Individually optimized multi-channel tDCS for targeting somatosensory cortex

Objective - Transcranial direct current stimulation (tDCS) is a non-invasive neuro-modulation technique that delivers current through the scalp by a pair of patch electrodes (2-Patch). This study proposes a new multi-channel tDCS (mc-tDCS) optimization method, the distributed constrained maximum intensity (D-CMI) approach. For targeting the P20/N20 somatosensory source at Brodmann area 3b, an integrated combined magnetoencephalography (MEG) and electroencephalography (EEG) source analysis is used with individualized skull conductivity calibrated realistic head modeling. - Methods - Simulated electric fields (EF) for our new D-CMI method and the already known maximum intensity (MI), alternating direction method of multipliers (ADMM) and 2-Patch methods were produced and compared for the individualized P20/N20 somatosensory target for 10 subjects. - Results - D-CMI and MI showed highest intensities parallel to the P20/N20 target compared to ADMM and 2-Patch, with ADMM achieving highest focality. D-CMI showed a slight reduction in intensity compared to MI while reducing side effects and skin level sensations by current distribution over multiple stimulation electrodes. - Conclusion - Individualized D-CMI montages are preferred for our follow up somatosensory experiment to provide a good balance between high current intensities at the target and reduced side effects and skin sensations. - Significance - An integrated combined MEG and EEG source analysis with D-CMI montages for mc-tDCS stimulation potentially can improve control, reproducibility and reduce sensitivity differences between sham and real stimulations

Reconfiguration of dominant coupling modes in mild traumatic brain injury mediated by δ-band activity: a resting state MEG study

During the last few years, rich-club (RC) organization has been studied as a possible brain-connectivity organization model for large-scale brain networks. At the same time, empirical and simulated data of neurophysiological models have demonstrated the significant role of intra-frequency and inter-frequency coupling among distinct brain areas. The current study investigates further the importance of these couplings using recordings of resting-state magnetoencephalographic activity obtained from 30 mild traumatic brain injury (mTBI) subjects and 50 healthy controls. Intra-frequency and inter-frequency coupling modes are incorporated in a single graph to detect group differences within individual rich-club subnetworks (type I networks) and networks connecting RC nodes with the rest of the nodes (type II networks). Our results show a higher probability of inter-frequency coupling for (δ–γ1), (δ–γ2), (θ–β), (θ–γ2), (α–γ2), (γ1–γ2) and intra-frequency coupling for (γ1–γ1) and (δ–δ) for both type I and type II networks in the mTBI group. Additionally, mTBI and control subjects can be correctly classified with high accuracy (98.6%), whereas a general linear regression model can effectively predict the subject group using the ratio of type I and type II coupling in the (δ, θ), (δ, β), (δ, γ1), and (δ, γ2) frequency pairs. These findings support the presence of an RC organization simultaneously with dominant frequency interactions within a single functional graph. Our results demonstrate a hyperactivation of intrinsic RC networks in mTBI subjects compared to controls, which can be seen as a plausible compensatory mechanism for alternative frequency-dependent routes of information flow in mTBI subjects

Improving the detection of mtbi via complexity analysis in resting - state magnetoencephalography

Diagnosis of mild Traumatic Brain Injury (mTBI) is difficult due to the variability of obvious brain lesions using imaging scans. A promising tool for exploring potential biomarkers for mTBI is magnetoencephalography which has the advantage of high spatial and temporal resolution. By adopting proper analytic tools from the field of symbolic dynamics like Lempel-Ziv complexity, we can objectively characterize neural network alterations compared to healthy control by enumerating the different patterns of a symbolic sequence. This procedure oversimplifies the rich information of brain activity captured via MEG. For that reason, we adopted neural-gas algorithm which can transform a time series into more than two symbols by learning brain dynamics with a small reconstructed error. The proposed analysis was applied to recordings of 30 mTBI patients and 50 normal controls in δ frequency band. Our results demonstrated that mTBI patients could be separated from normal controls with more than 97% classification accuracy based on high complexity regions corresponding to right frontal areas. In addition, a reverse relation between complexity and transition rate was demonstrated for both groups. These findings indicate that symbolic complexity could have a significant predictive value in the development of reliable biomarkers to help with the early detection of mTBI

