MEG Source Localization Research Articles

Unravelling the origin of the reward positivity: a human intracranial event-related brain potential study.

The reward positivity (RewP) is an event-related brain potential (ERP) component that emerges approximately 250 to 350 milliseconds (ms) after receiving reward-related feedback stimuli and is believed to be important for reinforcement learning and reward processing. Although numerous localization studies have indicated that the anterior cingulate cortex (ACC) is the neural generator of this component, other studies have identified sources outside of the ACC, fuelling a debate about its origin. Because the results of EEG and MEG source localization studies are severely limited by the inverse problem, we addressed this question by leveraging the high spatial and temporal resolution of intracranial EEG. We predicted that we would identify a neural generator of the RewP in the caudal ACC. We recorded intracranial EEG in 19 refractory epilepsy patients who underwent invasive video-EEG monitoring at Ghent University Hospital, Belgium. Participants engaged in the virtual T-maze task (vTMT), a trial-and-error task known to elicit a canonical RewP, while scalp and intracranial EEG were simultaneously recorded. The RewP was identified using a difference wave approach for both scalp and intracranial EEG. The data were aggregated across participants to create a virtual "meta-participant" that contained all the recorded intracranial ERPs (iERPs) with respect to their intracranial contact locations. We used both a hypothesis-driven (focused on ACC) and exploratory (whole-brain analysis) approach to segment the brain into regions of interest (ROI). For each ROI, we evaluated the degree to which the time course of the absolute current density (ACD) activity mirrored the time course of the RewP, and confirmed the statistical significance of the results using permutation analysis. The grand average waveform of the scalp data revealed a RewP at 309 ms after reward feedback with a frontocentral scalp distribution, consistent with the identification of this component as the RewP. The meta-participant contained iERPs recorded from 582 intracranial contacts in total. The ACD activity of the aggregated iERPs were most similar to the RewP in left caudal ACC, left dorsolateral prefrontal cortex, left frontomedial cortex, and left white matter, with the highest score attributed to caudal ACC, as predicted. To our knowledge, this is the first study that uses intracranial EEG aggregated across multiple human epilepsy patients and current source density analysis to identify the neural generator(s) of the RewP. These results provide direct evidence that the ACC is a neural generator of the RewP.

Deep learning based source imaging provides strong sublobar localization of epileptogenic zone from MEG interictal spikes

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.

Research on the anti-interference ability of optically pumped magnetometer

The anti-interference ability of OPM is systematically demonstrated by theoretical analysis and experimental study in this paper. The results show that the transverse modulation mode of the single-beam OPM has the strongest anti-interference ability compared with the dual-beam OPM’s non-modulation and longitudinal modulation modes. Additionally, the OPM built with a larger linewidth cell has a stronger anti-interference ability than that with a smaller linewidth cell. Moreover, a three-axis magnetic field closed-loop control mode of the single-beam OPM is proposed to further enhance the anti-interference ability. The experimental results prove that the OPM operating at the three-axis magnetic field closed-loop mode can provide real-time feedback on the magnetic field so that the performance of the OPM remains unchanged under biased magnetic fields, and the scale factor error is stable within 0.5%. The enhancement of OPM’s anti-interference ability is significant to improve the MEG source localization accuracy of OPM-MEG systems.

Automatic Sensor Array Calibration and Coregistration for On-Scalp MEG

Accurate MEG localization of neural electrical activity requires registration between the MEG sensors and MRI. To reduce the geometric error of the sensor array and realize automatic registration, an array geometry calibration algorithm based on prior knowledge is proposed, which automatically completes the position and orientation calibration of OPM-MEG array with the reference of the design model of MEG helmet. Full-process automatic co-registration based on optical scanning is realized. A position error of 0.23 ± 0.12mm and orientation error of 0.34 ± 0.14° are obtained in the calibration results of 85 sensors on-scalp MEG system. The proposed array geometric calibration algorithm is used to quantify the deviation between the manufactured array and the theoretical array for the first time. The results show a mean position deviation of 0.57mm and an angle deviation of 0.68° in the manufactured array, with the maximum position deviation of 1.45 mm and the maximum orientation deviation of 2.13°. It is indicated that the replacement of the manufactured array by the theoretical array in previous research may introduce significant errors in the MEG source localization results.

