Biology subjects

Rampp, S.

Publications and source records attributed to Rampp, S..

14 recordsLinked to original sources

bioRxiv2026

The Mechanical Fingerprint of Hippocampal Sclerosis Linking Neuronal Cell Loss and Gliosis to Tissue Stiffness

Hippocampal sclerosis (HS) is the most common pathology in drug-resistant temporal lobe epilepsy (TLE). However, clinical diagnosis, prevalent epileptogenicity, and drug drug-resistance in individuals with HS remain an ongoing challenge demanding multidisciplinary research efforts. In this study, we examined the mechanical properties of neurosurgically en bloc resected HS specimens (n=8) ex vivo under compression, tension, and torsional shear. We fitted a two-term Ogden hyperelastic model to the measured mechanical responses to quantify nonlinear mechanical tissue properties. The resulting parameters revealed higher strain stiffening under compression in HS compared to hippocampus obtained post mortem (n=7). The distinction was most noticeable in the large-strain regime, which has important implications for using mechanical tissue properties as valuable diagnostic biomarker. Furthermore, we correlated the tissue microstructure with mechanical parameters. We trained a deep-learning histopathology classifier to detect and classify neurons and glial cells from hematoxylin-stained whole slide images (WSI). We identified a strong association between the small-strain stiffness (shear modulus {micro}) and the overall cell density as well as the glial cell density. The negative relationship between the neuron-to-glia ratio and shear modulus is consistent with the hypothesis that neuronal cell loss and gliosis drives tissue stiffening, respectively. Magnetic resonance imaging (MRI) analysis of the specimens confirmed the previously reported negative association between MRI-derived fractional anisotropy and shear modulus {micro}. Taken together, our study establishes a direct link between tissue mechanics and microstructure, suggesting nonlinear continuum mechanics models as promising new tools for clinical diagnosis and novel research strategies.

bioengineering↗

bioRxiv2026

Towards patient-specific biomechanical human brain models

Reliable characterization of spatial variations in brain tissue stiffness is essential for predictive biomechanical modeling, yet most current methods rely on coarse regional parameter assignments based on invasive mechanical testing. In this study, we propose a new approach to obtain subject-specific mechanical properties at voxel resolution from in vivo diffusion tensor magnetic resonance imaging (DTI) based on a linear regression between the fractional anisotropy (FA) from DTI and experimentally measured stiffness values. To assess how such heterogeneity in mechanical properties influences simulated brain deformation, we construct a finite element model based on two material parameterizations of the same human brain: one employing nine anatomically defined regions, each with uniform material parameters, and another in which the shear modulus is assigned voxel-wise on the corresponding FA value. Applying this FA-stiffness mapping yields a smoothly varying mechanical property distribution that better captures local microstructural differences not represented by region-wise parameterizations. Both parameterizations are subjected to an identical atrophy-driven loading scenario. They exhibit comparable overall volume loss, but diverge in regional behavior. The voxel-resolved parameterization predicts more pronounced ventricular expansion and differs in the displacement and stretch distributions, indicating that variability in stiffness can alter local predicted responses even when global outcomes appear similar. This work presents a pipeline for estimating individualized mechanical properties directly from imaging protocols that are routinely performed on patients, with important implications for brain biomechanics. While the approach depends on a simplified linear FA-stiffness relation and assumes isotropic constitutive behavior, it provides a framework for integrating imaging-based microstructure into subject-specific simulations. Future validation against in vivo or experimental deformation data is needed to determine the fidelity and clinical utility of FA-derived stiffness fields.

