Psychiatry Computational Research | NYU Langone Health

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Department of Psychiatry Research Psychiatry Computational Research

Psychiatry Computational Research

Physician–scientists in the Computational Psychiatry Program in NYU Langone’s Department of Psychiatry use advanced methods to develop a better understanding of mental health conditions, from depression to post-traumatic stress disorder (PTSD).

Our co-directors, Zhe S. Chen, PhD, and Paul W. Glimcher, PhD, lead our interdisciplinary program that uses computation models and neuroimaging to identify functional connectivity in healthy and diseased brains. We combine mathematical tools with neuroanatomical, neurochemical, neuroimaging, and neurophysiological methods to improve our understanding, prediction, and treatment of psychiatric illnesses. This approach promotes collaborations between clinical scientists and practicing clinicians and advances translational research to develop neurotechnology for computational and digital phenotyping.

We use artificial intelligence (AI) and machine learning (ML) for biomarker discovery in mental health conditions and neuromodulation and behavioral intervention for psychiatric and neurologic disorders.

Our program staff works closely with PhD and MD/PhD students to provide a robust learning experience, and residents and postdoctoral fellows have the opportunity to conduct research with our faculty. Our curriculum encourages cross-department research, and we are launching our Computational Psychiatry Seminar Series, which is free and open to the public. For more information, please email Dr. Chen at zhe.chen@nyulangone.org.

Closing the Gap Between Animal Models and Humans

We conduct advanced computational neuroscience research using healthy and diseased animal models, under the leadership of Dr. Chen.

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 M. Osorio Suarez, 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 invasive or minimally invasive brain stimulations of targeted neural circuits to explore the effects of demand-based neuromodulation on acute and chronic pain in rodent models. Additionally, the Chen–Wang team uses the closed-loop brain–machine interface (BMI) to dissect cortical circuit mechanisms and their links to pain behavior.

Identification of Neurosignatures and Biomarkers in Translational Applications

AI and ML have played an increasingly important role in precision psychiatry, ranging from disease diagnosis, prognosis, and therapeutic treatment prediction. One active collaboration between the Chen lab and Dr. Wang is to develop advanced ML techniques to identify EEG-based biomarkers for patients with chronic back pain.

Currently, the Chen lab is also collaborating with a research team led by Orrin Devinsky, MD, at the Comprehensive Epilepsy Center to investigate the EEG- and ECG-based biomarkers of SUDEP (sudden unexpected death in epilepsy) in patients with epilepsy. In a new collaboration with Jacqueline A. French, MD, Dr. Devinsky, and other colleagues, the Chen lab is applying ML techniques to identify treatment-resistant biomarkers for epilepsy patients.

To promote translational applications, the Chen lab also hopes to develop mobile or portable smart devices for monitoring brain activity, promoting brain health, and enhancing memory and cognitive function.

Current Grants

Current grant funding comes from the National Institutes of Health and the National Science Foundation.

National Institute of Mental Health

An Integrative Study of Hippocampal–Neocortical Memory Coding During Sleep; R01MH118928

National Institute of Neurological Disorders and Stroke

CRCNS: Dissecting Neural Circuits for Acute Pain; NS100065

Cortical Information Integration as a Model for Pain Perception and Behavior; RF1NS121776

Advancing SUDEP Risk Prediction Using a Multicenter Case-Control Approach; R01NS123928

National Institute of Drug Abuse

Dissection of Spatiotemporal Activity from Large-scale, Multi-Modal, Multi-Resolution Hippocampal–Neocortical Recordings; RF1-DA056394

National Science Foundation

Computational Approaches to Uncover Neural Representation of Population Codes in Rodent Hippocampal–Cortical Circuits; 1443032

Closed-Loop Neuromodulation for Chronic Pain; 1835000

Research Training

Dr. Chen is an investigator at NYU Langone Health’s Neuroscience Institute and an associate faculty member at the Department of Biomedical Engineering, NYU Tandon School of Engineering. He is co-director of the newly established Computational Psychiatry Program and a mentoring faculty member for the MS in biomedical informatics program at NYU Langone’s Vilceck Institute of Graduate Biomedical Sciences. 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, NYU College of Arts and Science Dean’s Undergraduate Research Fund Freshman and Sophomore Training (FAST) grant, and the NIH Pathway to Independence Award (K99-AG073507). Dr. Chen also manages regular departmental neuromodulation, computational neuroscience, and computational psychiatry seminars.

