IMPACT-MH

Individually Measured Phenotypes to Advance Computational Translation in Mental Health (IMPACT-MH)

IMPACT-MH uses behavioral measures and computational methods to define novel clinical signatures that can be used for individual-level prediction and clinical decision making in mental disorders.

About IMPACT-MH

The Individually Measured Phenotypes to Advance Computational Translation in Mental Health (IMPACT-MH) consortium was established with a goal to harness the power of big data to address the complexity and heterogeneity of mental disorders, ultimately improving patient care and outcomes. The consortium includes eight U01 awardees from eight different institutions across the country and a U24 awardee located at Yale University, New Haven, CT.

The IMPACT-MH Data Coordination Center (DCC) is a partnership between Yale University, Mayo Clinic, University of Pennsylvania, and NIH/NIMH to bring together informaticians, data scientists, and mental health specialists to facilitate data collection, harmonization, and curation to improve health outcomes. 

Upcoming Events

Messsage from NIMH:
The
IMPACT-MH initiative aims to revolutionize precision medicine in psychiatry. This consortium seeks to enhance mental health care by integrating new data, such as performance on computerized behavioral tasks, with traditional clinical information. By focusing first on cost-effective and scalable behavioral measures that provide insights into individual brain functions, the initiative aims to improve predictions about treatment responses and outcomes, ultimately advancing the future of precision psychiatry.
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IMPACT-MH Projects

Project Name Contact PI Organization
Coordinating Individually Measured Phenotypes to Advance Mental Health Research Hua Xu Yale University
Phenotypes REimagined to Define Clinical Treatment and Outcome Research (PREDiCTOR) René S Kahn Icahn School of Medicine at Mount Sinai
IMPACT-MH: Clinical and behavioral fingerprints of psychopathology Christopher Pittenger Yale University
ACE-D: Accelerating Cognition-guided signatures to Enhance translation in Depression Leanne Williams Stanford University
JASPer-MH: Jointly Assessed Scalable Phenotypes for Mental Health Roy H. Perlis Massachusetts General Hospital
COMPASS: A comprehensive mobile precision approach for scalable solutions in mental health treatment Amy S. B. Bohnert University of Michigan at Ann Arbor
The Pediatric Precision Sleep Network Adriane M. Soehner University of Pittsburgh at Pittsburgh
Person-centered diagnostics and prediction for child dysregulatory psychopathology using novel phenotypes Bonnie J. Nagel Oregon Health & Science University
Developing Data-Driven Clinical Signatures for People Who Experience Hallucinations Dror Ben-Zeev University of Washington
Analyses to Reveal Trajectories and Early Markers of Imminent Shifts in Suicidal States Jessica Turner Ohio State University College of Medicine
SPARC-XP: Scalable Phenotyping to Assay Risk in Childhood after eXposures in Pregnancy Andrea Goldberg Edlow Massachusetts General Hospital
Duke Predictive Model of Adolescent Mental Health Jonathan Posner Duke University
TRACC-MH: Trajectories of Risk and Cognitive Change in Mental Health Laura Thi Germine Albert Einstein College of Medicine
REACH: Behavioral Phenoscreening: Remote Assessment of Child Mental Health Moriah E. Thomason New York University
Project Name Contact PI Organization
Coordinating Individually Measured Phenotypes to Advance Mental Health Research Hua Xu Yale University
Phenotypes REimagined to Define Clinical Treatment and Outcome Research (PREDiCTOR) René S Kahn Icahn School of Medicine at Mount Sinai
IMPACT-MH: Clinical and behavioral fingerprints of psychopathology Christopher Pittenger Yale University
ACE-D: Accelerating Cognition-guided signatures to Enhance translation in Depression Leanne Williams Stanford University
JASPer-MH: Jointly Assessed Scalable Phenotypes for Mental Health Roy H. Perlis Massachusetts General Hospital
COMPASS: A comprehensive mobile precision approach for scalable solutions in mental health treatment Amy S. B. Bohnert University of Michigan at Ann Arbor
The Pediatric Precision Sleep Network Adriane M. Soehner University of Pittsburgh at Pittsburgh
Person-centered diagnostics and prediction for child dysregulatory psychopathology using novel phenotypes Bonnie J. Nagel Oregon Health & Science University
Developing Data-Driven Clinical Signatures for People Who Experience Hallucinations Dror Ben-Zeev University of Washington
Analyses to Reveal Trajectories and Early Markers of Imminent Shifts in Suicidal States Jessica Turner Ohio State University College of Medicine
SPARC-XP: Scalable Phenotyping to Assay Risk in Childhood after eXposures in Pregnancy Andrea Goldberg Edlow Massachusetts General Hospital
Duke Predictive Model of Adolescent Mental Health Jonathan Posner Duke University
TRACC-MH: Trajectories of Risk and Cognitive Change in Mental Health Laura Thi Germine Albert Einstein College of Medicine
REACH: Behavioral Phenoscreening: Remote Assessment of Child Mental Health Moriah E. Thomason New York University