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.
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.
| 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 |
| Westfall, Jacob; Nichols, Thomas E; Yarkoni, Tal (2016) Fixing the stimulus-as-fixed-effect fallacy in task fMRI. Wellcome Open Res 1:23 |
| Westfall, Jacob; Yarkoni, Tal (2016) Statistically Controlling for Confounding Constructs Is Harder than You Think. PLoS One 11:e0152719 |
| de la Vega, Alejandro; Chang, Luke J; Banich, Marie T et al. (2016) Large-Scale Meta-Analysis of Human Medial Frontal Cortex Reveals Tripartite Functional Organization. J Neurosci 36:6553-62 |
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