Individual Patterns of Abnormality in Resting-State Functional Connectivity Reveal Two Data-Driven PTSD Subgroups

PTSD subtypes and their underlying neural biomarkers: a systematic review

Posttraumatic stress disorder (PTSD) is a heterogenous disorder with frequent diagnostic comorbidity. Research has deciphered this heterogeneity by identifying PTSD subtypes and their neural biomarkers. This review summarizes current approaches, symptom-based group-level and data-driven approaches, for generating PTSD subtypes, providing an overview of current PTSD subtypes and their neural correlates. Additionally, we systematically assessed studies to evaluate the influence of comorbidity on PTSD subtypes and the predictive utility of biotypes for treatment outcomes. Following the PRISMA guidelines, a systematic search was conducted to identify studies employing brain imaging techniques, including functional magnetic resonance imaging (fMRI), structural MRI, diffusion-weighted imaging (DWI), and electroencephalogram (EEG), to identify biomarkers of PTSD subtypes. Study quality was assessed using the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. We included 53 studies, with 44 studies using a symptom-based group-level approach, and nine studies using a data-driven approach. Findings suggest biomarkers across the default-mode network (DMN) and the salience network (SN) throughout multiple subtypes. However, only six studies considered comorbidity, and four studies tested the utility of biotypes in predicting treatment outcomes. These findings highlight the complexity of PTSD's heterogeneity. Although symptom-based and data-driven methods have advanced our understanding of PTSD subtypes, challenges remain in addressing the impact of comorbidities and the limited validation of biotypes. Future studies with larger sample sizes, brain-based data-driven approaches, careful account for comorbidity, and rigorous validation strategies are needed to advance biologically grounded biotypes across mental disorders.

Traumatic stress alters neural reactivity to visual stimulation

Traumatic stress is a precursor to the development of posttraumatic stress disorder (PTSD). Emergent research suggests visual processing regions may be relevant to PTSD development; however, no previous research to date has investigated the potential effects of trauma exposure on neural reactivity to non-affective visual stimulation. In the present study, 24 recently trauma-exposed (RTE) and 16 without recent exposure to trauma (NRTE) individuals completed functional magnetic resonance imaging during alternating blocks of flickering checkerboard presentations and attention/rest with an attentional check. RTE participants were recruited within ~2-4 weeks of trauma, and PTSD symptoms were assessed both at the time of the magnetic resonance imaging scan and 6 months following trauma exposure. RTE participants showed greater deactivation within the visual cortex compared to NRTE participants. Further, NRTE participants showed greater neural reactivity within the dorsomedial prefrontal cortex during stimulation compared to attention/rest, while no difference was observed in RTE participants. Connectivity analyses also revealed that visual cortex to paracentral gyrus connectivity was greater during stimulation compared to attention/rest, but only for the NRTE participants. Finally, neural reactivity to visual stimulation was negatively associated with PTSD symptoms within the RTE group. Our findings suggest that trauma exposure is associated with acute alterations in the neural function that underlies basic visual processing. Furthermore, trauma-induced variability in visual circuit function may be related to the development and expression of PTSD symptoms.

Neuroimaging-based variability in subtyping biomarkers for psychiatric heterogeneity

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.

Negative Affect Circuit Subtypes and Neural, Behavioral, and Affective Responses to MDMA: A Randomized Clinical Trial

Importance

Rapidly acting therapeutics like 3,4-methylenedioxymethamphetamine (MDMA) are promising treatments for disorders such as posttraumatic stress disorder (PTSD). However, understanding who benefits most and the underlying neural mechanisms remains a critical gap. Stratifying individuals by neural circuit profiles could help differentiate neural, behavioral, and affective responses to MDMA, enabling personalized treatment strategies.

Objective

To investigate whether baseline stratification of individuals based on negative affect circuit profiles, particularly in response to nonconscious threat stimuli, can differentiate acute responses to MDMA.

Design, Setting, and Participants

This randomized clinical trial, implementing a double-blinded, within-participant, placebo- and baseline-controlled design, was conducted at Stanford University School of Medicine between November 2, 2021, and November 9, 2022, for wave 1 data collection. Participants had used MDMA on at least 2 prior occasions, but not in the past 6 months, and had subthreshold PTSD symptoms and early life trauma but no current psychiatric disorders. Data were analyzed from March 1, 2023, to January 1, 2024.

Interventions

Participants completed 4 visits: 1 baseline session followed by 1 placebo session and 2 MDMA sessions in a randomized order, totaling 64 visits. Baseline functional magnetic resonance imaging (fMRI) assessed the negative affect circuit using a nonconscious threat processing task (NTN).

Main Outcomes and Measures

Primary outcomes included activity and connectivity of amygdala and subgenual anterior cingulate cortex (sgACC) defining the negative affect circuit. Secondary outcomes were behavioral measures of implicit threat bias, likability of threat expressions, and affective assessments.

Results

Sixteen participants (10 [63%] female; mean [SD] age, 40.8 [7.6] years) were stratified into subgroups with high and low levels of NTN activity in the amygdala (NTNA+ [n = 8] and NTNA- [n = 8], respectively), based on a median split of baseline nonconscious threat-evoked fMRI responses. Following administration of the 120 mg of MDMA vs placebo, the NTNA+ subgroup showed significant reductions in amygdala (contrast estimate [CE], -1.43; 95% CI, -2.60 to -0.27; Cohen d, -1.22; P = .02) and sgACC activity (CE, -1.48; 95% CI, -2.42 to -0.54; Cohen d, -1.56; P = .004), increased sgACC-amygdala connectivity (CE, 0.65; 95% CI, 0.02-1.28; Cohen d, 1.02; P = .04), and increased likability of threat expressions (CE, 14.38; 95% CI, 1.46-27.29; Cohen d, 0.86; P = .03) compared with the NTNA- subgroup.

