A competing renewal of R01 award MH074457 (currently in year 9) is requested. The R01 seeking renewal sustains the BrainMap Project (www.brainmap.org). The overall goal of the BrainMap Project is to provide the human brain mapping community with data sets, computational tools, and related resources that enable quantitative meta-analyses and co-activation mapping and functional decoding of neuroimaging data. The BrainMap Project manages two coordinate-based databases: 1) a functional activation repository of >11,000 published experiments (~45,000 subjects); and, 2) a voxel-based morphometry (VBM) repository of >2,700 published experiments (~63,000 subjects). The BrainMap Project provides a suite of tools (Sleuth, GingerALE, and Scribe) to access, curate, and analyze these datasets. To date, the tools and data have been used in >350 peer-reviewed meta-analytic publications, of which >170 were published by the community in the past two years (2012-2013). Four tool-development aims and four data sharing objectives are proposed.
Aim 1 proposes to improve anatomical specificity, null-distribution modeling, and normalization of contrast analyses computed using Activation Likelihood Estimation (ALE).
Aim 2 proposes to develop tools for modeling large-scale co-activation patterns (i.e., across thousands of experiment) in the BrainMap database to extract & map functionally connected brain networks. Our tool development strategy adopts both bottom up (regional) and top down (global) approaches.
Aim 3 proposes to develop tools that utilize BrainMap's location-linked behavioral metadata for functional interpretation of brain regions and networks.
Aim 4 proposes to model neural networks affected by psychiatric and neurological disorders, both within and between disorders. Sharing Objectives 1 & 2 provide user-oriented support for data entry and access for in-progress meta- analyses as well as sharing of useful products of this projects and their publications. Sharing Objectives 3 & 4 provide tools that facilitate the incorporation of Brain-Map derived tools and data into other image-analysis software environments.

Public Health Relevance

This project provides mathematical tools and data sets for large-scale mining of anatomical and functional imaging investigations of the human brain reported in the peer-reviewed scientific literature. These tools and data are shared through a widely used web portal (www.BrainMap.org) and by distribution to developers of highly used software systems. To date, more than 350 peer-reviewed, full-length publications have used these tools and data, including 79 in 2013 alone.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Research Project (R01)
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Neuroscience and Ophthalmic Imaging Technologies Study Section (NOIT)
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Friedman, Fred K
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University of Texas Health Science Center
Schools of Medicine
San Antonio
United States
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Camilleri, J A; Müller, V I; Fox, P et al. (2018) Definition and characterization of an extended multiple-demand network. Neuroimage 165:138-147
Acar, Freya; Seurinck, Ruth; Eickhoff, Simon B et al. (2018) Assessing robustness against potential publication bias in Activation Likelihood Estimation (ALE) meta-analyses for fMRI. PLoS One 13:e0208177
Trevisi, Gianluca; Eickhoff, Simon B; Chowdhury, Fahmida et al. (2018) Probabilistic electrical stimulation mapping of human medial frontal cortex. Cortex 109:336-346
Pool, Eva-Maria; Leimbach, Martha; Binder, Ellen et al. (2018) Network dynamics engaged in the modulation of motor behavior in stroke patients. Hum Brain Mapp 39:1078-1092
Bzdok, Danilo; Altman, Naomi; Krzywinski, Martin (2018) Statistics versus machine learning. Nat Methods 15:233-234
Wang, Hao-Ting; Poerio, Giulia; Murphy, Charlotte et al. (2018) Dimensions of Experience: Exploring the Heterogeneity of the Wandering Mind. Psychol Sci 29:56-71
Müller, Veronika I; Cieslik, Edna C; Laird, Angela R et al. (2018) Ten simple rules for neuroimaging meta-analysis. Neurosci Biobehav Rev 84:151-161
Genon, Sarah; Reid, Andrew; Li, Hai et al. (2018) The heterogeneity of the left dorsal premotor cortex evidenced by multimodal connectivity-based parcellation and functional characterization. Neuroimage 170:400-411
Riedel, Michael C; Yanes, Julio A; Ray, Kimberly L et al. (2018) Dissociable meta-analytic brain networks contribute to coordinated emotional processing. Hum Brain Mapp 39:2514-2531
Nostro, Alessandra D; Müller, Veronika I; Varikuti, Deepthi P et al. (2018) Predicting personality from network-based resting-state functional connectivity. Brain Struct Funct 223:2699-2719

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