Localization of beta power decrease as measure for lateralization in pre-surgical language mapping with magnetoencephalography, compared with functional magnetic resonance imaging and validated by Wada test.

Objective: Atypical patterns of language lateralization due to early reorganizational processes constitute a challenge in the pre-surgical evaluation of patients with pharmaco-resistant epilepsy. There is no consensus on an optimal analysis method used for the identification of language dominance in MEG. This study examines the concordance between MEG source localization of beta power desynchronization and fMRI with regard to lateralization and localization of expressive and receptive language areas using a visual verb generation task. Methods: Twenty-five patients with pharmaco-resistant epilepsy, including six patients with atypical language lateralization, and ten right-handed controls obtained MEG and fMRI language assessment. Fourteen patients additionally underwent the Wada test. We analyzed MEG beta power desynchronization in sensor (controls) and source space (patients and controls). Beta power decrease between 13 and 35 Hz was localized applying Dynamic Imaging of Coherent Sources Beamformer technique. Statistical inferences were grounded on cluster-based permutation testing for single subjects. Results: Event-related desynchronization of beta power in MEG was seen within the language-dominant frontal and temporal lobe and within the premotor cortex. Our analysis pipeline consistently yielded left language dominance with high laterality indices in controls. Language lateralization in MEG and Wada test agreed in all 14 patients for inferior frontal, temporal and parietal language areas (Cohen's Kappa = 1, p < 0.001). fMRI agreed with Wada test in 12 out of 14 cases (85.7%) for Broca's area (Cohen's Kappa = 0.71, p = 0.024), while the agreement for temporal and temporo-parietal language areas were non-significant. Concordance between MEG and fMRI laterality indices was highest within the inferior frontal gyrus, with an agreement in 19/24 cases (79.2%), and non-significant for Wernicke's area. Spatial agreement between fMRI and MEG varied considerably between subjects and brain regions with the lowest Euclidean distances within the inferior frontal region of interest. Conclusion: Localizing the desynchronization of MEG beta power using a verb generation task is a promising tool for the identification of language dominance in the pre-surgical evaluation of epilepsy patients. The overall agreement between MEG and fMRI was lower than expected and might be attributed to differences within the baseline condition. A larger sample size and an adjustment of the experimental designs are needed to draw further conclusions.

Bilateral Representation of Sensorimotor Responses in Benign Adult Familial Myoclonus Epilepsy: An MEG Study.