Conclusions and Relevance

In this randomized clinical trial of MDMA's acute profiles, 120 mg of MDMA acutely normalized negative affect circuit reactivity in participants stratified by heightened amygdala reactivity at baseline, demonstrating the potential of neuroimaging to identify prospective biomarkers and guide personalized MDMA-based therapies.

Trial Registration

ClinicalTrials.gov Identifier: NCT04060108.

Neurofind: using deep learning to make individualised inferences in brain-based disorders

Within precision psychiatry, there is a growing interest in normative models given their ability to parse heterogeneity. While they are intuitive and informative, the technical expertise and resources required to develop normative models may not be accessible to most researchers. Here we present Neurofind, a new freely available tool that bridges this gap by wrapping sound and previously tested methods on data harmonisation and advanced normative models into a web-based platform that requires minimal input from the user. We explain how Neurofind was developed, how to use the Neurofind website in four simple steps ( www.neurofind.ai ), and provide exemplar applications. Neurofind takes as input structural MRI images and outputs two main metrics derived from independent normative models: (1) Outlier Index Score, a deviation score from the normative brain morphology, and (2) Brain Age, the predicted age based on an individual's brain morphometry. The tool was trained on 3362 images of healthy controls aged 20-80 from publicly available datasets. The volume of 101 cortical and subcortical regions was extracted and modelled with an adversarial autoencoder for the Outlier index model and a support vector regression for the Brain age model. To illustrate potential applications, we applied Neurofind to 364 images from three independent datasets of patients diagnosed with Alzheimer's disease and schizophrenia. In Alzheimer's disease, 55.2% of patients had very extreme Outlier Index Scores, mostly driven by larger deviations in temporal-limbic structures and ventricles. Patients were also homogeneous in how they deviated from the norm. Conversely, only 30.1% of schizophrenia patients were extreme outliers, due to deviations in the hippocampus and pallidum, and patients tended to be more heterogeneous than controls. Both groups showed signs of accelerated brain ageing.

Affective Visual Circuit Dysfunction in Trauma and Stress-Related Disorders

Posttraumatic stress disorder (PTSD) is widely recognized as involving disruption of core neurocircuitry that underlies processing, regulation, and response to threat. In particular, the prefrontal cortex-hippocampal-amygdala circuit is a major contributor to posttraumatic dysfunction. However, the functioning of core threat neurocircuitry is partially dependent on sensorial inputs, and previous research has demonstrated that dense, reciprocal connections exist between threat circuits and the ventral visual stream. Furthermore, emergent evidence suggests that trauma exposure and resultant PTSD symptoms are associated with altered structure and function of the ventral visual stream. In the current review, we discuss evidence that both threat and visual circuitry together are an integral part of PTSD pathogenesis. An overview of the relevance of visual processing to PTSD is discussed in the context of both basic and translational research, highlighting the impact of stress on affective visual circuitry. This review further synthesizes emergent literature to suggest potential timing-dependent effects of traumatic stress on threat and visual circuits that may contribute to PTSD development. We conclude with recommendations for future research to move the field toward a more complete understanding of PTSD neurobiology.

Traumatic stress alters neural reactivity to visual stimulation

Traumatic stress is a precursor to the development of posttraumatic stress disorder (PTSD). Emergent research suggests visual processing regions may be relevant to PTSD development; however, no previous research to date has investigated the potential effects of trauma exposure on neural reactivity to non-affective visual stimulation. In the present study, 24 recently trauma-exposed (TE) and 16 without recent exposure to trauma (NTE) individuals completed functional magnetic resonance imaging during alternating blocks of flickering checkerboard presentations and rest with an attentional check. TE participants were recruited within 2-4 weeks of trauma, and PTSD symptoms were assessed both at the time of the magnetic resonance imaging scan and 6 months following trauma exposure. TE participants showed greater deactivation within the visual cortex compared to NTE participants. Further, NTE participants showed greater neural reactivity within the dorsomedial prefrontal cortex during stimulation compared to rest, while no difference was observed in TE participants. Connectivity analyses also revealed that visual cortex to paracentral gyrus connectivity was greater during stimulation compared to rest, but only for the NTE participants. Finally, neural reactivity to visual stimulation was negatively associated with PTSD symptoms within the TE group. Our findings suggest that trauma exposure is associated with acute alterations in the neural function that underlies basic visual processing. Furthermore, trauma-induced variability in visual circuit function may be related to the development and expression of PTSD symptoms.

Individualized resting-state functional connectivity abnormalities unveil two major depressive disorder subtypes with contrasting abnormal patterns of abnormality

Modern neuroimaging research has recognized that major depressive disorder (MDD) is a connectome disorder, characterized by altered functional connectivity across large-scale brain networks. However, the clinical heterogeneity, likely stemming from diverse neurobiological disturbances, complicates findings from standard group comparison methods. This variability has driven the search for MDD subtypes using objective neuroimaging markers. In this study, we sought to identify potential MDD subtypes from subject-level abnormalities in functional connectivity, leveraging a large multi-site dataset of resting-state MRI from 1276 MDD patients and 1104 matched healthy controls. Subject-level extreme functional connections, determined by comparing against normative ranges derived from healthy controls using tolerance intervals, were used to identify biological subtypes of MDD. We identified a set of extreme functional connections that were predominantly between the visual network and the frontoparietal network, the default mode network and the ventral attention network, with the key regions in the anterior cingulate cortex, bilateral orbitofrontal cortex, and supramarginal gyrus. In MDD patients, these extreme functional connections were linked to age of onset and reward-related processes. Using these features, we identified two subtypes with distinct patterns of functional connectivity abnormalities compared to healthy controls (p < 0.05, Bonferroni correction). When considering all patients together, no significant differences were found. These subtypes significantly enhanced case-control discriminability and showed strong internal discriminability between subtypes. Furthermore, the subtypes were reproducible across varying parameters, study sites, and in untreated patients. Our findings provide new insights into the taxonomy and have potential implications for both diagnosis and treatment of MDD.