neuroscience↗

bioRxiv2026

Fractional Anisotropy as a Surrogate Marker of Brain Mechanics

Understanding the mechanical properties of brain tissue may provide crucial insights into brain development, injury, disease and surgical planning. Conventionally, these properties are measured ex vivo or in vivo during surgical procedures, while non-invasive in vivo alternatives are sparse. This study investigates whether fractional anisotropy (FA) derived from diffusion-weighted magnetic resonance imaging can serve as a surrogate marker for brain tissue stiffness in healthy human brains. MRI data were collected from three body donor brains, 28 healthy adults, and a publicly available independent dataset of 26 adults. FA values were compared with mechanical properties from ex vivo mechanical testing of brain tissue. Statistical analysis revealed a strong negative correlation between FA and the mechanical response for small strains expressed as shear modulus of a one-term hyperelastic Ogden model, indicating that higher FA values are associated with lower tissue stiffness. The nonlinearity parameter alpha exhibited a qualitatively similar, but considerably weaker correlation with FA. These findings were consistent across datasets. The findings suggest that FA can be a robust, non-invasive marker for estimating mechanical properties of brain tissue, with potential applications in clinical diagnosis and computational modeling of brain mechanics and the study of brain development. Further research is needed to clarify the relationship in lesional tissues and to optimize clinical utility.

neuroscience↗

bioRxiv2026

Machine-Learning-Based spike marking in signal and source space EEG from a patient with focal epilepsy

Accurate detection of interictal epileptiform discharges (IEDs) in electroencephalography (EEG) plays a crucial role in epilepsy diagnosis. Our work investigates the classification of IEDs using Artificial Neural Networks (ANNs) trained on EEG data represented in both signal and source space. Source waveforms were computed using equivalent current dipole models fitted using either a 1-parameter fixed-orientation or a 3-parameter projection approach, both localized to a single best-fit position during the rising flank of the IED. The ANN was trained on raw and feature-extracted versions of signal space and source space data. Feature extraction significantly improved performance across all domains. The highest accuracy (0.98) was achieved in signal space using Katz Fractional Dimension (KFD). In source space analyses, the 1-parameter and 3-parameter models achieved a maximum accuracy of 0.84, with statistical features performing best for the fixed-orientation model and KFD for the free orientation model. Additionally, annotations from three independent expert markers showed considerable variability, with ANN performance falling within the range of inter-expert agreement. These findings support the potential of ANN-based tools to assist expert evaluation in future clinical workflows.

neuroscience↗

bioRxiv2025

Prediction, Syntax and Semantic Grounding in the Brain and Large Language Models

Language comprehension involves continuous prediction of upcoming words, with syntactic structure and semantic meaning intertwined in the human brain. To date, few studies have used combined magnetoencephalography (MEG) and electroen-cephalography (EEG) measurements to investigate how syntactic processing, predictive coding, and semantic grounding interact in real time. Here we present the first combined MEG-EEG investigation of syntactic processing and semantic grounding under naturalistic conditions. Twenty-nine healthy participants listened to a German audio book while their neural responses were recorded. Event-related fields and event-related potentials for four word classes - nouns, verbs, adjectives, and proper nouns - showed highly reproducible, characteristic spatio-temporal signatures, including significant pre-onset activity for nouns, suggesting enhanced predictability of this word class. Source-space analyses revealed pronounced activation in the pre- and post-central gyri for nouns, suggesting a deeper semantic grounding of nouns in e.g. sensory experiences than verbs. To further investigate predictive mechanisms, we analyzed the hidden representations of the large language model Llama. By comparing the transformer-based representations to neural responses, we explored the relationship between computational language models and human brain activity, offering new insights into syntactic and semantic prediction. These findings highlight the power of simultaneous MEG-EEG recordings in unraveling the predictive, syntactic, and semantic mechanisms that underlie the comprehension of natural language.

neuroscience↗

bioRxiv2024

Analyzing Differences in Processing Nouns and Verbs in the Human Brain using Combined EEG and MEG Measurements

Language and consequently the ability to transmit and spread complex information is unique to the human species. The disruptive event of the introduction of large language models has shown that the ability to process language alone leads to incredible abilities and, to some extent, to intelligence. However, how language is processed in the human brain remains elusive. Many insights originate from fMRI studies, as the high spatial resolution of fMRI devices provides valid information about where things happen. Nevertheless, the limited temporal resolution prevents us from gaining a deep understanding on the underlying mechanisms. In this study, we performed combined EEG and MEG measurements in 29 healthy right-handed human subjects during the presentation of continuous speech. We compared the evoked potentials (ERPs and ERFs) for different word types in source space and sensor space across the whole brain. We found characteristic spatio-temporal patterns for different word types (nouns, verbs) especially at latencies of 300ms to 1 s. This is further emphasized by the fact that we observe these effects in two pre-defined sub-samples of the data set (exploration and validation sample). We expect this study to be the starting point for further evaluations of semantic and syntactic processing in the brain.

