The mechanism by which gliomas disrupt the brain’s intrinsic rhythms, thereby altering the delicate balance of cortical excitability and neural synchrony at both local and global scales, remains a fundamental challenge. It is still unknown whether different glioma subtypes and malignancy grades impose distinct disruptions to network dynamics or whether these patterns reflect a shared functional fingerprint across tumor biology. Unraveling these questions is essential for glioma characterization and transforming clinical decision-making. Resting-state MEG was acquired in 135 glioma patients (WHO grade 2-4) and 100 matched healthy controls. We quantified local synchrony and cortical excitability through power spectral density and the aperiodic slope, an established surrogate marker of excitation-inhibition (E-I) balance, across canonical frequency bands. Long-range network integration was assessed using imaginary coherence. Spatially resolved, subtype-, grade-, and frequency-specific patterns were evaluated with voxel-wise non-parametric permutation testing. Glioma patients exhibited spatially widespread disruptions in neural synchrony and excitability. Across tumor subtypes, delta power is globally accentuated, with alpha power paradoxically increased at distant sites but suppressed near tumor-infiltrated cortex. High-grade GBMs exhibit the most dramatic spectral shifts, with marked increases in delta and prominent theta suppression near the tumor-infiltrated region. In contrast, oligodendrogliomas displayed a unique neurophysiological profile—high delta/gamma but attenuated alpha/theta. Long-range connectivity in the alpha and theta bands showed distinct, location- and subtype-dependent patterns: GBMs featured reduced theta coherence adjacent to tumor infiltration and decreased distal alpha coherence. Strikingly, aperiodic slope flattening, a marker of hyperexcitability, was flattened most in GBM, less so in astrocytoma, and least in oligodendroglioma, directly correlating network hyperexcitability to underlying malignancy grade. The most aberrant excitatory shifts were consistently localized to the tumor-infiltrated cortex when compared with the normal cortex. MEG mapping unveils striking, frequency-dependent patterns of network disruption and hyperexcitability signatures. These functional biomarkers could transform outcome prediction, risk assessment, and design precision for network-based therapies for patients

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.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.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.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.

Stereo-electroencephalography (SEEG) is the surgical implantation of electrodes in the brain to better localize the epileptic network in pharmaco-resistant epileptic patients. This technique has exquisite spatial and temporal resolution. Still, the number and the position of the electrodes in the brain is limited and determined by the semiology and/or preliminary non-invasive examinations, leading to a large number of unexplored brain structures in each patient. Here, we propose a new approach to reconstruct the activity of non-sampled structures in SEEG, based on independent component analysis (ICA) and dipole source localization. We have tested this approach with an auditory stimulation dataset in ten patients. The activity directly recorded from the auditory cortex served as ground truth and was compared to the ICA applied on all non-auditory electrodes. Our results show that the activity from the auditory cortex can be reconstructed at the single trial level from contacts as far as ∼40 mm from the source. Importantly, this reconstructed activity is localized via dipole fitting in the proximity of the original source. In addition, we show that the size of the confidence interval of the dipole fitting is a good indicator of the reliability of the result, which depends on the geometry of the SEEG implantation. Overall, our approach allows reconstructing the activity of structures far from the electrode locations, partially overcoming the spatial sampling limitation of intracerebral recordings.

Recording from deep neural structures such as hippocampus noninvasively and yet with high temporal resolution remains a major challenge for human neuroscience. Although it has been proposed that deep neuronal activity might be recordable during cognitive tasks using magnetoencephalography (MEG), this remains to be demonstrated as the contribution of deep structures to MEG recordings may be too small to be detected or might be eclipsed by the activity of large-scale neocortical networks. In the present study, we disentangled mesial activity and large-scale networks from the MEG signals thanks to blind source separation (BSS). We then validated the MEG BSS components using intracerebral EEG signals recorded simultaneously in patients during their presurgical evaluation of epilepsy. In the MEG signals obtained during a memory task involving the recognition of old and new images, we identified with BSS a putative mesial component, which was present in all patients and all control subjects. The time course of the component selectively correlated with stereo-electroencephalography signals recorded from hippocampus and rhinal cortex, thus confirming its mesial origin. This finding complements previous studies with epileptic activity and opens new possibilities for using MEG to study deep brain structures in cognition and in brain disorders.