Individual differences in functional connectivity during suppression of imagined threat

Functional magnetic resonance imaging studies typically rely on between-person analyses. To examine individual differences in functional connectivity, we used Group Iterative Multiple Model Estimation and its subgrouping function to analyze functional magnetic resonance imaging data of 54 participants who were suppressing imagined future threat. A two-stage random-effects meta-analytic approach was employed to examine individual differences. In addition to generalizable connections between brain regions, we identified individual differences in personalized models suggesting different pathways through which individuals suppress future threat. Two subgroups with distinct connectivity patterns emerged: One subgroup (n = 29; 53.70%), characterized by an additional lagged connection from the right to the left posterior cingulate cortex, exhibited comparatively higher anxiety and less brain connectivity, whereas the other subgroup (n = 25; 46.30%), showing an additional connection from the left posterior cingulate cortex to the ventromedial prefrontal cortex, was associated with lower anxiety levels and greater connectivity. This study points to individual differences in functional connectivity during emotion regulation.

Neuropsychiatric drug development: Perspectives on the current landscape, opportunities and potential future directions

Mental health represents a major challenge to our societies. One key difficulty associated with neuropsychiatric drug development is the lack of connection between the underlying biology and the disease. Nevertheless, there is growing optimism in this field with recent drug approvals (the first in decades) and renewed interest from pharmaceutical companies and investors. Here we review some of the most promising drug discovery and development endeavors currently deployed by industry. We also present elements illustrating the renewed interest from key stakeholders in neuropsychiatric drug development and provide potential future directions in this field.

The Functional Connectome and Long-Term Symptom Presentation Associated With Mild Traumatic Brain Injury and Blast Exposure in Combat Veterans

Mild traumatic brain injury (TBI) sustained in a deployment environment (deployment TBI) can be associated with increased severity of long-term symptom presentation, despite the general expectation of full recovery from a single mild TBI. The heterogeneity in the effects of deployment TBI on the brain can be difficult for a case-control design to capture. The functional connectome of the brain is an approach robust to heterogeneity that allows global measurement of effects using a common set of outcomes. The present study evaluates how differences in the functional connectome relate to remote symptom presentation following combat deployment and determines if deployment TBI, blast exposure, or post-traumatic stress disorder (PTSD) are associated with these neurological differences. Participants included 181 Iraq and Afghanistan combat-exposed Veterans, approximately 9.4 years since deployment. Structured clinical interviews provided diagnoses and characterizations of TBI, blast exposure, and PTSD. Self-report measures provided characterization of long-term symptoms (psychiatric, behavioral health, and quality of life). Resting-state magnetoencephalography was used to characterize the functional connectome of the brain individually for each participant. Linear regression identified factors contributing to symptom presentation including relevant covariates, connectome metrics, deployment TBI, blast exposure PTSD, and conditional relationships. Results identified unique contributions of aspects of the connectome to symptom presentation. Furthermore, several conditional relationships were identified, demonstrating that the connectome was related to outcomes in the presence of only deployment-related TBI (including blast-related TBI, primary blast TBI, and blast exposure). No conditional relationships were identified for PTSD; however, the main effect of PTSD on symptom presentation was significant for all models. These results demonstrate that the connectome captures aspects of brain function relevant to long-term symptom presentation, highlighting that deployment-related TBI influences symptom outcomes through a neurological pathway. These findings demonstrate that changes in the functional connectome associated with deployment-related TBI are relevant to symptom presentation over a decade past the injury event, providing a clear demonstration of a brain-based mechanism of influence.

Resolving Heterogeneity in Posttraumatic Stress Disorder Using Individualized Structural Covariance Network Analysis

The heterogeneity of posttraumatic stress disorder (PTSD) is an obstacle to both understanding and therapy, and this has prompted a search for internally homogeneous neuroradiological subgroups within the broad clinical diagnosis. We set out to do this using the individual differential structural covariance network (IDSCN). We constructed cortical thickness-based IDSCN using T1-weighted images of 89 individuals with PTSD (mean age 42.8 years, 60 female) and 89 demographically matched trauma-exposed non-PTSD (TENP) controls (mean age 43.1 years, 63 female). The IDSCN metric quantifies how the structural covariance edges in a patient differ from those in the controls. We examined the structural diversity of PTSD and variation among subtypes using a hierarchical clustering analysis. PTSD patients exhibited notable diversity in distinct structural covariance edges but mainly affecting three networks: default mode, ventral attention, and sensorimotor. These changes predicted individual PTSD symptom severity. We identified two neuroanatomical subtypes: the one with higher PTSD symptom severity showed lower structural covariance edges in the frontal cortex and between frontal, parietal, and occipital cortex-regions that are functionally implicated in selective attention, response selection, and learning tasks. Thus, deviations in structural covariance in large-scale networks are common in PTSD but fall into two subtypes. This work sheds light on the neurobiological mechanisms underlying the clinical heterogeneity and may aid in personalized diagnosis and therapeutic interventions.

Cognition and Ventral Attention Network Connectivity: Associations With Treatment Response in Posttraumatic Stress Disorder

OBJECTIVE

Posttraumatic stress disorder (PTSD) is a highly heterogeneous disorder, which makes it difficult to link clinical phenotypes with biomarkers to improve treatment outcomes. Findings from previous studies suggest that cognitive measures such as verbal memory or attention paired with within-ventral attention network (VAN) or salience network resting-state functional connectivity may predict treatment response among individuals with PTSD.

METHODS

In a sample comprising 20 individuals with PTSD and 10 healthy control group individuals, the investigators subtyped individuals by using both discriminant function analysis and standardized norms for a single measure of memory and neuropsychological batteries of memory, attention, and executive functioning; attempted to replicate previous findings of lower within-VAN connectivity among individuals with cognitive impairment; and explored whether within-VAN connectivity paired with cognitive impairment predicted treatment outcomes.

