Biology subjects

Raij, T.

Publications and source records attributed to Raij, T..

10 recordsLinked to original sources

bioRxiv2026

MEG Signatures of Differences in Auditory Cortex Responses to Trigger versus Non-Trigger Sounds in Misophonia

Misophonia is characterized by intense emotional and physiological reactions to specific trigger sounds that may vary across individuals. Despite greater recognition and increased research, the neural mechanisms underlying misophonia are still poorly understood. Using magnetoencephalography (MEG) with millisecond temporal resolution, we examined auditory cortex responses to trigger versus non-trigger sounds in 23 individuals (ages 14 - 32 years) that met criteria for misophonia on a screening assessment. We found that in the right hemisphere, trigger sounds elicited significantly smaller evoked responses, lower power of evoked alpha-band oscillations, and stronger alpha-gamma cross-frequency phase-amplitude coupling, compared to non-trigger sounds. We also found that evoked responses and power correlated with behaviorally assessed extent of misophonia symptoms. These findings support the idea that misophonia is characterized at least in part by altered neurophysiological processing of auditory trigger stimuli early in auditory cortex hierarchy. Our results provide novel insight into the neural basis of misophonia, and underscore the importance of examining brain activity even at the earliest levels of the cortical hierarchy, especially with high spatio-temporal resolution.

neuroscience↗

bioRxiv2026

The effect of EEG lead configuration on early TMS-EEG artifacts

Transcranial magnetic stimulation (TMS) combined with electroencephalography (EEG) has strong potential for recording cortical reactivity and connectivity. However, this promise is hampered by TMS-induced EEG artifacts. Here, we examine the origins of these artifacts with phantom TMS-EEG recordings and simulations. We focus on two major types of artifacts: (1) the TMS pulse artifact during each [~]0.2 ms TMS pulse and (2) the decay artifact that may last tens of milliseconds. We examine how these artifacts change as a function of the relative position between TMS coil windings and EEG electrode leads. We also examine the hypothesis that certain EEG lead configurations may reduce or even cancel out these artifacts. In experimental results across 23 different TMS coil / EEG lead configurations, the amplitudes between the TMS pulse artifact and the decay artifact were highly correlated (Spearman {rho} = 0.86, p < 0.001), suggesting that the decay artifact is caused by the TMS pulse artifact. As predicted, in certain EEG lead configurations, both the TMS pulse and decay artifacts were minimized. The simulations confirmed that the TMS pulse artifacts depended on the electromagnetic induction from the TMS coil windings to the EEG leads. These results illuminate the generator mechanisms of--and possible means to reduce--both artifacts.

neuroscience↗

bioRxiv2025

High-Definition MEG Source Estimation using the Reciprocal Boundary Element Fast Multipole Method

Magnetoencephalographic (MEG) source estimation relies on the computation of the gain (lead-field) matrix, which embodies the linear relationship between the amplitudes of the sources and the recorded signals. However, with a realistic forward model, the calculation of the gain matrix in a "direct" fashion is a computationally expensive task because the number of dipolar sources in standard MEG pipelines is often limited to [~]10,000. We propose a fast approach based on the reciprocal relationship between MEG and transcranial magnetic stimulation (TMS). This approach couples naturally with the charge-based boundary element fast multipole method (BEM-FMM), which allows us to efficiently generate gain matrices for high-resolution multi-layer non-nested meshes involving source spaces of up to a [~]1 million dipoles. We evaluate our approach by performing MEG source reconstruction against simulated data (at varying noise levels) obtained from the direct computation of MEG readings from 2000 different dipole positions over the cortical surface of 5 healthy subjects. Additionally, we test our methods with real MEG data from evoked somatosensory fields by right-hand median nerve stimulation in these same 5 subjects. We compare our experimental source reconstruction results against the standard MNE-Python source reconstruction pipeline.

neuroscience↗

bioRxiv2024

Fast and Accurate EEG/MEG BEM-Based Forward Problem Solution for High-Resolution Head Models

A BEM (boundary element method) based approach is developed to accurately solve an EEG/MEG forward problem for a modern high-resolution head model in approximately 60 seconds using a common workstation. The method utilizes a charge-based BEM with fast multipole acceleration (BEM-FMM) and a "smart" mesh pre-refinement (called b-refinement) close to the singular source(s). No costly matrix-filling or direct solution steps typical for the standard BEM are required; the method generates on-skin voltages as well as MEG magnetic fields for high-resolution head models in approximately 60 seconds after initial model assembly. The method is verified both theoretically and experimentally.

