# Next-Generation Thalamic Nuclei Visualization and Segmentation Methods

> **NIH NIH R01** · UNIV OF MASSACHUSETTS MED SCH WORCESTER · 2024 · $462,065

## Abstract

PROJECT SUMMARY/ABSTRACT
The thalamus is associated with critical neurological functions like regulation of consciousness, sleep, arousal,
and alertness in addition to relaying signals to the cortex. It is divided into multiple functionally specialized units
called thalamic nuclei which have been implicated in several psychiatric and neurodegenerative diseases such
as essential tremor, Parkinson’s disease, and schizophrenia.
Automated segmentation of thalamic nuclei from MRI data is not commonplace, due to their poor visibility in
conventional MRI. Segmentation methods based on functional MRI (fMRI) and Diffusion Tensor Imaging (DTI)
have been limited by the spatial resolution of the echo-planar imaging (EPI) acquisition and poor diffusion
anisotropy in the grey-matter dominated thalamus. As a result, most neuroimaging studies treat the thalamus
as a single entity, characterizing whole volume changes and using the whole thalamus as a seed for
connectivity analyses, significantly reducing sensitivity to nuclei-specific changes in pathology. We have
developed automated multi-atlas as well as deep-learning based thalamic nuclei segmentation techniques
based on a novel white-matter-nulled contrast scheme. The purpose of this grant is to develop the next-
generation methods for thalamic visualization and segmentation using multi-contrast imaging and cutting-edge
image processing techniques and testing it on data from healthy controls, and patients with Alzheimer’s
disease (AD) and mild cognitive impairment (MCI). This will be achieved using the following aims:
a) Development of a novel fast motion-robust multi-contrast imaging sequence which will provide co-registered
susceptibility weighted and MPRAGE images with different contrasts (e.g., white-matter and CSF-nulled)
b) Acquisition and creation of age-stratified atlases from subjects in the 18-80 age range
c) Development of a multi-contrast deep-learning based automatic segmentation scheme
d) Development of a contrast-synthesis strategy to segment conventional MPRAGE
e) Documenting changes in volumes, and structural/functional connectivity in healthy aging and across the AD
continuum using data from the OASIS database using the proposed segmentation methods.
The segmentation methods developed here can be used characterize thalamic atrophy in normal aging and in
disease populations with high sensitivity. The project is expected to yield new MR imaging biomarkers which
could be used in future studies for the identification and evaluation of novel therapeutic targets.

## Key facts

- **NIH application ID:** 10766357
- **Project number:** 5R01EB032674-03
- **Recipient organization:** UNIV OF MASSACHUSETTS MED SCH WORCESTER
- **Principal Investigator:** Manojkumar Saranathan
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $462,065
- **Award type:** 5
- **Project period:** 2022-09-01 → 2027-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10766357

## Citation

> US National Institutes of Health, RePORTER application 10766357, Next-Generation Thalamic Nuclei Visualization and Segmentation Methods (5R01EB032674-03). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10766357. Licensed CC0.

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