Patients with cortical reflex myoclonus manifest typical neurophysiologic characteristics due to primary sensorimotor cortex (S1/M1) hyperexcitability, namely, contralateral giant somatosensory-evoked potentials/fields and a C-reflex (CR) in the stimulated arm. Some patients show a CR in both arms in response to unilateral stimulation, with about 10-ms delay in the non-stimulated compared with the stimulated arm. This bilateral C-reflex (BCR) may reflect strong involvement of bilateral S1/M1. However, the significance and exact pathophysiology of BCR within 50 ms are yet to be established because it is difficult to identify a true ipsilateral response in the presence of the giant component in the contralateral hemisphere. We hypothesized that in patients with BCR, bilateral S1/M1 activity will be detected using MEG source localization and interhemispheric connectivity will be stronger than in healthy controls (HCs) between S1/M1 cortices. We recruited five patients with cortical reflex myoclonus with BCR and 15 HCs. All patients had benign adult familial myoclonus epilepsy. The median nerve was electrically stimulated unilaterally. Ipsilateral activity was investigated in functional regions of interest that were determined by the N20m response to contralateral stimulation. Functional connectivity was investigated using weighted phase-lag index (wPLI) in the time-frequency window of 30–50 ms and 30–100 Hz. Among seven of the 10 arms of the patients who showed BCR, the average onset-to-onset delay between the stimulated and the non-stimulated arm was 8.4 ms. Ipsilateral S1/M1 activity was prominent in patients. The average time difference between bilateral cortical activities was 9.4 ms. The average wPLI was significantly higher in the patients compared with HCs in specific cortico-cortical connections. These connections included precentral-precentral, postcentral-precentral, inferior parietal (IP)-precentral, and IP-postcentral cortices interhemispherically (contralateral region-ipsilateral region), and precentral-IP and postcentral-IP intrahemispherically (contralateral region-contralateral region). The ipsilateral response in patients with BCR may be a pathologically enhanced motor response homologous to the giant component, which was too weak to be reliably detected in HCs. Bilateral representation of sensorimotor responses is associated with disinhibition of the transcallosal inhibitory pathway within homologous motor cortices, which is mediated by the IP. IP may play a role in suppressing the inappropriate movements seen in cortical myoclonus.

Can MEG Source Localization Reveal the Time Course of Processing in the FFA, PPA, and EBA?

fMRI has provided a rich spatial map of category-selective responses across the ventral visual cortex, and EEG/MEG can inform the precise time course of visual processing. But what we really need is both temporal and spatial precision simultaneously. Can MEG source localization reveal the time course of processing in specific functional regions, distinguishing them from nearby regions? Here we addressed that question by collecting both MEG and fMRI responses from the same fifteen participants to the same images of faces, bodies, scenes, and objects. We used fMRI localizers to define the FFA, PPA, and EBA in each participant individually. We then computed source estimates from the MEG data using dynamic statistical parametric mapping, extracting the time courses of estimated MEG source amplitudes to each stimulus category within each fMRI-derived ROI. Source-localized MEG data showed a selective response to bodies peaking at about 160 ms in bilateral EBA that was not present in the FFA or PPA. A highly prominent right-lateralized face response peaking around 130 ms was detected in all three fROIs, while no clear peak response to scenes was present in any fROI. Analyses of source-localized responses across the whole cortex revealed a similar picture, with scene-selective responses spatially blurred across the occipital cortex, right-lateralized face-selective responses spanning much of the ventral surface of the cortex (extending well beyond the fusiform gyrus), and body-selective responses arising later on the lateral surface of both hemispheres in the vicinity of the EBA. These results suggest that right-lateralized face processing peaks on the ventral surface of the brain around 130 ms and body-selective processing on the lateral surface around 160 ms. However, they also indicate that MEG source localization is unable to detect fMRI-expected signals from some regions (PPA) or to precisely localize signals to the spatial grain of fMRI-defined functional regions.

Comparison of beamformer implementations for MEG source localization

Beamformers are applied for estimating spatiotemporal characteristics of neuronal sources underlying measured MEG/EEG signals. Several MEG analysis toolboxes include an implementation of a linearly constrained minimum-variance (LCMV) beamformer. However, differences in implementations and in their results complicate the selection and application of beamformers and may hinder their wider adoption in research and clinical use. Additionally, combinations of different MEG sensor types (such as magnetometers and planar gradiometers) and application of preprocessing methods for interference suppression, such as signal space separation (SSS), can affect the results in different ways for different implementations. So far, a systematic evaluation of the different implementations has not been performed. Here, we compared the localization performance of the LCMV beamformer pipelines in four widely used open-source toolboxes (MNE-Python, FieldTrip, DAiSS (SPM12), and Brainstorm) using datasets both with and without SSS interference suppression.We analyzed MEG data that were i) simulated, ii) recorded from a static and moving phantom, and iii) recorded from a healthy volunteer receiving auditory, visual, and somatosensory stimulation. We also investigated the effects of SSS and the combination of the magnetometer and gradiometer signals. We quantified how localization error and point-spread volume vary with the signal-to-noise ratio (SNR) in all four toolboxes.When applied carefully to MEG data with a typical SNR (3–15 ​dB), all four toolboxes localized the sources reliably; however, they differed in their sensitivity to preprocessing parameters. As expected, localizations were highly unreliable at very low SNR, but we found high localization error also at very high SNRs for the first three toolboxes while Brainstorm showed greater robustness but with lower spatial resolution. We also found that the SNR improvement offered by SSS led to more accurate localization.

