Biology subjects

Westner, B. U.

Publications and source records attributed to Westner, B. U..

12 recordsLinked to original sources

bioRxiv2026

Microstructure of white matter association tracts in the temporal lobe predicts the timing of electrophysiological activity and behaviour in picture naming

White matter (WM) microstructure has been mainly associated with accuracy of picture naming. However, little is known about how WM properties relate to the timing of picture naming and its underlying electrophysiological activity. Here, we linked diffusion-weighted, electrophysiological, and behavioural data, using time-based measures to conceptually connect WM deterioration to alterations in conduction speed and precision. Our sample included individuals of varying age, with or without stroke. We correlated fractional anisotropy (FA) of bilateral WM tracts traversing the temporal lobe with mean response times (RTs) and response time variability (coefficient of variation, CV) in picture naming, and with the onset latency of neuronal activity, represented by the electrophysiological P2 component time-locked to picture onset. We selected tracts showing associations with P2 latency and naming performance separately, on which we conducted path analyses examining the relationship between structure, neuronal activity and behaviour. Depending on the tract, higher FA was associated with faster and/or more consistent naming, suggesting that ventral tracts differentially contribute to naming speed and consistency. Importantly, path modelling for the right inferior fronto-occipital fasciculus (IFOF) revealed that FA predicted P2 latency, which in turn predicted RTs, possibly reflecting a structure-activity-behaviour relationship in lexical-semantic processing. P2 source reconstruction demonstrated anatomical proximity to posterior segments of the right IFOF, supporting these findings. Overall, this study provides a proof of concept for modelling structure-activity-behaviour relationships in language research, while highlighting the need for elaboration of such relations at a more mechanistic level.

neuroscience↗

bioRxiv2026

The Concise Language Paradigm in left and right hemisphere stroke: a longitudinal source-localized EEG study

The mechanisms underlying language recovery after stroke remain poorly understood. Most previous studies have either focused on language comprehension or production and/or relied on hemodynamic measures, with limited complementary electrophysiological evidence. Here, we combined the Concise Language Paradigm (CLaP), assessing comprehension and production, with individualized electroencephalography (EEG) source reconstruction to investigate post-stroke language function and changes in hemispheric dominance over time. In two sessions, we tested stroke-free controls, individuals with left hemisphere (LH) or right hemisphere (RH) stroke. Participants with stroke were assessed two and seven months post-stroke. EEG data were analyzed using event-related potentials and time-frequency representations, followed by source reconstruction based on state-of-the-art individual MRI-derived forward models. Laterality indices were calculated to assess changes in hemispheric dominance between sessions. Behaviorally, the LH group showed low performance at two months followed by overall improvement, whereas the RH group mainly improved in functional communication. EEG activity was significantly modulated by task condition across comprehension and production contrasts. Source-level results in controls were consistent with established language-related networks, serving as regions of interest for the stroke analyses. In the stroke groups, hemispheric dominance differed between LH and RH stroke participants, with LH participants showing systematic shifts from left- to right-hemisphere dominance between sessions, if not already right-hemisphere dominant at two months. Our study provides evidence on comprehension and production performance after stroke using a single paradigm. These findings demonstrate dynamic changes in hemispheric dominance during post-stroke recovery, ultimately highlighting the value of individualized EEG source reconstruction to investigate post-stroke plasticity.

neuroscience↗

bioRxiv2026

Posterior language areas share electrophysiological signatures of word retrieval in context-driven object and action naming

Claims about shared neural processing between object and action words have mainly been based on spatial overlap. Spatial overlap alone, however, provides an incomplete understanding of neural (dis)similarity. Here, we compared object and action word retrieval within participants utilising temporal, spectral, and spatial information in the electroencephalogram (EEG) recorded during context-driven object and action picture naming. Constrained sentence contexts elicited pre-picture lexical-semantic word planning for object and action words, indexed by power decreases in the alpha-beta frequency range (8 - 30 Hz). Using a novel approach based on mutual information and source-reconstructed EEG signal, we computed joint temporo-spectro-spatial (dis)similarity indices across object and action naming in the constrained condition where information retrieval occurred. Spatially, dissimilarities were found in bilateral frontal, anterior superior temporal, and right anterior-to-middle temporal areas. Similarity, by contrast, was linked to the precunei and right temporo-parietal areas, regions associated with lexical-semantic processing and word retrieval. Crucially, similarity in the precunei compared to the temporo-parietal regions was characterised by differential patterns of the alpha-beta activity, implying processing and, potentially, functional differences between the areas. This finding highlights how conclusions about shared neural processes depend on the degree of abstraction (e.g., spatial, spatial-spectral) chosen to define the compared neural mechanisms. We tentatively interpret the contribution of the right hemisphere and left frontal areas to (dis)similarity as coarser, less fine-grained lexical-semantic computations.

neuroscience↗

bioRxiv2026

Shared and distinct oscillatory fingerprints underlying episodic memory and word retrieval

