Spatiotemporal Signal Space Separation Research Articles

Spatiotemporal signal space separation for regions of interest: Application for extracting neuromagnetic responses evoked by deep brain stimulation.

Magnetoencephalography (MEG) recordings are often contaminated by interference that can exceed the amplitude of physiological brain activity by several orders of magnitude. Furthermore, the activity of interference sources may spatially extend (known as source leakage) into the activity of brain signals of interest, resulting in source estimation inaccuracies. This problem is particularly apparent when using MEG to interrogate the effects of brain stimulation on large-scale cortical networks. In this technical report, we develop a novel denoising approach for suppressing the leakage of interference source activity into the activity representing a brain region of interest. This approach leverages spatial and temporal domain projectors for signal arising from prespecified anatomical regions of interest. We apply this denoising approach to reconstruct simulated evoked response topographies to deep brain stimulation (DBS) in a phantom recording. We highlight the advantages of our approach compared to the benchmark-spatiotemporal signal space separation-and show that it can more accurately reveal brain stimulation-evoked response topographies. Finally, we apply our method to MEG recordings from a single patient with Parkinson's disease, to reveal early cortical-evoked responses to DBS of the subthalamic nucleus.

Extended Signal-Space Separation Method for Improved Interference Suppression in MEG.

Objective:Magnetoencephalography (MEG) signals typically reflect a mixture of neuromagnetic fields, subject-related artifacts, external interference and sensor noise. Even inside a magnetically shielded room, external interference can be significantly stronger than brain signals. Methods such as signal-space projection (SSP) and signal-space separation (SSS) have been developed to suppress this residual interference, but their performance might not be sufficient in cases of strong interference or when the sources of interference change over time.Methods:Here we suggest a new method, extended signal-space separation (eSSS), which combines a physical model of the magnetic fields (as in SSS) with a statistical description of the interference (as in SSP). We demonstrate the performance of this method via simulations and experimental MEG data.Results:The eSSS method clearly outperforms SSS and SSP in interference suppression regardless of the extent of a priori information available on the interference sources. We also show that the method does not cause location or amplitude bias in dipole modeling.Conclusion:Our eSSS method provides better data quality than SSP or SSS and can be readily combined with other SSS-based methods, such as spatiotemporal SSS or head movement compensation. Thus, eSSS extends and complements the interference suppression techniques currently available for MEG.Significance:Due to its ability to suppress external interference to the level of sensor noise, eSSS can facilitate single-trial data analysis, exemplified in automated analysis of epileptic data. Such an enhanced suppression is especially important in environments with large interference fields.

Magnetoencephalographic Source Localization of the Eye Area of the Motor Homunculus.

A patient with intractable epilepsy, previous right frontal resection, and active vagus nerve stimulation (VNS) developed new onset quasi-continuous twitching around the left eye. Electroencephalography showed no correlate to the orbicularis oculi twitches apart from myographic potentials at the left supraorbital and anterior frontal electrodes. Magnetoencephalography was performed using spatiotemporal signal space separation to suppress magnetic artifacts associated with the VNS apparatus. Magnetoencephalographic source imaging performed on the data back-averaged from the left supraorbital myographic potentials revealed an intrasulcal cortical generator situated in the posterior wall of the right precentral gyrus representing the eye area of the motor homunculus.

Feasibility of magnetoencephalographic source imaging in patients with thalamic deep brain stimulation for epilepsy

SummarySource localization of interictal spikes in patients with medically refractory epilepsy is the most common clinical application of magnetoencephalography (MEG). In recent decades, many patients with intractable epilepsy have been treated with various forms of neurostimulation, including thalamic deep brain stimulation (DBS). Patients with suboptimal seizure control after DBS might in some cases benefit from further investigations for resective epilepsy surgery, including MEG source imaging (MSI). We sought to determine the feasibility and accuracy of MSI in the setting of active thalamic DBS. Simultaneous EEG/MEG was obtained in a patient using an Elekta 306‐channel MEG system, with high‐frequency (100 Hz) DBS of the thalamic anterior nuclei cycling between on and off states. Magnetic artifacts associated with the DBS apparatus were successfully suppressed using the spatiotemporal signal space separation (tSSS) method. Electrical stimulation artifact was removed by standard digital low‐pass filtering. Dipole source modeling results for spike foci in frontal and posterior temporal regions were comparable between stimulation on and stimulation off states, and the source solutions corresponded well to the localization of spikes documented by intracranial EEG. MSI is thus feasible and source solutions can be accurate when performed in patients with active thalamic DBS for epilepsy.