Automatic Spatial Calibration of Ultra-Low-Field MRI for High-Accuracy Hybrid MEG-MRI.

With a hybrid magnetoencephalography (MEG)-MRI device that uses the same sensors for both modalities, the co-registration of MRI and MEG data can be replaced by an automatic calibration step. Based on the highly accurate signal model of ultra-low-field (ULF) MRI, we introduce a calibration method that eliminates the error sources of traditional co-registration. The signal model includes complex sensitivity profiles of the superconducting pickup coils. In the ULF MRI, the profiles are independent of the sample and therefore well-defined. In the most basic form, the spatial information of the profiles, captured in parallel ULF-MR acquisitions, is used to find the exact coordinate transformation required. We assessed our calibration method by simulations assuming a helmet-shaped pickup-coil-array geometry. Using a carefully constructed objective function and sufficient approximations, even with low-SNR images, sub-voxel and sub-millimeter calibration accuracy were achieved. After the calibration, distortion-free MRI and high spatial accuracy for MEG source localization can be achieved. For an accurate sensor-array geometry, the co-registration and associated errors are eliminated, and the positional error can be reduced to a negligible level.

Modulation of Neural Oscillatory Activity during Dynamic Face Processing.

Various neuroimaging and neurophysiological methods have been used to examine neural activation patterns in response to faces. However, much of previous research has relied on static images of faces, which do not allow a complete description of the temporal structure of face-specific neural activities to be made. More recently, insights are emerging from fMRI studies about the neural substrates that underpin our perception of naturalistic dynamic face stimuli, but the temporal and spectral oscillatory activity associated with processing dynamic faces has yet to be fully characterized. Here, we used MEG and beamformer source localization to examine the spatiotemporal profile of neurophysiological oscillatory activity in response to dynamic faces. Source analysis revealed a number of regions showing enhanced activation in response to dynamic relative to static faces in the distributed face network, which were spatially coincident with regions that were previously identified with fMRI. Furthermore, our results demonstrate that perception of realistic dynamic facial stimuli activates a distributed neural network at varying time points facilitated by modulations in low-frequency power within alpha and beta frequency ranges (8-30 Hz). Naturalistic dynamic face stimuli may provide a better means of representing the complex nature of perceiving facial expressions in the real world, and neural oscillatory activity can provide additional insights into the associated neural processes.

Neural Correlates of Early Sound Encoding and their Relationship to Speech-in-Noise Perception.

Speech-in-noise (SIN) perception is a complex cognitive skill that affects social, vocational, and educational activities. Poor SIN ability particularly affects young and elderly populations, yet varies considerably even among healthy young adults with normal hearing. Although SIN skills are known to be influenced by top-down processes that can selectively enhance lower-level sound representations, the complementary role of feed-forward mechanisms and their relationship to musical training is poorly understood. Using a paradigm that minimizes the main top-down factors that have been implicated in SIN performance such as working memory, we aimed to better understand how robust encoding of periodicity in the auditory system (as measured by the frequency-following response) contributes to SIN perception. Using magnetoencephalograpy, we found that the strength of encoding at the fundamental frequency in the brainstem, thalamus, and cortex is correlated with SIN accuracy. The amplitude of the slower cortical P2 wave was previously also shown to be related to SIN accuracy and FFR strength; we use MEG source localization to show that the P2 wave originates in a temporal region anterior to that of the cortical FFR. We also confirm that the observed enhancements were related to the extent and timing of musicianship. These results are consistent with the hypothesis that basic feed-forward sound encoding affects SIN perception by providing better information to later processing stages, and that modifying this process may be one mechanism through which musical training might enhance the auditory networks that subserve both musical and language functions.

