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Predicting conversion from MCI to AD by integrating rs-fMRI and structural MRI.

Seyed Hani Hojjati1, Ata Ebrahimzadeh1, Ali Khazaee2

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Summary

Integrating structural MRI and resting-state fMRI improves early Alzheimer's disease detection. Combining these imaging techniques achieved 97% accuracy in identifying patients with mild cognitive impairment who will progress to Alzheimer's disease.

Keywords:
Alzheimer's disease (AD)Graph theoryMachine learning approachMild cognitive impairment (MCI)Resting-state fMRIStructural MRI

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Area of Science:

  • Neuroimaging
  • Neurology
  • Radiology

Background:

  • Alzheimer's disease (AD) diagnosis relies on identifying early pathological changes.
  • Structural MRI (sMRI) and resting-state functional MRI (rs-fMRI) show potential for AD detection.
  • Integrating sMRI and rs-fMRI for early AD detection remains underexplored.

Purpose of the Study:

  • To evaluate the diagnostic performance of sMRI and rs-fMRI, individually and combined.
  • To classify patients with mild cognitive impairment (MCI) who convert to probable AD (MCI-C) from those who do not (MCI-NC).
  • To identify optimal neuroimaging features for early AD detection.

Main Methods:

  • Utilized cortical and subcortical measurements from sMRI (e.g., cortical thickness).
  • Extracted graph theory measures from rs-fMRI functional connectivity.
  • Trained a support vector machine classifier using selected sMRI and rs-fMRI features.

Main Results:

  • Single-modality classification achieved 89% accuracy for sMRI and 93% for rs-fMRI.
  • The combined multi-modality approach (sMRI + rs-fMRI) reached 97% accuracy.
  • The algorithm effectively identified MCI converters using a minimal set of optimal features.

Conclusions:

  • Integration of sMRI and rs-fMRI significantly enhances the accuracy of early AD detection.
  • This multi-modal approach offers a promising tool for identifying individuals at high risk of AD progression.
  • This study represents the first investigation into combining sMRI and rs-fMRI for early AD identification.