Alan Anticevic - US grants

DESCRIPTION (provided by applicant): The field of psychiatry has made substantial progress towards understanding mental illness using basic neuroscience methods, but the explanatory gap between cellular hypotheses and clinical phenomena remains vast. This gap is particularly evident in our understanding of schizophrenia, a devastating disorder whose core feature is disrupted cognition. Schizophrenia patients present with debilitating cognitive deficits, not adequately treated by available therapies. Understanding and restoring cognitive function is critical to improving patients' lives. One way to close this gap is to investigate the clinical phenomena by combining several scientific methodologies, at multiple levels of analysis. Therefore, this proposal broadly aims to align functional neuroimaging with biophysically-realistic computational models of neural function and to test model predictions using safe and reversible pharmacological manipulations in healthy volunteers. It further aims to directly compare findings to deficits observed in schizophrenia patients. Ultimately, the current proposal will bridge levels of explanation to mechanistically understand cognitive dysfunction in schizophrenia. One severely compromised cognitive operation in schizophrenia is working memory: the ability to temporarily hold and manipulate information in mind. Disruptions in working memory compromise patients' ability to track thoughts, ideas, and feelings, severely limiting even basic functioning. Although functional neuroimaging studies repeatedly link working memory disturbances to prefrontal dysfunction, synaptic mechanisms remain elusive. One leading hypothesis proposes disruption in the balance of excitation and inhibition in cortical micro-circuitry caused by hypo-function of the N-methyl-D-aspartate (NMDA) glutamate receptor. However, to test this hypothesis in relation to disrupted cognition, and to ultimately develop medications that alleviate cognitive dysfunction in schizophrenia, we need to go a step beyond neuroimaging. We need an understanding of working memory dysfunction at the level of cellular mechanisms, which is where treatments are developed. The specific aims of this proposal are: i) to extend an established biophysically-realistic computational model of working memory to 'mimic' hypothesized NMDA receptor pathology and use it to make behavioral and neural predictions regarding deficits observed in schizophrenia; ii) experimentally test those predictions using a leading safe pharmacological model of schizophrenia that perturbs the precise mechanism in healthy volunteers, namely transient NMDA antagonism via ketamine; iii) to relate these pharmacological results to deficits observed in patients using behavior and functional neuroimaging. The proposed project will close the explanatory gap and help develop a multi-level mechanistic understanding of cognitive dysfunction in schizophrenia. Ultimately, the success of this research will fertilize rationally-guided treatments and improve the lives of people suffering from this devastating disorder.

DESCRIPTION (provided by applicant): The diagnostic system for neuropsychiatric conditions embodied in the Diagnostic and Statistical Manual of Psychiatric Disorders (DSM) is based on clusters of symptoms rather than on underlying etiology or pathophysiology. The establishment of reliable diagnoses was a critical step in the advancement of psychiatric science three decades ago, but now it holds the field back by concealing relationships between brain biology and individual patients' symptoms - relationships that are obscure under the best of circumstances. This realization motivates a search for an alternative, brain-based diagnostic system, in the form of the NIMH's Research Domain Criteria (RDoC) initiative. The development of such an alternative diagnostic framework is in its infancy, and new strategies are needed for the rational categorization of pathophysiological states. We have successfully used data-driven analysis of functional connectivity data, derived from functional neuroimaging of the brain at rest. This approach has revealed neural dysconnectivity across several neuropsychiatric conditions. We will apply these data-driven approaches, in conjunction with leading machine learning algorithms, to quantify dysconnectivity patterns across and within major DSM disorders. We have assembled a dataset of 707 resting-state scans, performed on state-of-the-art 3T scanners and passing rigorous quality control standards, comprising five major DSM diagnoses: schizophrenia, bipolar disorder, major depressive disorder, obsessive-compulsive disorder, and post-traumatic stress disorder, with matched controls for each. Accompanying symptom assessments were administered by highly skilled personnel. This large hybrid dataset permits an unprecedented cross-diagnostic, data-driven search for shared or distinct dysconnectivity across diagnoses. Specifically, we will employ a powerful multi-tiered analytic approach using: fully data-driven connectivity analysis, focusing on networks defined a priori by work in healthy subjects, and a seed-based approach focused on circuits associated with the constituent DSM diagnoses. We hypothesize several possible outcomes. First, patient groups derived from the data-driven connectivity analyses may indeed map onto symptom-based DSM diagnoses. This would be a validation of a symptom- focused nosology, at least across these conditions. Second, data-driven analysis may identify new categories that cut across DSM diagnoses. Third, results may follow continua of dysconnectivity, such as those proposed by the RDoC framework. A more complex outcome that blends these patterns is also probable. Finally, emergent patterns will be correlated against symptom measures, within and across disorders. Irrespective of the ultimate pattern, results of this project will critically inform ongoing effort to refine a diagnostic scheme for psychiatric disorders that is firmly grounded in their pathophysiology. Furthermore, the methodology will be applicable to other datasets. We anticipate that this approach will provide a key pillar to the development of a brain-based understanding of the heterogeneity of psychiatric disease.