Impact of different registration methods in MEG source analysis

Abstract For this study the impact of different co-registration procedures on MEG source localization of somatosensory evoked fields was evaluated. Two different co-registration procedures were used to calculate the transformation matrix which specifies how to align the MRI data to the MEG head coordinate system. In order to depict the differences, caused by the method, the Euclidian distance between the reconstructed sources was noted. It was shown that, erroneous MRI and MEG data co- registration effects source localization results. Most dipoles are located more posterior and superior when the more advanced registration procedure was applied. In conclusion the results show, that an iterative matching procedure allows an accurate knowledge of the MEG gradiometer sensor position relative to the head which is crucial to correctly reconstruct neuronal activity derived from MEG measurements.

Usefulness of multiple frequency band source localizations in ictal MEG

ObjectiveWe evaluated the diagnostic value of multiple frequency band MEG source localization within a wide time window during the preictal period. MethodsData for 13 epilepsy patients who showed an ictal event during MEG were analyzed. Several seconds of preictal data were localized in the theta, alpha, beta, and gamma bands by using wavelet transformation and the sLORETA algorithm. The same analysis was performed with narrow time and frequency band. Localization concordances to the surgically resected area were compared. ResultsSource localization in the gamma band for a 10s window before ictal onset showed best concordance to the resection cavity. Eight of 13 patients showed sub-lobar concordance in the 10s gamma band localization, whereas 3 showed concordance in the narrow time and frequency analysis. Four of 7 patients with focal cortical dysplasia (FCD) achieved seizure-free outcome, and all 4 showed sub-lobar concordance. ConclusionsA 10s time window gamma source localization method can be used to delineate the epileptogenic zone. SignificanceThe use of a long period during preictal gamma source localization has the potential to become a localizing biomarker of the epileptogenic zone in candidates for surgical intervention, especially in MRI-suspected FCD.

S15: Sensitivity and validity of MEG source localization in focal epilepsy: dependencies on source size and location

S15: Sensitivity and validity of MEG source localization in focal epilepsy: dependencies on source size and location

MEG and EEG Sensitivity in a Case of Medial Occipital Epilepsy

Interictal or ictal events in partial epilepsies may project on scalp EEG contralaterally to the side of the epileptogenic lesion. Such paradoxical lateralization can be observed in case of para-sagittal generators, and is likely due to the spatial orientation of the generator, presenting an oblique projection towards the midline. We present here a case of medial occipital epilepsy investigated using EEG, MEG and stereoelectroencephalography (SEEG). MRI displayed a focal cortical dysplasia in the superior margin of the right calcarine fissure. SEEG demonstrated bilateral medial occipital interictal spikes, with an inversion of polarity at the level of the lesion and a contralateral propagation occurring in 10ms. Interictal iterative EEG cartographies showed a large posterior field, with a maximum contralateral to the initial generator (EEG paradoxical lateralization). With the same number of channels, interictal iterative MEG cartographies were more precise and more complex than EEG ones, indicating an onset accurately lateralized. A few milliseconds later, MEG cartographies were quadripolar, thus indicating two homotopic active generators. These MEG and EEG cartographies have been reproduced using BESA dipole simulator. Relative merits of MEG and EEG are still debated. With 151 channels, MEG source localizations indicated the right medial occipital area, as demonstrated by SEEG. An investigation with a corresponding number of EEG channels was not performed. After a down sampling to 64 sensors, this precision was lost. MEG and EEG source localization results, both with 64 channels, were quite comparable, indicating both medial occipital areas. However, a careful analysis of MEG/EEG iterative cartographies, performed with the same number of channels in both modalities, demonstrated that, in this configuration, MEG sensitivity was superior to the EEG one, allowing separating two medial occipital sources, characterized in SEEG by a time delay of 10ms.

