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Identifying patients with cognitive motor dissociation using resting-state temporal stability.

Hang Wu1, Qiuyou Xie2, Jiahui Pan3

  • 1Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, Guangdong, 510631, China.

Neuroimage
|March 24, 2023
PubMed
Summary

Temporal stability in brain activity may help identify covert awareness in disorders of consciousness (DOC). This resting-state fMRI study found impaired temporal stability in DOC patients, enabling accurate detection of cognitive motor dissociation (CMD).

Keywords:
Cognitive motor dissociationDisorders of consciousnessDynamic functional connectivityResting-state fMRITemporal stability

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Area of Science:

  • Neuroscience
  • Cognitive Science
  • Medical Imaging

Background:

  • Disorders of Consciousness (DOC) present diagnostic challenges, particularly identifying covert awareness in patients with cognitive motor dissociation (CMD).
  • Existing task-dependent neuroimaging methods may yield false negatives in CMD patients due to cognitive impairments.
  • High temporal stability of brain functional organization is increasingly recognized as crucial for consciousness.

Purpose of the Study:

  • To investigate alterations in brain temporal stability in patients with DOC.
  • To assess the potential of temporal stability metrics derived from resting-state fMRI (rs-fMRI) for detecting CMD.
  • To establish a generalizable machine learning model for identifying consciousness in DOC.

Main Methods:

  • rs-fMRI data from 119 participants across three independent sites were analyzed.
  • A sliding-window approach quantified global and regional brain temporal stability.
  • A Support Vector Machine (SVM) classifier was trained to discriminate DOC from controls and then tested for CMD detection.

Main Results:

  • DOC patients exhibited significantly impaired global and regional temporal stability, particularly in key brain networks (e.g., default-mode network, salience network).
  • The SVM model demonstrated high accuracy (90% and 84%) in discriminating DOC from controls across two independent datasets.
  • The SVM model achieved high accuracy (91%) in identifying CMD in a separate dataset.

Conclusions:

  • Brain temporal stability is a promising neuroimaging biomarker for assessing consciousness in DOC.
  • Temporal stability analysis using rs-fMRI can aid in the diagnosis of cognitive motor dissociation (CMD).
  • This approach offers a potential tool to improve diagnostic accuracy and understanding of consciousness in severe brain injury.