Highlights • MEG in the form of Magnetic Source Imaging (MSI) can increase the diagnostic yield of MRIs. • MSI-guided re-review of supposedly negative MRIs may reveal significant pathology including ...focal cortical dysplasia (FCD). • Clinical magnetoencephalographers (“MEG practitioners”) and the referring epilepsy teams (“MEG users”) should change their evaluation protocols accordingly.
This study investigates variations in hippocampal barque occurrence during sleep and compares findings to respective variations of their scalp manifestation as 14&6/sec positive spikes.
From 11 ...epilepsy patients, 12 non-epileptogenic hippocampi with barques were identified for this study. Using the first seizure-free whole-night sleep stereo-encephalography (sEEG) recording, we performed sleep staging and measured the occurrence of barques and 14&6/sec positive spikes variants.
Hippocampal barques (total count: 9,183; mean count per record: 765.2 ± 251.2) occurred predominantly during non-rapid eye movement (NREM) II sleep (total: 5,744; mean: 478.6 ± 176.1; 62.2 ± 6.0%) and slow-wave sleep (SWS) (total: 2,950; mean: 245.83 ± 92.9; 32.0 ± 6.2%), with rare to occasional occurrence in NREM I (total: 85; mean: 7.0 ± 2.8; 0.9 ± 0.4%), rapid eye movement (REM) (total: 153; mean: 12.75 ± 4.0; 1.7 ± 0.6) and wakefulness (total: 251; mean: 20.9 ± 6.3; 2.9 ± 0.9%). Barque rate increased during SWS (mean: 2.7 ± 1.0 per min) compared to NREM II (2.2 ± 1.0 per min) and other states (wakefulness: 0.1 ± 0.0 per min; NREM I: 0.3 ± 0.1 per min; REM: 0.1 ± 0.0 per min). The 14&6/sec positive spikes variant (total count: 2,406; mean: 343.7 ± 106.7) was present in NREM II (total: 2,059; mean: 249.1 ± 100.2, 84.9 ± 3.6%) and SWS (total: 347; mean: 49.5 ± 12.8, 15.0 ± 3.6%) stages, and absent from the rest of sleep and wakefulness. While all 14&6/sec positive spikes correlated with barques, only 44.7 ± 6.1% of barques manifested as 14&6/sec positive spikes.
Hippocampal barques are predominant in NREM II and SWS, and tend to increase their presence during SWS. Their scalp manifestation as 14&6/sec positive spikes is confounded by wakefulness, REM and NREM I stages, and "masked" by the co-occurrence of NREM II and SWS slow waves, and overlapping reactive micro-arousal elements.
Our study highlighted the overnight profile of hippocampal barques, in relation to the respective profile of their scalp manifestation, the 14&6/sec positive spikes variant.
•DL and neural mass model are integrated to accomplish MEG source imaging.•DL models were trained with or without personalized head geometry information.•Both models provided robust interictal spike ...imaging results with sublobar concordance.•Personalized model offers excellent performance in epilepsy source localization.
Electromagnetic source imaging (ESI) offers unique capability of imaging brain dynamics for studying brain functions and aiding the clinical management of brain disorders. Challenges exist in ESI due to the ill-posedness of the inverse problem and thus the need of modeling the underlying brain dynamics for regularizations. Advances in generative models provide opportunities for more accurate and realistic source modeling that could offer an alternative approach to ESI for modeling the underlying brain dynamics beyond equivalent physical source models. However, it is not straightforward to explicitly formulate the knowledge arising from these generative models within the conventional ESI framework. Here we investigate a novel source imaging framework based on mesoscale neuronal modeling and deep learning (DL) that can learn the sensor-source mapping relationship directly from MEG data for ESI. Two DL-based ESI models were trained based on data generated by neural mass models and either generic or personalized head models. The robustness of the two DL models was evaluated by systematic computer simulations and clinical validation in a cohort of 29 drug-resistant focal epilepsy patients who underwent intracranial EEG (iEEG) evaluation or surgical resection. Results estimated from pre-operative MEG interictal spikes were quantified using the overlap with resection regions and the distance to the seizure-onset zone (SOZ) defined by iEEG recordings. The DL-based ESI provided robust results when no personalized head geometry is considered, reaching a spatial dispersion of 21.90 ± 19.03 mm, sublobar concordance of 83 %, and sublobar sensitivity and specificity of 66 and 97 % respectively. When using personalized head geometry derived from individual patients’ MRI in the training data, personalized DL-based ESI model can further improve the performance and reached a spatial dispersion of 8.19 ± 8.14 mm, sublobar concordance of 93 %, and sublobar sensitivity and specificity of 77 and 99 % respectively. When compared to the SOZ, the localization error of the personalized approach is 15.78 ± 5.54 mm, outperforming the conventional benchmarks. This work demonstrates that combining generative models and deep learning enables an accurate and robust imaging of epileptogenic zone from MEG recordings with strong sublobar precision, suggesting its added value to enhancing MEG source localization and imaging, and to epilepsy source localization and other clinical applications.
