Clinical Neurophysiology最新文献

Objective

Somatosensory evoked spikes (SESs) have been reported only in children aged under 14 years and are considered as an age-dependent phenomenon. However, we detected SESs in adult patients with epilepsy using magnetoencephalography (MEG). The present study investigated whether MEG can detect SESs in normal adults.

Methods

Spontaneous MEG was recorded during measurement of somatosensory evoked fields (SEFs) for bilateral electrical median nerve stimuli in 30 healthy adults.

Results

Bilateral SESs were observed in 10 adults but none in the other 20 subjects. SESs consisted of one or two peaks, and the first peak latency corresponded to that of the second peak (M2) of SEFs. The first SES peak was identical to the M2 in isofield map pattern, as well as location and orientation of the equivalent current dipole (ECD). M2 ECD strength in the 10 subjects with SESs was larger (p <0.0001) than in 20 without SESs.

Conclusions

All-or-nothing detection of bilateral SESs by MEG in normal adults must depend on the signal-to-noise issue of symmetrical SEFs and background brain activity.

Significance

Our results further confirm the higher sensitivity of MEG compared to scalp EEG for the detection of focal cortical sources tangential to the scalp such as SESs.

Objective

Electroencephalography (EEG) can be used to estimate neonates’ biological brain age. Discrepancies between postmenstrual age and brain age, termed the brain age gap, can potentially quantify maturational deviation. Existing brain age EEG models are not well suited to clinical cot-side use for estimating neonates’ brain age gap due to their dependency on relatively large data and pre-processing requirements.

Methods

We trained a deep learning model on resting state EEG data from preterm neonates with normal neurodevelopmental Bayley Scale of Infant and Toddler Development (BSID) outcomes, using substantially reduced data requirements. We subsequently tested this model in two independent datasets from two clinical sites.

Results

In both test datasets, using only 20 min of resting-state EEG activity from a single channel, the model generated accurate age predictions: mean absolute error = 1.03 weeks (p-value = 0.0001) and 0.98 weeks (p-value = 0.0001). In one test dataset, where 9-month follow-up BSID outcomes were available, the average neonatal brain age gap in the severe abnormal outcome group was significantly larger than that of the normal outcome group: difference in mean brain age gap = 0.50 weeks (p-value = 0.04).

Conclusions

These findings demonstrate that the deep learning model generalises to independent datasets from two clinical sites, and that the model’s brain age gap magnitudes differ between neonates with normal and severe abnormal follow-up neurodevelopmental outcomes.

Significance

The magnitude of neonates’ brain age gap, estimated using only 20 min of resting state EEG data from a single channel, can encode information of clinical neurodevelopmental value.

Objective

Brivaracetam (BRV) is a recent antiseizure medication (ASM) approved as an add-on therapy for people with focal epilepsy. BRV has a good efficacy and safety profile compared to other ASMs. However, its specific effects on resting-state EEG activity and connectivity are unknown. The aim of this study is to evaluate quantitative EEG changes induced by BRV therapy in a population of adult people with drug-resistant epilepsy (PwE) compared to healthy controls (HC).

Methods

We performed a longitudinal, retrospective, pharmaco-EEG study on a population of 23 PwE and a group of 25 HC. Clinical outcome was dichotomized into drug-responders (i.e., >50% reduction in seizures’ frequency; RES) and non-responders (N-RES) after two years of BRV. EEG parameters were compared between PwE and HC at baseline (pre-BRV) and after three months of BRV therapy (post-BRV). We investigated BRV-related variations in EEG connectivity using the phase locking value (PLV).

Results

BRV therapy did not induce modifications in power spectrum density across different frequency bands. PwE presented lower PLV connectivity values compared to HC in all frequency bands. RES exhibited lower theta PLV connectivity compared to HC before initiating BRV and experienced an increase after BRV, eliminating the significant difference from HC.

Conclusions

This study shows that BRV does not alter the EEG power spectrum in PwE, supporting its favourable neuropsychiatric side-effect profile, and induces the disappearance of EEG connectivity differences between PwE and HC.

Significance

The integration of EEG quantitative analysis in epilepsy can provide insights into the efficacy, mechanism of action, and side effects of ASMs.

Objective

This systematic review aimed to evaluate if event-related potentials (ERPs) can be a relevant tool for cognitive dysfunction diagnosis in Multiple Sclerosis (MS).

Methods

Four databases were consulted (PubMed, Embase, Scielo, and Web of Science). The included studies should include adults with clear MS diagnoses, independently of having cognitive complaints, and all should have been submitted to ERPs (P300, N400 or mismatch negativity (MMN)). The main outcomes evaluated were ERPs’ amplitude and/or latency.

Results

425 studies were obtained initially from all databases, with 26 studies fulfilling the eligibility criteria. P300 was the most used ERP (25 studies), showing a reduced amplitude or an increased latency in 84% of those. N400 was evaluated in one study, showing also abnormal results. MMN was addressed in two studies with inconsistent findings. Some studies further suggest that ERPs may show earlier abnormal results compared with neuropsychological tests.

Conclusions

Most MS patient groups revealed ERP abnormalities, suggesting that these neurophysiological tests may be a relevant and appropriate diagnostic aid method for cognitive impairment in MS.

Significance

The use of ERPs in MS patients seems able to demonstrate cognitive impairment and its use should be considered as part of the regular patient evaluation.

Conventional electrophysiological methods, i.e. nerve conduction studies and electromyography are suitable methods for the diagnosis of neuromuscular disorders, however, they provide limited information about muscle fibre membrane properties and underlying disease mechanisms. Muscle excitability testing is a technique that provides in vivo information about muscle fibre membrane properties such as membrane potential and ion channel function.