AimsTo study the latency, amplitude, and source localization of magnetic evoked field (MEF) responses to visual, auditory, and somatosensory stimuli in Wilson's disease (WD) using magnetoencephalography (MEG) and compare it with "healthy" controls, and correlate the observations with disease severity and brain MRI.MethodsMEF of 28 patients with neurological WD (age: 22.82 ± 5.8 years; M:F = 12:16) and 21 matched controls (age: 25.0 ± 4.6 years; M:F = 10:11) were recorded using MEG. Source localization was performed using standard models on the components of M100, M20, and M100 for visual, somatosensory, and auditory evoked fields, respectively and its latency/amplitude was correlated with disease severity.ResultsThere were significant differences in source location between control and WD during visual evoked field (VEF) and auditory evoked field (AEF) studies. Latencies of M20 (right-p = 0.02; left-p = 0.04) and M32 (right-p = 0.01) components of SSEF were significantly prolonged. The amplitude of M20 was significantly reduced in patients bilaterally (P = 0.001). There was a trend for the prolonged latency of M100 of VEF in patients (P = 0.09). Five patients had reduced right M145 compared to 8 controls. The left somatosensory evoked fields (SSEF) latency correlated with disease severity (P = 0.04). There was no significant correlation between major components of other MEF with disease severity or MRI score.ConclusionsThis study, first of its kind to use MEF analysis in a large cohort of patients with WD, detected subclinical but a variable degree of abnormalities, most consistently of SSEF. It provides valuable insights of functioning and localization of various pathways in a disease known to have protean clinical manifestations and widespread MRI changes.

Magnetoencephalography (MEG) is a powerful tool for estimating brain connectivity with both good spatial and temporal resolution. It is particularly helpful in epilepsy to characterize non-invasively the epileptic networks. However, using MEG to map brain networks requires solving a difficult inverse problem that introduces uncertainty in the activity localization and connectivity measures. Our goal here was to compare independent component analysis (ICA) followed by dipole source localization and the linearly constrained minimum-variance beamformer (LCMV-BF) for characterizing regions with interictal epileptic activity and their dynamic connectivity. After a simulation study, we compared ICA and LCMV-BF results with intracerebral EEG (stereotaxic EEG, SEEG) recorded simultaneously in 8 epileptic patients, which provide a unique 'ground truth' to which non-invasive results can be confronted. We compared the signal time courses extracted applying ICA and LCMV-BF on MEG data to that of SEEG, both for the actual signals and the dynamic connectivity computed using cross-correlation (evolution of links in time). With our simulations, we illustrated the different effect of the temporal and spatial correlation among sources on the two methods. While ICA was more affected by the temporal correlation but robust against spatial configurations, LCMV-BF showed opposite behavior. Moreover, ICA seems more suited to retrieve the simulated networks. In case of real patient data, good MEG/SEEG correlation and good localization were obtained in 6 out of 8 patients. In 4 of them ICA had the best performance (higher correlation, lower localization distance). In terms of dynamic connectivity, the evolution in time of the cross-correlation links could be retrieved in 5 patients out of 6, however, with more variable results in terms of correlation and distance. In two patients LCMV-BF had better results than ICA. In one patient the two methods showed equally good outcomes, and in the remaining two patients ICA performed best. In conclusion, our results obtained by exploiting simultaneous MEG/SEEG recordings suggest that ICA and LCMV-BF have complementary qualities for retrieving the dynamics of interictal sources and their network interactions.

In a general scenario, the brain images acquired from magnetic resonance imaging (MRI) may experience tilt, distorting brain MR images. The tilt experienced by the brain MR images may result in misalignment during image registration for medical applications. Manually correcting (or estimating) the tilt on a large scale is time-consuming, expensive, and needs brain anatomy expertise. Thus, there is a need for an automatic way of performing tilt correction in three orthogonal directions (X, Y, Z). The proposed work aims to correct the tilt automatically by measuring the pitch angle, yaw angle, and roll angle in X-axis, Z-axis, and Y-axis, respectively. For correction of the tilt around the Z-axis (pointing to the superior direction), image processing techniques, principal component analysis, and similarity measures are used. Also, for correction of the tilt around the X-axis (pointing to the right direction), morphological operations, and tilt correction around the Y-axis (pointing to the anterior direction), orthogonal regression is used. The proposed approach was applied to adjust the tilt observed in the T1- and T2-weighted MR images. The simulation study with the proposed algorithm yielded an error of 0.40 ± 0.09°, and it outperformed the other existing studies. The tilt angle (in degrees) obtained is ranged from 6.2 ± 3.94, 2.35 ± 2.61, and 5 ± 4.36 in X-, Z-, and Y-directions, respectively, by using the proposed algorithm. The proposed work corrects the tilt more accurately and robustly when compared with existing studies.

