Biology subjects

Knoesche, T.

Publications and source records attributed to Knoesche, T..

14 recordsLinked to original sources

bioRxiv2026

An Approach to Measure Conductivity Gradients of Brain Tissue Transitions using Electrode Arrays

Accurate determination of surgical margins is critical in tumor resection to ensure complete removal of tumor-infiltrated tissue while preserving healthy tissue. Pathological assessment provides reliable information but is time-consuming. This study investigates the feasibility of using impedance spectroscopy to detect tissue transitions at a macroscopic level. Two electrode arrays--one-dimensional and two-dimensional--were applied to ex vivo porcine brain tissue. Measurements were performed using both two- and four-electrode configurations, and data were corrected using the multiple-load compensation method. Results demonstrate that the one-dimensional array provides continuous conductivity profiles corresponding to tissue transitions, while the two-dimensional array showed less consistent results. These findings suggest that impedance spectroscopy is a promising tool for intraoperative margin detection, but further optimization of electrode geometry and measurement data processing is required.

neuroscience↗

bioRxiv2026

Cortical Stimulation Strength and sgACC Connectivity Shape Neuro-Cardiac Responses to Prefrontal TMS

Autonomic dysregulation is a core feature of major depressive disorder and a promising physiological readout for early signs of clinical effectiveness of therapeutic TMS targeting prefrontal regions. Yet the stimulation dose and network properties required to optimally engage autonomic circuits remain elusive. Here, we applied a cortex- and circuit-centered dosing framework combining resting-state fMRI, simulations of the TMS-induced electric field, and electrocardiography acquired during repetitive TMS of six left dorsolateral prefrontal cortex (DLPFC) targets in healthy adults. Heart-brain coupling (HBC), defined as spectral power in the cardiac response locked to the TMS onset frequency, was modeled as a function of either conventional motor-threshold-based dose or cortical electric-field strength, with and without DLPFC connectivity to the subgenual anterior cingulate cortex (sgACC). Across linear and nonlinear models, cortical electric-field dose explained substantially more variance in HBC, indicating that prefrontal TMS dosing is best assessed at the cortical level. Incorporating sgACC connectivity further improved model fit and revealed site-specific electric-field-by-connectivity interactions, with the strongest and most reliable HBC modulation at DLPFC regions anticorrelated with sgACC. Voxel-wise TMS mapping identified individualized cortical hotspots spatially overlapping with sgACC-anticorrelated tissue. Notably, whereas negative connectivity showed the most robust and monotonic effects, nonlinear dose-response analyses suggested a confined stimulation range over which positively connected sites also exhibited HBC modulation. Together, these findings position HBC as a network-sensitive physiological readout of DLPFC-sgACC-vagal engagement and offer a principled answer to where and how much to stimulate, supporting an electric-field- and connectivity-informed dosing framework for refining prefrontal TMS in depression-related autonomic dysregulation.

neuroscience↗

bioRxiv2025

A 4-pole Theory-Based Compensation Method using Multiple Loads for Impedance Spectroscopy of Porcine Brain Tissue

Accurate determination of the electrical properties of brain tissue--conductivity and relative permittivity--is essential for modeling and optimizing neurostimulation and neuroimaging and modulation techniques such as EEG, MEG, TMS, and TES. However, large discrepancies in reported conductivity values highlight the methodological challenges associated with impedance measurements. In this study, we present a systematic evaluation of compensation methods in impedance spectroscopy to improve measurement accuracy using a self-developed four-point platinum electrode designed for biological tissue applications. Three compensation algorithms--open-short, open-short-load, and multiple-load--were implemented and compared using an RC parallel test circuit that emulated typical measurement conditions, with load resistances ranging from 250 {Omega} to 5000 {Omega}. Quantitative error analysis demonstrated that the multiple-load method achieved the lowest normalized root mean square error (3.4%) and superior stability across frequencies from 100 Hz to 1 MHz. The calibrated probe was subsequently applied to post mortem porcine brain tissue, yielding conductivity values of grey matter of 0.109 S/m and white matter of 0.074 S/m. These results are significantly lower than standard simulation parameters of 0.275 S/m, and 0.126 S/m for grey and white matter, respectively. Finite-element simulations revealed that using the measured conductivities increased electric field predictions by 2.1% for TMS and 46% for TES in the maximum electric field magnitude, respectively. Our findings demonstrate that accurate impedance compensation is critical for reliable characterization of brain tissue and for enhancing the precision of neurostimulation modeling in the future.

