# Functional-neuroanatomy of high-level visual cortex: a quantitative multimodal approach

> **NIH NIH R01** · STANFORD UNIVERSITY · 2021 · $379,064

## Abstract

Project Summary/Abstract
Perception of ecologically relevant visual stimuli such as faces and bodies is achieved through
two processing streams extending from early visual cortex (EVC) to lateral occipito-temporal
cortex (LOTC) and ventral temporal cortex (VTC), respectively. However, if and how the
underlying microstructure and white matter connections constrain the functional organization and
support neural computations in these visual streams remains poorly understood. Leveraging
advancements achieved in the prior funding period, we propose a unique multimodal approach,
combining functional magnetic resonance imaging (fMRI), quantitative MRI (qMRI), diffusion MRI
(dMRI), anatomical quantification, and innovative computational modeling to elucidate how
structural factors constrain the functional organization of LOTC and VTC. The research has three
main aims. Aim 1 will test a quantitative model of functional-anatomical correspondence in
high-level visual cortex. Using fMRI, analysis of micro- and macro-structure, the research will
quantify the correspondence between macroanatomical landmarks, cytoarchitecture, and
functional regions in LOTC and VTC. Aim 2 will determine how white matter connections
regulate the functional organization of high-level visual cortex. Using dMRI and fMRI this
aim will test (i) if different white matter connections from EVC to downstream regions contribute
to the segregation of functional regions within and across visual streams, and (ii) if the eccentricity
of the origin of these white matter connections impacts the visual field coverage of downstream
regions. Aim 3 will develop and test a spatiotemporal population receptive field model of
responses in visual cortex. This aim will provide not only an innovative approach using fMRI
and computational modeling to predict responses to a large range of stimuli that vary in size,
position, timing, and duration, but will also provide a quantitative framework to test the impact of
top-down attention on basic visual computations. Overall, the proposed research will significantly
advance understanding of high-level vision by filling in longstanding gaps in knowledge. The
research will (1) provide a parsimonious model of how the microstructure and connections
scaffold the function and computations of both ventral and lateral streams, (2) break new ground
in computational models of visual cortex, and (3) generate innovative multimodal in vivo methods
to quantify microstructural properties of visual cortex. Together, the research has important
implications for clinical conditions that are associated with malfunction of high-level vision
including developmental prosopagnosia, autism, and dyslexia.

## Key facts

- **NIH application ID:** 10087937
- **Project number:** 5R01EY023915-07
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Kalanit Grill-Spector
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $379,064
- **Award type:** 5
- **Project period:** 2014-06-01 → 2024-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10087937

## Citation

> US National Institutes of Health, RePORTER application 10087937, Functional-neuroanatomy of high-level visual cortex: a quantitative multimodal approach (5R01EY023915-07). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10087937. Licensed CC0.

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