Individualized fMRI connectivity defines signatures of antidepressant and placebo responses in major depression.

Though sertraline is commonly prescribed in patients with major depressive disorder (MDD), its superiority over placebo is only marginal. This is in part due to the neurobiological heterogeneity of the individuals. Characterizing individual-unique functional architecture of the brain may help better dissect the heterogeneity, thereby defining treatment-predictive signatures to guide personalized medication. In this study, we investigate whether individualized brain functional connectivity (FC) can define more predictable signatures of antidepressant and placebo treatment in MDD. The data used in the present work were collected by the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study. Patients (N = 296) were randomly assigned to antidepressant sertraline or placebo double-blind treatment for 8 weeks. The whole-brain FC networks were constructed from pre-treatment resting-state functional magnetic resonance imaging (rs-fMRI). Then, FC was individualized by removing the common components extracted from the raw baseline FC to train regression-based connectivity predictive models. With individualized FC features, the established prediction models successfully identified signatures that explained 22% variance for the sertraline group and 31% variance for the placebo group in predicting HAMD17 change. Compared with the raw FC-based models, the individualized FC-defined signatures significantly improved the prediction performance, as confirmed by cross-validation. For sertraline treatment, predictive FC metrics were predominantly located in the left middle temporal cortex and right insula. For placebo, predictive FC metrics were primarily located in the bilateral cingulate cortex and left superior temporal cortex. Our findings demonstrated that through the removal of common FC components, individualization of FC metrics enhanced the prediction performance compared to raw FC. Associated with previous MDD clinical studies, our identified predictive biomarkers provided new insights into the neuropathology of antidepressant and placebo treatment.

Neuroimaging-based classification of PTSD using data-driven computational approaches: A multisite big data study from the ENIGMA-PGC PTSD consortium.

BACKGROUND: Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. However, current machine learning studies limited to small homogeneous samples, different methodologies, and different imaging collection protocols, limit the ability to directly compare and generalize their results. Here we aimed to classify individuals with PTSD versus controls and assess the generalizability using a large heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group. METHODS: We analyzed brain MRI data from 3,477 structural-MRI; 2,495 resting state-fMRI; and 1,952 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality. RESULTS: We found lower performance in classifying PTSD vs. controls with data from over 20 sites (60 % test AUC for s-MRI, 59 % for rs-fMRI and 56 % for d-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history in each modality (75 % AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance. CONCLUSION: These results have the potential to provide a baseline classification performance for PTSD when using large scale neuroimaging datasets. Our findings show that the control group used can heavily affect classification performance. The DVAE framework provided better generalizability for the multi-site data. This may be more significant in clinical practice since the neuroimaging-based diagnostic DVAE classification models are much less site-specific, rendering them more generalizable.

The Role of the Dorsal-Lateral Prefrontal Cortex in Reward Sensitivity During Approach-Avoidance Conflict.

Approach-Avoidance conflict (AAC) arises from decisions with embedded positive and negative outcomes, such that approaching leads to reward and punishment and avoiding to neither. Despite its importance, the field lacks a mechanistic understanding of which regions are driving avoidance behavior during conflict. In the current task, we utilized transcranial magnetic stimulation (TMS) and drift-diffusion modeling to investigate the role of one of the most prominent regions relevant to AAC-the dorsolateral prefrontal cortex (dlPFC). The first experiment uses in-task disruption to examine the right dlPFC's (r-dlPFC) causal role in avoidance behavior. The second uses single TMS pulses to probe the excitability of the r-dlPFC, and downstream cortical activations, during avoidance behavior. Disrupting r-dlPFC during conflict decision-making reduced reward sensitivity. Further, r-dlPFC was engaged with a network of regions within the lateral and medial prefrontal, cingulate, and temporal cortices that associate with behavior during conflict. Together, these studies use TMS to demonstrate a role for the dlPFC in reward sensitivity during conflict and elucidate the r-dlPFC's network of cortical regions associated with avoidance behavior. By identifying r-dlPFC's mechanistic role in AAC behavior, contextualized within its conflict-specific downstream neural connectivity, we advance dlPFC as a potential neural target for psychiatric therapeutics.

