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Dynamic functional network connectivity in patients with a mismatch between white matter hyperintensity and cognitive

Siyuan Zeng1, Lin Ma1, Haixia Mao1

  • 1Medical Imaging Center, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi Medical Center, Nanjing Medical University, Wuxi People's Hospital, Wuxi, China.

Frontiers in Aging Neuroscience
|August 1, 2024
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Summary

Dynamic functional network connectivity (dFNC) analysis in cerebral small vessel disease (CSVD) reveals distinct patterns in patients with white matter hyperintensity (WMH) and cognitive impairment. These differences in dFNC metrics and underlying imaging features offer insights into cognitive mismatch mechanisms.

Keywords:
anatomy structurecerebral small vessel diseasedynamic functional network connectivityquantitative analysisresting-state functional magnetic resonance imaging

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Area of Science:

  • Neuroimaging
  • Neurology
  • Cognitive Neuroscience

Background:

  • White matter hyperintensity (WMH) is common in cerebral small vessel disease (CSVD) and linked to cognitive impairment.
  • However, WMH severity doesn't always correlate with cognitive deficits, suggesting other factors are involved.

Purpose of the Study:

  • To investigate differences in dynamic functional network connectivity (dFNC) between cognitively matched and mismatched CSVD patients with WMH.
  • To quantitatively explore the underlying mechanisms of cognitive mismatch in CSVD.

Main Methods:

  • Acquired resting-state functional magnetic resonance imaging (rs-fMRI) and cognitive assessments from 149 CSVD patients.
  • Performed dFNC analysis to derive metrics and correlated them with cognitive function.
  • Quantified CSVD imaging features, including perivascular spaces and medial temporal lobe atrophy (MTA).

Main Results:

  • Significant differences in dFNC metrics (fraction time, mean dwell time) were observed between matched and mismatched groups for both Type I and Type II CSVD.
  • dFNC metrics correlated with executive function and processing speed.
  • Type I mismatch was associated with perivascular spaces and MTA, while Type II mismatch related to MTA and education years.

Conclusions:

  • Type I cognitive mismatch in CSVD is linked to higher-order network alterations, potentially due to perivascular spaces and brain atrophy.
  • Type II mismatch involves primary network changes, possibly related to brain atrophy and educational background.