Objective: This study aims to evaluate the utility of magnetoencephalography in presurgical planning and in predicting post-surgical seizure outcome. Methods: This study included a cohort of 231 children (1-18 years) with focal drug-resistant epilepsy who underwent MEG as a part of their presurgical workup. Characteristics of MEG observations were described in all children. The concordance and agreement of Magnetic Source Imaging (MSI) of interictal discharges (IED) was estimated with either of the 3 subgroups - MRI lesion; presumed epileptogenic zone (EZ); or resection cavity. In operated children group, MEG dipole characteristics between good and poor outcome groups were assessed. Results: A total of 153 cases (66.2%) showed frequent IEDs (60 spikes/60 min). Of the 173 cases where MSI showed clusters (74.9%), 151 had lesions and 22 were non-lesional. amongst patients with lesional epilepsy and MEG clusters, class I concordance (MEG localization either completely included or overlapped at least 60% with the MRI lesion) was seen in 60.92% with a Cohen's kappa of 0.608. In non-lesional epilepsy, class I concordance of MEG with presumed EZ was found in (81.81%) with an agreement of 0.317. Fifty-three children underwent surgery of whom 39 (73.58%) showed a good outcome (Engel I). In operated children, concordance between MEG focus and resection cavity was observed in 23 (58.97%) with good outcome and in 12 (86.72%) with poor outcome with no significant difference (p>0.05). However, MEG cluster regular organization and clusterectomy are associated with good seizure outcome postoperatively (p< 0.05). Presence of scatters were associated with poor outcome (p<0.05) in children with focal cortical dysplasia. Conclusions: MEG provides useful information that can serve as a biomarker for prognosticating the surgical outcome in paediatric epilepsy. Cluster removal and regular cluster organization shows predictive power in postsurgical prognostication in children and the presence of scatters predicts poor outcome in children with focal cortical dysplasia.

Objective: P300 is an event-related potential, being explored as an objective tool to assess cognition. This study aimed to investigate the characteristics of auditory and visual P300 in patients with TLE having unilateral HS using electroencephalography (EEG) and to study its correlation with cognition. Methods: This is a cross-sectional case-control study, where P300 characteristics in thirty patients with unilateral hippocampal sclerosis with refractory epilepsy were compared with fifteen age-, gender-, and years of education-matched healthy controls (M: F-10:5, mean age-28 +/- 4.76 years). Among patients, 15 belonged to the right HS group (M: F-9:6, age at onset-12.92 +/- 10.22 years, duration of epilepsy 16.67 +/- 9.38 years) and 15 to the left HS group (M: F-8:7, age at onset-10.62 +/- 7.18 years, duration of epilepsy-15.53 +/- 10.14 years). All subjects underwent EEG-based auditory and visual oddball tasks and cognitive assessment. The P300 latencies (in milliseconds) as well as amplitudes (in microvolts) were predicted in EEG and were correlated with cognitive scores. Source localization of P300 was performed with the CLARA algorithm. Results: The auditory P300 latencies in controls, right HS, and left HS were 323.93 +/- 40.28, 351.06 +/- 47.23, and 328.80 +/- 36.03, respectively (p = 0.18) and its amplitudes were 2.3040 +/- 1.46, 2.77 +/- 1.19, and 2.68 +/- 1.78, respectively (p = 0.48). Visual P300 latencies in controls, right HS, and left HS were 365.87 +/- 47.37, 359.67 +/- 64.45, and 376.00 +/- 60.06, respectively (p = 0.51) and its amplitudes were 3.93 +/- 2.28, 2.09 +/- 1.45, and 3.56 +/- 1.74, respectively (p = 0.014). Further, when compared to the control group the cognitive scores were lower in the patient group (p < 0.05). Significance: In comparison to the controls, patients with right HS recorded lesser amplitude on visual P300 and lower scores on cognitive tests. P300 and cognitive parameters exhibited varied relationship. P300 could be a complementary objective tool to assess cognition in patients with TLE. (c) 2021 Elsevier Inc. All rights reserved.