EEG and MEG in mesial temporal lobe epilepsy: Where do the spikes really come from?

Objective There is persistent debate as to whether or not EEG and MEG recordings in patients with mesial temporal lobe epilepsy (MTLE) can detect mesial temporal interictal epileptiform discharges (spikes), and this issue is particularly relevant for source localization studies. With the aim of providing direct evidence pertinent to this debate we present detailed examples of the intracranial sources of spikes recorded with EEG and MEG in MTLE. Methods Spikes recorded in five different patients with MTLE during intracranial EEG ( n = 2), intraoperative electrocorticography (ECOG; n = 1), combined scalp-intracranial EEG ( n = 2) and combined EEG-MEG ( n = 1) were analyzed and the intracranial sources of the spike foci were matched with their corresponding extracranial EEG and/or MEG fields. EEG and MEG dipole source localization was performed on six independent spike foci identified in one representative patient with bilateral MTLE. Results Spikes with an electrical field maximal at F7/8, F9/10 ⩾ T3/4 were generated in the anterolateral temporal neocortex. The absence of coincident spiking at mesial locations indicated that these were not propagated from or to the hippocampus. Spikes with an electrical field maximal at T3/4 ⩾ T9/10 were generated in the lateral temporal neocortex and likewise did not involve the hippocampus. Individual spikes generated in the mesiobasal temporal neocortex, including the fusiform gyrus, were difficult to detect with EEG (low amplitude diphasic waves most apparent after spike averaging at T3/4, T9/10 ⩾ T5/6, P9/10) and only slightly more identifiable with MEG. Spikes generated within and confined to the mesial temporal structures, as confirmed by intracranial recordings, could not be detected with EEG or MEG. Notably, such spikes could not be detected even at intracranial recording sites on the lateral surface of the temporal lobe. Conclusions We present detailed evidence in a small case series showing that typical anterior temporal spikes recorded with EEG and MEG in MTLE arose from the anterolateral temporal neocortex and were neither propagated from nor to the hippocampus. Mid temporal EEG spikes were localized to the lateral temporal neocortex. Intracranially detected mesial temporal spikes were not detected with EEG or MEG. Significance The spikes recorded with EEG and MEG in MTLE are localized to neocortical foci, and not to the mesial temporal structures. Current noninvasive EEG and MEG source localization studies cannot accurately identify true mesial temporal spikes.

MEG and Intracranial EEG Source Localization with Particularized Frequency in Tuberous Sclerosis Complex