Pre-deployment risk factors for PTSD in active-duty personnel deployed to Afghanistan: a machine-learning approach for analyzing multivariate predictors.

Active-duty Army personnel can be exposed to traumatic warzone events and are at increased risk for developing post-traumatic stress disorder (PTSD) compared with the general population. PTSD is associated with high individual and societal costs, but identification of predictive markers to determine deployment readiness and risk mitigation strategies is not well understood. This prospective longitudinal naturalistic cohort study-the Fort Campbell Cohort study-examined the value of using a large multidimensional dataset collected from soldiers prior to deployment to Afghanistan for predicting post-deployment PTSD status. The dataset consisted of polygenic, epigenetic, metabolomic, endocrine, inflammatory and routine clinical lab markers, computerized neurocognitive testing, and symptom self-reports. The analysis was computed on active-duty Army personnel (N = 473) of the 101st Airborne at Fort Campbell, Kentucky. Machine-learning models predicted provisional PTSD diagnosis 90-180 days post deployment (random forest: AUC = 0.78, 95% CI = 0.67-0.89, sensitivity = 0.78, specificity = 0.71; SVM: AUC = 0.88, 95% CI = 0.78-0.98, sensitivity = 0.89, specificity = 0.79) and longitudinal PTSD symptom trajectories identified with latent growth mixture modeling (random forest: AUC = 0.85, 95% CI = 0.75-0.96, sensitivity = 0.88, specificity = 0.69; SVM: AUC = 0.87, 95% CI = 0.79-0.96, sensitivity = 0.80, specificity = 0.85). Among the highest-ranked predictive features were pre-deployment sleep quality, anxiety, depression, sustained attention, and cognitive flexibility. Blood-based biomarkers including metabolites, epigenomic, immune, inflammatory, and liver function markers complemented the most important predictors. The clinical prediction of post-deployment symptom trajectories and provisional PTSD diagnosis based on pre-deployment data achieved high discriminatory power. The predictive models may be used to determine deployment readiness and to determine novel pre-deployment interventions to mitigate the risk for deployment-related PTSD.

Assessing and improving public mental health literacy concerning rTMS.

BACKGROUND: Repetitive transcranial magnetic stimulation (rTMS) has received empirical support as a viable treatment alternative for treatment-resistant major depressive disorder. Nevertheless, patients and the public-at-large may be hesitant to adopt rTMS. In three studies, we sought to (1) assess and (2) improve public perceptions of rTMS as a treatment for depression. METHODS: In Study 1 (N = 107), we administered questionnaires on Amazon's Mechanical Turk (MTurk) to individuals from the US and Canada in a cross-sectional design to assess perceptions of rTMS compared to psychopharmacology, electroconvulsive therapy (ECT), and talk therapy. In Study 2 (N = 106), we again used an MTurk sample and a cross-sectional design to assess perceptions of rTMS after providing participants with a relatively long description of rTMS. In Study 3 (N = 308), we conducted an experiment in undergraduate students. Participants were randomized to one of four experimental conditions manipulating participants' understanding of the causal mechanisms of depression prior to assessing their perceptions of rTMS. RESULTS: Public perceptions of rTMS were more negative than pharmacotherapy and talk therapy but not ECT (Study 1). rTMS perceptions were notably better when participants were given thorough information about rTMS procedures, pain, and side-effects (Study 2), compared to the previous study when they were given a very brief description of rTMS. Finally, perceptions of rTMS were significantly better when participants were given a brain circuitry-based causal explanation of depression compared to when they were given a psychological explanation of the causes of depression (Study 3). CONCLUSIONS: Public perceptions of rTMS are relatively poor. To improve rTMS acceptability, practitioners should carefully consider patients' prior attitudes and beliefs when explaining rTMS as a treatment alternative. Given that beliefs can have powerful effects on treatment outcome (e.g., placebo, nocebo), future research should explore whether rTMS effects on depression can be improved by facilitating less negative perceptions of rTMS.

