Clinical MEG Analyses for Children with Intractable Epilepsy

Ochi, Ayako; Go, Cristina; Otsubo, Hiroshi

doi:10.5772/27256

Cited by 7 publications

(4 citation statements)

References 45 publications

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“…Fifteen 2-minute segments of resting-state data were recorded with concurrent MEG and 10-20 scalp EEG. The data were sampled at 625 Hz, as previously published, 17,18 and for a minority of patients, the data were sampled at 1,250 Hz and down-sampled to 625 Hz. Each epoch of data was classified by state of vigilance (i.e., awake or drowsy/asleep) by an experienced neurophysiologist.…”

Section: Methodsmentioning

confidence: 99%

Connectomic Profiles and Cognitive Trajectories After Epilepsy Surgery in Children

et al. 2022

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Objective:Neurocognitive outcomes following surgery for temporal lobe epilepsy in childhood are variable. Postoperative changes are not directly predicted by seizure-freedom and associations between epilepsy, neuropsychological function, and developing neural networks are poorly understood. Here, we leveraged whole-brain connectomic profiling in magnetoencephalography (MEG) to retrospectively study associations between brain connectivity and neuropsychological function in children with temporal lobe epilepsy undergoing resective surgery.Methods:Clinical and MEG data were retrospectively analyzed for children who underwent temporal lobe epilepsy surgery at the Hospital for Sick Children from 2000 to 2021. Resting-state connectomes were constructed from neuromagnetic oscillations via the weighted phase lag index. Using a partial least-squares (PLS) approach, multidimensional associations between patient connectomes, neuropsychological scores, and clinical covariates were assessed. Bootstrap resampling statistics were performed to assess statistical significance.Results:A total of 133 medical records were reviewed, and 5 PLS analyses were performed. Each PLS analysis probed a particular neuropsychological domain and the associations between its baseline and post-operative scores and the connectomic data. In each PLS analysis, a significant latent variable was identified, representing a specific percentage of the variance in the data, and relating neural networks to clinical covariates, which included changes in rote verbal memory (N=41, p = 0.01, σ2 = 0.38), narrative/verbal memory (N=57, p = 0.00, σ2 = 0.52), visual memory (N=51, p = 0.00, σ2 = 0.43), working memory (N=44, p = 0.00, σ2 = 0.52), and overall intellectual function (N=59, p = 0.00, σ2 = 0.55). Children with more diffuse, bilateral intrinsic connectivity across several frequency bands showed lower scores on all neuropsychological assessments but demonstrated a greater propensity for gains following resective surgery.Conclusion:Here, we report that connectomes characterized by diffuse connectivity, reminiscent of developmentally immature networks, are associated with lower pre-operative cognition and post-operative cognitive improvement. These findings provide a potential means to understand neurocognitive function in children with temporal lobe epilepsy and expected changes post-operatively.

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Section: Methodsmentioning

confidence: 99%

Connectomic Profiles and Cognitive Trajectories After Epilepsy Surgery in Children

et al. 2022

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“…Mostly for very high cycle fatigue, it is possible to find inside the fish-eye a fine granular structure particularly rough and circular around particles, which is called FGA (Fine Granular Area) [6]. Several names have been given to this zone: ODA (Optically Dark Area), GBF (Granular Bright Facet) or also RSA (Rough Surface Area) [3,[6][7][8][9]. The formation of the FGA is expected to enhance the fatigue lifetime for internal cracks [10].…”

Section: Introductionmentioning

confidence: 99%

On the crystallographic, stage I-like, character of fine granular area formation in internal fish-eye fatigue cracks

Abdesselam

Crépin

Pineau

et al. 2018

International Journal of Fatigue

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International audienceThe crystallographic character of fine granular area (FGA) formation from internal particles in martensitic high strength steel has been revealed by an assessment of the plastic zone size at the FGA border. This plastic zone size corresponded to about 3 times the martensite lath width. Tests at different temperatures (20 °C, 200 °C and 400 °C) revealed a decreasing FGA size with increasing temperature at constant applied stress amplitude. As a consequence, the critical stress intensity factor varied as the FGA decreased with temperature. In contrast, the critical plastic zone size remained constant and equal to the sizes of microstructural features. This represents a strong similarity between crystallographic, stage I-like, crack propagation and FGA formation in a vacuum

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“…The sets of candidate timepoints and sensors obtained were used for subsequent automated ECD estimation. Regarding the automated ECD estimation, we mimicked the workflow of routine manual ECD analysis and applied the following process to exclude artifacts and false positives [31]. First, we performed ECD estimation at 15 timepoints before and after 7 ms of the detected candidate time point and excluded those ECDs with Goodness of Fit (GoF) < 0.95 and an ECD intensity smaller than 50 nAm, or larger than 400 nAm.…”

Section: Famed Performancementioning

confidence: 99%

“…When the distance between each remaining ECD position and its center of gravity did not exceed 2 cm, the point with the highest intensity among the candidate points was adopted as the point at which the final ECD estimation should be performed. We set each of the parameters described above concerning the results of the segmentation performance and previous studies [31]- [33]. The parameters for applying DBSCAN clustering were set to eps as 0.03, and minPts as 4.…”

Section: Famed Performancementioning

confidence: 99%

Fully-Automated Spike Detection and Dipole Analysis of Epileptic MEG Using Deep Learning

Hirano

Emura

Nakata

et al. 2022

IEEE Trans. Med. Imaging

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Magnetoencephalography (MEG) is a useful tool for clinically evaluating the localization of interictal spikes. Neurophysiologists visually identify spikes from the MEG waveforms and estimate the equivalent current dipoles (ECD). However, presently, these analyses are manually performed by neurophysiologists and are time-consuming. Another problem is that spike identification from MEG waveforms largely depends on neurophysiologists' skills and experiences. These problems cause poor cost-effectiveness in clinical MEG examination. To overcome these problems, we fully automated spike identification and ECD estimation using a deep learning approach fully automated AI-based MEG interictal epileptiform discharge identification and ECD estimation (FAMED). We applied a semantic segmentation method, which is an image processing technique, to identify the appropriate times between spike onset and peak and to select appropriate sensors for ECD estimation. FAMED was trained and evaluated using clinical MEG data acquired from 375 patients. FAMED training was performed in two stages: in the first stage, a classification network was Manuscript

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Clinical MEG Analyses for Children with Intractable Epilepsy

Cited by 7 publications

References 45 publications

Connectomic Profiles and Cognitive Trajectories After Epilepsy Surgery in Children

Connectomic Profiles and Cognitive Trajectories After Epilepsy Surgery in Children

On the crystallographic, stage I-like, character of fine granular area formation in internal fish-eye fatigue cracks

Fully-Automated Spike Detection and Dipole Analysis of Epileptic MEG Using Deep Learning

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