Magnetoencephalography (MEG) is a noninvasive technique for exploring the spatiotemporal dynamics of brain networks with high temporal resolution as well as good spatial capacities, thanks to relative insensitivity to low skull conductivity. It is a tool of choice for both neuroscience research and clinical applications and is used routinely in epilepsy for localizing the sources of epileptiform discharges. Still, the capacity of capturing deep sources, such as hippocampus and amygdala that are key players in memory and emotion, has been for long a topic of debate. Thus, the fine characterization of deep structures has been up to now the reserved domain of intracerebral stereo electroencephalography (SEEG), performed during presurgical evaluation of patients with epilepsy. We review here the evidence for the detection of deep sources in MEG, with emphasis on simultaneous recordings of MEG and SEEG. In particular, we discuss how simultaneously recording depth and surface signals enables to investigate the correlation between MEG and invasive signals actually recorded in deep structures. We also discuss new venues in analysis and recording methods for reconstructing deep activities from MEG.

Background: Epilepsy is a neurological disorder which is characterised by recurrent and involuntary seizures. Magnetoencephalography (MEG) is clinically used as a presurgical tool in locating the epileptogenic zone by localising either interictal epileptic discharges (IEDs) or ictal activities. The localisation of ictal onset provides reliable and more accurate seizure onset zones rather than localising the IEDs. Ictals or seizures are presently detected during MEG analysis by manually inspecting the recorded data. This is laborious when the duration of recordings is longer. Methods: We propose a novel method which uses statistical features such as short-time permutation entropy (STPE), gradient of STPE (GSTPE), short-time energy (STE) and short-time mean (STM) extracted from the ictal and interictal MEG data of drug resistant epilepsy patients group. Since the data is heavily skewed, the RUSBoost algorithm with k-fold cross-validation is used to classify the data into ictal and interictal by using the four feature vectors. This method is further used for localising the epileptogenic region using region-specific classifications by means of the RUSBoost algorithm. Results: The accuracy obtained for seizure detection is 93.4%. The specificity and sensitivity for the same are 93%. The localisation accuracies for each lobe are in the range of 88.1-99.1%. Discussion: Through this ictus detection method, the current scenario of laborious inspection of the ictal MEG can be reduced. The proposed system, thus, can be implemented in real-time as a better and more efficient method for seizure detection and further it can prove to be highly beneficial for patients and health-care professionals during real-time MEG recording. Furthermore, the identification of the epileptogenic lobe can provide clinicians with useful insights, and a pre-cursor for source localisation.

In patients with medically refractory epilepsy, resective surgery is the mainstay of therapy to achieve seizure freedom. However, ∼20-50% of cases have intractable seizures post-surgery due to the imprecise determination of epileptogenic zone. Recent intracranial studies suggest that high frequency oscillations between 80 and 200 Hz could serve as one of the consistent epileptogenicity biomarkers for localization of the epileptogenic zone. However, these high frequency oscillations are not adopted in the clinical setting because of difficult non-invasive detection. Here, we investigated non-invasive detection and localization of high frequency oscillations and its clinical utility in accurate pre-surgical assessment and post-surgical outcome prediction. We prospectively recruited 52 patients with medically refractory epilepsy who underwent standard pre-surgical workup including magnetoencephalography (MEG) followed by resective surgery after determination of the epileptogenic zone. The post-surgical outcome was assessed after 22.14 ± 10.05 months. Interictal epileptic spikes were expertly identified, and interictal epileptic oscillations across the neural activity frequency spectrum from 8 to 200 Hz were localized using adaptive spatial filtering methods. Localization results were compared with epileptogenic zone and resected cortex for congruence assessment and validated against the clinical outcome. The concordance rate of high frequency oscillations sources (80-200 Hz) with the presumed epileptogenic zone and the resected cortex were 75.0% and 78.8%, respectively, which is superior to that of other frequency bands and standard dipole fitting methods. High frequency oscillation sources corresponding with the resected cortex, had the best sensitivity of 78.0%, positive predictive value of 100% and an accuracy of 78.84% to predict the patient's surgical outcome, among all other frequency bands. If high frequency oscillation sources were spatially congruent with resected cortex, patients had an odds ratio of 5.67 and 82.4% probability of achieving a favourable surgical outcome. If high frequency oscillations sources were discordant with the epileptogenic zone or resection area, patient has an odds ratio of 0.18 and only 14.3% probability of achieving good outcome, and mostly tended to have an unfavourable outcome (χ2 = 5.22; P = 0.02; φ = -0.317). In receiver operating characteristic curve analyses, only sources of high-frequency oscillations demonstrated the best sensitivity and specificity profile in determining the patient's surgical outcome with area under the curve of 0.76, whereas other frequency bands indicate a poor predictive performance. Our study is the first non-invasive study to detect high frequency oscillations, address the efficacy of high frequency oscillations over the different neural oscillatory frequencies, localize them and clinically validate them with the post-surgical outcome in patients with medically refractory epilepsy. The evidence presented in the current study supports the fact that HFOs might significantly improve the presurgical assessment, and post-surgical outcome prediction, where it could widely be used in a clinical setting as a non-invasive biomarker.

