# Cortical hyperexcitability in FTD and ALS: Exploring physiological biomarkers

> **NIH NIH R21** · MASSACHUSETTS GENERAL HOSPITAL · 2024 · $459,250

## Abstract

Summary/Abstract
The pathophysiology of C9orf72- and GRN-related Frontotemporal Degeneration links TDP43
proteinopathy to neural network dysfunction, which ultimately manifests as symptoms of motor,
cognitive, and/or behavioral impairment. Patients with ALS exhibit motor cortex hyper-
excitability, which has been used as a physiologic biomarker to assist in diagnosis and
outcomes monitoring in clinical trials. Most of this work has been done in patients with typical
ALS, with questions remaining about whether this abnormal physiologic feature is present in
patients with Frontotemporal Dementia and asymptomatic C9orf72 or GRN carriers.
In this pilot project, we aim to investigate family members of individuals with C9orf72- or GRN-
related FTD to determine whether this may be a universal feature of the illness and whether it is
present prior to the onset of symptoms. We will also examine its relationship to resting-state
functional MRI measures of large-scale brain network function.
The ultimate goal of this research is to begin to determine whether these biomarkers of cortical
physiology could be useful as an early, presymptomatic indicator of brain system dysfunction
and whether it could be useful as an outcome measure for treatment trials.

## Key facts

- **NIH application ID:** 10887882
- **Project number:** 1R21AG081925-01A1
- **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL
- **Principal Investigator:** Joan A Camprodon
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $459,250
- **Award type:** 1
- **Project period:** 2024-06-01 → 2026-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10887882

## Citation

> US National Institutes of Health, RePORTER application 10887882, Cortical hyperexcitability in FTD and ALS: Exploring physiological biomarkers (1R21AG081925-01A1). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10887882. Licensed CC0.

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