Data-driven topological filtering based on orthogonal minimal spanning trees: application to multi-group MEG resting-state connectivity

In the present study, a novel data-driven topological filtering technique is introduced to derive the backbone of functional brain networks relying on orthogonal minimal spanning trees (OMSTs). The method aims to identify the essential functional connections to ensure optimal information flow via the objective criterion of global efficiency minus the cost of surviving connections. The OMST technique was applied to multichannel, resting-state neuromagnetic recordings from four groups of participants: healthy adults (n = 50), adults who have suffered mild traumatic brain injury (n = 30), typically developing children (n = 27), and reading-disabled children (n = 25). Weighted interactions between network nodes (sensors) were computed using an integrated approach of dominant intrinsic coupling modes based on two alternative metrics (symbolic mutual information and phase lag index), resulting in excellent discrimination of individual cases according to their group membership. Classification results using OMST-derived functional networks were clearly superior to results using either relative power spectrum features or functional networks derived through the conventional minimal spanning tree algorithm

Altered cross-frequency coupling in resting-state MEG after mild traumatic brain injury

Cross-frequency coupling (CFC) is thought to represent a basic mechanism of functional integration of neural networks across distant brain regions. In this study, we analyzed CFC profiles from resting state Magnetoencephalographic (MEG) recordings obtained from 30 mild traumatic brain injury (mTBI) patients and 50 controls. We used mutual information (MI) to quantify the phase-to-amplitude coupling (PAC) of activity among the recording sensors in six nonoverlapping frequency bands. After forming the CFC-based functional connectivity graphs, we employed a tensor representation and tensor subspace analysis to identify the optimal set of features for subject classification as mTBI or control. Our results showed that controls formed a dense network of stronger local and global connections indicating higher functional integration compared to mTBI patients. Furthermore, mTBI patients could be separated from controls with more than 90% classification accuracy. These findings indicate that analysis of brain networks computed from resting-state MEG with PAC and tensorial representation of connectivity profiles may provide a valuable biomarker for the diagnosis of mTBI

The effect of experimental and modeling parameters on combined EEG/MEG source analysis and transcranial electric stimulation optimization of somatosensory and epilepsy activity

