Detecting epileptiform activity from deeper brain regions in spatially filtered MEG data

Hillebrand, Arjan; Nissen, Ida A.; Ris-Hilgersom, Irene H. M.; Sijsma, Ndedi; Ronner, H.E.; Dijk, Bob W. van; Stam, Cornelis J.

doi:10.1016/j.clinph.2016.05.272

Cited by 43 publications

(51 citation statements)

References 8 publications

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“…The insensitivity of MEG to sources in the mesiotemporal lobe has been stated before (Hillebrand and Barnes, 2002), also for ripples (Von Ellenrieder et al, 2016). However, even for two of these difficult cases we still found moderate concordance, suggesting that the improved SNR offered by beamforming (and xSSS in this study) may come to our aid, as shown previously for interictal spikes (Hillebrand et al, 2016). In the patients with poor outcome after surgery, ripples showed concordance with the resection area in two of the three patients, which can indicate that the resection was incomplete.…”

Section: Discussionsupporting

confidence: 81%

“…However the magnitude of the background noise in MEG, and the interference induced by electrical power lines, vehicles, or heart beats, for example, might deem this untrue (Vrba, 2002). Passive or active shielding, smart geometry of gradiometers and magnetometers, synthetic higher order gradiometers (Vrba, 2002), signal space separation (Taulu and Kajola, 2005, Taulu and Hari, 2009), and beamforming (Hillebrand et al, 2016, Van Klink et al, 2016a) can be used to improve the SNR. In a previous study we have shown that it is possible to identify epileptic ripples in the time domain in MEG data that was pre-processed (Van Klink et al, 2016a).…”

Section: Discussionmentioning

confidence: 99%

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Automatic detection and visualisation of MEG ripple oscillations in epilepsy

Klink

Rosmalen

Nenonen

et al. 2017

NeuroImage: Clinical

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High frequency oscillations (HFOs, 80–500 Hz) in invasive EEG are a biomarker for the epileptic focus. Ripples (80–250 Hz) have also been identified in non-invasive MEG, yet detection is impeded by noise, their low occurrence rates, and the workload of visual analysis. We propose a method that identifies ripples in MEG through noise reduction, beamforming and automatic detection with minimal user effort. We analysed 15 min of presurgical resting-state interictal MEG data of 25 patients with epilepsy. The MEG signal-to-noise was improved by using a cross-validation signal space separation method, and by calculating ~ 2400 beamformer-based virtual sensors in the grey matter. Ripples in these sensors were automatically detected by an algorithm optimized for MEG. A small subset of the identified ripples was visually checked. Ripple locations were compared with MEG spike dipole locations and the resection area if available. Running the automatic detection algorithm resulted in on average 905 ripples per patient, of which on average 148 ripples were visually reviewed. Reviewing took approximately 5 min per patient, and identified ripples in 16 out of 25 patients. In 14 patients the ripple locations showed good or moderate concordance with the MEG spikes. For six out of eight patients who had surgery, the ripple locations showed concordance with the resection area: 4/5 with good outcome and 2/3 with poor outcome. Automatic ripple detection in beamformer-based virtual sensors is a feasible non-invasive tool for the identification of ripples in MEG. Our method requires minimal user effort and is easily applicable in a clinical setting.

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Section: Discussionsupporting

confidence: 81%

Section: Discussionmentioning

confidence: 99%

Automatic detection and visualisation of MEG ripple oscillations in epilepsy

Klink

Rosmalen

Nenonen

et al. 2017

NeuroImage: Clinical

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“…Previous MEG studies have shown that subcortical brain activity can be estimated using beamforming (Engels et al, 2016, Tenney et al, 2013, Hillebrand et al, 2016b). Since the hippocampi play a key role in AD pathology (Hampel et al, 2008), and are also hub regions in the AD functional networks (Battaglia et al, 2011), we hypothesized that information flow would be impaired for these regions.…”

Section: Discussionmentioning

confidence: 99%

“…MEG is reference free, and its large number of sensors allows for sophisticated spatial filtering to accurately reconstruct neuronal activity for predefined cortical brain areas (Baillet et al, 2001, Hillebrand et al, 2005, Hillebrand et al, 2012, Hillebrand et al, 2016a, Hillebrand et al, 2016b). We have recently used this approach to reliably reconstruct oscillatory activity within the hippocampi of AD patients (Engels et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Directional information flow in patients with Alzheimer's disease. A source-space resting-state MEG study