Since the 1960s, various methodologies have been suggested to examine muscle membrane properties but technical difficulties have limited its use. In 2009, an automated, fast and simple application, the so-called multi-fibre muscle velocity recovery cycles (MVRC) has accelerated the use of muscle excitability testing. Later, frequency ramp and repetitive stimulation protocols have been developed. Though this method has been used mainly in research for revealing disease mechanisms across a broad range of neuromuscular disorders, it may have additional diagnostic uses; value has been shown particularly in muscle channelopathies.

This review will provide a description of the state-of-the art of methodological and clinical studies for muscle excitability testing.

Objective: Within the continuum of consciousness, patients in a Minimally Conscious State (MCS) may exhibit high-level behavioral responses (MCS+) or may not (MCS−). The evaluation of residual consciousness and related classification is crucial to propose tailored rehabilitation and pharmacological treatments, considering the inherent differences among groups in diagnosis and prognosis. Currently, differential diagnosis relies on behavioral assessments posing a relevant risk of misdiagnosis. In this context, EEG offers a non-invasive approach to model the brain as a complex network. The search for discriminating features could reveal whether behavioral responses in post-comatose patients have a defined physiological background. Additionally, it is essential to determine whether the standard behavioral assessment for quantifying responsiveness holds physiological significance. Methods: In this prospective observational study, we investigated whether low-density EEG-based graph metrics could discriminate MCS+/− patients by enrolling 57 MCS patients (MCS−: 30; males: 28). At admission to intensive rehabilitation, 30 min resting-state closed-eyes EEG recordings were performed together with consciousness diagnosis following international guidelines. After EEG preprocessing, graphs’ metrics were estimated using different connectivity measures, at multiple connection densities and frequency bands ($\alpha ,\theta ,\delta$). Metrics were also provided to cross-validated Machine Learning (ML) models with outcome MCS+/−. Results: A lower level of brain activity integration was found in the MCS− group in the $\alpha$ band. Instead, in the $\delta$ band MCS− group presented an higher level of clustering (weighted clustering coefficient) respect to MCS+. The best-performing solution in discriminating MCS+/− through the use of ML was an Elastic-Net regularized logistic regression with a cross-validation accuracy of $79\%$ (sensitivity and specificity of $74\%$ and $85\%$ respectively). Conclusion: Despite tackling the MCS+/− differential diagnosis is highly challenging, a daily-routine low-density EEG might allow to differentiate across these differently responsive brain networks. Significance: Graph-theoretical features are shown to discriminate between these two neurophysiologically similar conditions, and may thus support the clinical diagnosis.

Fibromyalgia Syndrome (FMS), Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) and Long COVID (LC) are similar multisymptom clinical syndromes but with difference in dominant symptoms in each individual. There is existing and emerging literature on possible functional alterations of the central nervous system in these conditions. This review aims to synthesise and appraise the literature on resting-state quantitative EEG (qEEG) in FMS, ME/CFS and LC, drawing on previous research on FMS and ME/CFS to help understand neuropathophysiology of the new condition LC.

A systematic search of MEDLINE, Embase, CINHAL, PsycINFO and Web of Science databases for articles published between December 1994 and September 2023 was performed.

Out of the initial 2510 studies identified, 17 articles were retrieved that met all the predetermined selection criteria, particularly of assessing qEEG changes in one of the three conditions compared to healthy controls. All studies scored moderate to high quality on the Newcastle-Ottawa scale. There was a general trend for decreased low-frequency EEG band activity (delta, theta, and alpha) and increased high-frequency EEG beta activity in FMS, differing to that found in ME/CFS. The limited LC studies included in this review focused mainly on cognitive impairments and showed mixed findings not consistent with patterns observed in FMS and ME/CFS.

Our findings suggest different patterns of qEEG brainwave activity in FMS and ME/CFS. Further research is required to explore whether there are phenotypes within LC that have EEG signatures similar to FMS or ME/CFS.

This could inform identification of reliable diagnostic markers and possible targets for neuromodulation therapies tailored to each clinical syndrome.

Objective

Periodic Discharges (PDs) in Status Epilepticus (SE) are historically related to negative outcome, and the Epidemiology-based Mortality Score in SE (EMSE) identifies PDs as an EEG feature associated with unfavorable prognosis. However, supportive evidence is conflicting. This study aims to evaluate the prognostic significance of interictal PDs during and following SE.

Methods

All 2020–2023 non-hypoxic-ischemic SE patients with available EEG during SE were retrospectively assessed. Interictal PDs during SE (SE-PDs) and PDs occurring 24–72 h after SE resolution (post-SE-PDs) were examined. In-hospital death was defined as the primary outcome.

Results

189 SE patients were finally included. SE-PDs were not related to outcome, while post-SE-PDs were related to poor prognosis confirmed after multiple regression analysis. EMSE global AUC was 0.751 (95%CI:0.680–0.823) and for EMSE-64 cutoff sensitivity was 0.85, specificity 0.52, accuracy 63%. We recalculated EMSE score including only post-SE-PDs. Modified EMSE (mEMSE) global AUC was 0.803 (95%CI:0.734–0.872) and for mEMSE-64 cutoff sensitivity was 0.84, specificity 0.68, accuracy 73%.

Conclusion

Interictal PDs during SE were not related to outcome whereas PDs persisting or appearing > 24 h after SE resolution were strongly associated to unfavorable prognosis. EMSE performed well in our cohort but considering only post-SE-PDs raised specificity and accuracy for mEMSE64 cutoff.

Significance

This study supports the utility of differentiating between interictal PDs during and after SE for prognostic assessment.