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Updated: Jul 14, 2025

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

Published on: June 9, 2018

Histopathologic brain age estimation via multiple instance learning.

Gabriel A Marx^1,2, Justin Kauffman^1,2, Andrew T McKenzie^1,2,3

¹Department of Pathology, Icahn School of Medicine at Mount Sinai, Friedman Brain Institute, 1 Gustave L. Levy Place, Box 1194, New York, NY, 10029, USA.

Acta Neuropathologica

|October 10, 2023

Summary

This summary is machine-generated.

Histopathological brain age estimation using deep learning on brain tissue images offers new insights into brain aging. This method reveals associations with clinical outcomes, outperforming epigenetic measures.

Keywords:

Aging Biological clock Digital pathology Machine learning Methylation

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Area of Science:

Neuroscience
Computational Biology
Pathology

Background:

Brain aging involves a discordance between biological and chronological age, termed age acceleration.
Histopathological data from whole slide images can be utilized for deep learning models to assess brain age acceleration.
Understanding brain aging is crucial for insights into normal physiology and age-related cognitive decline, including Alzheimer's disease.

Purpose of the Study:

To develop a histological brain age estimation model using digitized human post-mortem hippocampal sections.
To assess the utility of histopathological brain age acceleration as a metric for understanding brain aging and its clinical correlates.

Main Methods:

Digitized human post-mortem hippocampal sections were used to train a deep learning model.
The model estimated brain age based on histopathological features.
Attention weights were analyzed to identify neuroanatomical regions associated with age-related changes.

Main Results:

The developed model predicted brain age with a mean absolute error of 5.45 ± 0.22 years.
Attention weights highlighted neuroanatomical regions susceptible to age-related alterations.
Histopathological brain age acceleration showed significant associations with clinical and pathological outcomes, unlike epigenetic measures.

Conclusions:

Histopathological brain age is a robust and independent metric for evaluating factors contributing to brain aging.
This approach provides a powerful tool for studying brain aging mechanisms and identifying early disease markers.