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Highlights • Evaluation of inter-reader and algorithm versus human agreement for spike detection using pairwise comparisons. • A statistical Turing test evaluates for algorithm noninferiority versus ...skilled human performance. • The Persyst 13 spike detection algorithm proved noninferior to a set of three skilled human readers.
The objective of this study is to extract pathological brain networks from interictal period of E/MEG recordings to localize epileptic foci for presurgical evaluation. We proposed here a resting ...state E/MEG analysis framework, to disentangle brain functional networks represented by neural oscillations. By using an Embedded Hidden Markov Model, we constructed a state space for resting state recordings consisting of brain states with different spatiotemporal patterns. Functional connectivity analysis along with graph theory was applied on the extracted brain states to quantify the network features of the extracted brain states, based on which the source location of pathological states is determined. The method is evaluated by computer simulations and our simulation results revealed the proposed framework can extract brain states with high accuracy regarding both spatial and temporal profiles. We further evaluated the framework as compared with intracranial EEG defined seizure onset zone in 10 patients with drug-resistant focal epilepsy who underwent MEG recordings and were seizure free after surgical resection. The real patient data analysis showed very good localization results using the extracted pathological brain states in 6/10 patients, with localization error of about 15 mm as compared to the seizure onset zone. We show that the pathological brain networks can be disentangled from the resting-state electromagnetic recording and could be identified based on the connectivity features. The framework can serve as a useful tool in extracting brain functional networks from noninvasive resting state electromagnetic recordings, and promises to offer an alternative to aid presurgical evaluation guiding intracranial EEG electrodes implantation.
Summary
This document was developed by the members of the Committee to Revise the Guidelines for Services, Personnel, and Facilities at Specialized Epilepsy Centers. After discussions with the ...general membership they were adopted by the Board of the National Association of Epilepsy Centers. The Guidelines will be reviewed and updated when considered necessary by the Board.
EEG Findings in Coronavirus Disease Pilato, Madison S; Urban, Alexandra; Alkawadri, Rafeed ...
Journal of clinical neurophysiology,
02/2022, Volume:
39, Issue:
2
Journal Article
Peer reviewed
Neurologic manifestations of coronavirus disease (COVID-19) such as encephalopathy and seizures have been described. To our knowledge, detailed EEG findings in COVID-19 have not yet been reported. ...This report adds to the scarce body of evidence.
We identified eight COVID-19 positive patients who underwent EEG monitoring in our hospital system.
EEGs were most commonly ordered for an altered level of consciousness, a nonspecific neurologic manifestation. We observed generalized background slowing in all patients and generalized epileptiform discharges with triphasic morphology in three patients. Focal electrographic seizures were observed in one patient with a history of focal epilepsy and in another patient with no such history. Five of eight patients had a previous diagnosis of epilepsy, suggesting that pre-existing epilepsy can be a potential risk factor for COVID-19-associated neurological manifestations. Five of eight patients who underwent EEG experienced a fatal outcome of infection.
Our findings underscore previous observations that neurologic manifestations are common in severe cases. COVID-19 patients with epilepsy may have an increased risk of neurological manifestations and abnormal EEG.
•Barques are predominantly localized in the posterior part of the human hippocampus.•No independent barques were found to occur exclusively in the anterior hippocampus.•The posterio-lateral ...hippocampal barque phase reversal can explain the positive polarity of scalp 14&6/sec spikes.
To investigate whether barques can be localized across the hippocampal longitudinal axis with sufficient specificity.
We identified 51 focal epilepsy patients implanted with a minimum of two electrodes – unilateral anterior and posterior - in either hippocampus. We used visual inspection of the intracranial electroencephalogram (iEEG) and 3D brain volume spectrum-based statistical parametric mapping (SPM) to localize barques.
In 18/51 patients (35.29%), barques were identified in 22/70 (31.42%) hippocampi. In all hippocampi (100%), barques were present in the posterior hippocampus, while 9 (40.90%) showed concurrent non-independent barque activity anteriorly (P < 0.0001). Statistical parametric mapping confirmed the posterior barque localization, with significant differences in t-values (t(27) = 8.08, P < 0.0001) and z-scores (t(24) = 6.85, P < 0.0001) between anterior and posterior hippocampal barque activity. Posterior lateral extrahippocampal contacts demonstrated phase reversals of positive polarity during barque activity (P = 0.0092, compared to anterior extrahippocampal contacts).
This study highlights the posterior hippocampal predominance of barques. Our findings are concordant with the posterior distribution of the scalp manifestation of barques as “14&6/sec positive spikes”. The posterio-lateral hippocampal barque phase reversal can explain the positive polarity of scalp 14&6/sec spikes.
Understanding the properties of barques is critical for the iEEG interpretation in epilepsy surgery evaluations that include the hippocampus.
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