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  11. Resting-state Functional Connectivity In Lifelong Musicians

Resting-state functional connectivity in lifelong musicians

Cyrus Eierud1, Andrew Michael2, David Banks3

  • 1Linguistics Program, Duke University, Durham, NC 27708, USA.

Psychoradiology
|April 26, 2024

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View abstract on PubMed

Summary
This summary is machine-generated.

Lifelong musicianship is associated with increased functional network connectivity (FNC) in the aging brain, unlike in non-musicians. This suggests music training may build cognitive reserve, though more research is needed.

Area of Science:

  • Neuroscience
  • Cognitive Science
  • Neuroimaging

Background:

  • Musicianship is theorized to enhance brain and cognitive reserve.
  • Neural mechanisms underlying this effect remain poorly understood.
  • Lifelong musicianship offers a unique model to study brain network differences.

Purpose of the Study:

  • Investigate resting-state functional networks (FNs) in lifelong active musicians.
  • Compare FNs between musicians and matched healthy controls using fMRI.
  • Examine age-related differences in functional network connectivity (FNC).

Main Methods:

  • Derived FNs using independent component analysis.
  • Analyzed FNC between default mode (DMN), sensory-motor (SMN), visual (VSN), and auditory (AUN) networks.
  • Assessed age-trajectories of FNC using linear regressions.
Keywords:
cognitive reservedefault mode networkfunctional network connectivitylifelong musicianship

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Main Results:

  • Significant differences in age-related FNC trajectories were observed between musicians and controls (P=0.013).
  • Musicians exhibited increased average FNC with age, while controls showed a decrease.
  • Specific FNCs (DMN-AUN, DMN-VSN, SMN-VSN) increased with age in musicians.

Conclusions:

  • Provides preliminary evidence that lifelong musicianship may enhance brain and cognitive reserve.
  • Suggests distinct age-related functional network changes in musicians.
  • Highlights the need for replication with larger cohorts.
resting-state fMRI
sensory-motor network