Probing drug-evoked cortical plasticity with brain stimulation: A call for translation from animal to human medical research.

Drug evoked synaptic plasticity represents "memory traces" in the brain following abuse experiences. Preclinical studies have detailed the myriad changes in mesolimbic and cortical functioning, including alterations in synaptic transmission and plasticity. In humans, recent research advances utilizing a combination of non-invasive brain stimulation and neurophysiological readouts have opened a new avenue for the study cortical plasticity in clinical substance dependence states. These insights, along with findings in psychopathology more generally, uniquely positions brain stimulation as a key tool for understanding cortical function and plasticity in substance dependence.

Mapping causal circuit dynamics in stroke using simultaneous electroencephalography and transcranial magnetic stimulation.

BACKGROUND: Motor impairment after stroke is due not only to direct tissue loss but also to disrupted connectivity within the motor network. Mixed results from studies attempting to enhance motor recovery with Transcranial Magnetic Stimulation (TMS) highlight the need for a better understanding of both connectivity after stroke and the impact of TMS on this connectivity. This study used TMS-EEG to map the causal information flow in the motor network of healthy adult subjects and define how stroke alters these circuits. METHODS: Fourteen stroke patients and 12 controls received TMS to two sites (bilateral primary motor cortices) during two motor tasks (paretic/dominant hand movement vs. rest) while EEG measured the cortical response to TMS pulses. TMS-EEG based connectivity measurements were derived for each hemisphere and the change in connectivity (ΔC) between the two motor tasks was calculated. We analyzed if ΔC for each hemisphere differed between the stroke and control groups or across TMS sites, and whether ΔC correlated with arm function in stroke patients. RESULTS: Right hand movement increased connectivity in the left compared to the right hemisphere in controls, while hand movement did not significantly change connectivity in either hemisphere in stroke. Stroke patients with the largest increase in healthy hemisphere connectivity during paretic hand movement had the best arm function. CONCLUSIONS: TMS-EEG measurements are sensitive to movement-induced changes in brain connectivity. These measurements may characterize clinically meaningful changes in circuit dynamics after stroke, thus providing specific targets for trials of TMS in post-stroke rehabilitation.

The neural bases of emotion regulation.

Emotions are powerful determinants of behaviour, thought and experience, and they may be regulated in various ways. Neuroimaging studies have implicated several brain regions in emotion regulation, including the ventral anterior cingulate and ventromedial prefrontal cortices, as well as the lateral prefrontal and parietal cortices. Drawing on computational approaches to value-based decision-making and reinforcement learning, we propose a unifying conceptual framework for understanding the neural bases of diverse forms of emotion regulation.

ARTIST: A fully automated artifact rejection algorithm for single-pulse TMS-EEG data.

Concurrent single-pulse TMS-EEG (spTMS-EEG) is an emerging noninvasive tool for probing causal brain dynamics in humans. However, in addition to the common artifacts in standard EEG data, spTMS-EEG data suffer from enormous stimulation-induced artifacts, posing significant challenges to the extraction of neural information. Typically, neural signals are analyzed after a manual time-intensive and often subjective process of artifact rejection. Here we describe a fully automated algorithm for spTMS-EEG artifact rejection. A key step of this algorithm is to decompose the spTMS-EEG data into statistically independent components (ICs), and then train a pattern classifier to automatically identify artifact components based on knowledge of the spatio-temporal profile of both neural and artefactual activities. The autocleaned and hand-cleaned data yield qualitatively similar group evoked potential waveforms. The algorithm achieves a 95% IC classification accuracy referenced to expert artifact rejection performance, and does so across a large number of spTMS-EEG data sets (n = 90 stimulation sites), retains high accuracy across stimulation sites/subjects/populations/montages, and outperforms current automated algorithms. Moreover, the algorithm was superior to the artifact rejection performance of relatively novice individuals, who would be the likely users of spTMS-EEG as the technique becomes more broadly disseminated. In summary, our algorithm provides an automated, fast, objective, and accurate method for cleaning spTMS-EEG data, which can increase the utility of TMS-EEG in both clinical and basic neuroscience settings.

