Helen Mayberg - US grants

Depressive disorders are common in patients with Parkinson's disease (PD), affecting as many as half of a cross sectional sample of patients. Notwithstanding the selective and often subtle cognitive deficits previously identified in PD, there are substantive differences in cognitive performance between patients who are depressed and those who are not. It is widely accepted that depression will decrease motivation and attention, thereby impairing performance on many cognitive tasks. It has only recently been proposed that depression selectively impairs performance on tasks classically considered to localize to the frontal lobes. This clinical association in PD of depression and selective cognitive deficits may implicate common neurochemical and neuroanatomical pathways. This study proposes to examine the interaction between changes in regional glucose metabolism, mood, and cognitive function in depressed PD patients treated with the selective serotonin uptake blocker, Fluoxetine. The hypothesis is that mood is well as specific cognitive deficits will improve following treatment with Fluoxetine. Furthermore, specific regional changes in brain glucose metabolism will occur, and will predict clinical improvement in mood state and cognitive performance. This study, using a multidisciplinary design, will focus on the specific interactions between brain chemistry, anatomy, and behavior relevant to the depression identified in patients with PD. To this end, the proposed studies will determine: (1) whether measurements of regional glucose metabolism are a specific marker of depressive state or trait in patients with PD, (2) whether improved performance on some, but not all cognitive tasks accompanies resolution of depressive symptoms, and (3) whether distinct patterns of regional glucose metabolism identify brain regions associated with depression-sensitive and depression-independent cognitive functions. This proposal would be a first step in the mapping of specific anatomical and neurochemical pathways involved in the genesis and recovery from depressive illness in patients with PD.

Depressive disorders are common in patients with Parkinson's disease (PD), affecting as many as half of a cross sectional sample of patients. Notwithstanding the selective and often subtle cognitive deficits previously identified in PD, there are substantive differences in cognitive performance between patients who are depressed and those who are not. It is widely accepted that depression will decrease motivation and attention, thereby impairing performance on many cognitive tasks. It has only recently been proposed that depression selectively impairs performance on tasks classically considered to localize to the frontal lobes. This clinical association in PD of depression and selective cognitive deficits may implicate common neurochemical and neuroanatomical pathways. This study proposes to examine the interaction between changes in regional glucose metabolism, mood, and cognitive function in depressed PD patients treated with the selective serotonin uptake blocker, Fluoxetine. The hypothesis is that mood is well as specific cognitive deficits will improve following treatment with Fluoxetine. Furthermore, specific regional changes in brain glucose metabolism will occur, and will predict clinical improvement in mood state and cognitive performance. This study, using a multidisciplinary design, will focus on the specific interactions between brain chemistry, anatomy, and behavior relevant to the depression identified in patients with PD. To this end, the proposed studies will determine: (1) whether measurements of regional glucose metabolism are a specific marker of depressive state or trait in patients with PD, (2) whether improved performance on some, but not all cognitive tasks accompanies resolution of depressive symptoms, and (3) whether distinct patterns of regional glucose metabolism identify brain regions associated with depression-sensitive and depression-independent cognitive functions. This proposal would be a first step in the mapping of specific anatomical and neurochemical pathways involved in the genesis and recovery from depressive illness in patients with PD.

Early Life Stress (ELS) is a known risk factor for major depressive disorder (MDD). How this risk is expressed at the level of specific neural pathways is unknown. This Project will test the overarching hypothesis that depression at the brain systems-level involves selective dysfunction of highly integrated limbic-cortical-striatal circuits responsible for mediating adaptive motivational, emotional, and cognitive responses to everyday life events. The proposed studies will test two hypotheses: (1) that specific neural responses in depression-specific pathways are altered by exposure to ELS; and (2) that it is these specific neural system alterations that constitute a vulnerability factor for MDD. The planned imaging studies build and extend two main observations from the current CCNMD funding period: (1) abuse, neglect and parental loss at critical time points in early child development are associated with sensitized autonomic, endocrine and behavioral responses to various emotional stimuli; and (2) such sensitization appears to convey increased risk for the later development of various mood disorders, most notably major depressive disorder (MDD). In this context, the role of ELS as a depression risk factor will be examined by characterizing brain regions and associated pathways in a select subset of female subjects with well-characterized ELS from Project 0007. Neuroimaging probes of mood, self-reference and salience processing previously validated in healthy subjects will be used to investigate vulnerability and variability effects in patient subgroups stratified by MDD and ELS. The specific imaging probes were selected on the basis of their theoretical relevance to primary symptoms of major depression and their reliability in effecting changes in those brain regions most consistently identified across published structural MRI, post-mortem morphometric, resting state PET, and fMRI activation studies of depressed patient populations. In addition, the specific paradigms and planned analytic strategies have also been shown to be sensitive to depression subgroups, illness state and trait differences as well as treatment effects. The infrastructure provided by the CCNMD cores, combined with the careful characterization of demographic, genetic, endocrine and behavioral markers in ELS exposed subjects in Project 0007 provide an unparalleled opportunity to systematically test the impact of ELS on selective pathways of known involvement in MDD. These studies will provide critical data for defining brain-biomarkers of MDD vulnerability that may lead to potential pre-symptomatlc stress tests for individual at high illness risk. Identification of consistent neural systems correlates of MDD subtypes will lay foundation for the future development of brain-based algorithms to optimize diagnosis and treatment selection in individual depressed patients.

