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Related Concept Videos

Alzheimer's Disease: Overview01:26

Alzheimer's Disease: Overview

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Alzheimer's Disease (AD) is a continually advancing neurodegenerative disorder, distinguished by escalating memory loss, cognitive dysfunction, and dementia. The disease unfolds in three stages: preclinical, mild cognitive impairment (MCI), and dementia. Its onset is insidious, and the progression gradual, with the cause not well explained by other disorders.
The clinical diagnosis of AD hinges on the presence of memory and other cognitive impairments. Biomarkers, such as changes in Aβ...
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Alzheimer's Disease: Treatment01:22

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Alzheimer's Disease (AD), a neurodegenerative disorder, is pathologically identified by amyloid plaques and neurofibrillary tangles composed of tau protein. AD pharmacotherapy aims to manage cognitive symptoms, delay disease progression, and treat behavioral symptoms. The treatment is primarily symptomatic and palliative, with no definitive disease-modifying therapy available. Cholinesterase inhibitors, including donepezil (Aricept), rivastigmine (Exelon), and galantamine (Razadyne), are...
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Related Experiment Video

Updated: May 27, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
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A quantitatively interpretable model for Alzheimer's disease prediction using deep counterfactuals.

Kwanseok Oh1, Da-Woon Heo1, Ahmad Wisnu Mulyadi2

  • 1Department of Artificial Intelligence, Korea University, Seoul 02841, Republic of Korea.

Neuroimage
|February 15, 2025
PubMed
Summary

This study introduces a new framework for Alzheimer's disease (AD) prediction using counterfactual reasoning on MRI scans. It quantifies brain changes for better interpretability and comparable performance to deep learning models.

Keywords:
Alzheimer’s diseaseCounterfactual reasoningCounterfactual-guided attentionQuantitative feature-based in-depth analysis

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Area of Science:

  • Neuroimaging
  • Artificial Intelligence in Medicine
  • Medical Diagnostics

Background:

  • Deep learning (DL) models predict Alzheimer's disease (AD) but lack interpretability.
  • Counterfactual reasoning offers visual explanations but needs quantitative validation.
  • Current methods struggle to intuitively link visual maps to neuroscientific validity.

Purpose of the Study:

  • To develop a framework for interpretable AD prediction using counterfactual reasoning.
  • To quantitatively validate visual explanatory maps from DL models.
  • To enhance understanding of brain status in AD progression.

Main Methods:

  • Synthesized counterfactual-labeled structural MRIs using a novel framework.
  • Transformed MRIs into gray matter density maps to measure volumetric changes in regions of interest (ROIs).
  • Developed a lightweight linear classifier to boost ROI effectiveness and quantitative interpretation.

Main Results:

  • Achieved predictive performance comparable to existing DL methods.
  • Generated an "AD-relatedness index" for each ROI, quantifying disease association.
  • Demonstrated the framework's ability to provide intuitive understanding of brain status.

Conclusions:

  • The proposed framework enhances the interpretability of AD prediction models.
  • Quantitative features derived from counterfactual reasoning provide neuroscientific validity.
  • The "AD-relatedness index" offers a valuable tool for assessing AD progression.