Topological Detection of Alzheimer’s Disease Using Betti Curves

Rik Sarkar, Rayna Andreeva, Ameer Saadat-Yazdi

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Alzheimer’s disease is a debilitating disease in the elderly, and is an increasing burden to the society due to an aging population. In this paper, we apply topological data analysis to structural MRI scans of the brain, and show that topological invariants make accurate predictors for Alzheimer’s. Using the construct of Betti Curves, we first show that topology is a good predictor of Age. Then we develop an approach to factor out the topological signature of age from Betti curves, and thus obtain accurate detection of Alzheimer’s disease. Experimental results show that topological features used with standard classifiers perform comparably to recently developed convolutional neural networks. These results imply that topology is a major aspect of structural changes due to aging and Alzheimer’s. We expect this relation will generate further insights for both early detection and better understanding of the disease.
Original languageEnglish
Title of host publicationIMIMIC 2021, TDA4MedicalData 2021: Interpretability of Machine Intelligence in Medical Image Computing, and Topological Data Analysis and Its Applications for Medical Data
Number of pages10
ISBN (Electronic)978-3-030-87444-5
ISBN (Print)978-3-030-87443-8
Publication statusPublished - 21 Sep 2021
Event1st International Workshop on Topological Data Analysis and its Applications for Medical Data - Strasbourg, France
Duration: 27 Sep 202127 Sep 2021

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Workshop1st International Workshop on Topological Data Analysis and its Applications for Medical Data
Abbreviated titleTDA4MedicalData 2021
Internet address


  • Topological data analysis
  • Alzheimer’s disease
  • MRI


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