Cortico-muscular coherence in advanced Parkinson’s disease with deep brain stimulation

ObjectiveCortico-muscular coherence (CMC) is thought to reflect the interplay between cortex and muscle in motor coordination. In Parkinson’s disease (PD) patients, levodopa has been shown to enhance CMC. This study examined whether subthalamic nucleus (STN) deep brain stimulation (DBS) affects the CMC in advanced PD. MethodsMagnetoencephalography (MEG) and electromyography (EMG) measurements were done simultaneously both with DBS on and off to determine the CMC during wrist extension. The spatiotemporal signal space separation (tSSS) was used for artifact suppression. ResultsCMC peaks between 13 and 25Hz were found in 15 out of 19 patients. The effect of DBS on CMC was variable. Moreover, the correlation between CMC and motor performance was inconsistent; stronger CMC did not necessarily indicate better function albeit tremor and rigidity may diminish the CMC. Patients having CMC between 13 and 25Hz had the best motor scores at the group level. ConclusionsDBS modifies the CMC in advanced PD with large interindividual variability. SignificanceDBS does not systematically modify CMC amplitude in advanced PD. The results suggest that some components of the CMC may be related to the therapeutic effects of DBS.

Implanted medical devices or other strong sources of interference are not barriers to magnetoencephalographic recordings in epilepsy patients

ObjectiveLocalization accuracy in magnetoencephalography (MEG) recordings is highly dependent on signal to noise ratio, which is difficult to control. MethodsWe have post-processed our data in order to reduce noise to a level permitting adequate source localization with equivalent current dipole methods. In 30 consecutive epilepsy patients, MEG was recorded using a whole-head MEG system consisting of 204 planar gradiometer and 102 magnetometers, with simultaneous EEG. Data were reviewed to identify interictal spikes. The initial analysis was done after employing a spatiotemporal signal space separation (tSSS) method. A total of 18 dipole clusters in 15 patients were reanalyzed without tSSS, to compare the number, goodness of fit, and locations of acceptable dipoles before and after processing. ResultsIn 8 of 18 clusters, although acceptable dipole clusters were captured before processing, there was a clear improvement of all parameters with tSSS. In another 5 clusters, all from patients with vagus nerve stimulators, there were few or no acceptable dipoles before processing, but sufficient dipole clusters were obtained with tSSS. ConclusionIn contrast to volunteer research subjects, clinical patients cannot be expected to cooperate as fully, and their MEG data are likely to include more interference. This study demonstrates that processing the MEG data with a method to eliminate artifact arising from outside the brain significantly improves the data. SignificanceIn some cases, this improvement can mean the difference between satisfactory dipole fits vs no possible localization.

Validation of head movement correction and spatiotemporal signal space separation in magnetoencephalography

ObjectiveOur aim was to assess the effectiveness and reliability of spatiotemporal signal space separation (tSSS) and movement correction (MC) in magnetoencephalography (MEG) recordings disturbed by head movements and magnetized material on the head. MethodsWe recorded MEG from 20 healthy adults in stationary (reference) head position and during controlled head movements. Nearby magnetic interference sources were simulated by attaching magnetized particles on the subject’s head. Auditory and somatosensory stimuli were presented. MC, tSSS and averaging were performed to obtain auditory (AEF) and somatosensory (SEF) evoked fields. Neuronal sources were modeled as equivalent current dipoles. MC was also validated by reconstructing signals generated by current dipoles in a phantom. ResultsAfter MC, the AEF and SEF responses recorded during intermittent head movements were similar in amplitude to the reference recordings and differed by 5–7mm in source location. The tSSS method removed artifacts due to the attached magnetized particles but did not affect the reference data. ConclusionsThe methods are able to reliably recover MEG responses contaminated by movements and magnetic artifacts on the head. SignificanceThe combination of tSSS and MC methods is especially useful in clinical measurements, where movements and magnetic disturbances are commonly present.