DESCRIPTION (provided by applicant): While there are many effective options for treating a major depressive episode, there are no clinical markers that predict the likelihood of remission with an initial trial of either an antidepressant medication or psychotherapy. More critically, there are also no reliable predictors that might anticipate failure to such standard treatments either alone or in combination. In prioritizing a role for direct measures of brain functioning in the development of new algorithms for clinical management of depressed patients, a systematic characterization of pretreatment patterns predictive of unambiguous remission and non-remission to standard treatments is a necessary first step. This project will characterize imaging-based brain subtypes that distinguish groups of depressed patients who later remit or not to SSRI pharmacotherapy or cognitive behavior therapy (CBT), respectively. To define these subtypes, a prospectively-treated cohort of 100 patients will be randomized to receive either escitalopram (s-CIT) or CBT for the first 12 weeks, with non- remitters to either first treatment crossed over to receive an additional 12 weeks of treatment with the alternative intervention. Non-remitters to both treatments will thus define a relatively treatment resistant third subgroup. Resting-state 18F-fluoro-deoxyglucose (FDG) positron emission tomography (PET) scans will be acquired prior to initiating antidepressant therapy, with pre-treatment scan patterns associated with three possible outcomes (CBT remission, s-CIT remission, and non-remission to both) assessed using multivariate analytic methods. A second PET scan, acquired early in the treatment course, will be used to assess the likelihood of response to the specific treatment first assigned. The proposed studies are a first step towards defining brain-based biomarkers predictive of differential treatment outcome in major depression;most critically, patterns distinguishing patients at risk for treatment resistance. Identification of such biomarkers has additional implications for future testing of novel therapies in patients with distinct brain signatures, including development of evidence-based treatment algorithms for individual patients.

Early Life Stress (ELS) is a known risk factor for major depressive disorder (MDD). How this risk is expressed at the level of specific neural pathways is unknown. This Project will test the overarching hypothesis that depression at the brain systems-level involves selective dysfunction of highly integrated limbic-cortical-striatal circuits responsible for mediating adaptive motivational, emotional, and cognitive responses to everyday life events. The proposed studies will test two hypotheses: (1) that specific neural responses in depression-specific pathways are altered by exposure to ELS; and (2) that it is these specific neural system alterations that constitute a vulnerability factor for MDD. The planned imaging studies build and extend two main observations from the current CCNMD funding period: (1) abuse, neglect and parental loss at critical time points in early child development are associated with sensitized autonomic, endocrine and behavioral responses to various emotional stimuli; and (2) such sensitization appears to convey increased risk for the later development of various mood disorders, most notably major depressive disorder (MDD). In this context, the role of ELS as a depression risk factor will be examined by characterizing brain regions and associated pathways in a select subset of female subjects with well-characterized ELS from Project 0007. Neuroimaging probes of mood, self-reference and salience processing previously validated in healthy subjects will be used to investigate vulnerability and variability effects in patient subgroups stratified by MDD and ELS. The specific imaging probes were selected on the basis of their theoretical relevance to primary symptoms of major depression and their reliability in effecting changes in those brain regions most consistently identified across published structural MRI, post-mortem morphometric, resting state PET, and fMRI activation studies of depressed patient populations. In addition, the specific paradigms and planned analytic strategies have also been shown to be sensitive to depression subgroups, illness state and trait differences as well as treatment effects. The infrastructure provided by the CCNMD cores, combined with the careful characterization of demographic, genetic, endocrine and behavioral markers in ELS exposed subjects in Project 0007 provide an unparalleled opportunity to systematically test the impact of ELS on selective pathways of known involvement in MDD. These studies will provide critical data for defining brain-biomarkers of MDD vulnerability that may lead to potential pre-symptomatlc stress tests for individual at high illness risk. Identification of consistent neural systems correlates of MDD subtypes will lay foundation for the future development of brain-based algorithms to optimize diagnosis and treatment selection in individual depressed patients.

