The human functional neuroimaging literature has experienced explosive growth over the last two decades. In an effort to make sense of this literature, neuroimaging researchers have developed quantitative meta-analysis methods that can aggregate and synthesize the results of hundreds or thousands of studies. In the previous period of this project, we introduced a web-based platform called Neurosynth that supports automated meta- analysis of the fMRI literature at large scale. Neurosynth has become a widely used resource within then neuroimaging community; however, like other meta-analysis approaches to fMRI, it currently supports analysis only of sparse, discrete activations previously reported in published studies. This coordinate-based meta- analysis (CBMA) approach is inferior in many respects to image-based meta-analysis (IBMA) approaches that operate over continuous whole-brain statistical maps. A community-wide shift from CBMA to IBMA would considerably improve sensitivity and specificity, and allow a much broader range of mixed-effects meta- analysis models to be fit to fMRI data. Our overarching goal in the present project period is to contribute to such a shift by extending the existing Neurosynth platform into a turnkey solution for image-based meta- analysis.
In Aim 1, we will create a centralized database of whole-brain statistical maps, providing a rich data source for large-scale image-based meta-analyses.
In Aim 2, we will add new web-based interfaces to Neurosynth that enable users to easily (i) edit, validate, and annotate data from individual studies, and (ii) organize data from hundreds or thousands of studies into sophisticated image-based meta-analyses that can be readily executed on local or cloud computing resources.
In Aim 3, we will develop a reference open-source software package (PyCIBMA) for efficient mixed-effects meta-analysis of fMRI data, providing the community with a uniform interface for fMRI meta-analysis that complies with current open standards and specifications. Realizing these objectives will introduce powerful new tools for synthesizing the neuroimaging literature at a large scale and with unprecedented resolution. These tools will be freely and publicly available to anyone with an internet connection, enabling rapid and efficient application to a broad range of clinical and basic research applications.

Public Health Relevance

This project focuses on continued development of the Neurosynth framework for large-scale synthesis of functional MRI data. Neurosynth is a set of integrated web-based tools that enable users to summarize and synthesize the results of thousands of individual neuroimaging studies, making it easier for basic and clinical researchers to generate and test hypotheses about brain function.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Research Project (R01)
Project #
5R01MH096906-07
Application #
10075314
Study Section
Neuroscience and Ophthalmic Imaging Technologies Study Section (NOIT)
Program Officer
Bennett, Yvonne
Project Start
2012-08-10
Project End
2023-12-31
Budget Start
2021-01-18
Budget End
2021-12-31
Support Year
7
Fiscal Year
2021
Total Cost
Indirect Cost
Name
University of Texas Austin
Department
Psychology
Type
Schools of Arts and Sciences
DUNS #
170230239
City
Austin
State
TX
Country
United States
Zip Code
78759
de la Vega, Alejandro; Yarkoni, Tal; Wager, Tor D et al. (2018) Large-scale Meta-analysis Suggests Low Regional Modularity in Lateral Frontal Cortex. Cereb Cortex 28:3414-3428
Rubin, Timothy N; Koyejo, Oluwasanmi; Gorgolewski, Krzysztof J et al. (2017) Decoding brain activity using a large-scale probabilistic functional-anatomical atlas of human cognition. PLoS Comput Biol 13:e1005649
Poldrack, Russell A; Baker, Chris I; Durnez, Joke et al. (2017) Scanning the horizon: towards transparent and reproducible neuroimaging research. Nat Rev Neurosci 18:115-126
Cremers, Henk R; Wager, Tor D; Yarkoni, Tal (2017) The relation between statistical power and inference in fMRI. PLoS One 12:e0184923
Yarkoni, Tal; Westfall, Jacob (2017) Choosing Prediction Over Explanation in Psychology: Lessons From Machine Learning. Perspect Psychol Sci 12:1100-1122
Gilmore, Rick O; Diaz, Michele T; Wyble, Brad A et al. (2017) Progress toward openness, transparency, and reproducibility in cognitive neuroscience. Ann N Y Acad Sci 1396:5-18
Gorgolewski, Krzysztof J; Alfaro-Almagro, Fidel; Auer, Tibor et al. (2017) BIDS apps: Improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods. PLoS Comput Biol 13:e1005209
Pauli, Wolfgang M; O'Reilly, Randall C; Yarkoni, Tal et al. (2016) Regional specialization within the human striatum for diverse psychological functions. Proc Natl Acad Sci U S A 113:1907-12
Shine, James M; Koyejo, Oluwasanmi; Poldrack, Russell A (2016) Temporal metastates are associated with differential patterns of time-resolved connectivity, network topology, and attention. Proc Natl Acad Sci U S A 113:9888-91
Hirschman, Lynette; Fort, Karën; Boué, Stéphanie et al. (2016) Crowdsourcing and curation: perspectives from biology and natural language processing. Database (Oxford) 2016:

Showing the most recent 10 out of 28 publications