RESULTS

PTSD patients with cognitive impairment (defined by using a discriminant function analysis with verbal memory performance) had greater within-VAN resting-state functional connectivity compared with control group individuals and cognitively intact PTSD patients at a level that fell short of statistical significance (F=3.41; df=2, 21; ηp2=0.237). The interaction between verbal memory performance and within-VAN connectivity also predicted treatment-related change in PTSD symptoms at a level that also fell short of statistical significance (β=-0.442).

CONCLUSIONS

These findings somewhat support the clinical utility of identifying cognitive phenotypes within PTSD (by using discriminant function analysis and verbal memory performance) to predict treatment outcomes.

Neuroimaging of posttraumatic stress disorder in adults and youth: progress over the last decade on three leading questions of the field

Almost three decades have passed since the first posttraumatic stress disorder (PTSD) neuroimaging study was published. Since then, the field of clinical neuroscience has made advancements in understanding the neural correlates of PTSD to create more efficacious treatment strategies. While gold-standard psychotherapy options are available, many patients do not respond to them, prematurely drop out, or never initiate treatment. Therefore, elucidating the neurobiological mechanisms that define the disorder can help guide clinician decision-making and develop individualized mechanisms-based treatment options. To this end, this narrative review highlights progress made in the last decade in adult and youth samples on three outstanding questions in PTSD research: (1) Which neural alterations serve as predisposing (pre-exposure) risk factors for PTSD development, and which are acquired (post-exposure) alterations? (2) Which neural alterations can predict treatment outcomes and define clinical improvement? and (3) Can neuroimaging measures be used to define brain-based biotypes of PTSD? While the studies highlighted in this review have made progress in answering the three questions, the field still has much to do before implementing these findings into clinical practice. Overall, to better answer these questions, we suggest that future neuroimaging studies of PTSD should (A) utilize prospective longitudinal designs, collecting brain measures before experiencing trauma and at multiple follow-up time points post-trauma, taking advantage of multi-site collaborations/consortiums; (B) collect two scans to explore changes in brain alterations from pre-to-post treatment and compare changes in neural activation between treatment groups, including longitudinal follow up assessments; and (C) replicate brain-based biotypes of PTSD. By synthesizing recent findings, this narrative review will pave the way for personalized treatment approaches grounded in neurobiological evidence.

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.

Neural correlates of PTSD in women with childhood sexual abuse with and without PTSD and response to paroxetine treatment: A placebo-controlled, double-blind trial

Objective

Childhood sexual abuse is the leading cause of posttraumatic stress disorder (PTSD) in women, and is a prominent cause of morbidity and loss of function for which limited treatments are available. Understanding the neurobiology of treatment response is important for developing new treatments. The purpose of this study was to assess neural correlates of personalized traumatic memories in women with childhood sexual abuse with and without PTSD, and to assess response to treatment.

Methods

Women with childhood sexual abuse with (N = 28) and without (N = 17) PTSD underwent brain imaging with High-Resolution Positron Emission Tomography scanning with radiolabeled water for brain blood flow measurements during exposure to personalized traumatic scripts and memory encoding tasks. Women with PTSD were randomized to paroxetine or placebo followed by three months of double-blind treatment and repeat imaging with the same protocol.

Results

Women with PTSD showed decreases in areas involved in the Default Mode Network (DMN), a network of brain areas usually active when the brain is at rest, hippocampus and visual processing areas with exposure to traumatic scripts at baseline while women without PTSD showed increased activation in superior frontal gyrus and other areas (p < 0.005). Treatment of women with PTSD with paroxetine resulted in increased anterior cingulate activation and brain areas involved in the DMN and visual processing with scripts compared to placebo (p < 0.005).

Conclusion

PTSD related to childhood sexual abuse in women is associated with alterations in brain areas involved in memory and the stress response and treatment with paroxetine results in modulation of these areas.

Tensor response quantile regression with neuroimaging data

Collecting neuroimaging data in the form of tensors (i.e. multidimensional arrays) has become more common in mental health studies, driven by an increasing interest in studying the associations between neuroimaging phenotypes and clinical disease manifestation. Motivated by a neuroimaging study of post-traumatic stress disorder (PTSD) from the Grady Trauma Project, we study a tensor response quantile regression framework, which enables novel analyses that confer a detailed view of the potentially heterogeneous association between a neuroimaging phenotype and relevant clinical predictors. We adopt a sensible low-rank structure to represent the association of interest, and propose a simple two-step estimation procedure which is easy to implement with existing software. We provide rigorous theoretical justifications for the intuitive two-step procedure. Simulation studies demonstrate good performance of the proposed method with realistic sample sizes in neuroimaging studies. We conduct the proposed tensor response quantile regression analysis of the motivating PTSD study to investigate the association between fMRI resting-state functional connectivity and PTSD symptom severity. Our results uncover non-homogeneous effects of PTSD symptoms on brain functional connectivity, which cannot be captured by existing tensor response methods.

Resting-state fMRI signals contain spectral signatures of local hemodynamic response timing

Functional magnetic resonance imaging (fMRI) has proven to be a powerful tool for noninvasively measuring human brain activity; yet, thus far, fMRI has been relatively limited in its temporal resolution. A key challenge is understanding the relationship between neural activity and the blood-oxygenation-level-dependent (BOLD) signal obtained from fMRI, generally modeled by the hemodynamic response function (HRF). The timing of the HRF varies across the brain and individuals, confounding our ability to make inferences about the timing of the underlying neural processes. Here, we show that resting-state fMRI signals contain information about HRF temporal dynamics that can be leveraged to understand and characterize variations in HRF timing across both cortical and subcortical regions. We found that the frequency spectrum of resting-state fMRI signals significantly differs between voxels with fast versus slow HRFs in human visual cortex. These spectral differences extended to subcortex as well, revealing significantly faster hemodynamic timing in the lateral geniculate nucleus of the thalamus. Ultimately, our results demonstrate that the temporal properties of the HRF impact the spectral content of resting-state fMRI signals and enable voxel-wise characterization of relative hemodynamic response timing. Furthermore, our results show that caution should be used in studies of resting-state fMRI spectral properties, because differences in fMRI frequency content can arise from purely vascular origins. This finding provides new insight into the temporal properties of fMRI signals across voxels, which is crucial for accurate fMRI analyses, and enhances the ability of fast fMRI to identify and track fast neural dynamics.