neuroscience↗

bioRxiv2024

Temporal and Hemispheric Dynamics in Neural Processing of Auditory and Speech Stimuli Across Linguistic Complexity: A MEG Source Space Study

In this study we investigated the neural processing of auditory stimuli of varying complexity: a non-linguistic (pure tone), a simple linguistic (phoneme) and a complex linguistic (word) stimulus. We recorded brain activity of 30 healthy, righthanded participants using magnetoencephalography (MEG), and compared the resulting evoked fields (ERFs) in source space in three different time intervals, i.e. early (0-250ms), mid (250-500ms) and late (500-750ms) responses. Our results reveal a bilateral activation during early response and rightlateralized activation in the mid-phase for all stimuli. Hoewever, the late response exhibited lateralization variations. The pure tone predominantly activated the right hemisphere, consistent with pitch processing theories. The phoneme primarily engaged the left hemisphere, supporting its role in phonemic processing. Notably, the word elicited activation in both hemispheres, reflecting phonemic processing on the left and stress patterns on the right. These findings highlight the intricate interplay between temporal processing and hemispheric lateralization in speech perception, emphasizing the importance of stimulus complexity and temporal dynamics in understanding auditory and speech processing.

neuroscience↗

bioRxiv2024

Semantic integration demands modulate large-scale network interactions in the brain

The ability to integrate semantic information into the context of a sentence is essential for human communication. Several studies have shown that the predictability of a final keyword based on the sentence context influences semantic integration on the behavioral, neurophysiological, and neural level. However, the architecture of the underlying network interactions for semantic integration across the lifespan remains unclear. In this study, 32 healthy participants (30-75 years) performed an auditory cloze probability task during functional magnetic resonance imaging (fMRI), requiring lexical decisions on the sentences final words. Semantic integration demands were implicitly modulated by presenting sentences with expected, unexpected, anomalous, or pseudoword endings. To elucidate network interactions supporting semantic integration, we combined univariate task-based fMRI analyses with seed-based connectivity and between-network connectivity analyses. Behavioral data revealed typical semantic integration effects, with increased integration demands being associated with longer response latencies and reduced accuracy. Univariate results demonstrated increased left frontal and temporal brain activity for sentences with higher integration demands. Between-network interactions highlighted the role of task-positive and default mode networks for sentence processing with increased semantic integration demands. Furthermore, increasing integration demands led to a higher number of behaviorally relevant network interactions, suggesting that the increased between-network coupling becomes more relevant for successful task performance as integration demands increase. Our findings elucidate the complex network interactions underlying semantic integration across the aging continuum. Stronger interactions between various task-positive and default mode networks correlated with more efficient processing of sentences with increased semantic integration demands. These results may inform future studies with healthy old and clinical populations.

neuroscience↗

bioRxiv2023

Attentional modulation of the cortical contribution to the frequency-following response evoked by continuous speech

Selective attention to one of several competing speakers is required for comprehending a target speaker amongst other voices and for successful communication with them. Selective attention has been found to involve the neural tracking of low-frequency speech rhythms in the auditory cortex. Effects of selective attention have also been found in subcortical neural activities, in particular regarding the high-frequency neural response at the fundamental frequency of speech, the speech-FFR. Recent investigations have, however, shown that the speech-FFR contains cortical contributions as well. It remains unclear whether these are also modulated by selective attention. Here we employed magnetoencephalography (MEG) to assess the attentional modulation of the cortical contributions to the speech-FFR. We presented participants with two competing speech signals and analyzed the cortical responses during attentional switching between the two speakers. Our findings revealed robust attentional modulation of the cortical contribution to the speech-FFR: the neural responses were higher when the speaker was attended than when they were ignored. We also found that, regardless of attention, a voice with a lower fundamental frequency elicited a larger cortical contribution to the speech-FFR than a voice with a higher fundamental frequency. Our results show that the attentional modulation of the speech-FFR does not only occur subcortically but extends to the auditory cortex as well.

