The proposed research aims to develop a framework for integrating knowledge about brain, mind, and behavior across multiple disciplines, which we refer to as a }cognitive atlas." This framework extends the existing knowledge and software architecture for neuroanatomic atlas development that has been established and is now under major development in the UCLA Center for Computational Biology (CCB). A comprehensive approach to understanding mental illness requires translation between syndrome, cognition, behavior, and biology, but increasing specialization within each of these domains hinders interdisciplinary communication, insights and discoveries. The proposed project aims to bridge these gaps by developing a system for the representation of knowledge about cognitive tasks, cognitive processes, and brain structure. We propose to harness new technologies for collaborative knowledge-building along with automated methods of literature mining, to develop a flexible web-based system that will support the consolidation of distributed knowledge about cognitive phenotypes and allow creation of multi-level interdisciplinary links. This system will provide the foundation for involvement of experts in the formalization of their domain knowledge. The resulting knowledge base will provide an }atlas} that allows the mapping of cognitive phenotypes onto biomedical knowledge at manifold other levels, from genes to complex syndromes.

Public Health Relevance

The proposed research aims to improve public health by enabling interdisciplinary research on mental disease to result in discovery of treatments and preventions that would not otherwise be possible with current approaches. It will extend the work of the National Centers for Biological Computing to develop a knowledge base that relates psychological functions to brain systems. This knowledge base spanning syndromes, cognitive processes, neural systems, and molecular genetics will additionally serve as an evolving and up-to-date educational resource for scientists, practitioners, and patients. The system developed in this project will also enhance biomedical knowledge and discovery more broadly by providing new tools for collaborative knowledge building.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Research Project (R01)
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Special Emphasis Panel (ZRG1-BST-E (51))
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Freund, Michelle
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University of Texas Austin
Schools of Arts and Sciences
United States
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Poldrack, Russell A (2011) Inferring mental states from neuroimaging data: from reverse inference to large-scale decoding. Neuron 72:692-7
Poldrack, Russell A; Kittur, Aniket; Kalar, Donald et al. (2011) The cognitive atlas: toward a knowledge foundation for cognitive neuroscience. Front Neuroinform 5:17
Yarkoni, Tal; Poldrack, Russell A; Nichols, Thomas E et al. (2011) Large-scale automated synthesis of human functional neuroimaging data. Nat Methods 8:665-70
Schonberg, Tom; Fox, Craig R; Poldrack, Russell A (2011) Mind the gap: bridging economic and naturalistic risk-taking with cognitive neuroscience. Trends Cogn Sci 15:11-9
Bilder, Robert M (2011) Neuropsychology 3.0: evidence-based science and practice. J Int Neuropsychol Soc 17:7-13
Congdon, Eliza; Poldrack, Russell A; Freimer, Nelson B (2010) Neurocognitive phenotypes and genetic dissection of disorders of brain and behavior. Neuron 68:218-30
Yarkoni, Tal; Poldrack, Russell A; Van Essen, David C et al. (2010) Cognitive neuroscience 2.0: building a cumulative science of human brain function. Trends Cogn Sci 14:489-96
Bilder, Robert M; Sabb, Fred W; Parker, D Stott et al. (2009) Cognitive ontologies for neuropsychiatric phenomics research. Cogn Neuropsychiatry 14:419-50
Van Horn, John Darrell; Poldrack, Russell A (2009) Functional MRI at the crossroads. Int J Psychophysiol 73:3-9
Sabb, F W; Burggren, A C; Higier, R G et al. (2009) Challenges in phenotype definition in the whole-genome era: multivariate models of memory and intelligence. Neuroscience 164:88-107

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