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Aging effect on head motion: A Machine Learning study on resting state fMRI data.

Valeria Saccà1, Alessia Sarica1, Andrea Quattrone2

  • 1Neuroscience Research Center, Department of Medical and Surgical Sciences, University "Magna Graecia" of Catanzaro, Italy.

Journal of Neuroscience Methods
|January 28, 2021
PubMed
Summary
This summary is machine-generated.

Normal aging significantly increases head motion during resting-state fMRI scans, particularly affecting rotation psi and translations y and z. This finding is crucial for improving data quality in neuroimaging studies.

Keywords:
AgingHead motions correctionResting state fMRISupport vector machineTemporal Signal to noise ratio

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Area of Science:

  • Neuroimaging
  • Gerontology
  • Biomedical Engineering

Background:

  • Resting-state functional magnetic resonance imaging (fMRI) analyzes brain networks but is susceptible to physiological noise from head motion.
  • Understanding how aging affects head motion is critical for robust resting-state fMRI analysis.

Purpose of the Study:

  • To investigate the impact of normal aging on head motion signals in resting-state fMRI.
  • To identify specific motion parameters most sensitive to aging.

Main Methods:

  • Compared head motion parameters (translations x, y, z; rotations phi, theta, psi) between young (mean age 28) and elderly (mean age 66) subjects.
  • Employed univariate analysis and correlation to assess age-motion relationships.
  • Utilized Support-Vector Machine with bootstrap for feature importance analysis.

Main Results:

  • Significant associations found between aging and increased head motion, specifically rotation psi and translations y and z.
  • Support-Vector Machine analysis confirmed these findings with an Area Under the Curve (AUC) of 90%.

Conclusions:

  • Normal aging leads to a significant increase in specific head motion parameters (psi, y, z).
  • These findings highlight the critical impact of age-related motion on resting-state fMRI data quality.
  • This study provides a novel method for characterizing age-related motion, aiding in healthier control recruitment and reducing signal distortion.