neuroscience↗

bioRxiv2024

Accuracy of dipole source reconstruction in the 3-layerBEM model against the 5-layer BEM-FMM model

ObjectiveTo compare cortical dipole fitting spatial accuracy between the widely used yet highly simplified 3-layer and modern more realistic 5-layer BEM-FMM models with and without adaptive mesh refinement (AMR) methods. MethodsWe generate simulated noiseless 256-channel EEG data from 5-layer (7-compartment) meshes of 15 subjects from the Connectome Young Adult dataset. For each subject, we test four dipole positions, three sets of conductivity values, and two types of head segmentation. We use the boundary element method (BEM) with fast multipole method (FMM) acceleration, with or without (AMR), for forward modeling. Dipole fitting is carried out with the FieldTrip MATLAB toolbox. ResultsThe average position error (across all tested dipoles, subjects, and models) is [~]4 mm, with a standard deviation of [~]2 mm. The orientation error is [~]20{degrees} on average, with a standard deviation of [~]15{degrees}. Without AMR, the numerical inaccuracies produce a larger disagreement between the 3- and 5-layer models, with an average position error of [~]8 mm (6 mm standard deviation), and an orientation error of 28{degrees} (28{degrees} standard deviation). ConclusionsThe low-resolution 3-layer models provide excellent accuracy in dipole localization. On the other hand, dipole orientation is retrieved less accurately. Therefore, certain applications may require more realistic models for practical source reconstruction. AMR is a critical component for improving the accuracy of forward EEG computations using a high-resolution 5-layer volume conduction model. SignificanceImproving EEG source reconstruction accuracy is important for several clinical applications, including epilepsy and other seizure-inducing conditions.

bioinformatics↗

bioRxiv2024

Decoding auditory working memory content from EEG aftereffects of auditory-cortical TMS

Working memory (WM), short term maintenance of information for goal directed behavior, is essential to human cognition. Identifying the neural mechanisms supporting WM is a focal point of neuroscientific research. One prominent theory hypothesizes that WM content is carried in "activity-silent" brain states involving short-term synaptic changes. Information carried in such brain states could be decodable from content-specific changes in responses to unrelated "impulse stimuli". Here, we used single-pulse transcranial magnetic stimulation (spTMS) as the impulse stimulus and then decoded content maintained in WM from EEG using multivariate pattern analysis (MVPA) with robust non-parametric permutation testing. The decoding accuracy of WM content significantly enhanced after spTMS was delivered to the posterior superior temporal cortex during WM maintenance. Our results show that WM maintenance involves brain states, which are activity silent relative to other intrinsic processes visible in the EEG signal.

neuroscience↗

bioRxiv2023

Neuronal modeling of magnetoencephalography responses in auditory cortex to auditory and visual stimuli

Previous studies have demonstrated that auditory cortex activity can be influenced by crosssensory visual inputs. Intracortical recordings in non-human primates (NHP) have suggested a bottom-up feedforward (FF) type laminar profile for auditory evoked but top-down feedback (FB) type for cross-sensory visual evoked activity in the auditory cortex. To test whether this principle applies also to humans, we analyzed magnetoencephalography (MEG) responses from eight human subjects (six females) evoked by simple auditory or visual stimuli. In the estimated MEG source waveforms for auditory cortex region of interest, auditory evoked responses showed peaks at 37 and 90 ms and cross-sensory visual responses at 125 ms. The inputs to the auditory cortex were then modeled through FF and FB type connections targeting different cortical layers using the Human Neocortical Neurosolver (HNN), which consists of a neocortical circuit model linking the cellular- and circuit-level mechanisms to MEG. The HNN models suggested that the measured auditory response could be explained by an FF input followed by an FB input, and the crosssensory visual response by an FB input. Thus, the combined MEG and HNN results support the hypothesis that cross-sensory visual input in the auditory cortex is of FB type. The results also illustrate how the dynamic patterns of the estimated MEG/EEG source activity can provide information about the characteristics of the input into a cortical area in terms of the hierarchical organization among areas. Significance statementLaminar intracortical profiles of activity characterize feedforward- and feedback-type influences in the inputs to a cortical area. By combining magnetoencephalography (MEG) and biophysical computational neural modeling, we obtained evidence of cross-sensory visual evoked activity in human auditory cortex being of feedback type. The finding is consistent with previous intracortical recordings in non-human primates. The results illustrate how patterns of MEG source activity can be interpreted in the context of the hierarchical organization among cortical areas.

neuroscience↗

bioRxiv2022

High-Resolution EEG Source Reconstruction with Boundary Element Fast Multipole Method Using Reciprocity Principle and TES Forward Model Matrix

