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Dried Blood Spot Collection of Health Biomarkers to Maximize Participation in Population Studies
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Biomarkers.

Ethan Wong1, Liz Yuanxi Lee2, Marcella Montagnese2

  • 1University of Cambridge, Cambridge, Cambridgeshire, United Kingdom.

Alzheimer'S & Dementia : the Journal of the Alzheimer'S Association
|December 26, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a machine learning approach using Generalized Matrix Learning Vector Quantization (GMLVQ) to model frontotemporal dementia (FTD) progression. The GMLVQ model shows promise for improving FTD diagnosis and subtype classification.

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Area of Science:

  • Neuroscience
  • Computational Biology
  • Machine Learning

Background:

  • Frontotemporal dementia (FTD) involves cognitive decline affecting behavior, judgment, and language.
  • FTD subtypes (bvFTD, SD, nfvPPA) are often misdiagnosed, highlighting the need for better diagnostic tools.
  • Current FTD models lack continuous metrics and longitudinal data analysis.

Purpose of the Study:

  • To develop and validate a trajectory modeling approach for enhanced characterization of FTD cognitive progression.
  • To adapt and implement the Generalized Matrix Learning Vector Quantization (GMLVQ) algorithm for FTD.
  • To improve the accuracy of FTD diagnosis and subtyping using machine learning.

Main Methods:

  • Utilized the Neuroimaging in Frontotemporal Dementia (NIFD) dataset with longitudinal clinical, cognitive, and MRI data.
  • Applied Freesurfer to extract approximately 180 neuroimaging features, including cortical thickness and volume.
  • Extended the GMLVQ algorithm for both binary and multi-class classification of FTD subtypes.

    • Main Results:

    • Achieved 94.4% accuracy in binary classification distinguishing semantic dementia (SD) from other FTD subtypes.
    • Attained 79.9% accuracy in multi-class classification of all three FTD subtypes (bvFTD, SD, nfvPPA).
    • Demonstrated 52.2% accuracy in a six-class classifier including FTD subtypes, Alzheimer's disease, MCI, and controls.

    Conclusions:

    • GMLVQ trajectory modeling shows potential for advancing FTD diagnosis and assessment.
    • Further tuning of the multi-class model is recommended for improved performance.
    • The approach offers a promising direction for analyzing longitudinal neurodegenerative disease data.