Psychiatry Computational Research
Our physician–scientists in NYU Langone’s Department of Psychiatry use advanced computational approaches to develop a better understanding of mental health conditions, from depression to post-traumatic stress disorder (PTSD).
Closing the Gap Between Animal Models and Humans
We conduct advanced computational neuroscience research using healthy and diseased animal models, under the leadership of Zhe S. Chen, PhD.
One of the goals of computational psychiatry is to test causal relationships between brain manipulations and behavioral effects using closed-loop neuroscience experiments. These experiments are designed to reduce the gap in knowledge between animal models of mental health disorders—including depression, schizophrenia, and the psychiatric aspects of chronic pain—and the human experience of these conditions.
Another important goal of this research discipline is to apply computational approaches to the development of parametrically detailed and back-translatable (human to animal) behavioral assays across mental health–relevant domains of function.
Our researchers are also developing novel neuroengineering and neurotechnologies to improve understanding of brain oscillations in response to optimized acoustic stimulations during sleep. In collaboration with Ricardo S. Osorio, MD, and other colleagues, Dr. Chen is also analyzing changes in sleep spindles in patients with preclinical Alzheimer’s disease or schizophrenia. Their goal is to identify important biomarkers of these conditions and to provide new nonpharmacological therapeutic targets for brain disorders.
Another research initiative, conducted in close collaboration with Jing Wang, MD, PhD, is to employ noninvasive or minimally invasive brain stimulations of targeted neural circuits to explore the effects of demand-based neuromodulation on acute and chronic pain.
Currently, our lab is collaborating with a research team led by Orrin Devinsky, MD, at the Comprehensive Epilepsy Center to investigate the diagnosis and treatment of postoperative pain in epileptic patients.
Our researchers also hope to develop mobile or portable smart devices for monitoring brain activity, promoting brain health, and enhancing memory and cognitive function.
Assessing Clinical States and Predicting Psychiatric Risk
Isaac R. Galatzer-Levy, PhD, is investigating the measurement of emotional, cognitive, and clinical functioning in psychiatric patients using machine learning models and artificial intelligence that detect and assess facial expression, voice parameters, and speech patterns.
Although patient–clinician interactions can provide information about mood and cognitive function, we cannot yet automatically measure these interactions, which makes the collection of data for research purposes challenging.
Artificial intelligence may be able to assess emotional states and mood associated with psychiatric conditions such as depression, stress, anxiety, PTSD, and schizophrenia to help determine treatment effectiveness and to gauge the risk of outcomes such as hospitalization. As vice president of clinical and computational neuroscience at AiCure, Dr. Galatzer-Levy is developing software that allows these assessments to be conducted using patient cell phones.
Dr. Galatzer-Levy is also heavily involved in multiple National Institutes of Health–funded initiatives to build algorithms to predict clinical outcomes in trauma patients seen in the emergency medical setting as well as prediction of suicide risk using data in the electronic medical record and from biological data sources.
Current grant funding comes from the National Institutes of Health and the National Science Foundation.
National Institute of Neurological Disorders and Stroke
Dissecting Neural Circuits for Acute Pain; 5R01NS100065-03
National Institute of Mental Health
National Science Foundation
Dr. Chen is an investigator at the Neuroscience Institute and a faculty member at the Training Program in Computational Neuroscience at NYU. He offers undergraduate and graduate students the opportunity to work in his research laboratory throughout the year or as summer interns. His lab members have also received funding through the Irene and Eric Simon Brain Research Foundation Rolf Weil Fellowship, the Training Program in Computational Neuroscience at NYU, the China Scholar Council Scholarship, and NYU College of Arts and Science Dean’s Undergraduate Research Fund Freshman and Sophomore Training (FAST) grant. Dr. Chen is also organizing monthly departmental neuromodulation, computational neuroscience, and computational psychiatry seminars.
Dr. Galatzer-Levy teaches machine learning and clinical automated decision-making as part of the mentoring faculty for the MS in biomedical informatics program at NYU Langone’s Vilcek Institute of Graduate Biomedical Sciences.
Our computational neuroscience faculty are experts in the field.
For more information about our Department of Psychiatry’s neuroscience research initiatives, please contact Dr. Chen at 646-754-4765 or email@example.com.
Our psychiatry computational research faculty publish frequently in peer-reviewed journals. Here is a selection of our recent publications.
Dynamics of motor cortical activity during naturalistic feeding behavior
Journal of neural engineering. 2019 Apr ; 16:026038
A deep learning approach for real-time detection of sleep spindles
Journal of neural engineering. 2019 Feb 21; 16:036004
Cortical Pain Processing in the Rat Anterior Cingulate Cortex and Primary Somatosensory Cortex
Frontiers in cellular neuroscience. 2019 04 ; 13:165
Ensembles of change-point detectors: implications for real-time BMI applications
Journal of computational neuroscience. 2019 Feb ; 46:107-124
Data Science in the Research Domain Criteria Era: Relevance of Machine Learning to the Study of Stress Pathology, Recovery, and Resilience
Chronic stress. 2018 Jan-Dec; 2:
A Bayesian nonparametric approach for uncovering rat hippocampal population codes during spatial navigation
Journal of neuroscience methods. 2016 Apr 01; 263:36-47
Deciphering Neural Codes of Memory during Sleep
Trends in neurosciences. 2017 05 ; 40:260-275