Both episodic memory and word retrieval have been linked to power decreases in the alpha and beta oscillatory bands, but these patterns have rarely been related to each other, partly due to a lack of methodological approaches available. In this explorative study, we investigate the similarities and dissimilarities in the oscillatory fingerprints of the retrieval of words and episodes by directly comparing the activity patterns across time, frequency, and space. We acquired electroencephalography (EEG) data of participants performing a language and an episodic memory task based on the same stimulus material. With a newly developed approach, we directly compared the source-reconstructed oscillatory activity using mutual information and a feature-impact analysis. While left temporal and frontal regions showed dissimilarities between the tasks, right-hemispheric parietal regions exhibited similarities. We speculate that this could indicate a homologous function of these regions, potentially sharing less-specific representations between the tasks. We further uncovered a dissociation of the alpha and beta bands regarding the similarity across tasks. While the beta band was dissimilar between word and episodic memory retrieval, the alpha band seemed to contribute to the similarity we observed in right parietal regions. Whether this points to a task-unspecific function of the alpha band or a functional role in the retrieval process of the presumed representations, remains to be determined. In summary, we present an approach to study similarity across tasks using the temporal, spectral, and spatial dimensions of EEG data, and present results of exploring the shared oscillatory fingerprints between episodic memory and word retrieval.

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bioRxiv2023

Typical neural adaptation for familiar images in autistic adolescents

It has been proposed that autistic perception may be marked by a reduced influence of temporal context. Under this hypothesis, prior exposure to a stimulus should lead to a weaker or absent alteration of the behavioral and neural response to the stimulus in autism, compared to a typical population. To examine this hypothesis, we recruited two samples of human volunteers: a student sample (N=26), which we used to establish our analysis pipeline, and an adolescent sample (N=36), which consisted of a group of autistic (N=18) and a group of non-autistic (N=18) participants. All participants were presented with visual stimulus streams consisting of novel and familiar image pairs, while they attentively monitored each stream. We recorded task performance and used magnetoencephalography (MEG) to measure neural responses, and to compare the responses to familiar and novel images. We found behavioral facilitation as well as a reduction of event-related field (ERF) amplitude for familiar, compared to novel, images in both samples. Crucially, we found statistical evidence against between-group effects of familiarity on both behavioral and neural responses in the adolescent sample, suggesting that the influence of familiarity is comparable between autistic and non-autistic adolescents. These findings challenge the notion that perception in autism is marked by a reduced influence of prior exposure.

neuroscience↗

bioRxiv2023

The best of two worlds: Decoding and source-reconstructing M/EEG oscillatory activity with a unique model

The application of decoding models to electrophysiological data has become standard practice in neuroscience. The use of such methods on sensor space data can, however, limit the interpretability of the results, since brain sources cannot be readily estimated from the decoding of sensor space responses. Here, we propose a new method that combines the common spatial patterns (CSP) algorithm with beamformer source reconstruction for the decoding of oscillatory activity. We compare this method to sensor and source space decoding and show that it performs equally well as source space decoding with respect to both decoding accuracy and source localization without the extensive computational cost. We confirm our simulation results on a real MEG data set. In conclusion, our proposed method performs as good as source space decoding, is highly interpretable in the spatial domain, and has low computational cost.

neuroscience↗

bioRxiv2023

Towards a more robust non-invasive assessment of functional connectivity

Non-invasive evaluation of functional connectivity, based on source-reconstructed estimates of phase-difference-based metrics, is notoriously non-robust. This is due to a combination of factors, ranging from a misspecification of seed regions to suboptimal baseline assumptions, and residual signal leakage. In this work, we propose a new analysis scheme of source level phase-difference-based connectivity, which is aimed at optimizing the detection of interacting brain regions. Our approach is based on the combined use of sensor subsampling and dual-source beamformer estimation of all-to-all connectivity on a prespecified dipolar grid. First, a pairwise two-dipole model, to account for reciprocal leakage in the estimation of the localized signals, allows for a usable approximation of the pairwise bias in connectivity due to residual leakage of third party noise. Secondly, using sensor array subsampling, the recreation of multiple connectivity maps using different subsets of sensors allows for the identification of consistent spatially localized peaks in the 6-dimensional connectivity maps, indicative of true brain region interactions. These steps are combined with the subtraction of null coherence estimates to obtain the final coherence maps. With extensive simulations, we compared different analysis schemes for their detection rate of connected dipoles, as a function of signal-to-noise ratio, phase difference and connection strength. We demonstrate superiority of the proposed analysis scheme in comparison to single-dipole models, or an approach that discards the zero phase difference component of the connectivity. We conclude that the proposed pipeline allows for a more robust identification of functional connectivity in experimental data, opening up new possibilities to study brain networks with mechanistically inspired connectivity measures in cognition and in the clinic.

neuroscience↗

bioRxiv2021

Neural language processing across time, space, frequency and age: MEG-MVPA classification of intertrial phase coherence

