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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.

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Cocaine use disorder (CUD) is prevalent, and repetitive transcranial magnetic stimulation (rTMS) shows promise in reducing cravings. However, the association between a consistent CUD-specific functional connectivity signature and treatment response remains unclear. Here we identify a validated functional connectivity signature from functional magnetic resonance imaging to discriminate CUD, with successful independent replication. We found increased connectivity within the visual and dorsal attention networks and between the frontoparietal control and ventral attention networks, alongside reduced connectivity between the default mode and limbic networks in patients with CUD. These connections were associated with drug use history and cognitive impairments. Using data from a randomized clinical trial, we also established the prognostic value of these functional connectivities for rTMS treatment outcomes in CUD, especially involving the frontoparietal control and default mode networks. Our findings reveal insights into the neurobiological mechanisms of CUD and link functional connectivity biomarkers with rTMS treatment response, offering potential targets for future therapeutic development.

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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.

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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.

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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.

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Major depressive disorder (MDD) is a common and often severe condition that profoundly diminishes quality of life for individuals across ages and demographic groups. Unfortunately, current antidepressant and psychotherapeutic treatments exhibit limited efficacy and unsatisfactory response rates in a substantial number of patients. The development of effective therapies for MDD is hindered by the insufficiently understood heterogeneity within the disorder and its elusive underlying mechanisms. To address these challenges, we present a target-oriented multimodal fusion framework that robustly predicts antidepressant response by integrating structural and functional connectivity data (sertraline: R2 = 0.31; placebo: R2 = 0.22). Through the model, we identify multimodal neuroimaging biomarkers of antidepressant response and observe that sertraline and placebo show distinct predictive patterns. We further decompose the overall predictive patterns into constitutive network constellations with generalizable structural-functional co-variation, which exhibit treatment-specific association with personality traits and behavioral/cognitive task performance. Our innovative and interpretable multimodal framework provides novel insights into the intricate neuropsychopharmacology of antidepressant treatment and paves the way for advances in precision medicine and development of more targeted antidepressant therapeutics.

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The striatum, known as the input nucleus of the basal ganglia, is extensively studied for its diverse behavioral roles. However, the relationship between its neuronal and vascular activity, vital for interpreting functional magnetic resonance imaging (fMRI) signals, has not received comprehensive examination within the striatum. Here, we demonstrate that optogenetic stimulation of dorsal striatal neurons or their afferents from various cortical and subcortical regions induces negative striatal fMRI responses in rats, manifesting as vasoconstriction. These responses occur even with heightened striatal neuronal activity, confirmed by electrophysiology and fiber-photometry. In parallel, midbrain dopaminergic neuron optogenetic modulation, coupled with electrochemical measurements, establishes a link between striatal vasodilation and dopamine release. Intriguingly, in vivo intra-striatal pharmacological manipulations during optogenetic stimulation highlight a critical role of opioidergic signaling in generating striatal vasoconstriction. This observation is substantiated by detecting striatal vasoconstriction in brain slices after synthetic opioid application. In humans, manipulations aimed at increasing striatal neuronal activity likewise elicit negative striatal fMRI responses. Our results emphasize the necessity of considering vasoactive neurotransmission alongside neuronal activity when interpreting fMRI signal.

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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.

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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.

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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.

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The probabilistic reward task (PRT) has identified reward learning impairments in those with major depressive disorder (MDD), as well as anhedonia-specific reward learning impairments. However, attempts to validate the anhedonia-specific impairments have produced inconsistent findings. Thus, we seek to determine whether the Reward Behavior Disengagement (RBD), our proposed economic augmentation of PRT, differs between MDD participants and controls, and whether there is a level at which RBD is high enough for depressed participants to be considered objectively disengaged. Data were gathered as part of the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study, a double-blind, placebo-controlled clinical trial of antidepressant response. Participants included 195 individuals with moderate to severe MDD (Quick Inventory of Depressive Symptomatology (QIDS-SR) score ≥ 15), not in treatment for depression, and with complete PRT data. Healthy controls (n = 40) had no history of psychiatric illness, a QIDS-SR score < 8, and complete PRT data. Participants with MDD were treated with sertraline or placebo for 8 weeks (stage I of the EMBARC trial). RBD was applied to PRT data using discriminant analysis, and classified MDD participants as reward task engaged (n = 137) or reward task disengaged (n = 58), relative to controls. Reward task engaged/disengaged groups were compared on sociodemographic features, reward-behavior, and sertraline/placebo response (Hamilton Depression Rating Scale scores). Reward task disengaged MDD participants responded only to sertraline, whereas those who were reward task engaged responded to sertraline and placebo (F(1293) = 4.33, p = 0.038). Reward task engaged/disengaged groups did not differ otherwise. RBD was predictive of reward impairment in depressed patients and may have clinical utility in identifying patients who will benefit from antidepressants.

