# Representation of navigational and driving-related information across human brain

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA BERKELEY · 2022 · $443,524

## Abstract

ABSTRACT
Natural navigation is an important skill that engages many sensory, motor and cognitive systems. Because
aging and degenerative brain disease both diminish the capacity to navigate in the real world, a better
understanding of the brain mechanisms mediating navigation will improve diagnosis and monitoring of
neurological and neurodegenerative diseases. Neurophysiological studies in animals have led to fundamental
insights about the neural mechanisms mediating navigation. However, due to methodological limitations
neuroimaging studies of navigation in humans have generally been less compelling than the animal work. We
propose to overcome these limitations by using the NexGen 7T MRI scanner recently installed at UC Berkeley
to measure brain activity during a naturalistic driving task. Driving is an excellent target for fMRI studies
because is a common human navigation task that unfolds across a large and varied landscape, and on a
timescale commensurate with fMRI; it engages many navigational brain systems; and it is impacted by aging
and neurological diseases. Data will be analyzed by means of an innovative and powerful voxelwise modeling
framework developed in PI Gallant's lab over the past 10 years, and validated in many publications.
Computational models reflecting 33 different types of navigational features will be fit to the fMRI data
separately for each voxel and in each individual subject. Model prediction accuracy and generalization will be
cross-validated using separate data sets and subjects reserved for this purpose. The results will be used to test
dozens of specific hypotheses about navigation drawn from the theoretical and experimental literature on both
rodents and humans. These results will also be used to obtain a detailed functional parcellation of navigational
representations in each individual and across the group, and to identify functional networks that represent
specific navigation-related features. By combining naturalistic experiments, large-scale computational
modeling, multiple hypothesis testing, data-driven functional parcellation and functional network analysis, this
research will provide fundamental new information about the human brain mechanisms mediating navigation
and their relationship to prior findings from the animal literature.

## Key facts

- **NIH application ID:** 10392486
- **Project number:** 5R01EY031455-02
- **Recipient organization:** UNIVERSITY OF CALIFORNIA BERKELEY
- **Principal Investigator:** JACK L GALLANT
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $443,524
- **Award type:** 5
- **Project period:** 2021-05-01 → 2026-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10392486

## Citation

> US National Institutes of Health, RePORTER application 10392486, Representation of navigational and driving-related information across human brain (5R01EY031455-02). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10392486. Licensed CC0.

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