DESCRIPTION (provided by applicant): The diagnostic system for neuropsychiatric conditions embodied in the Diagnostic and Statistical Manual of Psychiatric Disorders (DSM) is based on clusters of symptoms rather than on underlying etiology or pathophysiology. The establishment of reliable diagnoses was a critical step in the advancement of psychiatric science three decades ago, but now it holds the field back by concealing relationships between brain biology and individual patients' symptoms - relationships that are obscure under the best of circumstances. This realization motivates a search for an alternative, brain-based diagnostic system, in the form of the NIMH's Research Domain Criteria (RDoC) initiative. The development of such an alternative diagnostic framework is in its infancy, and new strategies are needed for the rational categorization of pathophysiological states. We have successfully used data-driven analysis of functional connectivity data, derived from functional neuroimaging of the brain at rest. This approach has revealed neural dysconnectivity across several neuropsychiatric conditions. We will apply these data-driven approaches, in conjunction with leading machine learning algorithms, to quantify dysconnectivity patterns across and within major DSM disorders. We have assembled a dataset of 707 resting-state scans, performed on state-of-the-art 3T scanners and passing rigorous quality control standards, comprising five major DSM diagnoses: schizophrenia, bipolar disorder, major depressive disorder, obsessive-compulsive disorder, and post-traumatic stress disorder, with matched controls for each. Accompanying symptom assessments were administered by highly skilled personnel. This large hybrid dataset permits an unprecedented cross-diagnostic, data-driven search for shared or distinct dysconnectivity across diagnoses. Specifically, we will employ a powerful multi-tiered analytic approach using: fully data-driven connectivity analysis, focusing on networks defined a priori by work in healthy subjects, and a seed-based approach focused on circuits associated with the constituent DSM diagnoses. We hypothesize several possible outcomes. First, patient groups derived from the data-driven connectivity analyses may indeed map onto symptom-based DSM diagnoses. This would be a validation of a symptom- focused nosology, at least across these conditions. Second, data-driven analysis may identify new categories that cut across DSM diagnoses. Third, results may follow continua of dysconnectivity, such as those proposed by the RDoC framework. A more complex outcome that blends these patterns is also probable. Finally, emergent patterns will be correlated against symptom measures, within and across disorders. Irrespective of the ultimate pattern, results of this project will critically inform ongoing effort to refine a diagnostic scheme for psychiatric disorders that is firmly grounded in their pathophysiology. Furthermore, the methodology will be applicable to other datasets. We anticipate that this approach will provide a key pillar to the development of a brain-based understanding of the heterogeneity of psychiatric disease.