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Antidepressant medications yield unsatisfactory treatment outcomes in patients with major depressive disorder (MDD) with modest advantages over the placebo. This modest efficacy is partly due to the elusive mechanisms of antidepressant responses and unexplained heterogeneity in patient's response to treatment - the approved antidepressants only benefit a portion of patients, calling for personalized psychiatry based on individual-level prediction of treatment responses. Normative modeling, a framework that quantifies individual deviations in psychopathological dimensions, offers a promising avenue for the personalized treatment for psychiatric disorders. In this study, 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. 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 treatment responses. Our findings and highly generalizable framework advance the neurobiological understanding in the potential pathways of antidepressant responses, enabling more targeted and effective MDD treatment.

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Cocaine use disorder (CUD) is a prevalent substance abuse disorder, and repetitive transcranial magnetic stimulation (rTMS) has shown potential in reducing cocaine cravings. However, a robust and replicable biomarker for CUD phenotyping is lacking, and the association between CUD brain phenotypes and treatment response remains unclear. Our study successfully established a cross-validated functional connectivity signature for accurate CUD phenotyping, using resting-state functional magnetic resonance imaging from a discovery cohort, and demonstrated its generalizability in an independent replication cohort. We identified phenotyping FCs involving increased connectivity between the visual network and dorsal attention network, and between the frontoparietal control network and ventral attention network, as well as decreased connectivity between the default mode network and limbic network in CUD patients compared to healthy controls. These abnormal connections correlated significantly with other drug use history and cognitive dysfunctions, e.g., non-planning impulsivity. We further confirmed the prognostic potential of the identified discriminative FCs for rTMS treatment response in CUD patients and found that the treatment-predictive FCs mainly involved the frontoparietal control and default mode networks. Our findings provide new insights into the neurobiological mechanisms of CUD and the association between CUD phenotypes and rTMS treatment response, offering promising targets for future therapeutic development.

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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.

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Post-traumatic stress disorder (PTSD) is a mental disorder diagnosed by clinical interviews, self-report measures and neuropsychological testing. Traumatic brain injury (TBI) can have neuropsychiatric symptoms similar to PTSD. Diagnosing PTSD and TBI is challenging and more so for providers lacking specialized training facing time pressures in primary care and other general medical settings. Diagnosis relies heavily on patient self-report and patients frequently under-report or over-report their symptoms due to stigma or seeking compensation. We aimed to create objective diagnostic screening tests utilizing Clinical Laboratory Improvement Amendments (CLIA) blood tests available in most clinical settings. CLIA blood test results were ascertained in 475 male veterans with and without PTSD and TBI following warzone exposure in Iraq or Afghanistan. Using random forest (RF) methods, four classification models were derived to predict PTSD and TBI status. CLIA features were selected utilizing a stepwise forward variable selection RF procedure. The AUC, accuracy, sensitivity, and specificity were 0.730, 0.706, 0.659, and 0.715, respectively for differentiating PTSD and healthy controls (HC), 0.704, 0.677, 0.671, and 0.681 for TBI vs. HC, 0.739, 0.742, 0.635, and 0.766 for PTSD comorbid with TBI vs HC, and 0.726, 0.723, 0.636, and 0.747 for PTSD vs. TBI. Comorbid alcohol abuse, major depressive disorder, and BMI are not confounders in these RF models. Markers of glucose metabolism and inflammation are among the most significant CLIA features in our models. Routine CLIA blood tests have the potential for discriminating PTSD and TBI cases from healthy controls and from each other. These findings hold promise for the development of accessible and low-cost biomarker tests as screening measures for PTSD and TBI in primary care and specialty settings.