Accurate delineation of epileptogenic zone(EZ) is crucial in patients with medically refractory epilepsy (MRE) warranting surgery and at least 33% will have seizure recurrence post-surgery. Intracranial EEG studies had showed that high frequency oscillations (HFO; 80–200 Hz) can accurately localize the EZ and patients had better post-surgical seizure freedom, if brain regions generating HFOs were resected out. However, none of the noninvasive studies evaluated the clinical utility of HFOs. So, we investigated epileptic HFOs source imaging to delineate the presurgical EZ and predict the seizure freedom. Fifty-two patients (M: F = 37:15; age = 23 ± 8.9 years; disease duration 11.4 ± 7.7 years) with MRE were longitudinally recruited and studied with MEG. Each interictal epileptic discharge were marked by assessing spatial topography. Subject specific semi-realistic head model and a 3D grid voxels of 10 mm resolution was constructed. Source reconstruction was performed with frequency beamformer, by attenuating the source power at a specified location, subject to unit-gain constraint. Gain matrix was determined and cross-spectral density matrix on a Fourier transformed data was calculated at 7 distinct frequency bands; 8–14 Hz, 14–30 Hz, 1–30 Hz, 30–54 Hz, 55–80 Hz, 30–80 Hz, and 80–200 Hz. From the available clinical data, EZ was defined, respective surgery was performed and surgical outcome was assessed. The congruency of source construction with the EZ and surgically resected cortex was established at four levels, for each band & strength was quantified by kappa statistic. Sensitivity, positive predictive value (PPV) and accuracy was evaluated. Source localization error (SLE) was determined on the basis of minimum euclidean distance between the margin of reconstructed sources with the volume of the resected cortex. With surgery, 44 (84.6%) patients had good (Engel-I) and 8 (15.4%) patients had poor (Engel-II-IV) clinical outcome at 22.1 ± 10 months follow up. Compared to other frequency bands, interictal HFO imaging accurately delineated the epileptogenic cortex in 80.3% (k = 0.44 ± 0.12) of the patients, with 80.8% sensitivity, 100% PPV, and 80.32% accuracy. The highest possible concordance rate of 78.8% (k = 0.46 ± 0.1) with the surgically resected cortical volume, was observed for the reconstructed HFO sources. HFO source reconstruction did correlate with the seizure freedom (p < 0.05) with 78% sensitivity, 100% PPV and 78.84% accuracy. Interestingly, among patients (n = 8) who had recurrent seizures following surgery, 75% (n = 6) of the patients showed HFO activity outside the resection area. SLE was negatively correlated with source strength in 8–14 Hz (r −0.39; p = 0.008), but didn’t correlate with the outcome. To date, this is the first ever largest prospective & noninvasive (MEG) study to investigate the clinical role of the HFOs, where HFOs precisely delineated the EZ & predicted the patients who might achieve seizure freedom with surgery, emerging as a key noninvasive epilepsy biomarker.

