Notice of Availability of Administrative Supplements for Advancing Computational Modeling and Data Analytics Relevant to Mental Health

Notice Number: NOT-MH-17-011

Key Dates
Release Date:  December 22, 2016

Related Announcements
NOT-MH-17-010

Issued by
National Institute of Mental Health (NIMH)

Purpose

The National Institute of Mental Health (NIMH) announces the opportunity for investigators with relevant active NIMH-supported research project grants (R01, R03, R15, R21, R21/33, and R37), research centers (P-grants) and cooperative agreements (U-grants) to submit administrative supplements submitted to PA-16-287, Administrative Supplements to Existing NIH Grants and Cooperative Agreements  (Admin Supp),  for funded projects that could benefit from: (1) explanatory computational models (theory- and/or data-driven) to test underlying brain and behavioral mechanisms; and (2) analytical approaches leveraging complex datasets within and across levels of analysis (e.g., genes, molecules, cells, circuits, physiology, and behavior). 

This Notice will support the implementation of computational, theoretical, and analytical approaches in existing basic, translational, clinical neuroscience and neuropsychiatry, and mental health services grants. The intent of these supplements is to support the addition of computational approaches for interpreting mental health-relevant data. Applicants for the administrative supplements are encouraged to form new collaborations between computational modelers, clinicians, neuroscientists, biologists, biostatisticians, mathematicians, engineers, etc.

Examples of the types of supplements that are of interest include, but are not limited to the following:

  • Integrate deep-learning algorithms with effective explanatory techniques.
  • Integrate theory-driven models with data-driven models.
  • Integrate bottom-up models with top-down models. 
  • Integrate explanatory models of spatiotemporal dynamics across multiple levels of analysis.
  • Integrate multi-modal data fusion algorithms (e.g., multi-kernel learning) to link distinct levels of analysis to one or multiple outcome measures.
  • Apply/develop/validate biophysically realistic bio-structural and functional models enabling both wide-angle investigations (of the full system dynamics in high-resolution) and focused perspectives on specific components, leveraging data from neuro-technologies, such as high-resolution transmission electron microscopy, voltage/calcium indicators, array tomography, etc.
  • Apply/develop/validate methods to assess fundamental features in large non-linear systems (e.g., phenotyping activity-patterns of molecules, cells, circuits) based on hybrid mathematical systems.
  • Apply/develop/validate algorithms performing trial-by-trial, individual-level, and population-level predictions of behavior from neural data.
  • Apply/develop/validate predictive analytic approaches to existing data from healthcare systems (e.g., EMR data) to identify subgroups at high risk (e.g., suicidal behavior; poor treatment response; relapse) in order to consider earlier intervention targets.
  • Apply/develop/validate approaches for integrating/linking large data sets (such as, public records, e.g., death records, arrest records) in order to examine the down-stream impact of community-based interventions (e.g., community- or state-level health-promotion or prevention programs).

 

Develop/apply innovative computational and analytical approaches to:

  • Ascribe potential functional roles of genetic variants and incorporate many loci, accounting for pleiotropic effects, additive effects, and epistatic interactions, to increase understanding of the genetic risk architecture for mental illness.
  • Systematically evaluate the functional impact of rare variants, both coding and non-coding, on mental illness.
  • Improve prediction in determining functional effects of mental illness-associated genetic changes in the regulatory regions of the down-stream pathways, to explain the origin of pathophysiological state associated with mental disorders.
  • Analyze single cell genomics, transcriptomics, epigenomics for analysis of postmortem mental disorder brain samples.
  • Model the integration of multiple molecular/cellular/circuit/behavioral systems and how they might be impacted by perturbations. 
  • Integrate genomic and/or epigenomic data with other cell/molecular or functional phenotypic information to promote a greater understanding of cell types in the brain.  
  • Integrate across multiple data types and/or levels of analysis (genetic, molecular, cellular, circuit) to identify potential new therapeutic targets.
  • Examine cross-disorder analyses to provide insights into the phenotypic landscape of mental disorders.

Funds may be available for administrative supplements to meet increased costs that are within the scope of the approved award, but were unforeseen when the new or renewal application or grant progress report for non-competing continuation support was submitted.  Applications for administrative supplements are considered prior approval requests (as described in Section 8.1.2.11 of the NIH Grants Policy Statement) and will be routed directly to the Grants Management Officer of the parent award. There is no guarantee that funds are available from NIMH or for any specific grant. All applicants are encouraged to discuss potential requests with their program official. Additionally, prior to submission, applicants must review NIHM's web site to ensure they meet the IC's requirements. 

NIMH encourages applications for administrative supplements to be submitted prior to May 1, 2017. For additional reference, see the parent program announcement
Administrative Supplements to Existing NIH Grants and Cooperative Agreements  (Admin Supp)
 PA-16-287.

 

Inquiries

Please direct all inquiries to:

Michele Ferrante, PhD
National Institute of Mental Health (NIMH)
Telephone: 301-435-6782
Email: michele.ferrante@nih.gov