neuroscience↗

bioRxiv2025

A New Method for Optimal Placement of Tumor Treating Fields Electrodes

OverviewTumor Treating Fields (TTFields) provide a non-invasive treatment option for newly diagnosed glioblastoma. While optimization of electrode placement is important to increase treatment efficacy, clinical therapy planning is done using an undisclosed and proprietary software (NovoTAL(R)), which is clinically unvalidated. This study investigates a new computational approach for optimizing TTFields electrode placement and is compared to the current clinical standard. MethodsWe developed a new computational pipeline integrating patient-specific anatomical data to optimize electrode configurations in five representative glioblastoma cases with diverse tumor locations and sizes. Two optimization strategies were employed: one maximizing electric field intensity at the tumor, and another enhancing coverage of the adjacent brain while maintaining sufficient tumor intensity. Results were compared to electrode placements generated by NovoTAL(R). Additional simulations with artificial tumors assessed the effects of tumor size and location. ResultsOptimized electrode placements improved electric field intensity in tumors by 18%-34% compared to the clinical standard. Coverage-weighted optimizations provided broader field coverage without significantly compromising tumor intensity. Smaller or surface-adjacent tumors benefited most from optimization, achieving precise targeting and enhanced coverage. Extensive randomized placement analyses highlighted the superior performance of the optimized configurations. Analysis of artificial models showed consistent improvements across varying tumor locations and sizes. ConclusionPersonalized optimization of TTFields electrode placement significantly improves electric field targeting of tumors and adjacent brain regions. This approach outperforms standardized planning software and clinical practices and supports future development of adaptive, automated strategies for individualized TTFields therapy in glioblastoma. Keypoints- Optimized TTFields array placement enhanced field intensity by 18-34% vs. clinical standard. - Optimized TTFields planning improved field coverage in tumor-adjacent regions. - Optimized TTFields planning outperformed standard methods and random placement. Importance of the studyTTFields are increasingly used as adjuvant therapy for glioblastoma. However, current individualized treatment planning relies on proprietary, undisclosed, and clinically unvalidated software, limiting transparency, optimization, and innovation in the field. This study introduces an individualized, semi-automated, and open-source method for optimal electrode placement based on standard MRI data, addressing a critical need for validated and adaptable planning tools. Our approach increased field intensity in tumors by 18-34% and achieved broader coverage compared to the standard clinical tool (NovoTAL(R)), without compromising therapeutic strength. Notably, the method also consistently outperformed extensive random electrode placements across diverse tumor types and sizes, highlighting its robustness and ability to achieve true optimal configurations. Open-source availability enhances reproducibility and clinical translation, representing a significant step toward more effective, individualized TTFields therapy. This advancement has the potential to improve outcomes for glioblastoma patients and underscores the importance of technology validation in neuro-oncology.

