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Estimating brain age from structural MRI and MEG data: Insights from dimensionality reduction techniques.

Alba Xifra-Porxas1, Arna Ghosh2, Georgios D Mitsis3

  • 1Graduate Program in Biological and Biomedical Engineering, McGill University, Montréal, Canada; Center for Interdisciplinary Research in Rehabilitation of Greater Montreal (CRIR), Montréal, Canada.

Neuroimage
|February 7, 2021
PubMed
Summary

Combining brain imaging techniques like MRI and MEG improves brain age prediction. This approach offers a more accurate measure of brain aging and potential biomarkers for neurodegenerative diseases.

Keywords:
Age predictionBrain agingCanonical correlation analysisMachine learningMagnetic resonance imagingMagnetoencephalography

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Area of Science:

  • Neuroscience
  • Medical Imaging
  • Biomarkers

Background:

  • Brain age prediction estimates cognitive decline and disease using neuroimaging.
  • Previous studies primarily used magnetic resonance imaging (MRI).

Purpose of the Study:

  • To investigate if combining structural MRI with functional magnetoencephalography (MEG) enhances brain age prediction.
  • To identify optimal methods for analyzing high-dimensional neuroimaging data.

Main Methods:

  • Utilized a large cohort (N=613, ages 18-88) from the Cam-CAN repository.
  • Applied dimensionality reduction (PCA) and multivariate association (CCA) techniques.
  • Developed a stacking model combining MRI and MEG features.

Main Results:

  • Combining MRI and MEG features improved age prediction accuracy (MAE 4.88 years) compared to MRI alone (MAE 5.33 years) or MEG alone (MAE 9.60 years).
  • Canonical Correlation Analysis (CCA) with Gaussian process regression outperformed Principal Component Analysis (PCA).
  • Subcortical MRI features and spectral MEG measures were more reliable predictors than cortical features and MEG connectivity metrics, respectively.

Conclusions:

  • Integrating structural MRI and functional MEG data significantly enhances brain age prediction accuracy.
  • CCA is a powerful tool for analyzing multimodal neuroimaging data and identifying key predictive features.
  • This multimodal approach holds promise for developing reliable biomarkers for age-related neurodegenerative diseases.