Contrastive Self-supervised Learning for Neurodegenerative Disorder Classification | JoVE Visualize

Area of Science:

Neuroimaging
Artificial Intelligence
Neurology

Background:

Neurodegenerative diseases like Alzheimer's disease (AD) and frontotemporal lobar degeneration (FTLD) cause distinct brain volume loss detectable via MRI.
Supervised machine learning for disease classification requires extensive, expertly labeled datasets, which are often difficult to obtain.
Self-supervised learning (SSL) presents a promising alternative for training models without requiring labeled data.

Purpose of the Study:

To investigate the application of SSL models for distinguishing between different neurodegenerative disorders using T1-weighted MRI scans.
To assess the interpretability of SSL models in identifying disease-specific brain atrophy patterns.
To evaluate the performance of SSL models compared to state-of-the-art supervised methods.

Main Methods:

A deep convolutional neural network was trained using contrastive self-supervised learning as a feature extractor.
A single-layer perceptron served as the classification head for downstream tasks.
The model was trained and validated on 2694 T1-weighted MRI scans from ADNI, AIBL, and FTLDNI cohorts, including cognitively normal controls, AD, and FTLD subtypes.

Main Results:

The SSL-trained feature extractor demonstrated generalizable and robust representations for classification.
The model achieved 82% balanced accuracy for AD vs. cognitively normal (CN) on test and 80% on holdout datasets.
For behavioral variant frontotemporal dementia (BV) vs. CN, the model attained 88% balanced accuracy.
Integrated Gradient analysis highlighted hallmark atrophy regions: temporal gray matter for AD and insular for BV.

Conclusions:

SSL methodology can effectively utilize unannotated neuroimaging datasets for training robust and interpretable machine learning models.
The developed SSL models perform comparably to supervised deep learning approaches for neurodegenerative disease classification.
SSL offers a viable strategy for leveraging large, unlabeled neuroimaging data in the study of neurological disorders.