# Hierarchical Bayesian Analysis of Retinotopic Maps of the Human Visual Cortex with Conformal Geometry

> **NIH NIH R01** · ARIZONA STATE UNIVERSITY-TEMPE CAMPUS · 2024 · $1

## Abstract

PROJECT SUMMARY / ABSTRACT
 As of 2015 there were 940 million people with some degree of visual impairment in the world. Visual
impairments generate considerable economic burden for the society. The World Health Organization estimates
that 80% of visual impairments are either preventable or curable with treatment. Noninvasive imaging
techniques have been used extensively by eye specialists for diagnosis and treatment of visual disorders and
imaging is one of the priorities in the six core program areas of the National Eye Institute. As a noninvasive
high spatial resolution technique for measuring brain activities, functional magnetic resonance imaging (fMRI)
has provided a wealth of data on visual cortical organizations. Although numerous studies have been devoted
to discovering and validating different retinotopic maps in the human visual system, limited progress has been
made in developing software tools that fully consider the intrinsic geometrical features of the underlying cortical
structures, enforce diffeomorphic mapping when constructing retinotopic maps and atlases, and integrate both
individual and population statistics for more robust data analysis. In preliminary work, we have developed a
complete and invertible description of retinotopic maps (U.S. Patent Application Nos. 16/230,284 and
63/004,721, supported by NSF collaborative research awards DMS-1413417 and DMS-1412722). This project
will continue developing and applying novel quasiconformal geometry and hierarchical Bayesian modeling
(HBM) algorithms to retinotopy data obtained from the Human Connectome Project (HCP), the largest high
resolution retinotopy dataset to date. We hypothesize that, by combining Beltrami smoothing, quasiconformal
mapping and HBM, the proposed approach will reduce manual annotation work and maximize the statistical
power of retinotopic mapping techniques. The project aims to: (1) Develop computational methods to
effectively smooth retinotopic maps across multiple visual areas based on Beltrami smoothing. With
Beltrami descriptions, the proposed method will simultaneously smooth eccentricity and polar angle retinotopy
data in V1, V2 and V3, while preserving the underlying topological continuity; (2) Develop computational
methods to effectively register retinotopic maps of multiple visual areas across subjects with
quasiconformal mapping. Unlike previous work that relied on either structural MRI (sMRI) or fMRI data only,
the proposed method will simultaneously register both sMRI and fMRI data from multiple visual areas across
subjects and ensure diffeomorphism; (3) Develop an HBM of the retinotopic maps to capture the hierarchy
at both the individual and group levels. The proposed HBM will help overcome measurement noise, reveal
both population properties and individual differences, and offer unprecedented accuracy on retinotopic map
analysis; (4) Develop and disseminate software tools and atlases of human retinotopic maps. The
developed open-source softw...

## Key facts

- **NIH application ID:** 10927341
- **Project number:** 5R01EY032125-04
- **Recipient organization:** ARIZONA STATE UNIVERSITY-TEMPE CAMPUS
- **Principal Investigator:** ZHONG-LIN LU
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $1
- **Award type:** 5
- **Project period:** 2021-09-01 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10927341

## Citation

> US National Institutes of Health, RePORTER application 10927341, Hierarchical Bayesian Analysis of Retinotopic Maps of the Human Visual Cortex with Conformal Geometry (5R01EY032125-04). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10927341. Licensed CC0.

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