Program Researchers

Our computational neuroscience faculty are experts in the field of addiction, anxiety, attention deficit hyperactivity disorder (ADHD), bipolar disorder, chronic pain, depression, epilepsy, PTSD, and stress.

Co-Directors

Zhe S. Chen, PhD
Paul W. Glimcher, PhD

Faculty

Benedetta Bigio, PhD
Orrin Devinsky, MD
Donald C. Goff, MD
Russel W. Hanson
Dan Iosifescu, MD
Eugene M. Laska, PhD
Matteo Malgaroli, PhD
Charles R. Marmar, MD
Samuel Neymotin, PhD
Ricardo M. Osorio Suarez, MD
Jaime Ramos Cejudo, PhD
Stephen Ross, MD
John Rotrosen, MD
Katharina Schultebraucks, PhD
Carole Siegel, PhD
Naomi M. Simon, MD
Jing Wang, MD, PhD
Xiao-Jing Wang, PhD
Zhenfu Wen, PhD

Postdoctoral Fellows and Residents

Thomas Hainmueller, MD, PhD
Xiaohan Zhang, PhD

Contact Us

For more information about the Department of Psychiatry’s neuroscience research initiatives, please contact Dr. Chen at 646-754-4765 or zhe.chen@nyulangone.org.

Featured Publications

Our psychiatry computational research faculty publish frequently in peer-reviewed journals. Here is a selection of our recent publications.

Deciphering Neural Codes of Memory during Sleep

Chen, Zhe; Wilson, Matthew A

Trends in neurosciences. 2017 05 ; 40:260-275

A deep learning approach for real-time detection of sleep spindles

Kulkarni, Prathamesh M; Xiao, Zhengdong; Robinson, Eric J; Sagarwa Jami, Apoorva; Zhang, Jianping; Zhou, Haocheng; Henin, Simon E; Liu, Anli A; Osorio, Ricardo S; Wang, Jing; Chen, Zhe Sage

Journal of neural engineering. 2019 Feb 21; 16:036004

Interictal EEG and ECG for SUDEP Risk Assessment: A Retrospective Multicenter Cohort Study

Chen, Zhe Sage; Hsieh, Aaron; Sun, Guanghao; Bergey, Gregory K; Berkovic, Samuel F; Perucca, Piero; D'Souza, Wendyl; Elder, Christopher J; Farooque, Pue; Johnson, Emily L; Barnard, Sarah; Nightscales, Russell; Kwan, Patrick; Moseley, Brian; O'Brien, Terence J; Sivathamboo, Shobi; Laze, Juliana; Friedman, Daniel; Devinsky, Orrin

Frontiers in neurology. 2022 ; 13:858333

A prototype closed-loop brain-machine interface for the study and treatment of pain

Zhang, Qiaosheng; Hu, Sile; Talay, Robert; Xiao, Zhengdong; Rosenberg, David; Liu, Yaling; Sun, Guanghao; Li, Anna; Caravan, Bassir; Singh, Amrita; Gould, Jonathan D; Chen, Zhe S; Wang, Jing

Nature biomedical engineering. 2021 Jun 21;

Data Science in the Research Domain Criteria Era: Relevance of Machine Learning to the Study of Stress Pathology, Recovery, and Resilience

Galatzer-Levy, Isaac R; Ruggles, Kelly; Chen, Zhe

Chronic stress. 2018 Jan-Dec; 2:

Uncovering spatial representations from spatiotemporal patterns of rodent hippocampal field potentials

Cao, Liang; Varga, Viktor; Chen, Zhe S

Cell reports methods. 2021 Nov 22; 1:

Closed-loop stimulation using a multiregion brain-machine interface has analgesic effects in rodents

Sun, Guanghao; Zeng, Fei; McCartin, Michael; Zhang, Qiaosheng; Xu, Helen; Liu, Yaling; Chen, Zhe Sage; Wang, Jing

Science translational medicine. 2022 Jun 29; 14:eabm5868