The Operations, Clinical Assessment and Administrative Core oversees this entire project and[unreadable] is responsible for the conduct of the clinical trial and the collection of biological and behavioral[unreadable] data. Based on studies of groups of major depressive disorder (MDD) patients, any single[unreadable] psychotherapeutic or pharmacological strategy in clinical practice today achieves remission in only[unreadable] a minority of patients. Thus the initial treatment an individual with MDD receives is largely an[unreadable] accident of chance rather than an evidence based choice. Over a 5-year period, 400 treatment[unreadable] naive patients with MDD will be recruited into a 12 week treatment trial of randomly assigned[unreadable] treatment with an SSRI, escitalopram, a dual 5-HT/NE re-uptake inhibitor, duloxetine or cognitivebehavior[unreadable] therapy (CBT). Subjects will be evaluated using state-of-the-art clinical assessments prior[unreadable] to, during and at the end of acute treatment period. This core describes the efforts that will result in[unreadable] the identification, recruitment of the patients, and assessment and delivery of the treatments. A[unreadable] unique component of this proposal is the study of individuals with MDD not contaminated by[unreadable] previous treatments that may confound measures of treatment response. Commensurate with the[unreadable] CIDAR goals, the data generated in this core will provide the critical foundation for hypothesis[unreadable] generation and testing in the other cores and projects.

Major depression (MDD) is a highly prevalent disease associated with significant morbidity and mortality and estimated to be one of the leading causes of disability worldwide. A variety of antidepressant drugs, psychotherapies, and non-drug somatic therapies have demonstrated efficacy in acute treatment trials. However, the majority of patients in these trials do not attain remission, increasing the risk for the development of chronic depression, suicide, substance abuse and several serious medical disorders. A major unmet need in the field is the identification of predictors of response to individual treatment modalities, as has been utilized other branches of medicine such as oncology and infectious disease, to improve patient outcome. In view of advances in functional brain imaging, molecular neurobiology and genetics, and a number of promising findings in small studies, it is propitious to conduct both hypothesis-generating and hypothesis-testing studies to determine whether a concatenation of factors, taken together, predict antidepressant treatment response. To achieve that goal, we propose a 3-arm, 12-week treatment trial led by Philip T. Ninan, M.D. of 400 treament naive adult depressed patients randomized to one of the following treatments after a 1 week placebo treatment period: 1) escitalopram, an SSRI;2) duloxetine, an SNRI and 3) CBT. A series of behavioral and biological measures will be obtained that include functional brain imaging (fMRI) led by Helen S. Mayberg, M.D., several genetic polymorphisms led by Joseph F. Cubells, M.D., Ph.D. and Elisabeth Binder, M.D., Ph.D., indices of HPA axis activity, markers of immune and inflammatory function, as well as measures of personality, early life trauma, depression and anxiety, and cognitive function. In addition, in a study led by Michael J. Owens, Ph.D. both PET and an ex vivo method will be utilized to determine the relationship of the magnitude of SERT occupancy to clinical response in the escitalopram and duloxetine treatment groups and, moreover, the ex vivo method will be used to assess the importance of NET occupancy in treatment response to duloxetine. A Special Scientific Procedures, Statistical Modeling Core, led by Mary Kelley, Ph.D. will seek to determine which measure and/or combination of measures leads to predictors of response to the three treatments under study. Delineation of patient characteristics that predict treatment response to a specific treatment modality will dramatically improve patient outcomes and reduce the risk of inadequate treatment.

Although there are several effective treatments for a major depressive episode, there are no reliable predictors of the likelihood of remission, response or non-response with an initial trial of either an antidepressant medication or psychotherapy. In prioritizing a role for direct measures of brain functioning in the development of new algorithms for clinical management of depressed patients, a systematic characterization of pretreatment patterns predictive of unambiguous remission to standard treatments is a necessary first step. This project will characterize imagingbased brain subtypes that distinguish groups of never-treated depressed patients who subsequently respond to pharmacotherapy or cognitive behavior therapy (CBT), respectively. A prospectively-treated cohort of 400 never-treated depressed patients randomized to receive either escitalopram, duloxetine or CBT for 1.2 weeks will define these subtypes. Resting-state BOLD functional magnetic resonance imaging (fMRI) scans will be acquired prior to initiating antidepressant therapy and at a fixed, early time point specific for each treatment. Pre-treatment scan patterns derived using multivariate analyses and associated with the six possible response outcomes (3 types of response;3 types of nonresponse) will be used to determine whether pretreatment brain patterns can distinguish among outcome groups. A second fMRI scan, acquired early in the treatment course, will be used to assess the likelihood of response to the specific treatment assigned. The proposed studies are a first step towards defining brain-based subtypes predictive of differential treatment outcome in major depression. The data from these studies will also be entered into more complex algorithms integrating imaging findings with behavioral, environmental, biochemical and genetic information for individual patients.