Event Abstract Back to Event MEG and Intracranial EEG Source Localization with Particularized Frequency in Tuberous Sclerosis Complex Hisako Fujiwara1*, Douglas Rose1, Ki Lee1, Francesco Mangano1, Nat Hemasilpin1 and David Franz1 1 University of Cincinnati, Cincinnati Children's Hospital Medical Center, United States Tuberous sclerosis complex (TSC) presents multifocally in both hemispheres and patients can have several types of medically intractable seizures. . Identification of the dominant focal seizure and the corresponding epileptogenic zone is crucial for surgical success. We compared TSC patients with interictal/ictal MEG discharges and ictal onset of intracranial EEG (ICEEG) source localization and evaluated both the distance to nearest tuber and distance to the ictal onset zone (IOZ). Sixteen patients with medically intractable epilepsy secondary to TSC underwent presurgical evaluation followed by resective surgery. We reviewed ictal semiology, scalp interictal/ictal EEG lateralization/localization, MRI lesions (presence of prominent tubers/calcification). We performed MEG interictal/ictal source localization with synthetic aperture magnetometry (SAM) for all patients, and single rotating current dipole (SRCD), standardized low resolution brain electromagnetic tomography (sLORETA) and multiple signal classification (MUSIC) for all patients. Five of 16 patients who had one area of limited corticectomy showed MEG source localization within 2 cm maximum distance from the tuber and the center of the IOZ according to visual inspection. In 7 patients who had multilobe corticectomy/lobectomy MEG sources were part of the resected areas. For only one patient with epileptic spasms, MEG failed to localize the epileptogenic zone. For two patients, MEG had technical difficulty localizing sources because of excessive diffuse beta activities. One patient who had a seizure during the MEG recording showed closer and better source localization in terms of the depth of source. MEG source localization with multiple algorithms was able to localize IOZ in patients who had both single and multiple epileptogenic zones. These methods may be helpful to reveal not only IOZ but also distributed epileptogenic zones in TSC. Conference: Biomag 2010 - 17th International Conference on Biomagnetism , Dubrovnik, Croatia, 28 Mar - 1 Apr, 2010. Presentation Type: Poster Presentation Topic: MEG: Clinical applications Citation: Fujiwara H, Rose D, Lee K, Mangano F, Hemasilpin N and Franz D (2010). MEG and Intracranial EEG Source Localization with Particularized Frequency in Tuberous Sclerosis Complex. Front. Neurosci. Conference Abstract: Biomag 2010 - 17th International Conference on Biomagnetism . doi: 10.3389/conf.fnins.2010.06.00299 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 05 Apr 2010; Published Online: 05 Apr 2010. * Correspondence: Hisako Fujiwara, University of Cincinnati, Cincinnati Children's Hospital Medical Center, Cincinnati, United States, Hisako.Fujiwara@cchmc.org Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Hisako Fujiwara Douglas Rose Ki Lee Francesco Mangano Nat Hemasilpin David Franz Google Hisako Fujiwara Douglas Rose Ki Lee Francesco Mangano Nat Hemasilpin David Franz Google Scholar Hisako Fujiwara Douglas Rose Ki Lee Francesco Mangano Nat Hemasilpin David Franz PubMed Hisako Fujiwara Douglas Rose Ki Lee Francesco Mangano Nat Hemasilpin David Franz Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Comparison of MEG and BOLD Signals in a Visual-Tactile Discrimination Task

Event Abstract Back to Event Comparison of MEG and BOLD Signals in a Visual-Tactile Discrimination Task Yiwen L. Hegner1*, Ying Lee1, Erick Ortiz2 and Christoph Braun3 1 University of Tuebingen, MEG Centre, Institute of Medical Psychology and Behavioural Neurobiology, Germany 2 University of Tuebingen, MEG and fMEG Center, Germany 3 University of Trento, Center for Mind/Brain Sciences, Italy Previous studies have shown a complex relationship between scalp electromagnetic activation and BOLD signals[1,2]. Accordingly, not only evoked fields but also event-related de-/synchronization are reflected in positive BOLD responses. In order to further explore the relationship between MEG and fMRI signals, a combined event-related fMRI and 275 channel (CTF) whole-head MEG study was conducted in 12 right-handed subjects. The experimental setup for both studies was identical except that longer inter-trial intervals were used in the fMRI study to account for the slow BOLD signal. Subjects were instructed to match one out of two visual patterns with a tactile pattern. The visual patterns were presented 1 s prior to the presentation of the tactile pattern, which lasted 0.6s and were presented simultaneously at both middle fingertips. One second after the offset of the tactile stimulus, a rectangular response cue encircled one of the two visual patterns for 1s and indicated which of the them had to be compared with the tactile stimulus. Half of the subjects responded to matching visual-tactile stimuli, the other half to non-match. There were two sessions and each session had 96 (48 easy, 48 difficult) trials. MEG source localization was performed using the multiple sparse prior method in SPM8. Both the source localization of the transient evoked tactile response (e.g. 40-60ms and 60-80ms post tactile stimulus onset) and of the induced oscillatory activity in the beta (16-22Hz) and gamma range (56-66Hz) during 20-80ms post tactile stimulus onset correspond largely with the location of the BOLD responses. The contrast between easy and difficult task conditions revealed that the sources of the beta activity match best with the same contrast of the BOLD activation. Our study thus demonstrates that in the current visual-tactile pattern matching task, the BOLD signals are mostly represented by the beta oscillatory activity shortly after the tactile stimulus onset.

