Carmen Andreescu, M.D.

Carmen Andreescu, M.D.
Position

Professor of Psychiatry

University

University of Pittsburgh

Grant or Prize

Scientific Council Member (Joined 2023)

Grant or Prize

2009 Young Investigator Grant

Dr. Carmen Andreescu is a Professor in the Department of Psychiatry at the University of Pittsburgh. She is a licensed psychiatrist with additional expertise in Geriatric and Interventional Psychiatry. Dr. Andreescu is a faculty member in the Center for Neuroscience at the University of Pittsburgh and for the Research Career Institute in the Mental Health of Aging (CIMA) directed by NIMH and Weill Cornell Medical Center. She is the director of the ARGO Neuroscience of Aging Research lab [https://argo.pitt.edu]. Her research focus is on mapping the neural circuitry associated with mood/anxiety phenotypes in late-life, identifying neural markers of treatment response in late-life depression and anxiety, and describing the pathways through which  anxiety accelerates brain aging. Her research has been funded by BBRF, NIMH and NIA. Dr. Andreescu serves as a member on the editorial board of the American Journal of Geriatric Psychiatry, and she is a board member of the Federation Global Initiative on Psychiatry and the president of its American chapter [https://www.gip-global.org]. Dr.

Beatriz Luna, Ph.D.

Beatriz Luna, Ph.D.
Position

Professor of Psychiatry and Psychology

University

University of Pittsburgh

Grant or Prize

Scientific Council Member (Joined 2023)

Grant or Prize

1997 Young Investigator Grant

Beatriz Luna, Ph.D. is the Distinguished Staunton Professor of Psychiatry and Pediatrics and Professor of Psychology and BioEngineering at the University of Pittsburgh. She is the founder and Director of the Laboratory for Neurocognitive Development, the founder and acting past president of the Flux Society for Developmental Cognitive Neuroscience, and Editor and Chief of the journal Developmental Cognitive Neuroscience. Dr. Luna uses multimodal neuroimaging to investigate the neurobiological mechanisms that support the transition from adolescence to adulthood when lifetime trajectories are determined to inform basic processes of normative development and abnormal trajectories such as in mental illness. She has received numerous awards including the Presidential Early Career Award in Science and Engineering, the Provost’s Award for Excellence in Doctoral Mentoring, and Distinguished Professor of Psychiatry. Her research has been continuously supported by the National Institutes of Mental Health and has informed US Supreme Court briefs regarding extended sentencing in the juvenile justice system.

Andrea Goldschmidt, Ph.D.

Andrea Goldschmidt, Ph.D.
Position

Associate Professor of Psychiatry

University

University of Pittsburgh

Grant or Prize

Scientific Council Member (Joined 2023)

Dr. Goldschmidt is a clinical psychologist whose research focuses on eating behaviors that are associated with poor weight-related outcomes, particularly in children and adolescents. One of her research aims is to understand loss of control eating (i.e., the experience of feeling unable to control what or how much one is eating) as it contributes to excess weight gain in youth. Her work utilizes innovative approaches (e.g., laboratory paradigms, ecological momentary assessment, neuroimaging) to understand the etiology and correlates of loss of control eating, including its association with neurocognitive functioning and other self-regulation factors. A secondary line of research focuses on disseminating and implementing evidence-based treatments for adolescents with restrictive eating disorders in community settings to increase their accessibility, particularly for under-resourced families. The overarching goal of her research is to refine onset and maintenance models of eating and weight disorders in youth to inform neurodevelopmentally sensitive interventions.

Understanding Resilience to Schizophrenia through Genetics

Over the past decade, the field of psychiatric genetics has undergone significant advancements, particularly in our understanding of schizophrenia. These genetics studies have not only expanded our knowledge, but also sparked many new questions. One question that we are eagerly exploring is: What factors allow some individuals to evade schizophrenia despite having inherited a substantial burden of risk genes for the disorder? In this talk, Dr.

Jonathan Hess, Ph.D.

Jonathan Hess, Ph.D.
Position

Assistant Professor, Department of Psychiatry

University

SUNY Upstate Medical University

Grant or Prize

2020 Young Investigator Grant

Jonathan Hess, Ph.D. is an Assistant Professor in the Department of Psychiatry & Behavioral Sciences at SUNY Upstate Medical University. He specializes in computational neuroscience, dedicating his research to developing and applying novel computational genomics methods with the objective of uncovering factors contributing to the risk for and resilience against neuropsychiatric disorders and dementia, with a specific emphasis on schizophrenia and Alzheimer's disease. Dr. Hess currently holds the role of principal investigator on several projects encompassing both federal and non-federal funding sources. His laboratory was established after receiving a 2020 NARSAD Young Investigator Grant.

A Problem With Energy-Producing Mitochondria May Increase Risk for Schizophrenia

One of the most important objectives in brain research is to understand in detail how genetic abnormalities impact the ability of brain cells and circuits to function properly. This is one of the keys to unlocking the mysteries of causation in a range of psychiatric illnesses, especially those thought to have a strong genetic component.

Interpretation of Brain Scan Results Suggests Ways of Optimizing Psychotherapies for OCD

Abnormalities in several brain networks have been shown in past research to be present in individuals with obsessive-compulsive disorder (OCD). In the effort to find more effective treatments, there has been considerable interest in whether connectivity patterns in these disturbed networks can predict whether a given individual will respond to available treatments.