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The sleep cycle, an integral part of human health, consists of several stages with distinct characteristics and functions. It begins with a transition from wakefulness to sleep, known as the light sleep phase, followed by the restorative deep sleep phase, essential for physical recovery and growth. The cycle concludes with the Rapid Eye Movement (REM) phase, characterized by high brain activity and vivid dreaming. Insomnia, a prevalent sleep disorder, involves difficulty falling asleep, staying...
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Multivariate Pattern Classification of Primary Insomnia Using Three Types of Functional Connectivity Features.

Chao Li1, Yuanqi Mai2, Mengshi Dong3

  • 1Department of Medical Imaging, Guangdong Second Provincial General Hospital, Guangzhou, China.

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|October 22, 2019
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Functional connectivity (FC) patterns can distinguish primary insomnia (PI) patients from healthy individuals. Abnormal FC in specific brain regions may serve as a potential biomarker for PI diagnosis.

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Area of Science:

  • Neuroscience
  • Medical Imaging
  • Psychiatry

Background:

  • Primary insomnia (PI) is a prevalent sleep disorder.
  • Identifying reliable biomarkers for PI is crucial for diagnosis and treatment.
  • Current diagnostic methods may not fully capture the underlying neurobiological mechanisms of PI.

Purpose of the Study:

  • To investigate the potential of functional connectivity (FC) as a biomarker for classifying primary insomnia (PI) at an individual level.
  • To utilize multivariate pattern analysis (MVPA) to differentiate PI patients from healthy controls (HC) based on brain activity patterns.

Main Methods:

  • Resting-state functional MRI was used in 38 drug-naive PI patients and 44 HC.
  • Voxel-wise functional connectivity strength (FCS), large-scale FC, and regional homogeneity (ReHo) were calculated.
  • Support vector machine (SVM) with MVPA was employed for classification, followed by group comparisons and correlation analyses with clinical scales.

Main Results:

  • The best classifier achieved 81.5% accuracy, 84.9% sensitivity, 79.1% specificity, and an AUC of 83.0% (P < 0.001).
  • Key regions with high classification weights included the right anterior insular cortex (BA48) and left middle frontal gyrus (BA8).
  • Abnormal FCS in these regions correlated with insomnia severity and depressive symptoms.

Conclusions:

  • Abnormal functional connectivity strength in the right anterior insular cortex (BA48) and left middle frontal gyrus (BA8) shows promise as a potential neuromarker for primary insomnia.
  • MVPA of resting-state fMRI data can effectively differentiate individuals with PI.
  • These findings contribute to understanding the neurobiological underpinnings of PI and may inform future diagnostic strategies.