Individual Deviations from Normative Electroencephalographic Connectivity Predict Antidepressant Response

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.

Childhood trauma and cortical thickness in healthy women, women with post-traumatic stress disorder, and women with borderline personality disorder

BACKGROUND

Structural brain changes have been associated with childhood trauma (CT) and several trauma-associated mental disorders. It is not known whether specific brain alterations are rather associated with CT as such or with disorders that are common sequelae of CT. In this study, we characterized cortical thickness in three distinct groups with CT: healthy women (HC/CT), women with posttraumatic stress disorder (PTSD/CT) and women with borderline personality disorder (BPD/CT). These three CT-exposed groups were compared with healthy controls not exposed to CT (HC).

METHODS

We recruited 129 women (n = 70 HC, n = 25 HC/CT, n = 14 PTSD/CT, and n = 20 BPD/CT) and acquired T1-weighted anatomical images. FreeSurfer was used for conducting whole-brain cortical thickness between-group comparisons, applying separate generalized linear models to compare cortical thickness of each CT-exposed group with HC.

RESULTS

The HC/CT group had lower cortical thickness in occipital lobe areas (right lingual gyrus, left lateral occipital lobe) than the HC group. The BPD/CT group showed a broader pattern of reduced cortical thickness compared to the HC group, including the bilateral superior frontal gyrus, and bilateral isthmus, the right posterior, and left caudal anterior of the cingulate cortex as well as the right lingual gyrus of the occipital lobe. We found no differences between PTSD/CT and HC.

CONCLUSIONS

Cortical thickness reduction in the right lingual gyrus of the occipital lobe seem to be related to CT but is also present in BPD patients even after adjusting for severity of CT. Possibly, reduced cortical thickness in the lingual gyrus presents a CT-related vulnerability factor for CT-related adult psychopathologies such as BPD. Reduced cortical thickness in the frontal and cingulate cortex may represent unique neuroanatomical markers of BPD possibly related to difficulties in emotion regulation.

Data driven clusters derived from resting state functional connectivity: Findings from the EMBARC study

BACKGROUND

To address the clinical heterogeneity of Major Depressive Disorder (MDD), this investigation determined whether resting state functional magnetic resonance imaging (fMRI) could be deployed to identify circuit based homogeneous subgroups, and whether subgroups identified show differential treatment outcomes.

METHODS

Pretreatment resting state fMRIs obtained from 278 outpatients with nonpsychotic MDD from Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care for Depression Study were used to create data-driven subgroups using CLICK clustering. These subgroups were then compared using baseline clinical data, as well as baseline-to-week 8 changes in depression severity measured using the 17-item Hamilton Rating Scale for Depression (HAMD17) and response/remission rates by treatment group.

RESULTS

Three subgroups were identified. Cluster-1 was characterized by overallhyperconnectivity coupled with profound hypoconnectivity between the supramarginal gyrus (executive control network; ECN) and the superior frontal cortex (dorsal attention network; DAN). Cluster-2 was characterized by overall hypoconnectivity coupled with hyperconnectivity between supramarginal gyrus (ECN) and superior frontal cortex (DAN). Cluster-3 showed hypoconnectivity, especially profound between the angular cortex (default mode network; DMN) and middle frontal cortex (ECN). While baseline clinical measures did not differentiate the three clusters, Cluster-3 had the remission rate (51.6%) compared to Cluster-1 and Cluster-2 (32.7% and 31.9%) when treated with sertraline.

LIMITATIONS

Due to the exploratory nature of these analyses, there were no adjustments for multiple comparisons.

CONCLUSIONS

Baseline functional connectivity can be used to subgroup patients with MDD that differ in acute phase treatment outcomes. Measures of connectivity may address the heterogeneity of MDD.

Evaluating the Evidence for Brain-Based Biotypes of Psychiatric Vulnerability in the Acute Aftermath of Trauma

OBJECTIVE

The weak link between subjective symptom-based diagnostic methods for posttraumatic psychopathology and objectively measured neurobiological indices forms a barrier to the development of effective personalized treatments. To overcome this problem, recent studies have aimed to stratify psychiatric disorders by identifying consistent subgroups based on objective neural markers. Along these lines, a promising 2021 study by Stevens et al. identified distinct brain-based biotypes associated with different longitudinal patterns of posttraumatic symptoms. Here, the authors conducted a conceptual nonexact replication of that study using a comparable data set from a multimodal longitudinal study of recent trauma survivors.

METHODS

A total of 130 participants (mean age, 33.61 years, SD=11.21; 48% women) admitted to a general hospital emergency department following trauma exposure underwent demographic, clinical, and neuroimaging assessments 1, 6, and 14 months after trauma. All analyses followed the pipeline outlined in the original study and were conducted in collaboration with its authors.

RESULTS

Task-based functional MRI conducted 1 month posttrauma was used to identify four clusters of individuals based on profiles of neural activity reflecting threat and reward reactivity. These clusters were not identical to the previously identified brain-based biotypes and were not associated with prospective symptoms of posttraumatic psychopathology.

CONCLUSIONS

Overall, these findings suggest that the original brain-based biotypes of trauma resilience and psychopathology may not generalize to other populations. Thus, caution is warranted when attempting to define subtypes of psychiatric vulnerability using neural indices before treatment implications can be fully realized. Additional replication studies are needed to identify more stable and generalizable neuroimaging-based biotypes of posttraumatic psychopathology.

