# Unbiased longitudinal neuromorphometry for clinical decision support

> **NIH NIH R42** · CORTICOMETRICS, LLC · 2020 · $733,415

## Abstract

Project Summary
Normal human neuroanatomy is incredibly variable, and increases with age. This impedes the
ability of neuroimaging to detect effects in neurological conditions such as Alzheimer's disease
(AD), Huntington's disease (HD), multiple sclerosis (MS) and schizophrenia. Most of the recently
available state-of-the-art quantitative imaging tools still use cross-sectional methods to analyze
repeated scans. These tools lack the sensitivity to monitor subtle progressive changes because
such approaches do not account for the large intrinsic variability of normal neuroanatomy. The
goal of this project is to commercialize a longitudinal, neuro-morphometric image processing
pipeline for use in radiology, neurology and related clinical fields. The successful completion of
this project will result in a clinically useful neuro-morphometric longitudinal analysis stream with
more statistical power than is currently available commercially. This increase in power will
directly translate into an enhanced ability to detect and assess progression at both the individual
and group levels. It will also alleviate a major pain point in current longitudinal neuroradiology
reading workflows, reducing radiology report turnaround times (RTAT).

## Key facts

- **NIH application ID:** 10223528
- **Project number:** 4R42AG062026-02
- **Recipient organization:** CORTICOMETRICS, LLC
- **Principal Investigator:** Douglas Nowlin Greve
- **Activity code:** R42 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $733,415
- **Award type:** 4N
- **Project period:** 2019-07-01 → 2022-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10223528

## Citation

> US National Institutes of Health, RePORTER application 10223528, Unbiased longitudinal neuromorphometry for clinical decision support (4R42AG062026-02). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10223528. Licensed CC0.

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