Amit Etkin’s research while affiliated with Stanford University and other places

Background: Identifying robust neural signatures of posttraumatic stress disorder (PTSD) symptoms is important to facilitate precision psychiatry and help in understanding and treatment of the disorder. Emergent research suggests structural covariance of early visual regions is associated with later PTSD development. However, large-scale analyses are needed, in heterogeneous samples of trauma-exposed and trauma naive individuals, to determine if such a neural signature is a robust, and potentially a pretrauma, marker of vulnerability. Methods: We analyzed data from the ENIGMA-PTSD dataset (n = 2,814) and the Human Connectome Project Young Adult (HCP-YA) dataset (n = 890) to investigate whether structural covariance of early visual cortex is associated with either PTSD symptoms or perceived stress. Structural covariance was derived from a multimodal pattern previously identified in recent trauma survivors, and participant loadings on the profile were included in linear mixed effects models to evaluate associations with stress. Results: Early visual cortex covariance loadings were negatively associated with PTSD symptoms in the ENIGMA-PTSD dataset. The relationship persisted when accounting for prior childhood maltreatment; supporting PTSD symptom specificity, no relationship was observed with depressive symptoms and no association was observed between loadings and perceived stress measures in the HCP-YA dataset. Conclusion: Structural covariance of early visual cortex was robustly associated with PTSD symptoms across an international, heterogeneous sample of trauma survivors. Future studies should aim to identify specific mechanisms that underlie structural alterations in the visual cortex to better understand posttrauma psychopathology.

Neuroimaging-based subtyping is increasingly used to explain heterogeneity in psychiatric disorders. However, the clinical utility of these subtyping efforts remains unclear, and replication has been challenging. Here we examined how the choice of neuroimaging measures influences the derivation of neuro-subtypes and the consequences for clinical delineation. On a clinically heterogeneous dataset (total n = 566) that included controls (n = 268) and cases (n = 298) of psychiatric conditions, including individuals diagnosed with post-traumatic stress disorder (PTSD), traumatic brain injury (TBI), and comorbidity of both (PTSD&TBI), we identified neuro-subtypes among the cases using either structural, resting-state, or task-based measures. The neuro-subtypes for each modality had high internal validity but did not significantly differ in their clinical and cognitive profiles. We further show that the choice of neuroimaging measures for subtyping substantially impacts the identification of neuro-subtypes, leading to low concordance across subtyping solutions. Similar variability in neuro-subtyping was found in an independent dataset (n = 1642) comprised of major depression disorder (MDD, n = 848) and controls (n = 794). Our results suggest that the highly anticipated relationships between neuro-subtypes and clinical features may be difficult to discover.

Background: Posttraumatic stress disorder (PTSD) is a debilitating condition that disproportionately impacts individuals who are female. Prior research indicates that males with PTSD exhibit hypoconnectivity of frontal brain regions measured with resting electroencephalography (EEG). The present study examined functional connectivity among females with PTSD and trauma-exposed controls, as well as the impact of sex hormones. Methods: Participants included 61 females (Mage = 31.41, SD = 8.64) who endorsed Criterion A trauma exposure. Resting state EEG data were recorded for five minutes in the eyes open position. Using a Linear Mixed Effects model, paired region-of-interest power envelope connectivity of the theta band (4-7 Hz) served as the response variables. Results: Compared to controls, the PTSD group displayed hyperconnectivity between visual brain regions and the rest of the cerebral cortex (pFDR < 0.05). Additionally, participants with PTSD demonstrated enhanced connectivity between the default mode network and frontoparietal control network compared to controls (pFDR < 0.05), as well as increased connectivity between the ventral attention network and the rest of the cerebral cortex (pFDR < 0.05). Estradiol was associated with higher connectivity, while progesterone was associated with lower connectivity, but these did not survive correction. Conclusions: Results are consistent with prior research indicating that PTSD is associated with altered connectivity in visual brain regions, which may reflect disrupted visual processing related to reexperiencing symptoms (e.g., intrusive memories). Our findings provide additional support for the relevance of the theta frequency range in PTSD given its role in fear learning and regulation processes.

Neuroimaging and cognitive neuroscience studies have identified neural circuits linked to anxiety, mood, and trauma-related symptoms and focused on their interaction with the medial prefrontal default mode circuitry. Despite these advances, developing new neuromodulatory treatments based on neurocircuitry remains challenging. It remains unclear which nodes within and controlling these circuits are affected and how their impairment is connected to psychiatric symptoms. Concurrent single-pulse (sp) TMS/fMRI offers a promising approach to probing and mapping the integrity of these circuits. In this study, we present concurrent sp-TMS/fMRI data along with structural MRI scans from 152 participants, including both healthy and depressed individuals. The sp-TMS was administered to 11 different cortical sites, providing a dataset that allows researchers to investigate how brain circuits are modulated by spTMS.