? DESCRIPTION (provided by applicant): Schizophrenia (SCZ) is a disabling neurodevelopmental disorder causing profound cognitive impairment. SCZ is hypothesized to arise from synaptic disturbances affecting large-scale neural connectivity. This view is supported by neuroimaging studies that repeatedly show alterations in prefrontal cortex (PFC) function and connectivity and disruptions across thalamo-cortical and associative cortex circuits. However, the complex neurobiology of early-course SCZ remains uncharacterized, limiting treatments for early illness phases when intervention is crucial. This is a major objective for improving targeted therapies, predicting prognosis, and promoting early detection. Our overarching goal is to longitudinally characterize concurrent functional and structural dysconnectivity in early-course SCZ in relation to cognitive deficits via state-of-the-art neuroimaging. In turn, we aim to inform synaptic hypotheses underlying clinical neuroimaging effects via biophysically-based computational modeling scaled to the level of neural networks. To address these knowledge gaps, we will examine longitudinal progression of neural dysconnectivity in early-course SCZ patients after their initial admission into the Specialized Treatment Early in Psychosis (STEP) Clinic at Yale. In turn, we will follow patients longitudinally at 6, 12, and 24 months later in comparison with 50 matched healthy controls. To quantify dysconnectivity the project will use leading functional and structural methods optimized by the Human Connectome Project (HCP), in line with the NIMH Connectomes Related to Human Disease initiative. First, we aim to test if the recently identified PFC and thalamo-cortical markers exhibit concurrent (or dissociable) structural and functional alterations. This balanced longitudinal design can distinguish `state' versus `trait' neuroimaging markers during early illness course in relation to clinically-relevant variables. Specifically, examining effects of pharmacotherapy, treatment compliance, duration of untreated psychosis, and symptom severity, informs the clinical utility of these promising neuroimaging markers. Second, the project will test if these neuroimaging markers relate to severity of cognitive deficits - a hallmak clinical feature of SCZ. We aim to concurrently examine working memory (WM) via our validated neuroimaging paradigms to test if specific aspects of structural and functional dysconnectivity predict WM deficits. This provides a much-needed link between dysconnectivity and cognitive impairment in SCZ. Finally, to inform synaptic hypotheses behind neural dysconnectivity, such as cortical excitation-inhibition (E/I) imbalance resulting from hypo-function of the N-methyl-D-aspartate glutamate receptor (NMDAR), we aim to use biophysically-based computational models that incorporate relevant cellular detail. We aim to iteratively explore synaptic parameters governing E/I balance by fitting in silico effects with in vivo clinical neuroimaging findings. This computational psychiatry approach can help interpret dynamic neural dysconnectivity in SCZ via computational fits and yield new synaptic targets for treatment studies focused on early SCZ stages, when intervention is most vital.

PROJECT SUMMARY Schizophrenia (SCZ) is a profoundly disabling neurodevelopmental disorder causing marked functional impairment. SCZ is hypothesized to arise from synaptic disturbances affecting large-scale neural connectivity along cortico?thalamic- striatal?cortical (CTSC) pathways. Neuroimaging evidence supports this view by showing alterations in associative cortices and connectivity disruptions across CTSC circuits in chronic SCZ. Yet, the complex evolving neurobiology of early-course SCZ remains uncharacterized. This limits treatments for early illness phases when intervention is crucial by capitalizing on the narrow `window' of opportunity to halt disease progression. Thus, understanding the neurobiology of early-course SCZ is a major objective for early detection, prognosis prediction and targeted individualized therapy. A major complicating factor in many SCZ studies is the confounding presence of antipsychotic treatment. Thus, our goal is to characterize co-occurring functional and structural dysconnectivity in unmedicated early-course SCZ and quantify neural changes in relation to cardinal SCZ symptoms, cognitive deficits and treatment response. To achieve this, we will examine longitudinal progression of neural dysconnectivity in 150 unmedicated early-course SCZ patients after their initial admission into clinics affiliated with West China Hospital. We will follow patients longitudinally at 6, 12, and 24 months later in comparison with 150 matched healthy controls. We will use state-of-the-art functional and structural methods optimized by the Human Connectome Project to achieve cutting-edge multi-modal neuroimaging integration. As noted, mounting evidence implicates CTSC loops in SCZ, particularly higher-order prefrontal and thalamic regions (e.g. medio-dorsal structures), suggesting mechanistic links between CTSC dysfunction and SCZ symptoms. Thus, first we aim to test if the identified CTSC markers exhibit concurrent (or dissociable) structural and functional alterations in unmedicated SCZ patients and if these circuits alter longitudinally. Second, we will test if structural and functional neuroimaging alterations relate to severity of cardinal SCZ symptoms and cognitive deficits. This provides a much-needed mapping between longitudinal CTSC dysconnectivity, symptoms and cognition in SCZ. Critically, this balanced longitudinal design can distinguish `state' versus `trait' neuroimaging markers during early illness course in relation to clinically relevant variables. Finally, it is well established that many SCZ patients do not respond well to antipsychotics. Yet, the neural markers of poor treatment response remain unmapped (and conversely treatment response). A key advantage of the proposed U.S.-China partnership is precisely the capacity to longitudinally study large sample sizes starting from medication-free observations, afforded by extensive and robust recruitment infrastructure at West China Hospital. Thus, our third aim is to quantify longitudinal structural and functional CTSC dysconnectivity in relation to treatment response. Collectively, this study will map longitudinal `state' versus `trait' neural markers starting from medication-free early illness stages (Aim 1), relate these changes to symptom dynamics (Aim 2) and treatment response (Aim 3). This mapping is vital to inform future work aimed at maximizing individualized early intervention strategies.