neuroscience↗

bioRxiv2025

Optimizing and assessing multichannel TMS focality

BackgroundMultichannel transcranial magnetic stimulation (mTMS) enables electronic steering of induced electric fields across multiple cortical targets without physical coil repositioning, addressing key limitations of conventional single-channel TMS (sTMS). However, determining optimal input currents for focal stimulation remains challenging, and different mTMS systems have not been systematically compared under realistic hardware constraints. ObjectiveTo develop an user-centric framework for optimizing and assessing mTMS focality by introducing a generic optimization algorithm, establishing meaningful quality metrics, and comparing mTMS coil arrays with traditional single-channel TMS across cortical targets. MethodsWe developed a fast optimization framework incorporating target E-field constraints via parametrization of degenerated hyperellipsoids, explicitly integrating current-rate limits, for example from stimulator electronics and coil heating. Using high-resolution finite-element models of nine individual brains, we compared two mTMS designs (5-channel planar and 6/12-channel spherical systems) with standard sTMS figure-of-eight coils through E-field simulations. Three complementary metrics quantified stimulation quality: Focality, Target2Max, and OverstimulatedArea. ResultsDespite using a single optimized placement for all region-of-interest targets, mTMS achieved focality comparable to repositioned single-channel TMS in this in-silico study. For superficial targets, single-channel TMS showed slightly better stimulation, but for deeper cortical targets (>25 mm skin-cortex distance), mTMS performed similarly. More mTMS channels improved focality but required stronger current-rate constraints. The planar design performed better for deeper targets, while spherical designs improved with additional channels. ConclusionmTMS systems demonstrate remarkable performance comparable to standard TMS, enabling efficient multi-target stimulation without repositioning. Our open-source framework provides practical tools for designing and evaluating mTMS systems, supporting goal-directed mTMS development and effective application.

neuroscience↗

bioRxiv2025

Target-Specificity and Repeatability in Neuro-Cardiac-Guided TMS for Heart-Brain Coupling

The dorsolateral prefrontal cortex (DLPFC) is a principal target for transcranial magnetic stimulation (TMS) in treating major depressive disorder, with therapeutic effects thought to be mediated by its connectivity with the subgenual anterior cingulate cortex. As both regions are involved in autonomic regulation, short-term heart rate changes following DLPFC stimulation may serve as physiological markers to identify stimulation targets. We employed neuro-cardiac guided TMS in a cohort of healthy participants to examine the effects of stimulation intensity and DLPFC target specificity on heart-brain coupling (HBC). We used generalized additive models to assess nonlinear effects of stimulation intensity and target location on HBC, while accounting for pain ratings and other side effects. Intra-subject repeatability across three sessions was evaluated using intraclass correlation coefficients. We observed a non-linear modulation of HBC depending on stimulation intensity and target location, with greater effects at the F3 lateral and F3 posterior targets compared to sham. By evaluating these effects across sessions within participants, we demonstrate the robustness of our results beyond the influence of pain and other side effects on HBC modulation. Exploratory analyses of the directionality show a consistent decrease in HR only at the F3 lateral target with suprathreshold stimulation. These results demonstrate that HBC is modulated in a target- and intensity-specific manner, with particularly consistent effects at F3 lateral sites within the DLPFC. The findings enhance the understanding of TMS-modulated heart-brain interactions, offering a potential framework for optimizing individualized rTMS treatment protocols for depression.

neuroscience↗

bioRxiv2024

Effects of Electric Field Direction on TMS-based Motor Cortex Mapping

BackgroundTranscranial magnetic stimulation (TMS) modulates brain activity by inducing electric fields (E-fields) that can elicit action potentials in cortical neurons. Neuronal responses to TMS depend not only on the magnitude of the induced E-field but also on various physiological factors. In this study, we incorporated a novel average response model that efficiently estimates the firing threshold of neurons based on their orientation relative to the applied E-field, thereby advancing TMS mapping for motor function. MethodsWe conducted a regression-based TMS mapping experiment with fourteen subjects to localize cortical origins of motor evoked potential (MEP) on the first dorsal interosseous (FDI) muscle. Firing thresholds were estimated for excitatory neurons in cortical layers 2/3 and 5 via an average response model. Regression was performed between MEPs and three E-field quantities: the magnitude (magnitude model), the normal component (cosine model), and the effective E-field, which scales the E-field magnitude based on the firing thresholds specific to the neuronal orientation (neuron model). To validate, we applied TMS to ten subjects with optimized coil placements based on these three models to determine which model could yield the highest MEPs. ResultsThe magnitude and neuron models performed similarly, while the cosine model showed lower explained variance in regression results, required more TMS trials for stable mapping, and yielded the lowest MEP in the validation. ConclusionThis study is the first to advance TMS modeling by incorporating neuron-specific factors at the individual level. Results show that on the motor cortex, the magnitude model is-as expected-a good approximation of cortical TMS effects as it shows similar results as the neuron model. In contrast, the classic cosine model exhibited lower performance and required more TMS trials for stable results, and is not recommended for future studies.