Magnetoencephalography in epilepsy patients carrying a vagus nerve stimulator

Due to technical constraints, magnetoencephalography (MEG) is challenging in vagus nerve stimulation (VNS) patients. This study evaluates (1) the feasibility of MEG in VNS patients and (2) the added value of MEG in their presurgical evaluation (PE). Ten VNS patients were studied by MEG using the spatiotemporal signal space separation (tSSS) method. Equivalent current dipoles (ECD) were classified "clustered"/"scattered". It was evaluated whether MEG (1) confirmed localisation of the hypothesized epileptogenic zone (HEZ), (2) improved delineation of the HEZ, or (3) identified 1 out of multiple HEZs. Finally it was evaluated whether adding MEG to the PE improved patient management by changing or supporting the hypothesis. In 7/10 patients, tSSS allowed to obtain interpretable MEG data, with interictal epileptiform discharges in 6/7. ECD clustered within 1 lobe in 4/6; confirming the localisation of the HEZ in 2/4 and improving delineation of the HEZ in 2/4. When ECD clustered within 2 lobes (1/6) or were scattered (1/6), MEG could not identify 1 out of multiple HEZs. In 2 patients, MEG changed management to invasive video-EEG monitoring (IVEM) and resective surgery (RS). In 4 patients, MEG further supported the management; IVEM in 2/4 and unsuitability for RS in 2/4. So far IVEM, performed in 2, resulted into RS. This study demonstrates the feasibility of MEG in VNS patients. MEG changed management in 20% and further supported the proposed management in 40% illustrating the clinical value of MEG in the PE of VNS patients.

Effects of DBS on auditory and somatosensory processing in Parkinson's disease

Motor symptoms of Parkinson's disease (PD) can be relieved by deep brain stimulation (DBS). The mechanism of action of DBS is largely unclear. Magnetoencephalography (MEG) studies on DBS patients have been unfeasible because of strong magnetic artifacts. An artifact suppression method known as spatiotemporal signal space separation (tSSS) has mainly overcome these difficulties. We wanted to clarify whether tSSS enables noninvasive measurement of the modulation of cortical activity caused by DBS. We have studied auditory and somatosensory-evoked fields (AEFs and SEFs) of advanced PD patients with bilateral subthalamic nucleus (STN) DBS using MEG. AEFs were elicited by 1-kHz tones and SEFs by electrical pulses to the median nerve with DBS on and off. Data could be successfully acquired and analyzed from 12 out of 16 measured patients. The motor symptoms were significantly relieved by DBS, which clearly enhanced the ipsilateral auditory N100m responses in the right hemisphere. Contralateral N100m responses and somatosensory P60m responses also had a tendency to increase when bilateral DBS was on. MEG with tSSS offers a novel and powerful tool to investigate DBS modulation of the evoked cortical activity in PD with high temporal and spatial resolution. The results suggest that STN-DBS modulates auditory processing in advanced PD. Hum Brain Mapp, 2011. © 2010 Wiley-Liss, Inc.

The Effect of Spatiotemporal Signal Space Separation (tSSS) on the Localization of Interictal Spikes

Event Abstract Back to Event The Effect of Spatiotemporal Signal Space Separation (tSSS) on the Localization of Interictal Spikes Michael Funke1* and Samu Taulu2 1 University of Utah, United States 2 Elekta Oy, Finland Introduction: An increasing number of patients with intractable epilepsy are being treated with the vagus nerve stimulator (VNS) while in pursuit of resective neurosurgery. In addition, significant artifact contamination can be caused due to prior craniotomies and dental work. Processing such artifact contaminated clinical data using tSSS seems a promising solution for interference removal. But can localization results obtained from tSSS processed data be trusted? In this study, we investigate the effect of the tSSS on the localization of interictal transients in uncontaminated data sets. Methods: MEG data from ten (10) clinical patients with intractable epilepsy were acquired with a 306-channel whole-head MEG system. Simultaneous EEG was recorded using a 60-channel electrode array. Sleep was induced by prior sleep deprivation. Approximately 60 minutes of continuous data were recorded. None of the patients demonstrated significant artifacts. The spatiotemporal signal space separation method (tSSS) [1], an extended version of the spatial SSS [2], was applied off-line to the raw data. For each patient data were and bandpass filtered 1-70 Hz and ten (10) interictal spikes were localized using all 102 magnetometers and 204 gradiometers. Localization results for a single dipole fit, Goodness of Fit (GOF) as well as the Confidence Volume were compared for raw data as well as the for tSSS processed data. Resuts: The localization results between raw data and tSSS processed data across subjects showed a difference of 1.69 mm (SD: 0.52 mm; range: 1.10 mm - 2.41 mm). The GOF differences across subjects improved on average by 12.59% (SD: 4.82%; range: 5.55% - 18.10%). The Confidence volume decreased by 0.02 mm³ (SD: 0.04 mm³; range: 0 mm³ - 0.08 mm³). Conclusions: Source localization of interictal spikes in tSSS filtered data changed the localization on average by less than 2 mm, compared to unfiltered data, while GOF of the fitted dipoles increased noticeably. The Confidence Volume decreased minimally. Overall, tSSS seems to have very little effect on the localization of interictal spikes but improves the GOF due to noise reduction, ergo increasing slightly the SNR of epileptiform transients.