Developing a clinical translational neuroscience taxonomy for anxiety and mood disorder: protocol for the baseline-follow up Research domain criteria Anxiety and Depression ("RAD") project.

BACKGROUND: Understanding how brain circuit dysfunctions relate to specific symptoms offers promise for developing a brain-based taxonomy for classifying psychopathology, identifying targets for mechanistic studies and ultimately for guiding treatment choice. The goal of the Research Domain Criteria (RDoC) initiative of the National Institute of Mental Health is to accelerate the development of such neurobiological models of mental disorder independent of traditional diagnostic criteria. In our RDoC Anxiety and Depression ("RAD") project we focus trans-diagnostically on the spectrum of depression and anxiety psychopathology. Our aims are a) to use brain imaging to define cohesive dimensions defined by dysfunction of circuits involved in reactivity to and regulation of negatively valenced emotional stimulation and in cognitive control, b) to assess the relationships between these dimension and specific symptoms, behavioral performance and the real world capacity to function socially and at work and c) to assess the stability of brain-symptom-behavior-function relationships over time. METHODS AND DESIGN: Here we present the protocol for the "RAD" project, one of the first RDoC studies to use brain circuit functioning to define new dimensions of psychopathology. The RAD project follows baseline-follow up design. In line with RDoC principles we use a strategy for recruiting all clients who "walk through the door" of a large community mental health clinic as well as the surrounding community. The clinic attends to a broad spectrum of anxiety and mood-related symptoms. Participants are unmedicated and studied at baseline using a standardized battery of functional brain imaging, structural brain imaging and behavioral probes that assay constructs of threat reactivity, threat regulation and cognitive control. The battery also includes self-report measures of anxiety and mood symptoms, and social and occupational functioning. After baseline assessments, therapists in the clinic apply treatment planning as usual. Follow-up assessments are undertaken at 3 months, to establish the reliability of brain-based subgroups over time and to assess whether these subgroups predict real-world functional capacity over time. First enrollment was August 2013, and is ongoing. DISCUSSION: This project is designed to advance knowledge toward a neural circuit taxonomy for mental disorder. Data will be shared via the RDoC database for dissemination to the scientific community. The clinical translational neuroscience goals of the project are to develop brain-behavior profile reports for each individual participant and to refine these reports with therapist feedback. Reporting of results is expected from December 2016 onward. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02220309 . Registered: August 13, 2014.

Causal interactions between fronto-parietal central executive and default-mode networks in humans.

Information processing during human cognitive and emotional operations is thought to involve the dynamic interplay of several large-scale neural networks, including the fronto-parietal central executive network (CEN), cingulo-opercular salience network (SN), and the medial prefrontal-medial parietal default mode networks (DMN). It has been theorized that there is a causal neural mechanism by which the CEN/SN negatively regulate the DMN. Support for this idea has come from correlational neuroimaging studies; however, direct evidence for this neural mechanism is lacking. Here we undertook a direct test of this mechanism by combining transcranial magnetic stimulation (TMS) with functional MRI to causally excite or inhibit TMS-accessible prefrontal nodes within the CEN or SN and determine consequent effects on the DMN. Single-pulse excitatory stimulations delivered to only the CEN node induced negative DMN connectivity with the CEN and SN, consistent with the CEN/SN's hypothesized negative regulation of the DMN. Conversely, low-frequency inhibitory repetitive TMS to the CEN node resulted in a shift of DMN signal from its normally low-frequency range to a higher frequency, suggesting disinhibition of DMN activity. Moreover, the CEN node exhibited this causal regulatory relationship primarily with the medial prefrontal portion of the DMN. These findings significantly advance our understanding of the causal mechanisms by which major brain networks normally coordinate information processing. Given that poorly regulated information processing is a hallmark of most neuropsychiatric disorders, these findings provide a foundation for ways to study network dysregulation and develop brain stimulation treatments for these disorders.