Engels

Stam

et al. 2017

NeuroImage: Clinical

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In a recent magnetoencephalography (MEG) study, we found posterior-to-anterior information flow over the cortex in higher frequency bands in healthy subjects, with a reversed pattern in the theta band. A disruption of information flow may underlie clinical symptoms in Alzheimer's disease (AD). In AD, highly connected regions (hubs) in posterior areas are mostly disrupted. We therefore hypothesized that in AD the information flow from these hub regions would be disturbed. We used resting-state MEG recordings from 27 early-onset AD patients and 26 healthy controls. Using beamformer-based virtual electrodes, we estimated neuronal oscillatory activity for 78 cortical regions of interest (ROIs) and 12 subcortical ROIs of the AAL atlas, and calculated the directed phase transfer entropy (dPTE) as a measure of information flow between these ROIs. Group differences were evaluated using permutation tests and, for the AD group, associations between dPTE and general cognition or CSF biomarkers were determined using Spearman correlation coefficients. We confirmed the previously reported posterior-to-anterior information flow in the higher frequency bands in the healthy controls, and found it to be disturbed in the beta band in AD. Most prominently, the information flow from the precuneus and the visual cortex, towards frontal and subcortical structures, was decreased in AD. These disruptions did not correlate with cognitive impairment or CSF biomarkers. We conclude that AD pathology may affect the flow of information between brain regions, particularly from posterior hub regions, and that changes in the information flow in the beta band indicate an aspect of the pathophysiological process in AD.

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“…Indeed, cumulative evidence suggests that hippocampal sources can be identified in MEG, an observation made both in simulations (Attal and Schwartz, 2013, Chupin et al, 2002, Mills et al, 2012, Quraan et al, 2011, Stephen et al, 2005), and empirical data (Adjamian et al, 2004, Backus et al, 2016, Cornwell et al, 2012, Cornwell et al, 2008, Engels et al, 2016, Guitart-Masip et al, 2013, Hillebrand et al, 2016, Kaplan et al, 2012, Korczyn et al, 2013, Mills et al, 2012, Moses et al, 2011, Poch et al, 2011, Quraan et al, 2011, Riggs et al, 2009, Tesche and Karhu, 2000). Despite this body of theoretical support and empirical evidence, the sufficiency of the spatial precision of MEG for deep source reconstruction is still being debated (Mikuni et al, 1997, Mills et al, 2012, Riggs et al, 2009).…”

Section: Introductionmentioning

confidence: 97%

Using generative models to make probabilistic statements about hippocampal engagement in MEG

et al. 2017

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Magnetoencephalography (MEG) enables non-invasive real time characterization of brain activity. However, convincing demonstrations of signal contributions from deeper sources such as the hippocampus remain controversial and are made difficult by its depth, structural complexity and proximity to neocortex. Here, we demonstrate a method for quantifying hippocampal engagement probabilistically using simulated hippocampal activity and realistic anatomical and electromagnetic source modelling. We construct two generative models, one which supports neuronal current flow on the cortical surface, and one which supports neuronal current flow on both the cortical and hippocampal surface. Using Bayesian model comparison, we then infer which of the two models provides a more likely explanation of the dataset at hand. We also carry out a set of control experiments to rule out bias, including simulating medial temporal lobe sources to assess the risk of falsely positive results, and adding different types of displacements to the hippocampal portion of the mesh to test for anatomical specificity of the results. In addition, we test the robustness of this inference by adding co-registration error and sensor level noise. We find that the model comparison framework is sensitive to hippocampal activity when co-registration error is <3 mm and the sensor-level signal-to-noise ratio (SNR) is >−20 dB. These levels of co-registration error and SNR can now be achieved empirically using recently developed subject-specific head-casts.

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Detecting epileptiform activity from deeper brain regions in spatially filtered MEG data

Cited by 43 publications

References 8 publications

Automatic detection and visualisation of MEG ripple oscillations in epilepsy

Automatic detection and visualisation of MEG ripple oscillations in epilepsy

Directional information flow in patients with Alzheimer's disease. A source-space resting-state MEG study

Using generative models to make probabilistic statements about hippocampal engagement in MEG

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