Language is a key part of human cognition. Whereas many neurocognitive abilities decline with age, for language the picture is much less clear and how exactly language processing changes with aging is still unknown. To investigate this, we employed magnetoencephalography (MEG) and recorded neuromagnetic brain responses to auditory linguistic stimuli in healthy participants of younger and older age using a passive task-free paradigm and a range of different linguistic stimulus contrasts, which enabled us to assess neural language processes at multiple levels (lexical, semantic, morphosyntactic). By using machine learning-based classification algorithms to scrutinise intertrial phase coherence of MEG responses in source space, we found significant differences between younger and older participants across several frequency bands and for all tested processing types, which shows multiple changes in the brains neurolinguistic circuits which may be due to both healthy aging in general and compensatory processes in particular.

neuroscience↗

bioRxiv2021

Contactless recordings of retinal activity using optically pumped magnetometers

Optically pumped magnetometers (OPMs) have been adopted for the recording of brain activity. Without the need to be cooled to cryogenic temperatures, an array of these sensors can be placed more flexibly, which allows for the recording of neuronal structures other than neocortex. Here we use eight OPM sensors to record human retinal activity following flash stimulation. We compare this magnetoretinographic (MRG) activity to the simultaneously recorded electroretinogram of the eight participants. The MRG shows the familiar flash-evoked potentials (a-wave and b-wave) and shares a highly significant amount of information with the electroretinogram recording (both in a simultaneous and separate recording). We conclude that OPM sensors have the potential to become a contactless alternative to fiber electrodes for the recording of retinal activity. Such a contactless solution can benefit both clinical and neuroscientific settings.

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bioRxiv2019

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 (FieldTrip, SPM12, Brainstorm, and MNE-Python) using datasets both with and without SSS interference suppression.\n\nWe 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 SNR in all four toolboxes.\n\nWhen 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. We also found that the SNR improvement offered by SSS led to more accurate localization.

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bioRxiv2017

Across-subjects classification of stimulus modality from human MEG high frequency activity

Single-trial analyses have the potential to uncover meaningful brain dynamics that are obscured when averaging across trials. However, low signal-to-noise ratio (SNR) can impede the use of single-trial analyses and decoding methods. In this study, we investigate the applicability of a single-trial approach to decode stimulus modality from magnetoencephalography (MEG) high frequency activity. In order to classify the auditory versus visual presentation of words, we combine beamformer source reconstruction with the random forest classification method. To enable group level inference, the classification is embedded in an across-subjects framework.\n\nWe show that single-trial gamma SNR allows for good classification performance (accuracy across subjects: 66.44 %). This implies that the characteristics of high frequency activity have a high consistency across trials and subjects. The random forest classifier assigned informational value to activity in both auditory and visual cortex with high spatial specificity. Across time, gamma power was most informative during stimulus presentation. Among all frequency bands, the 75-95 Hz band was the most informative frequency band in visual as well as in auditory areas. Especially in visual areas, a broad range of gamma frequencies (55-125 Hz) contributed to the successful classification.\n\nThus, we demonstrate the feasibility of single-trial approaches for decoding the stimulus modality across subjects from high frequency activity and describe the discriminative gamma activity in time, frequency, and space.\n\nAuthor SummaryAveraging brain activity across trials is a powerful way to increase signal-to-noise ratio in MEG data. This approach, however, potentially obscures meaningful brain dynamics that unfold on the single-trial level. Single-trial analyses have been successfully applied to time domain or low frequency oscillatory activity; its application to MEG high frequency activity is hindered by the low amplitude of these signals. In the present study, we show that stimulus modality (visual versus auditory presentation of words) can successfully be decoded from single-trial MEG high frequency activity by combining source reconstruction with a random forest classification algorithm. This approach reveals patterns of activity above 75 Hz in both visual and auditory cortex, highlighting the importance of high frequency activity for the processing of domain-specific stimuli. Thereby, our results extend prior findings by revealing high-frequency activity in auditory cortex related to auditory word stimuli in MEG data. The adopted across-subjects framework furthermore suggests a high inter-individual consistency in the high frequency activity patterns.

neuroscience↗

bioRxiv2017

Faster than the brain’s speed of light: Retinocortical interactions differ in high frequency activity when processing darks and lights

Visual processing of dark visual stimuli has been hypothesized to occur faster relative to bright stimuli. We investigated the timing, processing, and propagation of neural activity in response to darks and lights, operationalized as light offset and onset, in the human visual system by recording electroretinography (ERG) simultaneously with magnetoencephalography (MEG) in humans. We discovered that dark onset resulted in 75-95 Hz retinal activity that we call the dark retinal oscillatory potential, occurring with the same latency as the analogous but more broadband (55-195 Hz) oscillatory potential at light onset. Both retinal oscillations coupled with subsequent cortical activity of corresponding bandwidths, but cortical responses for darks indeed occurred earlier than for lights. Darks therefore propagate from retina to cortex more quickly than lights, potentially resulting from a thalamic advantage. Furthermore, we found that this propagation is effectuated by high frequency retinocortical coupling of narrow bandwidth for darks but wide bandwidth for lights.

neuroscience↗