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This application, in response to PAR-07-159 (National Cooperative Drug Discovery Groups for the Treatment of Mental Disorders, Drug or Alcohol Addiction), represents the resubmission of the competitive renewal application of The Emory-MSSM-GSK-NIMH Collaborative Mood and Anxiety Disorders Initiative, MH-069056. This unique opportunity to accelerate antidepressant and anxiolytic drug development brings together expertise of four complementary research groups: the Emory University School of Medicine Department of Psychiatry and Behavioral Sciences, the NIMH Intramural Mood and Anxiety Disorders Program, the Mt. Sinai School of Medicine Department of Psychiatry (MSSM) and the Center for Excellence in Drug Discovery (CEDD) in Psychiatry of GlaxoSmithKline (GSK), one of the largest multinational pharmaceutical companies. The two major goals of the current application are the development of innovative new models for basic and clinical research in mood and anxiety disorders and the intensive scrutiny of 3 novel GSK antidepressant/anxiolytic candidates [two structurally distinct CRP! receptor antagonists (GSK008 and CRF-002), and a 5-HTiA/IB/ID receptor antagonist (GSK-1), in preclinical and clinical paradigms. In addition to an Administrative and Genomics/Endocrine/Statistics Core, 5 research projects are proposed. The preclinical projects are based at Emory University and are led by established investigators including, Michael Davis, PhD (Rat Models of Anxiety), Donald Rainnie, PhD (Physiologic Actions of Novel Antidepressants/Anxiolytics in the Basolateral Amygdala), and Clinton D. Kilts, PhD and Mark Goodman, PhD, (CRFi receptor PET Ligand Development). One clinical project is based in the intramural NIMH program: Effects of novel antidepressants/anxiolytics on human startle in normal volunteers (Christian Grillon, PhD, PI). A placebo-controlled, double-blind trial (based at Emory: Barbara Rothbaum, PhD, PI and MSSM: Dennis Charney, MD, PI) will assess the efficacy of the CRFi receptor antagonist GSK008 in patients with post-traumatic stress disorder (PTSD). This proposal encompasses virtually all of the major goals outlined in the PAR, namely, development of new neurochemical tools including PET ligands, exploration of new models for drug development and facilitation of a partnership between academia, NIMH and industry.

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. This project evaluates the effects of corticotropin releasing factor 1 receptor (CRF 1) antagonists in our measures of fear and anxiety in rats and compare it to our measure of conditioned fear. We found that the GSK CRF 1 antagonist blocks the increase in acoustic startle amplitude when rats are given CRF intraventricular or when they are exposed to bright light for long periods of time (dependent on the bed nucleus of the stria terminalis (BNST) but not when startle is increased in the presence of a cue previously paired with footshock (dependent on the central nucleus of the amygdala (CeA). We found that over-expression of CRF in the central nucleus of the amygdala using viral vector gene transfer led to a persistant increase in several measures that suggested a persistent increase in anxiety. CRF neurons in the lateral division of the central nucleus of the amygdala project to the bed nucleus of the stria terminalis. Axons containing calcitonin gene related peptide (CGRP) project to these cells and local infusion of CGRP into the CeA facilitate startle, an effect that is blocked by a CRF1 antagonist. This grant has been renewed for another five years during which time we will continue to look at the role of CRF1 antagonists in anxiety.

