McLean Hospital 115 Mill Street Belmont, MA 02478
Mei-Hua Hall, PhD, is the director of the Psychosis Neurobiology Laboratory. Her specific expertise is in electrophysiology, psychiatric genetics, and cognitive neuroscience and she also has a broad background in clinical psychology, epidemiology, and statistical modeling. She receives funding from the National Institute of Mental Health and other sources for her research. Dr. Hall uses electrophysiology (EEG) techniques to study the neurobiological mechanisms underlying schizophrenia, bipolar, and psychotic disorders. She applies multimodal approaches to link patients’ neurobiological and clinical profiles with their functional recovery trajectories. The overarching goals of her lab are to identify individuals with different functional recovery paths and to develop individually tailored and effective treatments.
Dr. Hall has received several awards for her research, including Young Investigator Awards from the Brain and Behavior Research Foundation (BBRF), the Sidney R. Baer, Jr. Foundation, the Jerome Lyle Rappaport Research Award, and the Harvard Catalyst Clinical Research Center (HCCRC) Research Award.
Dr. Hall’s Psychosis Neurobiology Laboratory was founded in 2013. Dr. Hall is currently conducting several projects. One project uses an integrative approach, combining neurophysiological, neurochemical, genetics, clinical and behavioral measures, to understand the neurobiological mechanisms underlying schizophrenia and bipolar disorders. This project also aims to clarify how genetic and environmental risk factors influence psychopathology, neurocognitive traits, and brain function.
Robust genomewide genetic studies have identified many risk genes and variations within those genes associated with schizophrenia and bipolar disorder, though their effects on the brain are largely unknown. The lab collects a variety of neurophysiological, clinical, and cognitive functioning data on patients with these disorders. The goal is to link genetic risk factors associated with psychosis disorders to brain neurophysiological characteristics.
Another project of the lab uses multimodal approaches to stratify patients who experience their first episode psychosis into homogeneous subgroups based on patients’ unique neurobiological profiles and to relate these profiles to later functional recovery outcomes. This study longitudinally follows-up with patients with first episode of psychosis every six months for two years. An expanded biomarker panel (cognition, MRI, EEG, clinical symptoms) and functional outcome measures are collected in each patient. The goals are to identify and better characterize individual longitudinal patterns of functioning recovery, and to explore the potential risk and protective factors associated with functioning outcome.
In addition, the lab uses a computerized cognitive training program for patients with schizophrenia and bipolar disorder with the goal of improving certain cognitive functions (such as memory and attention) and psychosocial outcomes. In recent years, training programs based on learning-induced neuroplasticity have shown promising improvement effects on cognitive function. This suggests that cognitive rehabilitation training induces neurobiological remodeling in brain circuitry relevant to a range of cognitive functions. This project examines brain changes induced by cognitive training to reveal the underlying mechanisms mediating these changes.
A new line of research project, in collaboration with McLean OnTrack, focuses on using natural language processing (NLP) and machine learning techniques and aims to create tools for evaluating and predicting psychosis patient readmission risk as well as risk of harm. The project is currently creating a data analysis pipeline that extracts meaningful information from unstructured clinical narratives in patient electronic health records (EHR) and converts these data to features for use in training our machine learning models. This project includes experts in the fields of psychiatry and computational linguistics. Some of the questions this project aims to address include what are specific risk factors for readmission?, how to quantify these risk factors?, which machine learning models and feature representations exhibit the greatest compatibility with our unique sources of psychiatric EHR data?, how to accurately determine whether a patient’s condition is improving or deteriorating over time? Gaining knowledge toward answering these questions will inform the selection of treatment interventions and implement appropriate preventive measures.
Dr. Hall is collaborating with scientists internationally. She is involved in a project entitled “The Global Research Initiative on the Neurophysiology of Schizophrenia (GRINS)”, and a project entitled “Collaborations in Psychiatry: Fostering Research Opportunities Between McLean Hospital and China.”
Hall M-H, Smoller JW, Cook NR, Schulze K, Hyoun Lee P, Taylor G, Bramon E, Coleman MJ, Murray RM, Salisbury DF, Levy DL. Patterns of deficits in brain function in bipolar disorder and schizophrenia: A cluster analytic study. Psychiatry Research 2012;200: 272-280.
Hall M-H, Levy DL, Salisbury DF, Haddad S, Gallagher PJ, Lohan M, Cohen B, Öngür D, Smoller JW. Neurophysiologic effects of GWAS derived schizophrenia and bipolar risk variants. American Journal of Medical Genetics Part B, Neuropsychiatric Genetics 2014;165:9-18.
Lin Y-F, Chen C-Y, Öngür D, Betensky R, Smoller JW, Blacker D, Hall M-H. Polygenic pleiotropy and potential causal relationships between educational attainment, neurobiological profile, and positive psychotic symptoms. Translational Psychiatry 2018;8(1):97.
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