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Updated: Jan 10, 2026

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Predicting Future Brain Atrophy Based on Longitudinal MRI.

Maryam Hadji1, Elaheh Moradi1, Jussi Tohka1

  • 1A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio 70150, Finland.

Medrxiv : the Preprint Server for Health Sciences
|November 24, 2025
PubMed
Summary
This summary is machine-generated.

Predicting future brain atrophy using longitudinal MRI and risk factors shows promise for assessing cognitive decline risk and aiding clinical trial selection in Alzheimer's disease research.

Keywords:
DementiaHippocampal atrophyMRIMachine learningMild cognitive impairmentProgression predictionTotal gray matter atrophyventricle enlargement

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Area of Science:

  • Neuroimaging
  • Machine Learning
  • Neurodegenerative Diseases

Background:

  • Neuron loss and brain atrophy are hallmarks of neurodegenerative diseases like Alzheimer's disease (AD).
  • Magnetic resonance imaging (MRI) is crucial for detecting brain atrophy, essential for AD research.
  • Accurate measurement of brain atrophy is vital for understanding disease progression and evaluating interventions.

Purpose of the Study:

  • To forecast annual percentage changes in brain volumes (hippocampus, ventricles, total gray matter) using machine learning.
  • To compare the predictive power of baseline versus longitudinal MRI data combined with risk factors.
  • To assess the utility of predicted atrophy rates for predicting clinical status progression in AD and related dementias.

Main Methods:

  • Developed an elastic net linear regression model to predict future annual brain volume changes.
  • Evaluated two approaches: baseline (single-time-point) and longitudinal (multiple time points).
  • Compared MRI-only models with models incorporating risk factors (age, sex, APOE4, diagnosis), validated on external datasets.

Main Results:

  • The longitudinal MRI + risk factor model achieved high prediction accuracy (e.g., 0.62 for hippocampus).
  • Longitudinal models consistently outperformed baseline models; models with risk factors outperformed MRI-only models.
  • Predicted atrophy rates were superior to current volumes for predicting progression to mild cognitive impairment and dementia.

Conclusions:

  • Future atrophy prediction using longitudinal data and risk factors is a valuable tool for assessing cognitive decline risk.
  • This approach can aid in identifying individuals for clinical trials targeting disease-modifying therapies for AD.
  • Predicted atrophy rates offer a more sensitive marker of disease progression than static volume measurements.