DESCRIPTION (provided by applicant): Usual first-line treatments for major depressive disorder (MDD) show less than a 40% remission rate. For many patients, the wrong treatment has significant individual and societal costs due to continued distress, risk of suicide, loss of productivity, wasted resources and potential neural effects associated with 2-3 months of an ineffective strategy. Though treatments are highly effective in some individuals, there is no reliable way to match patients to their best option. Therefore, a long term goal is to develop a clinically viable algorithm that selects the best treatment and avoids ineffective treatments, whil also identifying patients that require alternatives to standard first-line options. The objective o this proposal is to test the efficacy of a novel imaging biomarker developed to stratify patients into two subtypes that predict the likelihood of remission to monotherapy with cognitive behavioral therapy (CBT) or escitalopram (sCIT)--two standard first-line treatments for MDD. Our central hypothesis is that use of insula metabolism as a treatment selection biomarker (TSB) to assign treatment will increase remission rates to at least 50%, exceeding the usual 35-40% commonly reported with usual care. Data from the previous funding period identified the insula as the best discriminator of remission/non response to CBT and sCIT. The data further indicated other potential biomarkers that identify patients who will fail to remit to combined treatment with both CBT and sCIT. We will examine two specific aims: 1) To prospectively test the efficacy of the insula TSB to assign individual MDD patients to treatment with either CBT or sCIT; and 2) To further characterize brain subtypes predictive of combined treatment failure., Treatment assignment will be determined by the level of glucose metabolism in the right anterior insula measured with positron emission tomography (FDG PET). We will derive each patient's insula TSB using a simple region/whole brain ratio approach and a fixed cut-off. Patients will be assigned to a 12-week treatment course of CBT or sCIT using the insula TSB value. Patients who do not remit to their TSB-assigned first treatment will receive a second 12-week course of combined CBT and sCIT. Baseline whole brain metabolism will be evaluated in patients who do not remit after both monotherapy and combination therapy to determine biomarkers of dual failure. Lastly, we will examine regional metabolic changes over the first 12 weeks of treatment to characterize mechanisms mediating remission across insula subtypes. This proposal is innovative because it tests a novel imaging biomarker strategy developed specifically to prospectively stratify patients into two distinct treatment specific subtypes that will predict the likelihood of response to two standard first line therapies for a major depressive episode. In addition, this study will further characterize a second biomarker that identifies patients who fail to remit to both treatments. The proposed research is significant because it directly addresses the need for evidence-based methods for selecting optimal treatments for individual depressed patients with the ultimate goal of improving treatment outcomes.

CRCNS Research Proposal: Collaborative Research: Modeling and Manipulating Dynamic Network Activity in the Brain

Connectome-based Dynamic Network Modeling (CDNM) is a recent approach in computational neuroscience, made possible by the availability of structural and functional brain connectivity data. This project aims to understand how the interaction between structure and dynamics of neural populations leads to brain functional networks and brain states. Understanding mechanistically and being able to predict how the combination of macroscale structure and local neural activity leads to complex whole-brain dynamics is a major research goal for every aspect of brain science, ranging from basic neuroscience to clinical psychiatry and neurology. This project can also have an important impact in understanding both how Major Depressive Disorder emerges from specific structural abnormalities, and the conditions under which Deep Brain Stimulation is an effective treatment. The developed methods can be also applied to numerous other mental and neurological disorders. The project will also develop and openly disseminate new computational models, and optimization methods for speeding up the simulation of complex CDNMs.

The project consists of three Aims: 1) Leverage dynamic functional connectivity to further constrain and evaluate CDNM: The first goal is to clearly separate the parameterization of a CDNM from the evaluation of its accuracy. It is possible that several models, or parameterizations of the same model, lead to realistic average functional connectivity. However, not all of these models may be able to reproduce the more complex, dynamic functional connectivity patterns observed in practice. The project relies on state-of-the-art methods that infer dynamic functional connectivity between brain regions, applying these methods to both empirical data and CDNM-based simulation results. Each candidate CDNM model will be evaluated in terms of how well it can reproduce the dynamic FC patterns observed in empirical data. 2) Using CDNM to understand the connection between structural and functional connectivity in Major Depression Disorder: The ultimate test for any model is its predictive power. The project will utilize structural and functional connectivity data for a patient group that exhibits known and significant differences from healthy controls. Starting with the best model from Aim-1, that CDNM will be run on a perturbed connectome that captures the major structural abnormalities in depression. Then, the CDNM results will be analyzed to determine if the model can reproduce the FC abnormalities observed in the group of patients. 3) Modeling the effects of interventions such as deep brain stimulation: The use of this experimental treatment in depression is a ?network intervention?. CDNM can play a significant role in understanding how and when it works as an effective treatment. The effect of deep brain stimulation will be modeled by modifying either the local dynamics of certain regions or the weights of specific connections in the model, such as increasing or decreasing the weight of the connection. The project will investigate whether there is a specific weight adjustment with which the stimulated model produces dynamics that resemble the normal FC of healthy subjects. If that adjustment needs to be in a very narrow range, it might explain why deep brain stimulation is unsuccessful in some patients.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.