Project Summary: Brain networks linking the cortex to thalamus are critical for cognitive, sensory, and motor functioning. Dysruption of thalamocortical networks has been implicated in the pathophysiology of neurodevelopmental disorders, including psychosis, and mechanisms of clinical phenotypes, especially cognitive impairment. This view is supported by neuroimaging, including a series of studies by the Co-PIs, which consistently find a combination of reduced thalamic connectivity with the prefrontal cortex (PFC) and sensorimotor-thalamic hyper-connectivity in schizophrenia and bipolar disorder. While significant progress has been made, critical knowledge gaps remain with respect to the normal developmental trajectory of thalamocortical networks and onset of connectivity disturbances in psychosis; relationship between cognitive functions supported by thalamocortical circuits and thalamocortical connectivity biomarkers; and clinical utility of imaging thalamocortical networks. The availability of several large-scale cross-sectional datasets containing multi-modal neuroimaging data and extensive phenotypic data on healthy and at-risk individuals has created an unprecedented opportunity to address these critical knowledge gaps. They include the Cambridge Center for Ageing and Neuroscience (Cam-CAN: n=656, ages 18-88); Nathan Kline Institute-Rockland Sample (NKI- RS: n=932, ages 6-85); Pediatric Imaging, Neurocognition, and Genetics dataset (PING: N=1239, ages 3-20); Philadelphia Neurodevelopmental Cohort (PNC: n=1601, ages 8-21), which includes psychosis spectrum (PS) youth; and the North American Prodromal Longitudinal Study (NAPLS: n=397). Building on our prior work in clinical populations and leveraging the considerable resources of these datasets, we propose to chart the development of thalamocortical networks in healthy subjects (Aim 1) and youth with PS symptoms (Aim 2), characterize the associations between thalamocortical biomarkers and cognition (Aim 3), and investigate the clinical utility of thalamocortical connectivity biomarkers at identifying atypical brain development in individual subjects across the psychosis continuum and predicting conversion to psychosis in clinical high risk individuals (Aim 4). The proposed Aims will: 1) establish critical normative lifespan development data for refining and testing etiological models of not just psychosis, but other neurodevelopmental disorders, as well as aging- related disorders; 2) inform the pathophysiology of psychotic disorders and contribute to dimensional models of psychosis; 3) help define the neural basis of executive cognitive abilities thereby providing the necessary foundation for mechanistic models of normal cognitive function and cognitive impairment in psychosis; and 4) potentially provide risk biomarkers and intervention targets for youth at risk for developing a psychotic disorder.

ABSTRACT Obsessions and compulsions affect ~30% of the population; when they become severe they lead to a diagnosis of obsessive-compulsive disorder (OCD), which affects one person in 40. Available treatments, including pharmacotherapy with the selective serotonin reuptake inhibitor (SSRI) antidepressants and specialized psychotherapy, are of benefit to many, but individualized response is heterogeneous and unpredictable. Understanding the brain mechanisms of therapeutic change is urgently needed and may guide the development of new interventions. Ultimately, the ability to predict who will respond to a particular treatment would be a major theoretical and clinical advance, would accelerate deployment of effective treatment, and would thereby greatly reduce morbidity. Early studies using perfusion imaging have hinted that baseline neural markers can predict response to pharmacotherapy. However, these studies have not harnessed modern network-focused analytic methods and have not yielded mechanistic insight or clinical utility. Neuropsychiatric disorders are hypothesized to derive from altered functional brain networks. Resting-state functional connectivity MRI (rs-fcMRI) has emerged as a powerful tool to characterize functional network architecture in humans. We propose to use rs-fcMRI, employing state-of-the-art methodologies pioneered by the Human Connectome Project, to map the relationship between functional neural networks and treatment response in OCD. Specifically, we aim to characterize rs-fcMRI connectivity profiles that map onto treatment-associated changes and that predict response. The feasibility of this project is supported by our pilot data. We focus on first-line SSRI pharmacotherapy with fluoxetine as a tractable first step; future studies will incorporate other treatment modalities, including psychotherapy. We propose an innovative clinical design that dissociates treatment from time effects, which is a major challenge in studies of treatment mechanism. 