Resting-state fMRI signals contain spectral signatures of local hemodynamic response timing

Functional magnetic resonance imaging (fMRI) has proven to be a powerful tool for noninvasively measuring human brain activity; yet, thus far, fMRI has been relatively limited in its temporal resolution. A key challenge is understanding the relationship between neural activity and the blood-oxygenation-level-dependent (BOLD) signal obtained from fMRI, generally modeled by the hemodynamic response function (HRF). The timing of the HRF varies across the brain and individuals, confounding our ability to make inferences about the timing of the underlying neural processes. Here we show that resting-state fMRI signals contain information about HRF temporal dynamics that can be leveraged to understand and characterize variations in HRF timing across both cortical and subcortical regions. We found that the frequency spectrum of resting-state fMRI signals significantly differs between voxels with fast versus slow HRFs in human visual cortex. These spectral differences extended to subcortex as well, revealing significantly faster hemodynamic timing in the lateral geniculate nucleus of the thalamus. Ultimately, our results demonstrate that the temporal properties of the HRF impact the spectral content of resting-state fMRI signals and enable voxel-wise characterization of relative hemodynamic response timing. Furthermore, our results show that caution should be used in studies of resting-state fMRI spectral properties, as differences can arise from purely vascular origins. This finding provides new insight into the temporal properties of fMRI signals across voxels, which is crucial for accurate fMRI analyses, and enhances the ability of fast fMRI to identify and track fast neural dynamics.

Resting-state connectivity subtype of comorbid PTSD and alcohol use disorder moderates improvement from integrated prolonged exposure therapy in Veterans

BACKGROUND

Posttraumatic stress disorder (PTSD) and alcohol use disorder (AUD) are highly comorbid and are associated with significant functional impairment and inconsistent treatment outcomes. Data-driven subtyping of this clinically heterogeneous patient population and the associated underlying neural mechanisms are highly needed to identify who will benefit from psychotherapy.

METHODS

In 53 comorbid PTSD/AUD patients, resting-state functional magnetic resonance imaging was collected prior to undergoing individual psychotherapy. We used a data-driven approach to subgroup patients based on directed connectivity profiles. Connectivity subgroups were compared on clinical measures of PTSD severity and heavy alcohol use collected at pre- and post-treatment.

RESULTS

We identified a subgroup of patients associated with improvement in PTSD symptoms from integrated-prolonged exposure therapy. This subgroup was characterized by lower insula to inferior parietal cortex (IPC) connectivity, higher pregenual anterior cingulate cortex (pgACC) to posterior midcingulate cortex connectivity and a unique pgACC to IPC path. We did not observe any connectivity subgroup that uniquely benefited from integrated-coping skills or subgroups associated with change in alcohol consumption.

CONCLUSIONS

Data-driven approaches to characterize PTSD/AUD subtypes have the potential to identify brain network profiles that are implicated in the benefit from psychological interventions - setting the stage for future research that targets these brain circuit communication patterns to boost treatment efficacy.

Human fear neurobiology reimagined: Can brain-derived biotypes predict fear-based disorders after trauma?

Human studies of fear neurobiology have established neural circuits that are activated to threatening stimuli, whether it be during Pavlovian fear conditioning or in response to naturally occurring threats. This circuitry involves the central and basolateral amygdala, as well as the bed nucleus of the stria terminalis, insula, hippocampus, and regulatory regions such as the anterior cingulate cortex and ventromedial prefrontal cortex. While research has found that fear-based disorders, such as anxiety and post-traumatic stress disorder, as associated with dysfunction in these circuits, there is substantial individual heterogeneity in the clinical presentation of symptoms. Recent work has used data-driven methods to derive brain biotypes that capitalize on the activity of the fear circuit and its interaction with other regions of the brain. These biotypes have great utility in both describing individual variation in psychopathology and in identifying individuals at greater risk for fear-based disorders after an environmental stressor, such as a traumatic event. The review discusses recent examples of how fear neurobiology studies can be leveraged to derive biotypes that may ultimately lead to improved treatment.

Modern views of machine learning for precision psychiatry

In light of the National Institute of Mental Health (NIMH)'s Research Domain Criteria (RDoC), the advent of functional neuroimaging, novel technologies and methods provide new opportunities to develop precise and personalized prognosis and diagnosis of mental disorders. Machine learning (ML) and artificial intelligence (AI) technologies are playing an increasingly critical role in the new era of precision psychiatry. Combining ML/AI with neuromodulation technologies can potentially provide explainable solutions in clinical practice and effective therapeutic treatment. Advanced wearable and mobile technologies also call for the new role of ML/AI for digital phenotyping in mobile mental health. In this review, we provide a comprehensive review of ML methodologies and applications by combining neuroimaging, neuromodulation, and advanced mobile technologies in psychiatry practice. We further review the role of ML in molecular phenotyping and cross-species biomarker identification in precision psychiatry. We also discuss explainable AI (XAI) and neuromodulation in a closed human-in-the-loop manner and highlight the ML potential in multi-media information extraction and multi-modal data fusion. Finally, we discuss conceptual and practical challenges in precision psychiatry and highlight ML opportunities in future research.

CRF serum levels differentiate PTSD from healthy controls and TBI in military veterans

Background and Objective

Posttraumatic stress disorder (PTSD) is a serious and frequently debilitating psychiatric condition that can occur in people who have experienced traumatic stressors, such as war, violence, sexual assault and other life‐threatening events. Treatment of PTSD and traumatic brain injury (TBI) in veterans is challenged by diagnostic complexity, partially due to PTSD and TBI symptoms overlap and to the fact that subjective self‐report assessments may be influenced by a patient's willingness to share their traumatic experiences and resulting symptoms. Corticotropin‐releasing factor (CRF) is one of the main mediators of hypothalamic pituitary adrenal (HPA)‐axis responses in stress and anxiety.