Neue experimentelle und modellierende Parameter werden eingeführt, um die Auswirkungen auf die kombinierte Elektroenzephalographie (EEG) und Magnetenzephalographie (MEG) zu untersuchen - EMEG-Quellenanalyse und Optimierung der transkraniellen elektrischen Stimulation (TES) von somatosensorisch evozierter und epileptischer Aktivität. Es werden simultane Daten gemessen, einschließlich somatosensorisch evozierter Potentiale (SEP) und Felder (SEF), die durch verschiedene Stimulationstypen für gruppenbasierte Sensitivitätsuntersuchungen und spontane EEG- und MEG-Messungen für die präoperative Epilepsiediagnose hervorgerufen werden. Bei der Lösung des Vorwärtsproblems der Quellenanalyse werden individualisierte Finite-Elemente-Kopfvolumenleitermodelle konstruiert. Zu diesem Zweck wird ein quasi-automatisches Bildverarbeitungsverfahren eingeführt, das T1-gewichtete und T2-gewichtete MRTs kombiniert. Zur realistischen Modellierung der leitfähigen Eigenschaften des Gehirns wird die Diffusionstensor-Bildgebung verwendet. Die Leitfähigkeit des Schädels wird aufgrund ihrer hohen Variabilität zwischen den Probanden und ihres Einflusses auf EEG- und EMEG-Quellenrekonstruktionen individuell kalibriert. Es wird auch dargestellt, wie unterschiedliche Stimulationsarten, Kopfmodelle und Messmodalitäten (EEG, MEG oder EMEG) die Quellenrekonstruktion der SEP/SEF-Antwort bei 20 ms nach dem Stimulus (P20/N20) und das Targeting bei der mehrkanaligen TES-Optimierung beeinflussen. Die Inter-Subjekt-Variabilität der Schädel-Leitfähigkeit und -Dicke über das Alter wird nicht-invasiv untersucht. Schließlich wird die EMEG-Quellenanalyse mit realistischen Kopfmodellen, die Schädelgratlöcher beinhalten, für die präoperative Diagnose eines medikamentenresistenten Epilepsiepatienten evaluiert. Die optimierte TES wird als Alternative zur Operation zur Unterdrückung epileptischer Anfälle untersucht. Die Ergebnisse zeigen, dass das MEG die P20/N20-Lokalisation stabilisiert und das EEG zur Bestimmung der Quellenorientierung beiträgt. Die Komplementarität beider Modalitäten im EMEG kann auf der Basis von detaillierten und individualisierten Kopfmodellen ausgenutzt werden. Anschließend wird berichtet, dass optimierte TES-Elektrodenmontagen von der P20/N20-Orientierungskomponente beeinflusst werden. Für die Kopfmodellierung wird dargestellt, dass die Variabilität der Leitfähigkeit und der Dicke des Schädels zwischen den Probanden groß ist und bei der Quellenanalyse und TES berücksichtigt werden sollte. In dieser Hinsicht sind das Alter der Probanden und die Schädeldicke signifikant mit der Leitfähigkeit des Schädels verbunden. Bei der präoperativen Epilepsiebeurteilung weist die EMEG-Quellenanalyse mit kalibrierten und anisotropen Kopfmodellen auf eine fokale kortikale Dysplasie (FCD) zu Beginn der epileptischen Spike-Spitze hin. Vereinfachte Kopfmodelle, die Verwendung einer einzelnen Modalität oder Zeitpunkte in der Nähe des Spike-Peaks verursachen nicht zu vernachlässigende Einflüsse auf die Bestimmung der FCD. Schließlich spiegeln Änderungen an der Kopfmodellierung erhebliche Einflüsse auf die optimierte TES und den Fluss der injizierten Gleichströme zur FCD wider.New experimental and modeling parameters are introduced to investigate effects on combined electroencephalography (EEG) and magnetoencephalography (MEG) - EMEG source analysis and transcranial electric stimulation (TES) optimization of somatosensory evoked and epileptic activity. Simultaneous data are measured, including somatosensory evoked potentials (SEP) and fields (SEF) elicited by different stimulation types for group-based sensitivity investigations and spontaneous EEG and MEG measures for presurgical epilepsy diagnosis. Individualized finite element head volume conductor models are constructed in the solution of the forward problem of source analysis. For this purpose, a quasi-automatic image processing procedure is introduced, combining T1-weighted and T2-weighted MRIs. For realistic modeling of the conductive properties of brain, diffusion tensor imaging is used. Skull conductivity is individually calibrated due to its high inter-subject variability and influence on EEG and EMEG source reconstructions. It is also presented how different stimulation types, head models and measurement modalities (EEG, MEG or EMEG) influence the source reconstruction of SEP/SEF response at 20 ms post-stimulus (P20/N20) and the targeting in multi-channel TES optimization. Inter-subject variability of skull conductivity and thickness over age are investigated non-invasively. Finally, EMEG source analysis with realistic head models that include skull burr holes are evaluated for the presurgical diagnosis of a drug-resistant epilepsy patient. Optimized TES is investigated as an alternative of surgery to suppress epileptic seizures. Results show that MEG stabilizes the P20/N20 location and EEG contributes to the determination of the source orientation. The complementarity of both modalities in EMEG can be utilized on the basis of detailed and individualized head models. Subsequently, optimized TES electrode montages are reported to be affected by the P20/N20 orientation component. For head modeling, it is presented that the inter-subject variability of conductivity and thickness of skull is large and it should be taken into account in source analysis and TES. In this regard, subjects' age and skull thickness are significantly related to the skull conductivity. For presurgical epilepsy evaluation, EMEG source analysis with calibrated and anisotropic head models indicates a focal cortical dysplasia (FCD) at the onset of the epileptic spike peak. Simplified head models, use of single modality or time points close to the spike peak cause non-negligible influences on the determination of the FCD. Finally, changes on the head modeling reflect considerable influences on the optimized TES and the flow of the injected direct currents towards the FCD