Clinical Application of MEG Source Localization Algorithms in Presurgical Epilepsy Evaluation: Strengths and Limitations

Event Abstract Back to Event Clinical Application of MEG Source Localization Algorithms in Presurgical Epilepsy Evaluation: Strengths and Limitations Douglas Rose1*, Hisako Fujiwara1, Nat Hemasilpin1, Ki Lee1 and Francesco Mangano1 1 University of Cincinnati, Cincinnati Children's Hospital Medical Center, United States MEG source localization algorithms perform well in simulation studies with low noise; little is known regarding relative strengths and limitations of algorithms for real clinical data. We compared algorithms with a common presurgical evaluation clinical dataset. We recorded MEG interictal discharges for 209 patients (ages 1 mo–26 years) with medically intractable epilepsy. We analyzed all recordings with equivalent current dipole models (ECD) and a beamformer, synthetic aperture magnetometry (SAM). We additionally analyzed recordings in 50 patients with dipole scan (DS), (multiple signal classification -MUSIC) and distributed dipole models (DD) (minimum norm estimate - MNE, standardized low resolution brain electromagnetic tomography - sLORETA). We compared each to presurgical scalp interictal/ictal EEG findings. We compared MEG to ECoG localization for 59 patients who had ECD and SAM only and for 17 patients studied with all 5 algorithms. For ECD and DD we had to carefully choose representative samples with low background noise. Use of segmented cortical mantle improved localization for ECD, DS, and DD, but also introduced potential errors. DD tended to localize a single contiguous source region for each patient. SAM tended to identify multiple locations and gave reasonable localizations in the presence of VNS artifact (24 patients) but failed to localize spikes under several conditions. Use of several methodologies limited errors from each; when multiple algorithms indicated a similar region, the localization more often agreed with ictal onset by ECoG. MEG source localization algorithms highlight different aspects of the irritative zone associated with interictal discharges based on different a priori assumptions and different approaches to the inverse problem. Use of several different MEG source localization methodologies may improve localization non-invasively of the irritative zone prior to placement of intracranial electrodes and prior to epilepsy surgery. Conference: Biomag 2010 - 17th International Conference on Biomagnetism , Dubrovnik, Croatia, 28 Mar - 1 Apr, 2010. Presentation Type: Poster Presentation Topic: Epilepsy Citation: Rose D, Fujiwara H, Hemasilpin N, Lee K and Mangano F (2010). Clinical Application of MEG Source Localization Algorithms in Presurgical Epilepsy Evaluation: Strengths and Limitations. Front. Neurosci. Conference Abstract: Biomag 2010 - 17th International Conference on Biomagnetism . doi: 10.3389/conf.fnins.2010.06.00377 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 08 Apr 2010; Published Online: 08 Apr 2010. * Correspondence: Douglas Rose, University of Cincinnati, Cincinnati Children's Hospital Medical Center, Cincinnati, United States, douglas.rose@cchmc.org Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Douglas Rose Hisako Fujiwara Nat Hemasilpin Ki Lee Francesco Mangano Google Douglas Rose Hisako Fujiwara Nat Hemasilpin Ki Lee Francesco Mangano Google Scholar Douglas Rose Hisako Fujiwara Nat Hemasilpin Ki Lee Francesco Mangano PubMed Douglas Rose Hisako Fujiwara Nat Hemasilpin Ki Lee Francesco Mangano Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Fundamentals of MEG Source Localization for Clinical Applications

Fundamentals of MEG Source Localization for Clinical Applications