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MRI-informed machine learning-driven brain age models for classifying mild cognitive impairment converters.

Hanna Lu1,2, Jing Li1

  • 1Department of Psychiatry, The Chinese University of Hong Kong, Hong Kong, China.

Journal of Central Nervous System Disease
|July 25, 2024
PubMed
Summary

Brain age models using MRI data show increased brain age in individuals with mild cognitive impairment (MCI) converters, aiding in early detection. These brain age predictions can help monitor neurodegeneration and personalize treatments.

Keywords:
Brain age modelageingmagnetic resonance imagingmild cognitive impairmentmorphometric featuresneurological diseasespredictive models

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Area of Science:

  • Neuroimaging
  • Biomarkers
  • Neurodegenerative Diseases

Background:

  • Brain age models estimate brain age and brain-predicted age difference (brain-PAD) as potential imaging markers.
  • These markers assist in monitoring normal aging and identifying individuals in the pre-diagnostic phase of neurodegenerative diseases.

Purpose of the Study:

  • To investigate brain age models in normal aging and mild cognitive impairment (MCI) converters.
  • To assess the utility of brain age models in classifying MCI conversion.

Main Methods:

  • Structural magnetic resonance imaging (MRI) data from Cam-CAN and OASIS-2 projects were used.
  • Brain age models were constructed using support vector machine (SVM) algorithm based on morphometric features like total intracranial volume (TIV) and gray matter volume (GMV).
  • Models were tested on normal aging (NA) adults and MCI converters at baseline and follow-up points.

Main Results:

  • MCI converters exhibited significantly increased TIV-based and left GMV-based brain age compared to NA adults across all time points.
  • Higher brain-predicted age difference (brain-PAD) scores correlated with poorer global cognition.
  • TIV-based and left GMV-based brain age models achieved acceptable performance in differentiating MCI converters from NA adults (AUC = 0.698 and 0.703, respectively).

Conclusions:

  • This study demonstrates feature-specific patterns in MRI-informed brain age models.
  • Increased GMV-based brain age in MCI converters offers potential for early neurodegeneration identification.
  • These findings enhance quantitative imaging markers for disease monitoring and personalized treatment.