Objective: Major depressive disorder (MDD) is a common psychiatric disorder for which pharmacologic standard-of-care treatments have limited efficacy, particularly among individuals with cognitive dysfunction. Cognitive dysfunction is observed in approximately 25%-50% of those with MDD, wherein response to standard-of-care medications is reduced. Vortioxetine is an approved antidepressant that has shown evidence of procognitive effects in patients. It is not known if it has greater clinical efficacy in MDD patients with cognitive dysfunction, a more difficult to treat population, than other antidepressants. Methods: This study was a reanalysis of 1,812 subjects with MDD across 4 placebo controlled trials. Baseline cognition was measured by the Digit Symbol Substitution Test (DSST), the primary measure used to demonstrate vortioxetine's procognitive effects in clinical studies. Analyses examined whether baseline cognitive function was associated with differences in treatment outcomes. Results: Baseline DSST did not predict placebo-adjusted treatment effects of vortioxetine on depressive symptoms (pooled Cohen d = -0.02, 95% CI = -0.12 to 0.07). Analyses of additional cognitive measures similarly did not predict placebo-adjusted treatment effects on depression (all 95% CI contained zero). Finally, analyses of trials with selective serotonin reuptake inhibitors (SSRIs)/serotonin and norepinephrine reuptake inhibitors (SNRIs) as active comparators also revealed no prediction of SSRI/SNRI adjusted treatment effects of vortioxetine on depression. Conclusions: These findings, taken together, suggest that cognitive function does not moderate depression outcomes in vortioxetine, with results comparable to other antidepressants.

Importance Advancing precision psychiatry, where treatments are based on an individual’s biology rather than solely their clinical presentation, requires attention to several key attributes for any candidate biomarker. These include test-retest reliability, sensitivity to relevant neurophysiology, cost-effectiveness, and scalability. Unfortunately, these issues have not been systematically addressed by biomarker development efforts that use common neuroimaging tools like magnetic resonance imaging (MRI) and electroencephalography (EEG). Here, the critical barriers that neuroimaging methods will need to overcome to achieve clinical relevance in the near to intermediate term are examined. Observations Reliability is often overlooked, which together with sensitivity to key aspects of neurophysiology and replicated predictive utility, favors EEG-based methods. The principal barrier for EEG has been the lack of large-scale data collection among multisite psychiatric consortia. By contrast, despite its high reliability, structural MRI has not demonstrated clinical utility in psychiatry, which may be due to its limited sensitivity to psychiatry-relevant neurophysiology. Given the prevalence of structural MRIs, establishment of a compelling clinical use case remains its principal barrier. By contrast, low reliability and difficulty in standardizing collection are the principal barriers for functional MRI, along with the need for demonstration that its superior spatial resolution over EEG and ability to directly image subcortical regions in fact provide unique clinical value. Often missing, moreover, is consideration of how these various scientific issues can be balanced against practical economic realities of psychiatric health care delivery today, for which embedding economic modeling into biomarker development efforts may help direct research efforts. Conclusions and Relevance EEG seems most ripe for near- to intermediate-term clinical impact, especially considering its scalability and cost-effectiveness. Recent efforts to broaden its collection, as well as development of low-cost turnkey systems, suggest a promising pathway by which neuroimaging can impact clinical care. Continued MRI research focused on its key barriers may hold promise for longer-horizon utility.

Neuroimaging, across positron emission tomography (PET), electroencephalography (EEG), and magnetic resonance imaging (MRI), has been a mainstay of clinical neuroscience research for decades, yet has penetrated little into psychiatric drug development beyond often underpowered phase 1 studies, or into clinical care. Simultaneously, there is a pressing need to improve the probability of success in drug development, increase mechanistic diversity, and enhance clinical efficacy. These goals can be achieved by leveraging neuroimaging in a precision psychiatry framework, wherein effects of drugs on the brain are measured early in clinical development to understand dosing and indication, and then in later-stage trials to identify likely drug responders and enrich clinical trials, ultimately improving clinical outcomes. Here we examine the key variables important for success in using neuroimaging for precision psychiatry from the lens of biotechnology and pharmaceutical companies developing and deploying new drugs in psychiatry. We argue that there are clear paths for incorporating different neuroimaging modalities to de-risk subsequent development phases in the near to intermediate term, culminating in use of select neuroimaging modalities in clinical care for prescription of new precision drugs. Better outcomes through neuroimaging biomarkers, however, require a wholesale commitment to a precision psychiatry approach and will necessitate a cultural shift to align biopharma and clinical care in psychiatry to a precision orientation already routine in other areas of medicine.

Large-scale networks underpin brain functions. How such networks respond to focal stimulation can help decipher complex brain processes and optimize brain stimulation treatments. To map such stimulation-response patterns across the brain non-invasively, we recorded concurrent EEG responses from single-pulse transcranial magnetic stimulation (i.e., TMS-EEG) from over 100 cortical regions with two orthogonal coil orientations from one densely-sampled individual. We also acquired Human Connectome Project (HCP)-styled diffusion imaging scans (six), resting-state functional Magnetic Resonance Imaging (fMRI) scans (120 mins), resting-state EEG scans (108 mins), and structural MR scans (T1- and T2-weighted). Using the TMS-EEG data, we applied network science-based community detection to reveal insights about the brain's causal-functional organization from both a stimulation and recording perspective. We also computed structural and functional maps and the electric field of each TMS stimulation condition. Altogether, we hope the release of this densely sampled (n=1) dataset will be a uniquely valuable resource for both basic and clinical neuroscience research.