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Increased sensitivity to age-related differences in brain functional connectivity during continuous multiple object

Erlend S Dørum1, Tobias Kaufmann2, Dag Alnæs2

  • 1Sunnaas Rehabilitation Hospital HT, Nesodden, Norway; NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychology, University of Oslo, Norway.

Neuroimage
|January 24, 2017
PubMed
Summary

Cognitive load during tasks enhances brain network analysis for aging research. Functional connectivity during tasks, not just rest, reveals significant age-related brain differences.

Keywords:
AgingBrain network connectivityMachine learningSDSAfMRI

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Area of Science:

  • Neuroscience
  • Cognitive Aging
  • Neuroimaging

Background:

  • Individual differences in cognitive agility and brain network connectivity are pronounced in aging.
  • Resting-state functional magnetic resonance imaging (fMRI) shows potential for identifying biomarkers of cognitive decline.
  • Resting-state paradigms offer limited experimental control, potentially underestimating age-related connectivity differences.

Purpose of the Study:

  • To test if increased cognitive load during tasks enhances sensitivity to age-related differences in brain connectivity.
  • To investigate how cognitive context and effort modulate age-dependent network configurations.
  • To identify discriminative network patterns associated with aging under varying cognitive demands.

Main Methods:

  • Acquired fMRI data from younger and older adults during rest and two levels of multiple object tracking (MOT).
  • Estimated brain network nodes and time-series using independent component analysis (ICA) and dual regression.
  • Defined network edges as regularized partial temporal correlations and used machine learning (rLDA) for classification.

Main Results:

  • Functional connectivity (FC) during MOT significantly improved group classification accuracy (82% young, 95% old) compared to resting-state (approx. 70%).
  • Machine learning indicated stronger task-related differentiation in younger adults, suggesting network dedifferentiation in older adults.
  • Task-modulation of FC primarily involved attention and sensorimotor networks, with reduced negative correlation between attention and default mode networks in older adults.

Conclusions:

  • Age-related differences in brain functional connectivity are context- and load-dependent.
  • Assessing brain connectivity across various cognitive contexts, beyond rest, is crucial for understanding cognitive aging.
  • Task-based fMRI provides a more sensitive approach to detecting age-related changes in brain network organization.