Coordinating individually measured phenotypes to advance mental health research

Ended Cooperative Agreement Awarded Last action

Award ID
U24MH136069
Recipient
Awarding agency
Obligated
$14,753,327

Award facts

Assistance listing
93.242 Mental Health Research Grants
Completion date
Place of performance
New Haven, CT 06511, USA

Award details come from USAspending.gov.

Description

As the agency reported it.

Abstract
Background: mental health research faces significant challenges, including the heterogeneity of diagnostic groups and the lack of precise characterization of individual patients, hindering effective clinical decision-making. However, data-driven approaches, such as machine learning and computational analyses, have emerged as crucial tools to address these challenges. By integrating data from behavioral assessments, clinical records, and biological markers, these approaches can generate more precise and objective clinical phenotypes, leading to improved diagnostic accuracy, personalized treatment selection, mechanistic insights, and enhanced monitoring and prognosis. The use of data-driven approaches holds immense importance in revolutionizing mental health research, enabling tailored interventions, and advancing our understanding and management of mental disorders. By integrating diverse data sources and leveraging advanced computational techniques, the individually measured phenotypes to advance computational translation in mental health (impact-MH) initiative was formed, with the goal to harness the power of big data to address the complexity and heterogeneity of mental disorders, ultimately improving patient care and outcomes. Research GAP: mental disorders exhibit complex characteristics, making IT difficult to represent, collect, and analyze heterogeneous data effectively. One major challenge is the absence of a unified representation for mental health data. While the research domain criteria framework (rdoc) aims to provide a systematic framework, a formal representation using existing biomedical standards, such as ontologies, is still lacking. Developing such standards is crucial to generate computational phenotypes. Additionally, once data standards and normalization methods are established, disseminating them to researchers is essential for promoting the generation of interoperable and reusable data. Moreover, ensuring the generalizability of phenotyping algorithms beyond their original development institute and minimizing bias associated with potential errors are critical factors for enabling broad applications of such algorithms in mental health research. Addressing these challenges is pivotal for advancing computational phenotyping in mental health and facilitating its broader utilization. Method: to tackle these challenges, we will establish three cores within our three aims: aim 1. Project coordination and data management core. This core will facilitate effective coordination and communication across impact-MH projects. Additionally, IT will build a robust data management system that encompasses the necessary infrastructure and pipelines to efficiently gather, integrate, store, and manipulate DE-identified multi-modal data from multiple impact-MH projects and submit them to NIH data repositories. Aim 2. Data standards core. This core will work on defining comprehensive data standards by leveraging the rdoc framework and existing ontologies. IT will also develop a consensus process and data harmonization methods aimed at maximizing the clinical, administrative, and scientific value of the various ascertainment and assessment practices used across the impact-MH projects. Aim 3. Data analytics core. This core will focus on conducting rigorous analyses on the aggregated data from the impact-MH projects. IT will develop methods to address potential biases associated with the datasets, algorithms, and applications used. By implementing sound analytical approaches, we aim to ensure the validity and reliability of the findings generated from the data.

Transactions

Action Obligated Date
Amendment $0
Amendment $5,585,608
Amendment $0
Amendment $977,811
Amendment $1,600,595
Amendment $1,600,595
Amendment $0
Amendment $0
Amendment $0
Amendment $0
Amendment $1,662,906
Amendment $1,662,906
Original award $1,662,906
Show all 13

Subawards

Subrecipient Amount Awarded
Mayo Clinic Jacksonville (A Nonprofit Corporation) "Dr. Cui Tao will serve as the Principal Investigator (PI) for the proposed project, leading the Mayo Clinic team and collaborating closely with the Yale… $236,250
The Trustees Of The University Of Pennsylvania "The UPenn team led by Dr. Yong Chen (MPI) will work closely with Dr. Hua Xu (contact PI at Yale) and Dr. Cui Tao (MPIs at UTHealth) on co-leading the Data… $239,060
The Trustees Of The University Of Pennsylvania The upenn team led by DR. Yong chen (MPI) will work closely with DR. HUA XU (contact PI at yale) and DR. CUI TAO (mpis at uthealth) on CO-leading the data… $402,387
Mayo Clinic Jacksonville (A Nonprofit Corporation) Dr. Tao will serve as a principal investigator (PI) for the proposed project. She will lead the Mayo Clinic team and work with the Yale University team to… $1,151,356
The Trustees Of The University Of Pennsylvania The UPenn team led by Dr. Yong Chen (MPI) will work closely with Dr. Hua Xu (contact PI at Yale) and Dr. Cui Tao (MPIs at UTHealth) on co-leading the Data… $402,500

Quick answers

Who received award U24MH136069?

U24MH136069 was awarded to Yale Univ by National Institute of Mental Health (National Institutes of Health) on July 15, 2024.

How much is award U24MH136069 worth?

$14,753,327 has been obligated on U24MH136069.

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