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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Discovering subtypes with imaging signatures in the Motoric Cognitive Risk Syndrome Consortium using weakly

Bhargav Teja Nallapu^1,2, Ali Ezzati³, Helena M Blumen^4,5

¹Department of Neurology Albert Einstein College of Medicine Bronx New York USA.

Alzheimer'S & Dementia (Amsterdam, Netherlands)

|October 10, 2025

Summary

Brain imaging reveals distinct subgroups in individuals with Motoric Cognitive Risk Syndrome (MCR), showing varied brain atrophy and cognitive performance. This aids in early dementia risk stratification.

Keywords:

MCR cognitive complaints dementia gait machine learning volumetric imaging weakly‐supervised clustering

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Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

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Published on: September 25, 2019

Area of Science:

Neuroimaging
Gerontology
Neurology

Background:

Motoric Cognitive Risk Syndrome (MCR) heterogeneity impacts dementia risk assessment.
Understanding brain structure variations is crucial for improved risk prediction.

Purpose of the Study:

To identify distinct subgroups within the MCR population using structural MRI.
To explore the relationship between brain structure, gait speed, and cognitive function.

Main Methods:

Applied the HYDRA weakly-supervised clustering algorithm to volumetric MRI data from 1987 participants across six cohorts.
Analyzed gait speeds and cognitive test performance (Trail Making Test B, Free and Cued Selective Reminding Test).

Main Results:

Identified three distinct subgroups (Groups A, B, C) based on MRI-derived brain volumes.
Significant differences observed in regional brain volumes, gait speeds, and cognitive performance among subgroups.
Slower gait speeds correlated with greater cortical atrophy, higher MCR rates, and poorer cognitive outcomes.

Conclusions:

Structural MRI reveals significant heterogeneity in individuals with moderate to slow gait, including those with MCR.
A data-driven approach using neuroimaging can enhance dementia risk stratification at early, asymptomatic stages.
Findings have implications for developing precision health strategies for at-risk individuals.