The description below was taken from the Collaborative U01 version of this FOA:
New technologies, advances in computing, and the rise of ‘team science’ have led to parallel revolutions in human genetics and multiple fields of experimental biology, which are generating large, complex datasets. Large team science efforts, such as the Psychiatric Genetics Consortium (PGC) and the Autism Sequencing Consortium (ASC), have made significant progress in delineating the genetic architecture of mental disorders by identifying robust and reliable genetic associations across the allelic spectrum for several DSM disorders. In schizophrenia, early successes in gene discovery have highlighted the contribution of many individual loci of small effect, while in the case of autism spectrum disorders, most of the progress has focused on very rare or de novo coding mutations of large(r) effect.
The circuit-level understanding of behavioral traits has recently started to advance, driven by large-scale initiatives in connectomics and technology development (Human Connectome, http://www.humanconnectomeproject.org/, and BRAIN, https://www.braininitiative.nih.gov/). Innovative computational models have started to link brain network level data to specific phenotypic outcomes for disorders like autism and schizophrenia. Despite the advancements at each level of analysis, understanding the etiology of serious mental illness and its many manifestations, such as psychosis, remains elusive. Additionally, phenotypic heterogeneity and genetic pleiotropy are known phenomena in neuropsychiatric disorders that pose a major hurdle in understanding the underlying pathophysiology. To decode the biological underpinnings of psychopathology, we must tackle the “hard” problem of linking causal explanations of biological phenomenon across multiple levels of analysis from genetic factors to cells, circuits, networks, and behavioral (including cognitive and/or affective) phenotypes.
Currently, well-founded and informative probabilistic or causal models that cross levels of analysis are rare, as most cross-level analyses are correlative and insufficient with respect to understanding causality. Understanding how factors at each level of analysis work together to ultimately produce changes in behavioral, cognitive, and affective phenotypes will require innovative and paradigm-shifting approaches that can effectively integrate multi-level datasets into experimental paradigms and computational models to establish probabilistic and causal links across multiple levels of analysis.
In the convergent neuroscience (CN) framework, inter/transdisciplinary teams composed of members from the neurosciences and orthogonal fields (e.g., physics, engineering, astrophysics, mathematics, computer science, environmental sciences, etc.) will work together on the “hard” problems in neuropsychiatry. By breaking down intellectual silos, CN will capitalize on advances in neuropsychiatry (e.g., from BRAIN, Connectome, RDoC, etc.) and recent progress in complementary fields, such as applied mathematics or theoretical and computational sciences (e.g., dynamical systems theory, machine learning, estimation theory, hierarchical Bayesian inference). Exploring causal links across multiple levels of analysis through the CN framework will require innovation in computational methods and the integration of molecular- and genomic-level tools into circuit- and systems-level neuroscience. Novel convergent approaches will accelerate our understanding of neurobiological systems and processes that are relevant in health and disease, and may provide a path to identify novel avenues of inquiry to improve the diagnosis and treatment of neuropsychiatric disorders.
The overall objective of this FOA is to provide a mechanistic understanding of the key drivers of psychopathology, across disorders and throughout neurodevelopment, by establishing causal and/or probabilistic linkages across contiguous levels of analysis. The projects under this FOA will develop novel methods, theories, and approaches through a CN team framework, bringing together highly synergistic inter/transdisciplinary teams from multiple disciplines including neuroscience, data/computational science, physics, engineering, mathematics, and environmental sciences. Additionally, a goal of this program is to advance research in convergent neuroscience by creating a shared community framework of resources which may be used by the broader research community to further research, as such, successful team will have robust plan for sharing data and other resources. In order to move beyond cataloging statistical associations to defining causation, successful teams will utilize, combine, expand upon, or develop conceptual frameworks and theoretical approaches, and build explanatory computational models that connect contiguous levels of analysis. Such frameworks, theories, and computational explanatory models should be validated through experimental approaches to elucidate biological underpinnings of complex behavioral (including cognitive and affective) outcomes in psychopathology. Applicants are strongly encouraged to link at least three contiguous levels of analysis and base their aims on findings from unbiased genetic or genomic studies (e.g., large-scale genome-wide association studies, rare de novo mutations and structural variations, and tissue-specific or cell-type specific transcriptional and epigenetic profiles).
Levels of analysis include, but are not limited to:
- genomic (e.g., DNA variation, epigenomic, transcriptomic, etc.)
- molecular interactions and pathways (e.g., signaling pathways, neurotransmitter-receptor interactions, protein-protein interactions, etc.)
- single cell and small-scale multi-cellular function (micro-circuitry, etc.)
- cell population interactions across circuits (macro-circuitry, etc.)
- system-wide connections (neural networks, etc.)
- behavioral/cognitive/affective phenotypes relevant to mental health and mental illness
Deadlines: May 1, 2017 and then Standard dates apply (full proposals; letters of intent are due 30 days prior to the deadline)
- Collaborative U01 – https://grants.nih.gov/grants/guide/pa-files/PAR-17-176.html
- U01 – https://grants.nih.gov/grants/guide/pa-files/PAR-17-179.html
Filed Under: Funding Opportunities