neuroscience↗

bioRxiv2023

Modeling the Effects of Transcranial Magnetic Stimulation on Spatial Attention

ObjectivesTranscranial magnetic stimulation (TMS) has been widely used to modulate brain activity in healthy and diseased brains, but the underlying mechanisms are not fully understood. Previous research leveraged biophysical modeling of the induced electric field (E-field) to map causal structure-function relationships in the primary motor cortex. This study aims at transferring this localization approach to spatial attention, which helps to understand the TMS effects on cognitive functions, and may ultimately optimize stimulation schemes. ApproachThirty right-handed healthy participants underwent a functional magnetic imaging (fMRI) experiment, and seventeen of them participated in a TMS experiment. The individual fMRI activation peak within the right inferior parietal lobule (rIPL) during a Posner-like attention task defined the center target for TMS. Thereafter, participants underwent 500 Posner task trials. During each trial, a 5-pulse burst of 10 Hz repetitive TMS (rTMS) was given over the rIPL to modulate attentional processing. The TMS-induced E-fields for every cortical target were correlated with the behavioral modulation to identify relevant cortical regions for attentional orientation and reorientation. Main resultsWe did not observe a robust correlation between E-field strength and behavioral outcomes, highlighting the challenges of transferring the localization method to cognitive functions with high neural response variability and complex network interactions. Nevertheless, TMS selectively inhibited attentional reorienting in five out of seventeen subjects, resulting in task-specific behavioral impairments. The BOLD-measured neuronal activity and TMS-evoked neuronal effects showed different patterns, which emphasizes the principal distinction between the neural activity being correlated with (or maybe even caused by) particular paradigms, and the activity of neural populations exerting a causal influence on the behavioral outcome. SignificanceThis study is the first to explore the mechanisms of TMS-induced attentional modulation through electrical field modeling. Our findings highlight the complexity of cognitive functions and provide a basis for optimizing attentional stimulation protocols.

neuroscience↗

bioRxiv2022

Laminar Neural Dynamics of Auditory Evoked Responses: Computational Modeling of Local Field Potentials in Auditory Cortex of Non-Human Primates

Evoked neural responses to sensory stimuli have been extensively investigated in humans and animal models both to enhance our understanding of brain function and to aid in clinical diagnosis of neurological and neuropsychiatric conditions. Recording and imaging techniques such as electroencephalography (EEG), magnetoencephalography (MEG), local field potentials (LFPs), and calcium imaging provide complementary information about different aspects of brain activity at different spatial and temporal scales. Modeling and simulations provide a way to integrate these different types of information to clarify underlying neural mechanisms. In this study, we aimed to shed light on the neural dynamics underlying auditory evoked responses by fitting a rate-based model to LFPs recorded via multi-contact electrodes which simultaneously sampled neural activity across cortical laminae. Recordings included neural population responses to best-frequency (BF) and non-BF tones at four representative sites in primary auditory cortex (A1) of awake monkeys. The model considered major neural populations of excitatory, parvalbumin-expressing (PV), and somatostatin-expressing (SOM) neurons across layers 2/3, 4, and 5/6. Unknown parameters, including the connection strength between the populations, were fitted to the data. Our results revealed similar population dynamics, fitted model parameters, predicted equivalent current dipoles (ECD), tuning curves, and lateral inhibition profiles across recording sites and animals, in spite of quite different extracellular current distributions. We found that PV firing rates were higher in BF than in non-BF responses, mainly due to different strengths of tonotopic thalamic input, whereas SOM firing rates were higher in non-BF than in BF responses due to lateral inhibition. In conclusion, we demonstrate the feasibility of the model-fitting approach in identifying the contributions of cell-type specific population activity to stimulus-evoked LFPs across cortical laminae, providing a foundation for further investigations into the dynamics of neural circuits underlying cortical sensory processing.

