Intrinsic functional connectivity differentiates minimally conscious from unresponsive patients

Area of Science:

• Neuroscience
• Clinical Neurology
• Medical Imaging

Background:

• Resting-state functional magnetic resonance imaging (fMRI) shows promise for diagnosing disorders of consciousness (DoC).
• Implementing fMRI on an individualized clinical basis remains a challenge.
• A systems-level approach is needed to assess the clinical relevance of fMRI in DoC.

Purpose of the Study:

• To assess the clinical relevance of resting-state fMRI in patients with DoC using a systems-level approach.
• To investigate the discriminative capacity of functional brain networks in differentiating minimally conscious state (MCS) from vegetative state/unresponsive wakefulness syndrome (VS/UWS).
• To validate the diagnostic utility of fMRI for single-patient diagnostics.

Main Methods:

• Collected data from 73 patients across three clinical centers with DoC (MCS, VS/UWS, coma).
• Investigated functional connectivity in default mode, frontoparietal, salience, auditory, sensorimotor, and visual networks using a multiple-seed correlation approach.
• Employed inferential statistics and machine learning to differentiate MCS from VS/UWS, validating findings with independent datasets.

Main Results:

• All investigated networks demonstrated high discriminative capacity (>80%) between MCS and VS/UWS.
• The auditory network exhibited the highest discriminative power.
• Preserved auditory-visual crossmodal connectivity in the auditory network was identified as a key differentiator, accurately classifying 20 out of 22 independent patients.

Conclusions:

• Resting-state fMRI, particularly focusing on the auditory network and auditory-visual crossmodal connectivity, is clinically relevant for diagnosing DoC.
• Multisensory integration and top-down processing abilities are significant in minimal consciousness.
• This study promotes the clinical utility of resting-state fMRI for single-patient diagnostics in DoC.