MEG of Parkinsonian patients with deep brain stimulation

Event Abstract Back to Event MEG of Parkinsonian patients with deep brain stimulation Katja Airaksinen1, 2*, Jyrki P. Mäkelä1, Samu Taulu3, Antti Ahonen3, Juha Pohjola4, Jussi Nurminen1, Alfons Schnitzler5, 6 and Eero Pekkonen2 1 Helsinki University Central Hospital, BioMag laboratory, Finland 2 Helsinki University Central Hospital, Department of Neurology, Finland 3 Elekta Neuromag, Finland 4 Helsinki University Central Hospital, Department of Neurosurgery, Finland 5 Heinrich-Heine-University, Department of Neurology, Germany 6 Heinrich-Heine-Univeristy, Institute of Clinical Neuroscience and Medical Psychology, Germany Our aim was to clarify the mechanism of action of deep brain stimulation (DBS) of subthalamic nucleus (STN) in patients with Parkinson's disease (PD). We studied 12 advanced PD patients with STN DBS using a 306-channel MEG-system (Elekta Neuromag®) both when the stimulator was on and off. Auditory evoked fields (AEFs) were elicited by 1-kHz tones delivered to each ear separately and somatosensory evoked fields (SEFs) by electrical pulses delivered to the median nerve at both wrists. Spontaneous MEG was recorded in combination with EMG from extended arm of one patient before and after DBS implantation. The magnetic artifacts caused by DBS were suppressed by the spatiotemporal signal space separation (tSSS) -method. Single dipole models for AEFs and SEFs were constructed. The source strengths of AEF N100m and SEF P60m were calculated both with DBS on and off. Coherence spectra between MEG signals and wrist extensor EMG were studied when DBS was on and off both before and after operation. DBS significantly enhanced AEF N100m to ipsilateral stimulation in the right hemisphere (49 ± 17 nAm DBS on, 43 ± 16 nAm DBS off, p=0.02). The mean source strengths of contralateral N100m also increased when DBS was on (n.s.). The mean SEF source strengths of P60m were stronger when stimulator was on than off (n.s.). In one patient the MEG-EMG coherence was abolished 0.5 months after DBS implantation. Four months after implantation the coherence reappeared when DBS was off, whereas it was suppressed when DBS was on. DBS significantly diminished motor symptoms in the patients. MEG with tSSS allows to study DBS effects in PD. DBS appears to modulate auditory processing in PD patients, and have a modest effect on SEFs. Data of one patient suggests that DBS suppresses the MEG-EMG coherence. In line with data from animal models, DBS modulates cortical functions of the human brain. Conference: Biomag 2010 - 17th International Conference on Biomagnetism , Dubrovnik, Croatia, 28 Mar - 1 Apr, 2010. Presentation Type: Poster Presentation Topic: MEG: Clinical applications Citation: Airaksinen K, Mäkelä JP, Taulu S, Ahonen A, Pohjola J, Nurminen J, Schnitzler A and Pekkonen E (2010). MEG of Parkinsonian patients with deep brain stimulation. Front. Neurosci. Conference Abstract: Biomag 2010 - 17th International Conference on Biomagnetism . doi: 10.3389/conf.fnins.2010.06.00285 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: Katja Airaksinen, Helsinki University Central Hospital, BioMag laboratory, Helsinki, Finland, katja.airaksinen@helsinki.fi 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 Katja Airaksinen Jyrki P Mäkelä Samu Taulu Antti Ahonen Juha Pohjola Jussi Nurminen Alfons Schnitzler Eero Pekkonen Google Katja Airaksinen Jyrki P Mäkelä Samu Taulu Antti Ahonen Juha Pohjola Jussi Nurminen Alfons Schnitzler Eero Pekkonen Google Scholar Katja Airaksinen Jyrki P Mäkelä Samu Taulu Antti Ahonen Juha Pohjola Jussi Nurminen Alfons Schnitzler Eero Pekkonen PubMed Katja Airaksinen Jyrki P Mäkelä Samu Taulu Antti Ahonen Juha Pohjola Jussi Nurminen Alfons Schnitzler Eero Pekkonen 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.