COGNITION-CHILDHOOD MALTREATMENT INTERACTIONS IN THE PREDICTION OF ANTIDEPRESSANT OUTCOMES IN MAJOR DEPRESSIVE DISORDER PATIENTS: RESULTS FROM THE iSPOT-D TRIAL.

BACKGROUND: Childhood maltreatment (CM) history has been associated with poor treatment response in major depressive disorder (MDD), but the mechanisms underlying this relationship remain opaque. Dysfunction in the neural circuits for executive cognition is a putative neurobiological consequence of CM that may contribute importantly to adverse clinical outcomes. We used behavioral and neuroimaging measures of executive functioning to assess their contribution to the relationship between CM and antidepressant response in MDD patients. METHODS: Ninety eight medication-free MDD outpatients participating in the International Study to Predict Optimized Treatment in Depression were assessed at baseline on behavioral neurocognitive measures and functional magnetic resonance imaging during tasks probing working memory (continuous performance task, CPT) and inhibition (Go/No-go). Seventy seven patients completed 8 weeks of antidepressant treatment. Baseline behavioral and neuroimaging measures were assessed in relation to CM (history of childhood physical, sexual, and/or emotional abuse) and posttreatment depression outcomes. RESULTS: Patients with maltreatment exhibited decreased modulation of right dorsolateral prefrontal cortex (DLPFC) activity during working memory updating on the CPT, and a corresponding impairment in CPT behavioral performance outside the scanner. No between-group differences were found for imaging or behavior on the Go/No-go test of inhibition. Greater DLPFC activity during CPT significantly predicted posttreatment symptom improvement in patients without maltreatment, whereas the relationship between DLPFC activity and symptom change was nonsignificant, and in the opposite direction, in patients with maltreatment. CONCLUSIONS: The effect of CM on prefrontal circuitry involved in executive function is a potential predictor of antidepressant outcomes.

The effects of age, sex, and hormones on emotional conflict-related brain response during adolescence.

While cognitive and emotional systems both undergo development during adolescence, few studies have explored top-down inhibitory control brain activity in the context of affective processing, critical to informing adolescent psychopathology. In this study, we used functional magnetic resonance imaging to examine brain response during an Emotional Conflict (EmC) Task across 10-15-year-old youth. During the EmC Task, participants indicated the emotion of facial expressions, while disregarding emotion-congruent and incongruent words printed across the faces. We examined the relationships of age, sex, and gonadal hormones with brain activity on Incongruent vs. Congruent trials. Age was negatively associated with middle frontal gyrus activity, controlling for performance and movement confounds. Sex differences were present in occipital and parietal cortices, and were driven by activation in females, and deactivation in males to Congruent trials. Testosterone was negatively related with frontal and striatal brain response in males, and cerebellar and precuneus response in females. Estradiol was negatively related with fronto-cerebellar, cingulate, and precuneus brain activity in males, and positively related with occipital response in females. To our knowledge, this is the first study reporting the effects of age, sex, and sex steroids during an emotion-cognition task in adolescents. Further research is needed to examine longitudinal development of emotion-cognition interactions and deviations in psychiatric disorders in adolescence.

Attitudes toward neuroscience education in psychiatry: a national multi-stakeholder survey.

