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

Notice Number: NOT-MH-18-023

Key Dates
Release Date: February 20, 2018

Related Announcements

Issued by
National Institute of Mental Health (NIMH)


The National Institute of Mental Health (NIMH) announces the opportunity for investigators with relevant active NIMH-supported research project grants (R01, R03, R15, R21, R61/33, R21/33, R33, R34, and R37), research centers (P-grants) and cooperative agreements (U-grants) to submit administrative supplements according to PA-18-591, Administrative Supplements to Existing NIH Grants and Cooperative Agreements (Parent Admin Supp Clinical Trial Optional), 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 intervention and services research 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, geneticists, etc. as such, funds should be used to pay for effort of computational modeler or addition of post-doc; to pay for computational resources/equipment necessary for building/testing models related to already acquired clinical data-sets; for something directly related to time/effort needed to design new pilot experiments. Administrative supplements must add value to the science proposed in the aims of the original project, as such they must be within the scientific scope of the parent grant.
In general, NIMH is we are interested in integrating: deep-learning algorithms with effective explanatory techniques; theory-driven models with data-driven models; bottom-up models with top-down models; and in multi-modal data fusion algorithms (e.g., multi-kernel learning) to mechanistically link distinct levels of analysis to one or multiple outcome measures. Examples of the types of supplements that are of interest include, but are not limited to the following:

  • Develop adaptive/closed-loop (supervised and/or un-supervised) computational methods to optimize neuro-stimulation protocols (for TMS, ECoG, etc.,) to either: test innovative theories of circuit functions; correct pathological neuro-behavioral signals; or improve performance in any mental function by modulating neural circuits.   
  • 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).
  • 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 (e.g., public records, 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).
  • Apply computational methods to EHR data to predict pathways of care and/or to identify modifiable risk factors that can lead to clinical and/or services interventions to improve quality of care.
  • Integrate and analyze data captured through technology-based approaches (e.g., sensor-based, passive monitoring of the individual’s autonomic state, activity level, location and environmental context) to create digital phenotypes, advance understanding of risk/etiological factors and illness trajectories, and guide the delivery of timely intervention. 
  • Model the integration of multiple molecular/cellular/circuit/behavioral systems and how they might be impacted by perturbations. 
  • Examine cross-disorder analyses to provide insights into the phenotypic landscape of mental disorders.
  • Integrate existing data from the (NIMH data Archive or other sources) for novel secondary analyses aimed at mental health priorities. We are particularly interested in identifying potential biological, experiential, and other predictors and moderators of suicide risk. The use of dimensional variables and inclusion of multiple levels of analyses is particularly encouraged.
  • Analyze risk trajectories that point to timing or intervention approaches that could interrupt mediation pathways for suicide risk, such as acute intoxication, interpersonal distress, anxiety and depression.
  • Analyze modifiable risk and protective factors and moderators across development to better understand opportunities to intervene earlier to prevent later risk for suicide.
  • Maximize the information yield from randomized clinical trials and extend causal mediation pathway analysis.
Supplements to projects involving genetic/genomic data may also be proposed to develop/apply innovative computational and analytical approaches to:
  • Increase understanding of the genetic risk architecture for mental illness by assessing the relative contribution of additive, pleiotropic and epistatic effects.
  • Systematically evaluate the functional, molecular, cell-type specific and/or systems level impact of rare and common variants, both coding and non-coding, associated with mental illness.
  • Analyze single cell genomics (e.g., transcriptomes, methylomes) for analysis and interpretation of postmortem brain samples from patients.
  • Integrate across multiple data types and/or levels of analysis (genetic, molecular, cellular, circuit) to identify potential new therapeutic targets.
  • Develop a robust, comparative approach to identify the molecular pathways and cell-types enriched for mental illness risk variants that are shared or unique across species.
  • Build predictive models of disease risk and progression using state-of-the-art machine learning algorithms and systems biology approaches that leverage largescale datasets (genetic, epigenomic, transcriptomic, proteomic, phenotypic).
Apply/develop/validate artificial intelligence and/or quantum algorithms that leverage novel computational platforms (e.g., quantum processors) to address any of the above, or related, areas. 
These administrative supplements are available 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 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. 

Budget and Available Funds
Supplement budgets are limited to one year and may not exceed $100,000 in direct costs (including costs for all applications combined in a collaborative set), and are expected to reflect actual needs of the proposed project.
Projects well-aligned with this Notice but requiring larger budgets (and/or longer time frames) or an expansion of the project’s approved scope or research protocol may submit a Revision Application (formerly termed Competitive Supplement). Revision applications are submitted using the forms, instructions, and guidelines detailed in the parent grant’s originating FOA. Should the parent grant’s FOA be expired, applicants are encouraged to apply via the guidelines for the appropriate parent or omnibus announcement.
NIMH intends to commit approximately $1 million in direct cost in FY18 to support approximately 10 administrative supplements.
Submitting an Application
Applicants should begin their application by stating: “This application is being submitted in response to NOT-MH-18-023" . For additional reference, see the parent program announcement to Administrative Supplements to Existing NIH Grants and Cooperative Agreements (Parent Admin Supp Clinical Trial Optional) PA-18-591
Applications are due by 5:00 PM local time on April 1, 2018.


Please direct all inquiries to:

Michele Ferrante, PhD 
National Institute of Mental Health ( NIMH )
Telephone: 301-435-6782