Signal Space Separation Algorithm and Its Application on Suppressing Artifacts Caused by Vagus Nerve Stimulation for Magnetoencephalography Recordings

Magnetoencephalography (MEG) has been successfully applied to presurgical epilepsy foci localization and brain functional mapping. Because the neuronal magnetic signals from the brain are extremely weak, MEG measurement requires both low environment noise and the subject/patient being free of artifact-generating metal objects. This strict requirement makes it hard for patients with vagus nerve stimulator, or other similar medical devices, to benefit from the presurgical MEG examinations. Therefore, an approach that can effectively reduce the environmental noise and faithfully recover the brain signals is highly desirable. We applied spatiotemporal signal space separation method, an advanced signal processing approach that can recover bio-magnetic signal from inside the MEG sensor helmet and suppress external disturbance from outside the helmet in empirical MEG measurements, on MEG recordings from normal control subjects and patients who has vagus nerve stimulator. The original MEG recordings were heavily contaminated, and the data could not be assessed. After applying temporal signal space separation, the strong external artifacts from outside the brain were successfully removed, and the neuronal signal from the human brain was faithfully recovered. Both of the goodness-of-fit and 95% confident limit volume confirmed the significant improvement after temporal signal space separation. Hence, temporal signal space separation makes presurgical MEG examinations possible for patients with implanted vagus nerve stimulator or similar medical devices.

Fine tuning the correlation limit of spatio-temporal signal space separation for magnetoencephalography

Head, jaw and tongue movements contribute to speech artifacts in magnetoencephalography (MEG). Their sources lay close to MEG sensors, therefore, the spatio-temporal signal space separation method (tSSS), specifically suppressing nearby artifacts, can be used for speech artifact suppression. After data reconstruction by signal space separation (referred as SSS), tSSS identifies artifacts by their correlated temporal behavior inside and outside the sensor helmet. The artifacts to be eliminated are thresholded by the quantitative level of this correlation determined by correlation limit (CL). Unnecessarily high CL value may result in suboptimal interference suppression. We evaluated the performance of tSSS with different CLs on MEG data containing speech artifacts. MEG was recorded with 204 planar gradiometers and 102 magnetometers in two subjects counting aloud. The recorded data were processed by tSSS using CLs 0.98, 0.8 and 0.6, and traces were compared. The speech artifact was increasingly suppressed with decreasing CL, but sufficient suppression was achieved at different CL in each subject. Alpha rhythm was not suppressed with CL 0.98 or 0.8; some amplitude reduction with CL 0.6 occurred in one subject. The tSSS is a robust tool suppressing MEG artifacts. It can be fine tuned for challenging artifacts which, after insufficient rejection might resemble brain signals.

Spatiotemporal signal space separation method for rejecting nearby interference in MEG measurements

Limitations of traditional magnetoencephalography (MEG) exclude some important patient groups from MEG examinations, such as epilepsy patients with a vagus nerve stimulator, patients with magnetic particles on the head or having magnetic dental materials that cause severe movement-related artefact signals. Conventional interference rejection methods are not able to remove the artefacts originating this close to the MEG sensor array. For example, the reference array method is unable to suppress interference generated by sources closer to the sensors than the reference array, about 20–40 cm. The spatiotemporal signal space separation method proposed in this paper recognizes and removes both external interference and the artefacts produced by these nearby sources, even on the scalp. First, the basic separation into brain-related and external interference signals is accomplished with signal space separation based on sensor geometry and Maxwell's equations only. After this, the artefacts from nearby sources are extracted by a simple statistical analysis in the time domain, and projected out. Practical examples with artificial current dipoles and interference sources as well as data from real patients demonstrate that the method removes the artefacts without altering the field patterns of the brain signals.