Altered brain functional connectivity in vegetative state and minimally conscious state

Area of Science:

• Neuroscience
• Medical Imaging
• Network Science

Background:

• Disorders of consciousness (DoC) present diagnostic challenges due to limitations of traditional behavioral scales.
• Pathological mechanisms underlying DoC subtypes, such as vegetative state (VS) and minimally conscious state (MCS), remain incompletely understood.
• Accurate differentiation between VS and MCS is crucial for patient care and prognosis.

Purpose of the Study:

• To investigate the utility of topological characterization of brain functional networks in elucidating DoC pathophysiology.
• To determine if network metrics can effectively distinguish between VS and MCS patients.

Main Methods:

• Resting-state functional magnetic resonance imaging (fMRI) data were acquired from normal controls, VS patients, and MCS patients.
• Weighted brain functional networks were constructed and analyzed for global and local network characteristics.
• A support vector machine (SVM) classifier was trained to differentiate between VS and MCS based on network metrics.

Main Results:

• DoC patients exhibited reduced average connection strength, global efficiency, local efficiency, and clustering coefficients, with increased characteristic path length.
• Nodal efficiency and clustering coefficients were decreased in specific brain regions, including frontoparietal areas, limbic structures, and occipital/temporal lobes.
• The SVM classifier achieved high accuracy (89.83%), sensitivity (78.95%), and specificity (95%) in differentiating VS from MCS patients.

Conclusions:

• Altered brain network structures are associated with clinical symptoms in DoC.
• Topological network analysis provides a feasible method for differentiating between VS and MCS, offering potential for improved diagnostic accuracy.