neuroscience↗

bioRxiv2023

Neurophysiological correlates of Somatosensory tinnitus modulation

Tinnitus is the subjective perception of a sound in absence of corresponding external acoustic stimuli. Research highlights the influence of the sensorimotor system on tinnitus perception. Associated neuronal processes, however, are insufficiently understood and it remains unclear how and at which hierarchical level the sensorimotor system interacts with the tinnitus-processing auditory system. We therefore asked 23 patients suffering from chronic tinnitus (11 males) to perform specific exercises, aimed at relaxing or tensing the jaw area, which temporarily modulated tinnitus perception. Associated neuronal processes were assessed using Magnetencephalography. Results show that chronic tinnitus patients experienced their tinnitus as weaker and less annoying after completion of relaxing compared to tensing exercises. Furthermore, (1) sensorimotor alpha power and alpha-band connectivity directed from the somatosensory to the auditory cortex increased, and (2) gamma power in the auditory cortex, reduced, which (3) related to reduced tinnitus annoyance perception on a trial-by-trial basis in the relaxed state. No effects were revealed for 23 control participants without tinnitus (6 males) performing the same experiment. We conclude that the increase in directed alpha-band connectivity from somatosensory to auditory cortex is most likely reflecting the transmission of inhibition from somatosensory to auditory cortex during relaxation, where concurrently tinnitus-related gamma power reduces. We suggest that revealed neuronal processes are transferable to other tinnitus modulating systems beyond the sensorimotor one that are e.g. involved in attentional or emotional tinnitus modulation and provide deeper mechanistic insights into how and through which channels phantom sound perception might be modulated on a neuronal level. Significance StatementTinnitus describes the perception of auditory phantom sounds. Research suggests that the sensorimotor system impacts on tinnitus perception, associated neuronal mechanisms, however, have remained unclear. Here, chronic tinnitus patients performed exercises with the jaw temporarily reducing (versus increasing) tinnitus perception. Tinnitus reduction was accompanied by an increase of alpha-band connectivity directed from the somatosensory to the auditory cortex and gamma power reduction in the auditory cortex. We suggest that the increase in alpha-band connectivity, when tinnitus is reduced, reflects the transmission of inhibition from somatosensory to auditory cortex, where, in parallel, probably tinnitus related, gamma power reduces. The findings have important implications both for the understanding of phantom sound perception and, more generally, of top-down modulation in healthy and impaired cognition.

neuroscience↗

bioRxiv2023

Decreasing alpha flow releases task-specific processing paths

Directing and maintaining attention toward relevant information and away from non-relevant information is a critical competence of higher-order nervous systems. Here, we used directed connectivity to investigate how the human brain prioritizes appropriate processing paths when participants are performing a behavioral task requiring attention in the visual or the auditory modality. We show that the efficient transfer of information relevant for the task is mediated by a region- and task-specific decrease of alpha band connectivity from parietal and, in case a motor response is required, motor cortex to the relevant sensory cortices. Further, alpha-band connectivity modulations reliably predict alpha power modulations in the task-related sensory cortices, particularly where the task-irrelevant cortex is inhibited via local alpha power increases. We suggest that the task- and region-specific modulation of alpha-band connectivity in the present study is a basic neuronal mechanism orchestrating the allocation of task-relevant neuronal resources related to and possibly underlying the previously reported sensory alpha power modulations in association with the allocation of competing attentional resources.

neuroscience↗

bioRxiv2023

Cortical Activity Associated With Acute Development of Phantom Auditory Percepts via Unilateral Deprivation

Subjective tinnitus describes the experience of hearing phantom sounds (e.g., tones, buzzing, noise). While the majority of those who report having experienced phantom sounds claim that these percepts have not lasted but are transient, some experience this chronically, with others even describing their tinnitus as severe enough to negatively impact their well-being and daily lives. Currently, no permanent solution has been discovered for preventing or curing tinnitus. This is due to several factors, including an insufficient understanding of the mechanisms at play that give rise to such auditory sensations, as well as a lack of research investigating the corresponding changes in neural activity associated with the onset and development of tinnitus. Taking advantage of the high spatial and temporal resolution of magnetoencephalography (MEG), we measured cortical activity associated with the development of acute tinnitus-like percepts induced via unilateral auditory deprivation. Over the course of four days, participants continuously wore a silicone earplug in one ear, which led to the experience of phantom sounds in 15 of 16 participants. Frequency analysis of source-localized continuous MEG data revealed a significant increase of gamma power in primary auditory cortices (A1) during the tinnitus condition (p=0.02), which most likely reflects the neuronal processing correlated with tinnitus perception.

