using both anatomical and functional seed ROIs. While group-averaged neural activation maps were consistent across time, only one anatomical ROI and two functional ROIs showed good or excellent individual-level reliability for neural activation. The anatomical ROI was the right medioventral fusiform gyrus for the FACES contrast (ICC ​= ​0.60). The functional ROIs were the left and the right fusiform face area (FFA) for both FACES-SHAPES and FACES (Left FFA ICCs ​= ​0.69 & 0.79; Right FFA ICCs ​= ​0.68 & 0.66). Poor reliability (ICCs ​< ​0.4) was identified for almost all other anatomical and functional ROIs, with some exceptions showing fair reliability (ICCs ​= ​0.4–0.59). Whole-brain voxel-wise analysis of neural activation identified voxels with good (ICCs ​= ​0.6–0.74) to excellent reliability (ICCs ​> ​0.75) that were primarily located in visual cortex, with several clusters in bilateral dorsal lateral prefrontal cortex (DLPFC). Whole-brain voxel-wise analyses of functional connectivity for amygdala and fusiform gyrus identified very few voxels with good to excellent reliability using both anatomical and functional seed ROIs. Exceptions included clusters in right cerebellum and right DLPFC that showed reliable connectivity with left amygdala (ICCs ​> ​0.6). In conclusion, results indicate that visual cortical regions demonstrate good reliability at the individual level for neural activation, but reliability is generally poor for salience regions often focused on within psychiatric research (e.g., amygdala). Given these findings, future clinical neuroimaging studies using emotional faces tasks to examine individual differences might instead focus on visual regions and their role in psychiatric disorders. •Group-averaged neural activation to emotional face matching is consistent over time.•Test-retest reliability of individual amygdala activation is often poor.•Test-retest reliability of individual visual cortical activation is often good.•Emotional faces contrasted to baseline was more reliable than contrasted to shapes.•Amygdala-DLPFC functional connectivity values had good reliability for some ROIs.

Early life adversity (ELA) disrupts default mode network (DMN) integrity subserving self-referential processes involved in emotional awareness and regulation. Mindfulness training (MT) reduces self-referential processing and down-regulates the DMN. We employed neurofeedback-augmented mindfulness training (NAMT), combining a core mindfulness strategy (focusing on breath) with real-time fMRI neurofeedback (rtfMRI-nf) to modulate DMN by targeting the posterior cingulate cortex (PCC). ELA-exposed (ELA; n  = 43) and healthy control (HC; n  = 40) adolescents completed a scan with three conditions: (a) Focus-on-breath (MT): rtfMRI-nf was presented as a variable-height bar, and adolescents attempted to lower the bar; (b) Describe: engaging self-referential processing; and (c) Rest. ELA were single-blind randomized to active PCC rtfMR-nf (NF; n  = 22) or artificial feedback (SHAM; n  = 21). Adolescents reported perceived stress, state mindfulness, and affect at baseline, post-training, and one-week follow-up. General linear models (GLMs) examined group differences (ELA vs. HC; NF vs. SHAM) on neural (MT vs. Describe) and self-report measures. ELA showed greater difficulty in PCC down-regulation relative to HC. For ELA, SHAM evidenced similar PCC down-regulation as active NF. All adolescents reported increased state mindfulness post-training. Relative to HC, ELA reported greater improvements in positive affect, negative affect and stress at follow-up. There was no difference in self-reported measures between active and SHAM. PCC responses in ELA confirm the region’s utility as a potential treatment target. NAMT was feasible and acceptable for ELA-exposed adolescents, but may not enhance mindfulness training more than SHAM. Optimal strategies for enhancing PCC regulation in ELA may be elucidated with future research.