Degrees of functional connectome abnormality in disorders of consciousness.

Understanding the neuronal basis of disorders of consciousness can help improve the accuracy of their diagnosis, indicate potential targets for therapeutic interventions, and provide insights into the organization of normal conscious information processing. Measurements of brain activity have been used to find associations of the levels of consciousness with brain complexity, topological features of functional connectomes, and disruption of resting-state networks. However, obtainment of a detailed picture of activity patterns underlying the vegetative state/unresponsive wakefulness syndrome and the minimally conscious state remains a work in progress. We here aimed at finding the aspects of fMRI-based functional connectivity that differentiate these states from each other and from the normal condition. A group of 22 patients was studied (9 minimally conscious state and 13 vegetative state/unresponsive wakefulness syndrome). Patients were shown to have reduced connectivity in most resting-state networks and disrupted patterns of relative connection strengths as compared to healthy subjects. Differences between the unresponsive wakefulness syndrome and the minimally conscious state were found in the patterns formed by a relatively small number of strongest positive correlations selected by thresholding. These differences were captured by measures of functional connectivity disruption that integrate area-specific abnormalities over the whole brain. The results suggest that the strong positive correlations between the functional activities of specific brain areas observed in healthy individuals may be critical for consciousness and be an important target of disruption in disorders of consciousness.

Neuroimaging Correlates of Post-COVID-19 Symptoms: A Functional MRI Approach.

BACKGROUNDS AND PURPOSE: Post-COVID syndrome is characterized by persistent symptoms, including fatigue and cognitive impairment. These symptoms may be experienced by up to 80% of patients. We aimed to identify possible patterns of brain activation underlying post-COVID fatigue. METHODS: The study used functional MRI (Siemens MAGNETOM Prisma 3T scanner with a specially created protocol) of the brain in 30 patients with post-COVID fatigue syndrome and 20 healthy volunteers. Task functional MRI (fMRI) was performed using a cognitive paradigm (modified Stroop test). Eligible patients included adults aged 18-50 years with a >12 weeks before enrolment (less than 12 months) prior history of documented COVID-19 with symptoms of fatigue not attributable to any other cause, and with MFI-20 score > 30 and MoCA at first visit. Healthy control participants had no prior history of COVID-19 and negative tests for severe acute coronavirus respiratory syndrome with MFI-20 score < 30 and MoCA at first visit. Task fMRI data were processed using the SPM12 software package based on MATLAB R2022a. RESULTS: Cognitive task fMRI analysis showed significantly higher activation in the post-COVID group versus healthy volunteers' group. Between-group analysis showed significant activation differences. Using a threshold of T > 3 we identified eight clusters of statistically significant activation: supramarginal gyri, posterior cingulate cortex, opercular parts of precentral gyri and cerebellum posterior lobe bilaterally. CONCLUSIONS: Post-COVID fatigue syndrome associated with subjective cognitive impairment could show changes in brain functional activity in the areas connected with information processing speed and quality.

Detecting the Potential for Consciousness in Unresponsive Patients Using the Perturbational Complexity Index.

The difficulties of behavioral evaluation of prolonged disorders of consciousness (DOC) motivate the development of brain-based diagnostic approaches. The perturbational complexity index (PCI), which measures the complexity of electroencephalographic (EEG) responses to transcranial magnetic stimulation (TMS), showed a remarkable sensitivity in detecting minimal signs of consciousness in previous studies. Here, we tested the reliability of PCI in an independently collected sample of 24 severely brain-injured patients, including 11 unresponsive wakefulness syndrome (UWS), 12 minimally conscious state (MCS) patients, and 1 emergence from MCS patient. We found that the individual maximum PCI value across stimulation sites fell within the consciousness range (i.e., was higher than PCI*, which is an empirical cutoff previously validated on a benchmark population) in 11 MCS patients, yielding a sensitivity of 92% that surpassed qualitative evaluation of resting EEG. Most UWS patients (n = 7, 64%) showed a slow and stereotypical TMS-EEG response, associated with low-complexity PCI values (i.e., ≤PCI*). Four UWS patients (36%) provided high-complexity PCI values, which might suggest a covert capacity for consciousness. In conclusion, this study successfully replicated the performance of PCI in discriminating between UWS and MCS patients, further motivating the application of TMS-EEG in the workflow of DOC evaluation.

Feasibility of Non-Gaussian Diffusion Metrics in Chronic Disorders of Consciousness.

Diagnostic accuracy of different chronic disorders of consciousness (DOC) can be affected by the false negative errors in up to 40% cases. In the present study, we aimed to investigate the feasibility of a non-Gaussian diffusion approach in chronic DOC and to estimate a sensitivity of diffusion kurtosis imaging (DKI) metrics for the differentiation of vegetative state/unresponsive wakefulness syndrome (VS/UWS) and minimally conscious state (MCS) from a healthy brain state. We acquired diffusion MRI data from 18 patients in chronic DOC (11 VS/UWS, 7 MCS) and 14 healthy controls. A quantitative comparison of the diffusion metrics for grey (GM) and white (WM) matter between the controls and patient group showed a significant (p < 0.05) difference in supratentorial WM and GM for all evaluated diffusion metrics, as well as for brainstem, corpus callosum, and thalamus. An intra-subject VS/UWS and MCS group comparison showed only kurtosis metrics and fractional anisotropy differences using tract-based spatial statistics, owing mainly to macrostructural differences on most severely lesioned hemispheres. As a result, we demonstrated an ability of DKI metrics to localise and detect changes in both WM and GM and showed their capability in order to distinguish patients with a different level of consciousness.