The effect of stimulation type, head modeling, and combined EEG and MEG on the source reconstruction of the somatosensory P20/N20 component

Contains fulltext : 207068.pdf (publisher's version ) (Open Access)Modeling and experimental parameters influence the Electro‐ (EEG) and Magnetoencephalography (MEG) source analysis of the somatosensory P20/N20 component. In a sensitivity group study, we compare P20/N20 source analysis due to different stimulation type (Electric‐Wrist [EW], Braille‐Tactile [BT], or Pneumato‐Tactile [PT]), measurement modality (combined EEG/MEG – EMEG, EEG, or MEG) and head model (standard or individually skull‐conductivity calibrated including brain anisotropic conductivity). Considerable differences between pairs of stimulation types occurred (EW‐BT: 8.7 ± 3.3 mm/27.1° ± 16.4°, BT‐PT: 9 ± 5 mm/29.9° ± 17.3°, and EW‐PT: 9.8 ± 7.4 mm/15.9° ± 16.5° and 75% strength reduction of BT or PT when compared to EW) regardless of the head model used. EMEG has nearly no localization differences to MEG, but large ones to EEG (16.1 ± 4.9 mm), while source orientation differences are non‐negligible to both EEG (14° ± 3.7°) and MEG (12.5° ± 10.9°). Our calibration results show a considerable inter‐subject variability (3.1–14 mS/m) for skull conductivity. The comparison due to different head model show localization differences smaller for EMEG (EW: 3.4 ± 2.4 mm, BT: 3.7 ± 3.4 mm, and PT: 5.9 ± 6.8 mm) than for EEG (EW: 8.6 ± 8.3 mm, BT: 11.8 ± 6.2 mm, and PT: 10.5 ± 5.3 mm), while source orientation differences for EMEG (EW: 15.4° ± 6.3°, BT: 25.7° ± 15.2° and PT: 14° ± 11.5°) and EEG (EW: 14.6° ± 9.5°, BT: 16.3° ± 11.1° and PT: 12.9° ± 8.9°) are in the same range. Our results show that stimulation type, modality and head modeling all have a non‐negligible influence on the source reconstruction of the P20/N20 component. The complementary information of both modalities in EMEG can be exploited on the basis of detailed and individualized head models

Ανάλυση μαγνητοεγκεφαλογραφήματος με χρήση προβολής ανεξαρτήτων συνιστωσών (ICA)

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Parametrizing the Conditionally Gaussian Prior Model for Source Localization with Reference to the P20/N20 Component of Median Nerve SEP/SEF

In this article, we focused on developing the conditionally Gaussian hierarchical Bayesian model (CG-HBM), which forms a superclass of several inversion methods for source localization of brain activity using somatosensory evoked potential (SEP) and field (SEF) measurements. The goal of this proof-of-concept study was to improve the applicability of the CG-HBM as a superclass by proposing a robust approach for the parametrization of focal source scenarios. We aimed at a parametrization that is invariant with respect to altering the noise level and the source space size. The posterior difference between the gamma and inverse gamma hyperprior was minimized by optimizing the shape parameter, while a suitable range for the scale parameter can be obtained via the prior-over-measurement signal-to-noise ratio, which we introduce as a new concept in this study. In the source localization experiments, the primary generator of the P20/N20 component was detected in the Brodmann area 3b using the CG-HBM approach and a parameter range derived from the existing knowledge of the Tikhonov-regularized minimum norm estimate, i.e., the classical Gaussian prior model. Moreover, it seems that the detection of deep thalamic activity simultaneously with the P20/N20 component with the gamma hyperprior can be enhanced while using a close-to-optimal shape parameter value.publishedVersionPeer reviewe