# The neural architecture of pragmatic processing

> **NIH NIH R01** · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · 2021 · $232,638

## Abstract

PROJECT SUMMARY
Functional magnetic resonance imaging (fMRI) has been invaluable for illuminating the brain’s functional
architecture. It has been especially important for cognitive abilities where animal models have limited utility, like
language. However, the field of human cognitive neuroscience has been struggling in that many findings are
not replicable, suffer from statistical flaws, or are difficult to compare across studies due to the use of divergent
analytic approaches. Many have now recognized the need for more robust, replicable, and meaningful science.
We here propose the development and dissemination of a powerful tool that can improve the field’s ability to
establish a robust and cumulative research enterprise. Leveraging the data collected in our lab over the last
ten years (>800 neurotypical participants across >1,200 scanning sessions), we propose to develop and
make publicly available probabilistic functional atlases for four brain networks critical for high-level
cognition: the language-selective network (which supports language processing; Fedorenko et al., 2010,
2011), the domain-general Multiple Demand (MD) network (which supports executive functions like cognitive
control; Duncan, 2010), the Default Mode network (DMN) (which supports internally-directed cognition and
construction of situation models; Buckner & DiNicola, 2019), and the Theory of Mind network (which supports
general social inference; Saxe & Kanwisher, 2003). These atlases will be created based on large numbers of
individual activation maps for well-established and extensively validated ‘localizer’ tasks targeting these
networks (700+ participants for the first three networks, and ~150 participants for the ToM network) and can be
used to estimate the probability that any given location in the common brain space belongs to a particular
functional network. In Aim 1, we will develop these probabilistic atlases. To do so, we will aggregate all the
relevant data for each of the localizer tasks, preprocess it through a uniform pipeline across two most
commonly used software packages (SPM, Friston, 1997; and FreeSurfer, Dale et al., 1999), and overlay the
individual activation maps in the relevant volume and surface spaces. We will additionally extract a set of key
individual-level neural markers, so that their distributions can be used normatively for comparisons with other
populations. In Aim 2, we will make the atlases (and constituent individual activation maps and neural markers)
publicly available. To do so, we will create a robust and interactive web-based platform for the dissemination of
the atlases. The proposed project is a critical step to bridge two fundamentally different and currently
disjoint analytic traditions in functional brain imaging—group-averaging approaches and functional
localization in individual brains—by providing common reference frames: probabilistic functional atlases based
on well-established and widely used localizers for four high-...

## Key facts

- **NIH application ID:** 10406543
- **Project number:** 3R01DC016607-04S1
- **Recipient organization:** MASSACHUSETTS INSTITUTE OF TECHNOLOGY
- **Principal Investigator:** Evelina Fedorenko
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $232,638
- **Award type:** 3
- **Project period:** 2018-05-21 → 2023-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10406543

## Citation

> US National Institutes of Health, RePORTER application 10406543, The neural architecture of pragmatic processing (3R01DC016607-04S1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10406543. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*