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Aberrant alterations in any of the two dimensions of consciousness, namely awareness and arousal, can lead to the emergence of disorders of consciousness (DOC). The development of DOC may arise from more severe or targeted lesions in the brain, resulting in widespread functional abnormalities. However, when it comes to classifying patients with disorders of consciousness, particularly utilizing resting-state electroencephalogram (EEG) signals through machine learning methods, several challenges surface. The non-stationarity and intricacy of EEG data present obstacles in understanding neuronal activities and achieving precise classification. To address these challenges, this study proposes variational mode decomposition (VMD) of EEG before feature extraction along with machine learning models. By decomposing preprocessed EEG signals into specified modes using VMD, features such as sample entropy, spectral entropy, kurtosis, and skewness are extracted across these modes. The study compares the performance of the features extracted from VMD-based approach with the frequency band-based approach and also the approach with features extracted from raw-EEG. The classification process involves binary classification between unresponsive wakefulness syndrome (UWS) and the minimally conscious state (MCS), as well as multi-class classification (coma vs. UWS vs. MCS). Kruskal-Wallis test was applied to determine the statistical significance of the features and features with a significance of p < 0.05 were chosen for a second round of classification experiments. Results indicate that the VMD-based features outperform the features of other two approaches, with the ensemble bagged tree (EBT) achieving the highest accuracy of 80.5% for multi-class classification (the best in the literature) and 86.7% for binary classification. This approach underscores the potential of integrating advanced signal processing techniques and machine learning in improving the classification of patients with disorders of consciousness, thereby enhancing patient care and facilitating informed treatment decision-making.

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Dystonia has been described in a few cases with SSPE, but there are only very few reports with status dystonicus and none from South India. Here, we report a six-year-old child presenting with severe dystonic posturing of all four limbs and trunk for 10 days duration following a febrile illness and initially treated elsewhere as viral encephalitis. Scalp EEG showed periodic high-amplitude slow wave discharges. MRI brain showed T2/FLAIR hyperintensity in bilateral frontal, left parietal, and deep white matter, extending across the corpus collosum with diffuse cerebral atrophy. The titer for IgG antibodies to measles virus by ELISA was 1:625, suggestive of SSPE. With medications, dystonia used to subside transiently; however, the patient had worsening of symptoms and showed gradual deterioration.

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OBJECTIVES: This study aimed to localise the eloquent cortex and measure evoked field (EF) parameters using magnetoencephalography in patients with epilepsy and tumours near the eloquent cortex. METHODS: A total of 41 patients (26 with drug-refractory epilepsy and 15 with tumours), with a mean age of 33 years, were recruited. Visual evoked field (VEF), auditory evoked field (AEF), sensory evoked field (SSEF), and motor-evoked field (MEF) latencies, amplitudes, and localisation were compared with those of a control population. Subgroup analyses were performed based on lobar involvement. Evoked Field parameters on the affected side were compared with those on the opposite side. The effect of distance from the lesion on nearby and distant evoked fields was evaluated. RESULTS: AEF and VEF amplitudes and latencies were reduced bilaterally (p < 0.05). Amplitude in the ipsilateral SSEF was reduced by 29.27% and 2.16% in the AEF group compared to the contralateral side (p = 0.02). In patients with temporal lobe lesions, the SSEF amplitude was reduced bilaterally (p < 0.02), and latency was prolonged compared with controls. The MEF amplitude was reduced and latency was prolonged in patients with frontal lobe lesions (p = 0.01). EF displacement was 32%, 57%, 21%, and 16% for AEF, MEF, VEF, and SSEF respectively. Patients in the epilepsy group had distant EF abnormalities. CONCLUSIONS: EF amplitude was reduced and latency was prolonged in the involved hemisphere. Distant EF amplitudes were more affected than latencies in epilepsy. Amplitude and distance from the lesion had negative correlation for all EF. EF changes indicated eloquent cortical displacement which may not be apparent on MRI.

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OBJECTIVE: Eating epilepsy presents various imaging and electrophysiological features along with various seizure triggers. As such, network changes in eating epilepsy have not been comprehensively explored. This study was conducted to illustrate resting state network changes in eating epilepsy and to study the changes in network configurations during eating. METHODS: Magnetoencephalography recordings of nineteen patients with drug-resistant eating epilepsy were compared with healthy controls during resting state. A subgroup of nine patients and 12 controls had MEG recordings during eating. Network changes were analyzed using phase lag index across 5 frequency bands [delta, theta, alpha, beta, and gamma] using clustering coefficient (CC), betweenness centrality (BC), path length (PL), modularity (Q), and small worldness (SW). RESULTS: During the resting state, PL was decreased in patients with epilepsy in the delta, theta, and gamma band. Q was lower in patients with epilepsy in the beta and gamma bands. During eating, in patients with epilepsy, PL and SW were increased in all frequency bands, and Q was decreased in the beta band and increased in the rest of the frequency bands. Patients with mixed types of seizures showed higher PL in all bands except alpha, higher Q in all bands, and higher SW in the alpha and beta bands. Node-wise changes in CC and BC implicated changes in DMN and 'eating' networks. CONCLUSION: Reflex Eating epilepsy presents with a hyperconnected network that exacerbates during eating. The cause of seizure onset and loss of consciousness in eating epilepsy might be due to aberrant network interaction between the regions of the brain involved with eating, such as the sensorimotor cortex, lateral parietal cortex, and insula with the limbic cortex and default mode network across multiple frequency bands.

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