BackgroundAccurate high-resolution EEG source reconstruction (localization) is important for several tasks, including rigorous and rapid mental health screening. ObjectiveThe present study has developed, validated, and applied a new source localization algorithm utilizing a charge-based boundary element fast multipole method (BEM-FMM) coupled with the Helmholtz reciprocity principle and the transcranial electrical stimulation (TES) forward solution. MethodsThe unknown cortical dipole density is reconstructed over the entire cortical surface by expanding into global basis functions in the form of cortical fields of active TES electrode pairs. These pairs are constructed from the reading electrodes. An analog of the minimum norm estimation (MNE) equation is obtained after substituting this expansion into the reciprocity principle written in terms of measured electrode voltages. Delaunay (geometrically balanced) triangulation of the electrode cap is introduced first. Basis functions for all electrode pairs connected by the edges of a triangular mesh are precomputed and stored in memory. A smaller set of independent basis functions is then selected and employed at every time instant. This set is based on the highest voltage differences measured. ResultsThe method is validated against the classic, yet challenging problem of median nerve stimulation and the tangential cortical sources located at the posterior wall of the central sulcus for an N20/P20 peak (2 scanned subjects). The method is further applied to perform source reconstruction of synthesized tangential cortical sources located at the posterior wall of the central sulcus (12 different subjects). In the second case, an average source reconstruction error of 7 mm is reported for the best possible noiseless scenario. ConclusionsOnce static preprocessing with TES electrodes has been done (the basis functions have been computed), our method requires fractions of a second to complete the accurate high-resolution source localization.

neuroscience↗

bioRxiv2020

A Software Toolkit for TMS Electric-Field Modeling with Boundary Element Fast Multipole Method: An Efficient MATLAB Implementation

BackgroundTranscranial magnetic stimulation (TMS) is currently the only non-invasive neurostimulation modality that enables painless and safe supra-threshold stimulation by employing electromagnetic induction to efficiently penetrate the skull. Accurate, fast, and high resolution modeling of the electric fields (E-fields) may significantly improve individualized targeting and dosing of TMS and therefore enhance the efficiency of existing clinical protocols as well as help establish new application domains. ObjectiveTo present and disseminate our TMS modeling software toolkit, including several new algorithmic developments, and to apply this software to realistic TMS modeling scenarios given a high-resolution model of the human head including cortical geometry and an accurate coil model. MethodThe recently developed charge-based boundary element fast multipole method (BEM-FMM) is employed as an alternative to the 1st order finite element method (FEM) most commonly used today. The BEM-FMM approach provides high accuracy and unconstrained field resolution close to and across cortical interfaces. Here, the previously proposed BEM-FMM algorithm has been improved in several novel ways. Results and ConclusionsThe improvements resulted in a threefold increase in computational speed while maintaining the same solution accuracy. The computational code based on the MATLAB(R) platform is made available to all interested researchers, along with a coil model repository and examples to create custom coils, head model repository, and supporting documentation. The presented software toolkit may be useful for post-hoc analyses of navigated TMS data using high-resolution subject-specific head models as well as accurate and fast modeling for the purposes of TMS coil/hardware development.

bioengineering↗

bioRxiv2019

Software Toolkit for Fast High-Resolution TMS Modeling

Computational modeling of Transcranial Magnetic Stimulation (TMS) is a tradeoff between computational speed vs. spatial precision. In this study, we introduce a software toolkit for high-resolution TMS modeling, which may offer the best of both. The toolkit employs the recently developed boundary element fast multipole method (BEM-FMM) with accurate solution computations close to tissue interfaces. It operates within the MATLAB platform and is designed for a broad, medically-oriented computational research community. To enable easy, subject-specific operation, the package is compatible with human head models generated with automated tools such as SimNIBS and FreeSurfer.\n\nBoth coil design in free space as well as efficacy and focality of TMS for a specific subject could be modeled by the package, including optional user-defined parametric loops. Detailed and widely used coil models, generated in both CAD and wire formats, may include several hundred thousand elementary current elements and observation spaces with approximately 1 M field points; the corresponding computational times are on the order of 1 sec. Detailed head models with approximately 1 M triangular facets and a mesh resolution of 0.6 points per mm2 are simulated in approximately 1.5 min which is arguably the fastest time to date. Further reduction of computational times is foreseen. The toolkit is augmented with a population of 16 ready-to-use head models for performing simulations for computational studies that do not involve MRI data collection. The toolkit is also augmented with a coil geometry generator capable of creating accurate coil models.

neuroscience↗