Magnetoencephalography (MEG) data is subject to many sources of environmental noise, and interference rejection is a necessary step in the processing of MEG data. Large amplitude interference caused by sources near the brain have been common in clinical settings and are difficult to reject. Artifact from vagal nerve stimulators (VNS) is a prototypical example. In this study, we describe a novel MEG interference rejection algorithm called dual signal subspace projection (DSSP), and evaluate its performance in clinical MEG data from people with epilepsy and implanted VNS. The performance of DSSP was evaluated in a retrospective cohort study of patients with epilepsy and VNS who had MEG scans for source localization of interictal epileptiform discharges. DSSP was applied to the MEG data and compared with benchmark for performance. We evaluated the clinical impact of interference rejection based on human expert detection and estimation of the location and time-course of interictal spikes, using an empirical Bayesian source reconstruction algorithm (Champagne). Clinical recordings, after DSSP processing, became more readable and a greater number of interictal epileptic spikes could be clearly identified. Source localization results of interictal spikes also significantly improved from those achieved before DSSP processing, including meaningful estimates of activity time courses. Therefore, DSSP is a valuable novel interference rejection algorithm that can be successfully deployed for the removal of strong artifacts and interferences in MEG.

Purpose: Magnetoencephalography (MEG) was used to record and localize the sources of the epileptiform discharges, in absence epilepsy, at three different time intervals to infer the sources of involvement during generation and propagation. Methods: Twenty patients with absence epilepsy (M:F = 1:1; age: 10.2 +/- 3.4 years), which included 12 patients with childhood absence epilepsy (CAE) and 8 patients with juvenile absence epilepsy (JAE), were recruited in this prospective MEG based study. MEG epileptiform discharges were divided into three sub-groups based on the duration viz., 1 a (very short), > 1-9.9 s (short) and >= 10 s (long) and the discharges of each group were averaged independently in each patient. MEG source analysis was performed on these averaged discharges, of each of the subgroups, at the onset, during middle and offset. Results: The source locations obtained, in lobar and gyri levels, were compared across these three groups of varying duration of discharges and in the CAE and JAE subjects. It was observed that the most frequent location of sources from the sublobar, limbic and frontal lobes in all the discharge groups at different time intervals. Also, it was noted that there were only subtle and variable degree of the differences of source localization of epileptic discharges among CAE and JAE subgroups. Conclusion: The study provided novel findings regarding origin and propagation of sources of epileptiform discharges in patients with childhood and juvenile absence epilepsies. Such analysis further improves the understanding of network involvement of subcortical and cortical regions in these patients.

Specificity of ictal high-frequency oscillations (HFOs) in identifying epileptogenic abnormality is significant, compared to the spikes and interictal HFOs. The objectives of the study were to detect and to localize ictal HFOs by magnetoencephalography (MEG) for identifying the seizure onset zone (SOZ), evaluate the cortical excitability from preictal to ictal transition, and establish HFO concordance rates with other modalities and postsurgical resection. Sixty-seven patients with drug-resistant epilepsy had at least 1 spontaneous seizure each during MEG acquisition, and analysis was carried out on 20 seizures from 20 patients. Ictal MEG data were bandpass filtered (80–200 Hz) to visualize, review, and analyze the HFOs co-occurring with ictal spikes. Source montages were generated on both hemispheres, mean fast Fourier transform was computed on virtual time series for determining the preictal to ictal spectral power transition, and source reconstruction was performed with sLORETA and beamformers. The concordance rates of ictal MEG HFOs (SOZ) was estimated with 4 reference epileptogenic regions. In each subject, transient bursts of high-frequency oscillatory cycles, distinct from the background activity, were observed in the periictal continuum. Time–frequency analysis showed significant spectral power surge (85–160 Hz) during ictal state (P < .05) compared to preictal state, but there was no variation in the peak HFO frequencies (P > .05) for each subgroup and at each source montage. HFO source localization was consistent between algorithms (k = 0.8570 ± 0.138), with presumed epileptogenic zone (EZ) comparable to other modalities. In patients who underwent surgery (n = 6), MEG HFO SOZ was concordant with the presumed EZ HFOs could reliably be detected in the MEG periictal state, and its sources were accurately localized in almost 95% of the patients with medically refractory epilepsy. During preictal to ictal transition, HFOs exhibited dynamic augmentation in intrinsic epileptogenicity. Spatial overlap of ictal HFO sources was consistent with EZ determinants including the presumed EZ and the surgical resection area.