Methods and Results

We analyzed serum CRF levels in 230 participants including heathy controls (64), and individuals with PTSD (53), TBI (70) or PTSD + TBI (43) by enzyme immunoassay (EIA). Significantly lower CRF levels were found in both the PTSD and PTSD + TBI groups compared to healthy control (PTSD vs. Controls: P = 0.0014, PTSD + TBI vs. Controls: P = 0.0011) and chronic TBI participants (PTSD vs. TBI: P < 0.0001, PTSD + TBI vs. TBI: P < 0.0001), suggesting a PTSD‐related mechanism independent from TBI and associated with CRF reduction. CRF levels negatively correlated with PTSD severity on the Clinically Administered PTSD Scale (CAPS‐5) scale in the whole study group.

Conclusions

Hyperactivation of the HPA axis has been classically identified in acute stress. However, the recognized enhanced feedback inhibition of the HPA axis in chronic stress supports our findings of lower CRF in PTSD patients. This study suggests that reduced serum CRF in PTSD should be further investigated. Future validation studies will establish if CRF is a possible blood biomarker for PTSD and/or for differentiating PTSD and chronic TBI symptomatology.

The HPA axis is activated under acute stress conditions, but an enhanced feedback inhibition may be prevalent in chronic stress conditions such as PTSD.

We observed a reduction in serum CRF levels in veterans with PTSD and PTSD + TBI, but not in veterans with chronic TBI alone.

A serum CRF reduction may be indicative of CNS mechanisms specific to PTSD and should be further evaluated as a possible peripheral biomarker.

Reversed patterns of resting state functional connectivity for females vs. males in posttraumatic stress disorder

Background

Posttraumatic stress disorder (PTSD) is twice as prevalent among females as compared to males following potentially traumatic events. While there is evidence for aberrant functional connectivity between hubs of the central executive network (CEN), salience network (SN), and the default mode network (DMN) in PTSD, little is known regarding sex-specificity of this connectivity. The current study aims to directly examine sex-specific resting-state functional connectivity (rs-FC) in trauma exposed males and females, with and without PTSD.

Methods

One hundred and seventy-eight individuals underwent functional magnetic resonance imaging (fMRI) at rest, of them 85 females (45 with PTSD) and 93 males (57 with PTSD). We conducted whole-brain seed-based analysis using CEN (lateral prefrontal cortex [lPFC]), SN (anterior cingulate cortex [ACC], insula, amygdala [AMG]), and DMN (medial prefrontal cortex [mPFC], posterior parietal cortex [PCC], and hippocampus [HIP]) hubs as seed regions. Group-by-Sex ANOVA was conducted.

Results

The amygdala-precuneus, ACC-precuneus, and hippocampus-precuneus pathways exhibited significant group-by-sex interaction effects, with females with PTSD consistently differing in connectivity patterns from males with PTSD and from trauma-exposed healthy females.

Conclusions

Sex-specific neural connectivity patterns were found within and between key nodes of the CEN, DMN, and the SN, suggesting opposite patterns of connectivity in PTSD and trauma-exposed controls as a function of sex as a biological variable (SABV). This may point to mechanistic sex differences in adaptation following trauma and may inform differential neural targets for treatment of females and males with PTSD.

Neural correlates of anger expression in patients with PTSD

Anger is a common and debilitating symptom of post-traumatic stress disorder (PTSD). Although studies have identified brain circuits underlying anger experience and expression in healthy individuals, how these circuits interact with trauma remains unclear. Here, we performed the first study examining the neural correlates of anger in patients with PTSD. Using a data-driven approach with resting-state fMRI, we identified two prefrontal regions whose overall functional connectivity was inversely associated with anger: the left anterior middle frontal gyrus (aMFG) and the right orbitofrontal cortex (OFC). We then used concurrent TMS-EEG to target the left aMFG parcel previously identified through fMRI, measuring its cortical excitability and causal connectivity to downstream areas. We found that low-anger PTSD patients exhibited enhanced excitability in the left aMFG and enhanced causal connectivity between this region and visual areas. Together, our results suggest that left aMFG activity may confer protection against the development of anger, and therefore may be an intriguing target for circuit-based interventions for anger in PTSD.

Translating Across Circuits and Genetics Toward Progress in Fear- and Anxiety-Related Disorders

(Reprinted with permission from Am J Psychiatry 2020; 177:214-222).

Impaired executive function exacerbates neural markers of posttraumatic stress disorder

BACKGROUND

A major obstacle in understanding and treating posttraumatic stress disorder (PTSD) is its clinical and neurobiological heterogeneity. To address this barrier, the field has become increasingly interested in identifying subtypes of PTSD based on dysfunction in neural networks alongside cognitive impairments that may underlie the development and maintenance of symptoms. The current study aimed to determine if subtypes of PTSD, based on normative-based cognitive dysfunction across multiple domains, have unique neural network signatures.

METHODS

In a sample of 271 veterans (90% male) that completed both neuropsychological testing and resting-state fMRI, two complementary, whole-brain functional connectivity analyses explored the link between brain functioning, PTSD symptoms, and cognition.

RESULTS

At the network level, PTSD symptom severity was associated with reduced negative coupling between the limbic network (LN) and frontal-parietal control network (FPCN), driven specifically by the dorsolateral prefrontal cortex and amygdala Hubs of Dysfunction. Further, this relationship was uniquely moderated by executive function (EF). Specifically, those with PTSD and impaired EF had the strongest marker of LN-FPCN dysregulation, while those with above-average EF did not exhibit PTSD-related dysregulation of these networks.

CONCLUSION

These results suggest that poor executive functioning, alongside LN-FPCN dysregulation, may represent a neurocognitive subtype of PTSD.

