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Related Experiment Video

Updated: Nov 8, 2025

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

Longitudinal self-supervised learning.

Qingyu Zhao¹, Zixuan Liu², Ehsan Adeli³

¹Department of Psychiatry & Behavioral Sciences, Stanford University, Stanford, CA 94305, USA.

Medical Image Analysis

|April 21, 2021

Summary

This summary is machine-generated.

This study introduces a new machine learning method for analyzing brain scans over time without needing many labels. The approach effectively identifies changes in brain age from MRI data, aiding in understanding neurodegenerative diseases.

Keywords:

Brain age Factor disentanglement Longitudinal neuroimaging Self-supervised learning

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Area of Science:

Neuroimaging
Machine Learning
Computational Neuroscience

Background:

Supervised learning for longitudinal neuroimaging requires extensive ground-truth labels, which are often scarce in neuroscience.
Existing methods struggle with the inherent challenges of missing or costly labels in analyzing brain changes over time.

Purpose of the Study:

To develop a novel self-supervised learning framework for analyzing longitudinal neuroimaging data without relying on ground-truth labels.
To disentangle key factors, such as brain age, from neuroimaging data to identify individual changes across multiple MRI acquisitions.

Main Methods:

Proposed a new definition of factor disentanglement using a multivariate mapping between MRI-associated factors and latent image representations.
Implemented Longitudinal Self-Supervised Learning (LSSL) using an autoencoding structure with cosine loss to separate brain age from image representation.
Utilized self-supervised learning to ensure changes in a single factor induce directional changes in the representation space.

Main Results:

Successfully applied LSSL to two longitudinal neuroimaging studies, demonstrating its ability to extract brain-age information from MRI.
Revealed informative characteristics associated with neurodegenerative and neuropsychological disorders using the disentangled representations.
Showcased that LSSL-learned representations enhance supervised classification, leading to faster convergence and comparable or superior prediction accuracy.

Conclusions:

LSSL offers a powerful, label-efficient approach for analyzing longitudinal neuroimaging data, particularly for understanding brain aging and disease progression.
The method effectively disentangles dynamic factors like brain age, providing valuable insights into neurodegenerative and neuropsychological conditions.
Learned representations from LSSL improve downstream supervised learning tasks, offering a significant advancement in neuroimaging analysis.