In selected patients with drug-resistant focal epilepsies (DRFE), who otherwise are likely to be excluded from epilepsy surgery (ES) because of the absence of a magnetic resonance imaging (MRI)-demonstrable lesion or discordant anatomo-electro-clinical (AEC) data, magnetoencephalography (MEG) may help to generate an AEC hypothesis and stereo-electroencephalography (SEEG) may help to verify the hypothesis and proceed with ES. The sensitivity of MEG is much better in localizing the spiking zone in relation to lateral temporal and extratemporal cortical regions compared to the mesial temporal structures. MEG has a dominant role in the presurgical evaluation of patients with MRI-negative DRFEs, insular epilepsies, and recurrent seizures after failed epilepsy surgeries, and in guiding placement of invasive electrodes. Moreover, postoperative seizure freedom is better if MEG spike source localized cortical region is included in the resection. When compared to subdural grid electrode recording, SEEG is less invasive and safer. Those who are otherwise destined to suffer from uncontrolled seizures and their consequences, SEEG guided ES is a worthwhile and a cost-effective option. Depending on the substrate pathology, there is > 80-90% chance of undergoing ES and 60-80% chance of becoming seizure-free following SEEG. Recent noninvasive techniques aimed at better structural imaging, delineating brain connectivity and recording specific intracerebral EEG patterns such as high frequency oscillations might decrease the need for SEEG; but more importantly, make SEEG exploration more goal-directed and hypothesis-driven.

Objective: The purpose of this study is to localize the sources of epileptiform discharges (EDs), in juvenile myoclonic epilepsy (JME) using Magnetoencephalography (MEG), at three different time instances and analyze the propagation of EDs, from onset to offset, for inferring the cortical and subcortical region of involvement.Methods: Twenty patients (age 23.5 +/- 6.3 years old) with JME were recruited in this prospective study. MEG source analysis was performed on the independently collected EDs of each patient. The distributed source model was employed for source localization using low resolution electromagnetic brain tomography (LORETA). In each EDs, the onset (leading edge of the spike from baseline), peak and offset (trailing edge of the spike), with time window of 8 ms, were subjected for source localization in order to study the propagation of the EDs. The obtained source location coordinates, from each individual MRI, were transformed in Talairach space and the distribution of region of source involvement was analysed.Results: The frequency pattern of lobar distribution at onset, peak and offset respectively suggest that discharges most commonly localized at onset from sublobar region, at peak from frontal lobe and at offset from the sublobar region. It was observed that the maximum involvement of sources from the sublobar, limbic and frontal lobes at different time instances. It indicates that the restricted cortical-subcortical involvement during the generation and propagation of EDs in JME.Significance: This MEG study supported the cortical-subcortical region of involvement and provided further insights in our understanding the network involvement in generation and propagation of EDs in JME. (C) 2016 Elsevier B.V. All rights reserved.

Objective: Specificity of ictal high-frequency oscillations (HFOs) in identifying epileptogenic abnormality is significant, compared to the spikes and interictal HFOs. The objectives of the study were to detect and to localize ictal HFOs by magnetoencephalography (MEG) for identifying the seizure onset zone (SOZ), evaluate the cortical excitability from preictal to ictal transition, and establish HFO concordance rates with other modalities and postsurgical resection. Methods: Sixty-seven patients with drug-resistant epilepsy had at least 1 spontaneous seizure each during MEG acquisition, and analysis was carried out on 20 seizures from 20 patients. Ictal MEG data were bandpass filtered (80-200Hz) to visualize, review, and analyze the HFOs co-occurring with ictal spikes. Source montages were generated on both hemispheres, mean fast Fourier transform was computed on virtual time series for determining the preictal to ictal spectral power transition, and source reconstruction was performed with sLORETA and beamformers. The concordance rates of ictal MEG HFOs (SOZ) was estimated with 4 reference epileptogenic regions. Results: In each subject, transient bursts of high-frequency oscillatory cycles, distinct from the background activity, were observed in the periictal continuum. Time-frequency analysis showed significant spectral power surge (85-160Hz) during ictal state (P < .05) compared to preictal state, but there was no variation in the peak HFO frequencies (P > .05) for each subgroup and at each source montage. HFO source localization was consistent between algorithms (k = 0.857 +/- 0.138), with presumed epileptogenic zone (EZ) comparable to other modalities. In patients who underwent surgery (n = 6), MEG HFO SOZ was concordant with the presumed EZ and the surgical resection site (100%), and all were seizure-free during follow-up. Significance: HFOs could be detected in the MEG periictal state, and its sources were accurately localized. During preictal to ictal transition, HFOs exhibited dynamic augmentation in intrinsic epileptogenicity. Spatial overlap of ictal HFO sources was consistent with EZ determinants and the surgical resection area.