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Psychiatric disorders share neurobiology and frequently co-occur. This neurobiological and clinical overlap highlights opportunities for transdiagnostic treatments. In this study, we used coordinate and lesion network mapping to test for a shared brain network across psychiatric disorders. In our meta-analysis of 193 studies, atrophy coordinates across six psychiatric disorders mapped to a common brain network defined by positive connectivity to anterior cingulate and insula, and by negative connectivity to posterior parietal and lateral occipital cortex. This network was robust to leave-one-diagnosis-out cross-validation and specific to atrophy coordinates from psychiatric versus neurodegenerative disorders (72 studies). In 194 patients with penetrating head trauma, lesion damage to this network correlated with the number of post-lesion psychiatric diagnoses. Neurosurgical ablation targets for psychiatric illness (four targets) also aligned with the network. This convergent brain network for psychiatric illness may partially explain high rates of psychiatric comorbidity and could highlight neuromodulation targets for patients with more than one psychiatric disorder.

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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.

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OBJECTIVE: The authors evaluated AKL-T03, an investigational digital intervention delivered through a video game-based interface, designed to target the fronto-parietal network to enhance functional domains for attentional control. AKL-T03 was tested in adult patients with major depressive disorder and a demonstrated cognitive impairment at baseline. METHODS: Adults ages 25-55 years on a stable antidepressant medication regimen with residual mild to moderate depression and an objective impairment in cognition (as measured using the symbol coding test) were enrolled in a double-blind randomized controlled study. Participants were randomized either to AKL-T03 or to an expectation-matched digital control intervention. Participants were assessed at baseline and after completion of their 6-week at-home intervention. The primary outcome measure was improvement in sustained attention, as measured by the Test of Variables of Attention (TOVA). RESULTS: AKL-T03 (N=37) showed a statistically significant medium-effect-size improvement in sustained attention compared with the control intervention on the TOVA primary outcome (N=37) (partial eta-squared=0.11). Additionally, a composite score derived from all cognitive measures demonstrated significant improvement with AKL-T03 over the control intervention. Individual secondary and exploratory endpoints did not demonstrate statistically significant between-group differences. No serious adverse events were reported, and two patients (5.5%) in the AKL-T03 group reported an intervention-related adverse event (headache). CONCLUSIONS: Treatment with AKL-T03 resulted in significant improvement in sustained attention, as well as in cognitive functioning as a whole, compared with a control intervention. AKL-T03 is a safe digital intervention that is effective in the treatment of cognitive impairment associated with major depression. Further research will be needed to understand the clinical consequences of this treatment-induced change.

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BACKGROUND: The brain circuitry of depression and anxiety/fear is well-established, involving regions such as the limbic system and prefrontal cortex. We expand prior literature by examining the extent to which four discrete factors of anxiety (immediate state anxiety, physiological/panic, neuroticism/worry, and agitation/restlessness) among depressed outpatients are associated with differential responses during reactivity to and regulation of emotional conflict. METHODS: A total of 172 subjects diagnosed with major depressive disorder underwent functional magnetic resonance imaging while performing an Emotional Stroop Task. Two main contrasts were examined using whole brain voxel wise analyses: emotional reactivity and emotion regulation. We also evaluated the association of these contrasts with the four aforementioned anxiety factors. RESULTS: During emotional reactivity, participants with higher immediate state anxiety showed potentiated activation in the rolandic operculum and insula, while individuals with higher levels of physiological/panic demonstrated decreased activation in the posterior cingulate. No significant results emerged for any of the four factors on emotion regulation. When re-analyzing these statistically-significant brain regions through analyses of a subsample with (n = 92) and without (n = 80) a current anxiety disorder, no significant associations occurred among those without an anxiety disorder. Among those with an anxiety disorder, results were similar to the full sample, except the posterior cingulate was associated with the neuroticism/worry factor. CONCLUSIONS: Divergent patterns of task-related brain activation across four discrete anxiety factors could be used to inform treatment decisions and target specific aspects of anxiety that involve intrinsic processing to attenuate overactive responses to emotional stimuli.

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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.

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