Developing Multimodal Dynamic Functional Connectivity as a Neuroimaging Biomarker

Background: In spite of increasing evidence highlighting the role of dynamic functional connectivity (FC) in characterizing mental disorders, there is a lack of (a) reliable statistical methods to compute dynamic connectivity and (b) rigorous dynamic FC-based approaches for predicting mental health outcomes in heterogeneous disorders such as post-traumatic stress disorder (PTSD). Methods: In one of the first such efforts, we develop a reliable and accurate approach for estimating dynamic FC guided by brain structural connectivity (SC) computed using diffusion tensor imaging data and investigate the potential of the proposed multimodal dynamic FC to predict continuous mental health outcomes. We develop concrete measures of temporal network variability that are predictive of PTSD resilience, and identify regions whose temporal connectivity fluctuations are significantly related to resilience. Results: Our results illustrate that the multimodal approach is more sensitive to connectivity change points, it can clearly detect localized brain regions with the dynamic network features such as small-worldedness, clustering coefficients, and efficiency associated with resilience, and that it has superior predictive performance compared with existing static and dynamic network models when modeling PTSD resilience. Discussion: While the majority of resting-state network modeling in psychiatry has focused on static FC, our novel multimodal dynamic network analyses that are sensitive to network fluctuations allowed us to provide a model of neural correlates of resilience with high accuracy compared with existing static connectivity approaches or those that do not use brain SC information, and provided us with an expanded understanding of the neurobiological causes for PTSD. Impact statement The methods developed in this article provide reliable and accurate dynamic functional connectivity (FC) approaches by fusing multimodal imaging data that are highly predictive of continuous clinical phenotypes in heterogeneous mental disorders. Currently, there is very little theoretical work to explain how network dynamics might contribute to individual differences in behavior or psychiatric symptoms. Our analysis conclusively discovers localized brain resting-state networks, regions, and connections where variations in dynamic FC (that is estimated after incorporating brain structural connectivity information) are associated with post-traumatic stress disorder resilience, which could potentially provide valuable tools for the development of neural circuit modeling in psychiatry in the future.

Identification of psychiatric disorder subtypes from functional connectivity patterns in resting-state electroencephalography

The understanding and treatment of psychiatric disorders, which are known to be neurobiologically and clinically heterogeneous, could benefit from the data-driven identification of disease subtypes. Here, we report the identification of two clinically relevant subtypes of post-traumatic stress disorder (PTSD) and major depressive disorder (MDD) on the basis of robust and distinct functional connectivity patterns, prominently within the frontoparietal control network and the default mode network. We identified the disease subtypes by analysing, via unsupervised and supervised machine learning, the power-envelope-based connectivity of signals reconstructed from high-density resting-state electroencephalography in four datasets of patients with PTSD and MDD, and show that the subtypes are transferable across independent datasets recorded under different conditions. The subtype whose functional connectivity differed most from those of healthy controls was less responsive to psychotherapy treatment for PTSD and failed to respond to an antidepressant medication for MDD. By contrast, both subtypes responded equally well to two different forms of repetitive transcranial magnetic stimulation therapy for MDD. Our data-driven approach may constitute a generalizable solution for connectome-based diagnosis.

Resting-state functional network models for posttraumatic stress disorder

Four recent articles were examined for their use of resting-state functional magnetic resonance imaging on participants with posttraumatic symptoms. Theory-driven computations were complemented by the novel use of network metrics, which revealed reduced global centrality and higher efficiency within the default mode network for participants with posttraumatic symptoms. Data-driven methods from other studies revealed associations between functional networks and posttraumatic stress disorder (PTSD) symptoms and clusters of functional activation corresponding to different PTSD presentations.

Parsing inter- and intra-individual variability in key nervous system mechanisms of stress responsivity and across functional domains

Exposure to stressful events is omnipresent in modern human life, yet people show considerable heterogeneity in the impact of stress exposure(s) on their functionality and overall health. Encounter with stressor(s) is counteracted by an intricate repertoire of nervous-system responses. This narrative review starts with a brief summary of the vast evidence that supports heart rate variability, cortisol secretion, and large-scale cortical network interactions as kay physiological, endocrinological, and neural mechanisms of stress responsivity, respectively. The second section highlights potential sources for inter-individual variability in these mechanisms, by focusing on biological, environmental, social, habitual, and psychological factors that may influence stress responsivity patterns and thus contribute to heterogeneity in the impact of stress exposure on functionality and health. The third section introduces intra-individually variability in stress responsivity across functional domains as a novel putative source for heterogeneity in the impact of stress exposure. Challenges and future directions are further discussed. Parsing inter- and intra-individual variability in nervous-system mechanisms of stress responsivity and across functional domains is critical towards potential clinical translation.

Translating Across Circuits and Genetics Toward Progress in Fear- and Anxiety-Related Disorders

Anxiety and fear-related disorders are common and disabling, and they significantly increase risk for suicide and other causes of morbidity and mortality. However, there is tremendous potential for translational neuroscience to advance our understanding of these disorders, leading to novel and powerful interventions and even to preventing their initial development. This overview examines the general circuits and processes thought to underlie fear and anxiety, along with the promise of translational research. It then examines some of the data-driven "next-generation" approaches that are needed for discovery and understanding but that do not always fit neatly into established models. From one perspective, these disorders offer among the most tractable problems in psychiatry, with a great deal of accumulated understanding, across species, of neurocircuit, behavioral, and, increasingly, genetic mechanisms, of how dysregulation of fear and threat processes contributes to anxiety-related disorders. One example is the progressively sophisticated understanding of how extinction underlies the exposure therapy component of cognitive-behavioral therapy approaches, which are ubiquitously used across anxiety and fear-related disorders. However, it is also critical to examine gaps in our understanding between reasonably well-replicated examples of successful translation, areas of significant deficits in knowledge, and the role of large-scale data-driven approaches in future progress and discovery. Although a tremendous amount of progress is still needed, translational approaches to understanding, treating, and even preventing anxiety and fear-related disorders offer great opportunities for successfully bridging neuroscience discovery to clinical practice.