OBJECTIVE: The objective of this study is to assess the attitudes of chairs of psychiatry departments, psychiatrists, and psychiatry trainees toward neuroscience education in residency programs and beyond in order to inform future neuroscience education approaches. METHOD: This multi-stakeholder survey captured data on demographics, self-assessments of neuroscience knowledge, attitudes toward neuroscience education, preferences in learning modalities, and interests in specific neuroscience topics. In 2012, the authors distributed the surveys: by paper to 133 US psychiatry department chairs and electronically through the American Psychiatric Association to 3,563 of its members (1,000 psychiatrists and 2,563 trainees). RESULTS: The response rates for the chair, psychiatrist, and trainee surveys were 53, 9, and 18 %, respectively. A large majority of respondents agreed with the need for more neuroscience education in general and with respect to their own training. Most respondents believed that neuroscience will help destigmatize mental illness and begin producing new treatments or personalized medicines in 5-10 years. Only a small proportion of trainees and psychiatrists, however, reported a strong knowledge base in neuroscience. Respondents also reported broad enthusiasm for transdiagnostic topics in neuroscience (such as emotion regulation and attention/cognition) and description at the level of neural circuits. CONCLUSIONS: This study demonstrates the opportunity and enthusiasm for teaching more neuroscience in psychiatry among a broad range of stakeholder groups. A high level of interest was also found for transdiagnostic topics and approaches. We suggest that a transdiagnostic framework may be an effective way to deliver neuroscience education to the psychiatric community and illustrate this through a case example, drawing the similarity between this neuroscience approach and problem-based formulations familiar to clinicians.

Connectivity underlying emotion conflict regulation in older adults with 5-HTTLPR short allele: a preliminary investigation.

OBJECTIVE: The serotonin transporter polymorphism short (s) allele is associated with heightened emotional reactivity and reduced emotion regulation, which increases vulnerability to depression and anxiety disorders. We investigated behavioral and neural markers of emotion regulation in community-dwelling older adults, contrasting s allele carriers and long allele homozygotes. METHODS: Participants (N = 26) completed a face-word emotion conflict task during functional magnetic resonance imaging, in which facilitated regulation of emotion conflict was observed on face-word incongruent trials following another incongruent trial (i.e., emotional conflict adaptation). RESULTS: There were no differences between genetic groups in behavioral task performance or neural activation in postincongruent versus postcongruent trials. By contrast, connectivity between dorsal anterior cingulate cortex (ACC) and pregenual ACC, regions previously implicated in emotion conflict regulation, was impaired in s carriers for emotional conflict adaptation. CONCLUSION: This is the first demonstration of an association between serotonin transporter polymorphism and functional connectivity in older adults. Poor dorsal ACC-pregenual ACC connectivity in s carriers may be one route by which these individuals experience greater difficulty in implementing effective emotional regulation, which may contribute to their vulnerability for affective disorders.

Beyond the DSM: development of a transdiagnostic psychiatric neuroscience course.

OBJECTIVE: Clinical and neurobiological data suggest that psychiatric disorders, as traditionally defined in the Diagnostic and Statistical Manual of Mental Disorders (DSM), are (1) more comorbid than expected by chance, (2) often share neurobiological signatures, and (3) reflect alterations across multiple brain systems that mediate particular mental processes. As such, emerging conceptualizations such at the National Institute of Mental Health's Research Domain Criteria Project (RDoC) have suggested that a different way to understand psychopathology may be with respect to the degree of dysfunction in each of these brain systems, seen dimensionally, which both cross traditional diagnostic boundaries and extend to a healthy range of functioning. At present, however, this scientific perspective has not been incorporated into neuroscience education in psychiatry, nor has its relationship to clinical care been made clear. METHODS: We describe the rationale and implementation of a reformulated neuroscience course given to psychiatric residents at Stanford University centered on the conceptual framework of RDoC. Data are presented on resident feedback before and after revision of the course. RESULTS: A clear motivation and rationale exists for teaching neuroscience in a transdiagnostic framework. This course was taken up well by the residents, with overall feedback significantly more positive than that prior to the course revision. CONCLUSION: This "proof of concept" neuroscience course illustrates a potential route for bridging between rapid advances in psychiatric neuroscience and the clinical education for trainees not otherwise versed in neuroscience but who are needed for scientific advances to translate to the clinic. The promise of this approach may be in part related to the similarity between this framework and problem-based approaches common in routine clinical care. In such approaches, clinicians focus on the expressed complaints of their individual patient and identify specific symptoms as the target of treatment--symptoms which are presumably the expression of dysfunction in specific brain systems.