80 medication-free OCD subjects will be randomized 1:1 to receive fluoxetine treatment starting either immediately or after a 6-week placebo lead-in phase. OCD subjects will undergo imaging at baseline and at 6, 12 and 18 weeks. All subjects will be pooled to identify correlates of symptom improvement. The immediate and delayed treatment groups will be contrasted to dissociate treatment-induced neural changes from the non-specific effects of therapeutic contact (i.e. placebo). 40 matched controls will be scanned once and compared with OCD subjects at baseline, prior to pharmacotherapy, to characterize connectivity alterations in the unmedicated state. Neuroimaging data will be analyzed using whole-brain general linear models (GLMs), including between-group and longitudinal effects to isolate effects of time, effects of drug exposure per se, and correlates of clinical improvement. Baseline imaging data will be examined for treatment response prediction, using both a GLM-based regression and via a recently optimized individual classifier, trained on 75% of the sample and then tested on the remaining 25%. This study will yield a rich multi-modal neuroimaging dataset elucidating the neural correlates of OCD symptomatology and of treatment response. If successful, we will identify network targets for novel treatments and take a major step towards the goal of developing predictive measures in the service of precision medicine in psychiatry.

PROJECT SUMMARY This UO1 application is a response to the NIMH Program Announcement intended to accelerate the development of a high priority therapeutic agent by establishing its dose-related pharmacodynamic effects on biomarkers designed to inform subse- quent clinical development. Dopamine D1 receptor (D1R) agonism is among the most highly prioritized adjunctive treatment mechanisms for schizophrenia. Currently, all D1R agonists are also D5R agonists. D1R/D5R agonists have pro-cognitive and antipsychotic-like effects in preclinical studies, reflecting their ability to stabilize prefrontal cortical network activity in the face of distractors, and to enhance the precision of spatial working memory (sWM) by enhancing inhibitory tuning of prefrontal cortical (PFC) functional connectivity (FC). Yet, dose-related benefits of D1R/D5R agonism in patients could not be demonstrated in prior pilot studies. This application proposes that the testing of D1R/D5R agonists requires both a more direct translational/computational neuroscience framework (i.e., the most appropriate biomarkers) and a precision medicine strategy (i.e., the appropriate subpopulation of patients). To accelerate the selection of an optimal dose, we propose a multi- center study that densely maps the dose-related effects of the D1R/D5R partial agonist, PF-06412562 immediate release (IR), on three informative translational functional neuroimaging (fMRI) biomarkers as primary outcome measures: i) sWM-related activation; ii) task-based FC; and iii) resting-state FC in early course schizophrenia patients. Primary Aim 1 will apply a mul- tivariate analytic strategy to these three outcome measures (sWM-related activation, task-based FC and resting-state FC) to test if PF-06412562 produces a dose-related effect. This multivariate translational neural marker is designed and powered to inform a clear Go/No-Go decision with regards to proceeding to a full-scale clinical trial. A Go decision will be indicated if there is a significant dose-related drug effect on the neural signal measured via the multivariate combination of task-evoked activation and FC during the sWM task and FC during rest. Conversely, a No-Go decision will be reached if there is an absence of a dose-related effect on the multivariate index. Secondary Aim 2 will quantify dose-related drug effects on sWM precision based on behavioral data collected during fMRI. Exploratory Aim 3 will model the biophysical properties of PF- 06412562 in a cortical circuit model capable of sWM simulations, which will simulate hypothesized molecular mechanisms governing pro-cognitive PF-06412562 effects on sWM. In turn, we will will test if the dose-related pattern of PF-06412562 effects on resting FC in patients maps onto D1R and D5R receptor transcriptomic profiles in humans derived from from Allen Human Brain Atlas. Finally, Exploratory Aim 4 will study potential clinical predictors and moderators of PF-06412562 ef- fects on neuroimaging biomarkers. Collectively, this translational biomarker study informs the highest priority experimental treatment mechanism identified by the NIMH MATRICS Initiative using a precision medicine strategy that targets a specific subpopulation of early course schizophrenia patients who may pro-cognitively respond to D1R/D5R agonism.