Diagnostic Developments in Differentiating Unresponsive Wakefulness Syndrome and the Minimally Conscious State

• Published in: Frontiers in neurology 2022-01, Vol.12, p.778951-778951
• Main Authors: Porcaro, Camillo, Nemirovsky, Idan Efim, Riganello, Francesco, Mansour, Zahra, Cerasa, Antonio, Tonin, Paolo, Stojanoski, Bobby, Soddu, Andrea
• Format: Article
• Language: English
• Subjects: disorder of consciousness (DOC); functional magnetic resonance imaging (fMRI); magneto-electroencephalography (M-EEG); minimally conscious state (MCS); Neurology; unresponsiveness wakefulness syndrome (UWS); vegetative state (VS)
• ISSN: 1664-2295; 1664-2295
• DOI: 10.3389/fneur.2021.778951

When treating patients with a disorder of consciousness (DOC), it is essential to obtain an accurate diagnosis as soon as possible to generate individualized treatment programs. However, accurately diagnosing patients with DOCs is challenging and prone to errors when differentiating patients in a Vegetative State/Unresponsive Wakefulness Syndrome (VS/UWS) from those in a Minimally Conscious State (MCS). Upwards of ~40% of patients with a DOC can be misdiagnosed when specifically designed behavioral scales are not employed or improperly administered. To improve diagnostic accuracy for these patients, several important neuroimaging and electrophysiological technologies have been proposed. These include Positron Emission Tomography (PET), functional Magnetic Resonance Imaging (fMRI), Electroencephalography (EEG), and Transcranial Magnetic Stimulation (TMS). Here, we review the different ways in which these techniques can improve diagnostic differentiation between VS/UWS and MCS patients. We do so by referring to studies that were conducted within the last 10 years, which were extracted from the PubMed database. In total, 55 studies met our criteria (clinical diagnoses of VS/UWS from MCS as made by PET, fMRI, EEG and TMS- EEG tools) and were included in this review. By summarizing the promising results achieved in understanding and diagnosing these conditions, we aim to emphasize the need for more such tools to be incorporated in standard clinical practice, as well as the importance of data sharing to incentivize the community to meet these goals.