Attitudes toward neuroscience education among psychiatry residents and fellows.

OBJECTIVE: The purpose of this study is to assess the attitudes of psychiatry trainees toward neuroscience education in psychiatry residency and subsequent training in order to inform neuroscience education approaches in the future. METHODS: This online survey was designed to capture demographic information, self-assessed neuroscience knowledge, attitudes toward neuroscience education, preferences in learning modalities, and interest in specific neuroscience topics. Volunteers were identified through the American Psychiatric Association, which invited 2,563 psychiatry trainees among their members. RESULTS: Four hundred thirty-six trainees completed the survey. Nearly all agreed that there is a need for more neuroscience education in psychiatry residency training (94%) and that neuroscience education could help destigmatize mental illness (91%). Nearly all (94%) expressed interest in attending a 3-day course on neuroscience. Many neuroscience topics and modes of learning were viewed favorably by participants. Residents in their first 2 years of training expressed attitudes similar to those of more advanced residents and fellows. Some differences were found based on the level of interest in a future academic role. CONCLUSIONS: This web-based study demonstrates that psychiatry residents see neuroscience education as important in their training and worthy of greater attention. Our results suggest potential opportunities for advancing neuroscience education.

An electroencephalographic signature predicts craving for methamphetamine.

Craving is central to methamphetamine use disorder (MUD) and both characterizes the disease and predicts relapse. However, there is currently a lack of robust and reliable biomarkers for monitoring craving and diagnosing MUD. Here, we seek to identify a neurobiological signature of craving based on individual-level functional connectivity pattern differences between healthy control and MUD subjects. We train high-density electroencephalography (EEG)-based models using data recorded during the resting state and then calculate imaginary coherence features between the band-limited time series across different brain regions of interest. Our prediction model demonstrates that eyes-open beta functional connectivity networks have significant predictive value for craving at the individual level and can also identify individuals with MUD. These findings advance the neurobiological understanding of craving through an EEG-tailored computational model of the brain connectome. Dissecting neurophysiological features provides a clinical avenue for personalized treatment of MUD.

Individual deviations from normative electroencephalographic connectivity predict antidepressant response.

BACKGROUND: Antidepressant medications yield unsatisfactory treatment outcomes in patients with major depressive disorder (MDD) with modest advantages over the placebo, partly due to the elusive mechanisms of antidepressant responses and unexplained heterogeneity in patient's response to treatment. Here we develop a novel normative modeling framework to quantify individual deviations in psychopathological dimensions that offers a promising avenue for the personalized treatment for psychiatric disorders. METHODS: We built a normative model with resting-state electroencephalography (EEG) connectivity data from healthy controls of three independent cohorts. We characterized the individual deviation of MDD patients from the healthy norms, based on which we trained sparse predictive models for treatment responses of MDD patients (102 sertraline-medicated and 119 placebo-medicated). Hamilton depression rating scale (HAMD-17) was assessed at both baseline and after the eight-week antidepressant treatment. RESULTS: We successfully predicted treatment outcomes for patients receiving sertraline (r = 0.43, p < 0.001) and placebo (r = 0.33, p < 0.001). We also showed that the normative modeling framework successfully distinguished subclinical and diagnostic variabilities among subjects. From the predictive models, we identified key connectivity signatures in resting-state EEG for antidepressant treatment, suggesting differences in neural circuit involvement between sertraline and placebo responses. CONCLUSIONS: Our findings and highly generalizable framework advance the neurobiological understanding in the potential pathways of antidepressant responses, enabling more targeted and effective personalized MDD treatment. TRIAL REGISTRATION: Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care for Depression (EMBARC), NCT#01407094.