neuroscience↗

bioRxiv2022

Network topology in brain tumor patients with and without structural epilepsy- a prospective MEG-study

It has been proposed that functional connectivity (FC) and network topology (NT) are altered in patients with glial brain tumors. So far there is no consensus on the pattern of these changes, furthermore data on FC in patients with brain metastases (BMs) as well as on the presence and absence of tumor-related epilepsy is lacking. We aimed to analyze preoperative NT of newly diagnosed, singular, supratentorial glial brain tumors (GBTs) and BMs with and without structural epilepsy. FC and NT derived from resting state magnetoencephalography (MEG) were compared between patients (PAs) and matched healthy controls (HCs), between GBTs and BMs as well as between patients with and without structural epilepsy. We investigated all patients, who met our inclusion criteria from February 2019 to March 2021. Clinical data was collected from patients electronic medical charts. We analyzed whole brain (wb) connectivity in six frequency bands, calculated three different network topological parameters (node degree (ND), shortest path length (L), clustering coefficient (CC)) and performed a stratification, where differences in the power were to be found. For data analysis we used Fieldtrip, Brain Connectivity Matlab toolboxes and in-house built scripts. We consecutively included 41 patients (21 men), mean age 60.1ys (range 23-82), who were operated on at our institution. Tumor histology included high-grade gliomas (n=18), low-grade gliomas (n=4), dysembryoplastic neuroepithelial tumor (DNET, n=1), BMs (n=14) and others (n=4). Statistical analysis revealed a significant decrease for wb ND in patients compared to healthy controls in every frequency range analyzed at the descriptive and corrected level (p1-30Hz=0.002, p{gamma}=0.002, p{beta}=0.002, p=0.002, p{theta}=0.024, p{delta}=0.002). Furthermore, at the descriptive level, we found a significant augmentation for wb CC (p1-30Hz=0.031, p{delta}=0.013) in PAs compared to HCs, which did not persist the false discovery rate (FDR) correction. There were no differences in the networks of glial brain tumors and metastases identified. However, we found a significant increase in wb CC in patients with structural epilepsy (p{theta}= 0.048), and significantly lower wb ND (p= 0.039) at the uncorrected level. Our data suggests that network topology is altered in brain tumor patients, which is in line with previous studies. Tumor histology per se might not influence the brains functional network, however, tumor-related epilepsy seems to do so. Longitudinal studies and in-depth analysis of possible factors and confounders might be helpful to further substantiate these findings.

neuroscience↗

bioRxiv2018

Spectral fingerprints or spectral tilt? Evidence for distinct oscillatory signatures of memory formation

Decreases in low frequency power (2-30 Hz) alongside high frequency power increases (>40 Hz) have been demonstrated to predict successful memory formation. Parsimoniously this change in the frequency spectrum can be explained by one factor, a change in the tilt of the power spectrum (from steep to flat) indicating engaged brain regions. A competing view is that the change in the power spectrum contains several distinct brain oscillatory fingerprints, each serving different computations. Here, we contrast these two theories in a parallel MEG-intracranial EEG study where healthy participants and epilepsy patients, respectively, studied either familiar verbal material, or unfamiliar faces. We investigated whether modulations in specific frequency bands can be dissociated in time, space and by experimental manipulation. Both, MEG and iEEG data, show that decreases in alpha/beta power specifically predicted the encoding of words, but not faces, whereas increases in gamma power and decreases in theta power predicted memory formation irrespective of material. Critically, these different oscillatory signatures of memory encoding were evident in different brain regions. Moreover, high frequency gamma power increases occurred significantly earlier compared to low frequency theta power decreases. These results speak against a \"spectral tilt\" and demonstrate that brain oscillations in different frequency bands serve different functions for memory encoding.

neuroscience↗