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bioRxiv2022

Effect of contact pressure on porcine postmortem brain tissue impedance

In this experimental study we demonstrate the influence of contact pressure on porcine postmortem brain tissue impedance using a movable electrode array and a load cell. We show that the variation of the contact pressure between the tissue and the measurement probe leads to a coefficient of variation in the measured impedance of under 3%. Its influence can therefore be neglected. Further, we fit the measured impedances to an equivalent circuit model and compare the resistance as well as the cell density of grey and white matter brain tissue based on the model parameters.

neuroscience↗

bioRxiv2022

White Matter Brain Structure Predicts Language Performance and Learning Success

Individual differences in the ability to deal with language have long been discussed. The neural basis of these, however, is yet unknown. Here we investigated the relationship between long-range white matter connectivity of the brain, as revealed by diffusion tractography, and the ability to process syntactically complex sentences in the participants native language as well as the improvement thereof by multi-day training. We identified specific network motifs that indeed related white matter tractography to individual language processing performance. First, for two such motifs, one in the left and one in the right hemisphere, their individual prevalence significantly predicted the individual language performance suggesting a predisposition for the individual ability to process syntactically complex sentences, which manifests itself in the white matter brain structure. Both motifs comprise a number of cortical regions, but seem to be dominated by areas known for the involvement in working memory rather than the classical language network itself. Second, we identified another left hemispheric network motif, whose change of prevalence over the training period significantly correlated with the individual change in performance, thus reflecting training induced white matter plasticity. This motif comprises diverse cortical areas including regions known for their involvement in language processing, working memory and motor functions. The present findings suggest that individual differences in language processing and learning can be explained, in part, by individual differences in the brains white matter structure. Brain structure may be a crucial factor to be considered when discussing variations in human cognitive performance, more generally.

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bioRxiv2020

Computational Properties of the Visual Microcircuit

The neocortex is organized around layered microcircuits consisting of a variety of excitatory and inhibitory neuronal types which perform rate-and oscillation based computations. Using modeling, we show that both superficial and deep layers of the primary mouse visual cortex implement two ultrasensitive and bistable switches built on mutual inhibitory connectivity motives between SST, PV and VIP cells. The switches toggle pyramidal neurons between high and low firing rate states that are synchronized across layers through translaminar connectivity. Moreover, inhibited and disinhibited states are characterized by low- and high frequency oscillations, respectively, with layer-specific differences in frequency and power which show asymmetric changes during state transitions. These findings are consistent with a number of experimental observations and embed firing rate together with oscillatory changes within a switch interpretation of the microcircuit.

neuroscience↗

bioRxiv2020

In situ impedance measurements on postmortem porcine brain

The paper presents an experimental study where the distinctness of grey and white matter of an in situ postmortem porcine brain by impedance measurements is investigated. Experimental conditions that would allow to conduct the same experiment on in vivo human brain tissue are replicated. https://doi.org/10.1515/cdbme-2019-XXXX

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bioRxiv2019

A novel approach to localize cortical TMS effects

Despite the widespread use of transcranial magnetic stimulation (TMS), the precise cortical location underlying the physiological and behavioral stimulation effects are still only coarsely known. So far, mapping strategies rely on center of gravity approaches and therefore localize the stimulated cortical site only approximately and indirectly. Focusing on the motor cortex, we present a novel method to reliably determine the effectively stimulated cortical site at the individual subject level. The approach combines measurements of motor evoked potentials (MEPs) at different coil positions and orientations with numerical modeling of induced electric fields. We identify sharply bounded cortical areas around the gyral crowns and rims of the motor hand area as the origin of MEPs and show that the tangential component and the magnitude of the electric field is most relevant for the observed effect. To validate our approach, we determined motor thresholds for coil positions and orientations for the predicted cortical target. Our methods allows for the identification of optimal coil positions and orientations. Moreover, we used extensive uncertainty and sensitivity analyses to verify the robustness of the method and identify the most critical model parameters. Our generic approach improves the localization of the cortex area stimulated by TMS and may be transferred to other modalities such as language mapping.

neuroscience↗