Biology subjects

Kuster, N.

Publications and source records attributed to Kuster, N..

13 recordsLinked to original sources

bioRxiv2026

Artefact-free EEG during temporal interference stimulation reveals phase-specific modulation of the human visual cortex

Background: Temporal interference (TI) stimulation applies kHz electric fields whose envelope is modulated at a difference frequency ({Delta}f), allowing non-invasive deep-brain targeting. Measuring the neural response during stimulation with EEG is essential for establishing mechanisms and for closed-loop control. However, non-linearities in the recording chain mix the kHz carriers, producing an artefact at exactly {Delta}f. Objectives: To establish a method for recording EEG during TI without the {Delta}f artefact, and to test whether TI modulates neural activity at {Delta}f. Methods: A front-end analogue low-pass filter (7th order; 90 Hz cut-off; [≥]130 dB attenuation at 1 kHz) was bench-tested. In 22 healthy participants, residual artefact was assessed per recording with a two-stage detection pipeline in stimulation-only blocks. TI targeted the right primary visual cortex at 10 or 15.5 Hz, in-phase or anti-phase with multi-frequency photic stimulation (4.5, 10, 15.5 Hz), during a choice reaction time task. Results: The filter suppressed the {Delta}f artefact below the noise floor on a resistive load. In vivo, residual artefact was confirmed in two participants and could not be excluded in two more; all four were excluded from EEG analyses. Steady-state visual evoked potential (SSVEP) amplitude at 11, 14.5 and 15.5 Hz predicted faster responses. In-phase 15.5 Hz TI increased SSVEP amplitude at 14.5 and 15.5 Hz and shortened response times relative to both sham and anti-phase TI, which did not differ from sham. Conclusions: With front-end filtering and per-recording verification, EEG can measure TI neuromodulation during stimulation and at {Delta}f, revealing phase-specific modulation of visual entrainment linked to behaviour.

neuroscience↗

bioRxiv2026

Hippocampal stimulation timed to memory reactivation shapes human sleep oscillatory dynamics and consolidation

Memory consolidation during sleep depends on the precisely timed coordination of hippocampal reactivation with cortical slow oscillations and spindles. In humans this coupling has been characterised correlationally, while causal investigations have required intracranial stimulation in restricted patient cohorts or used non-invasive approaches targeting cortical regions, often obscuring the underlying sleep rhythms. Here we stimulated the human hippocampus during sleep for the first time, applying temporal interference stimulation to the left hippocampal head either concurrently with or prior to induced memory reactivation. Stimulation concurrent with reactivation reduced associative memory forgetting relative to mistimed stimulation and enhanced fast spindle amplitude, preserving the association between slow oscillation-spindle coupling and memory. This behavioural benefit was mediated by a distributed, left-lateralised set of spindle and coupling features. Non-invasively engaging deep hippocampal circuits during sleep offers both a means to probe human memory and a scalable basis for intervention where consolidation fails.

neuroscience↗

bioRxiv2026

BraiNN: A Modern Simulator for Clinically Feasible Personalized Whole-Brain Network Modeling

Personalized whole-brain modeling aims to transform treatment planning for neurological disorders by enabling patient-specific simulations of brain network dynamics. Neural mass models (NMMs) offer a tractable compromise between biophysical detail and computational cost and can be directly linked to macroscopic observables such as EEG. However, scaling NMMs to whole-brain networks with realistic connectivity, conduction delays, and cortical surface resolution--and fitting them to individual patient data--imposes computational demands that existing frameworks cannot meet at clinically relevant timescales. Here we introduce BraiNN, a JAX-based Python framework for large-scale neural mass modeling that achieves speedups of up to two to three orders of magnitude over existing tools by leveraging GPU/TPU-accelerated, XLA-compiled array computation. BraiNN combines a region-level Jansen-Rit network with a subject-specific cortical surface mesh of coupled neural mass models and biophysically grounded EEG forward modeling via reciprocity-based lead fields. Its fully differentiable computational graph enables a hybrid personalization pipeline that pairs Bayesian optimization for global parameter exploration with gradient-based refinement, completing EEG-driven spectral fitting of an eight-dimensional parameter space in approximately 2-3 hours on a single consumer GPU--compared to multiple days with conventional neural mass modeling software. Numerical verification against established benchmarks confirms that BraiNN faithfully reproduces canonical synchronization and bifurcation dynamics of Jansen-Rit networks. By reducing the time requirements for personalizing a high-detail whole-brain surface model from days to a few hours on consumer-grade hardware, BraiNN brings personalized brain network modeling closer to practical use in clinical contexts. We anticipate that BraiNN will serve as a foundation for patient-specific digital twins and EEG-guided neuromodulation planning.

neuroscience↗

bioRxiv2026

Resonance-driven enhancement of sleep spindles using thalamic temporal interference stimulation

Sleep spindles are hallmarks of non-rapid eye movement sleep and support memory consolidation yet remain difficult to modulate non-invasively. Combining computational modeling and human sleep recordings, we show that thalamus-targeted temporal interference stimulation (TIS) with a 5Hz envelope increases spindle density via subthreshold resonance in thalamocortical relay neurons. Our results demonstrate a mechanistic framework for the rational design of interventions to selectively augment sleep spindles.

neuroscience↗

bioRxiv2025

Shaping a Collaborative, Sustainable, Accessible, and Reproducible Future for Computational Modeling

The o2S2PARC platform is an open-source, extensible, and scalable cloud-based platform developed in the context of the U.S. National Institutes of Health SPARC program to support collaborative, sustainable, FAIR (findable, accessible, interoperable, reusable) and reproducible computational modeling and analysis. This publication presents the main features of o2S2PARC, its underlying approaches and philosophy, innovative aspects of the developed technologies, while also drawing attention to its rapid adoption. The paper showcases a variety of applications and use cases enabled by the platform. These include hybrid electromagnetic-electrophysiology simulations of neural interfaces, personalized brain and spinal cord stimulation planning, in silico device safety assessments, the training and application of AI systems (e.g., for model-predictive control and medical image segmentation), hybridized surrogate modeling and multi-objective optimization in high-dimensional parameter spaces, sensitive and unbiased validation of measurement devices, and interactive data analysis as paper supplements.

neuroscience↗

bioRxiv2025

Human Skin Model from 15 GHz to 110 GHz

In the recently revised guidelines for electromagnetic safety, basic restrictions expressed in terms of the absorbed power density (APD) at frequencies higher than 6 GHz were introduced. Testing for APD compliance of wireless devices requires experimental and numerical body models or phantoms that conservatively reproduce the absorption characteristics of human skin. Previous studies of APD indicate that frequency-dependent impedance-matching effects are caused by the low-permittivity stratum corneum (SC) layer. The objective of this study is to complement previous work (Christ et al., 2020) and to develop dispersive dielectric models to represent reflection and absorption of electromagnetic fields at the surface of the skin across a frequency range up to 110 GHz. The reflection coefficient of the skin of human volunteers was measured at frequencies of 15 to 43 GHz with open waveguide probes, complementing previous data from 45 to 110 GHz (Christ et al., 2020). The volunteers represented both sexes and different age groups and occupations; measurements were made at various regions of the body. The statistical analysis of the results show that the reflection coefficient follows a normal distribution in regions where the SC is relatively thin, which permits development of a conservative skin model that covers the 95th percentile of the tested population. As expected, in regions where the SC is thicker, e.g., the palms, the reflection coefficient is not normally distributed, because the thickness of the SC depends on the mechanical stress and friction to which the hands are exposed during routine daily activities. There was no evidence of relevant differences due to sex, but there is evidence for a slight age-dependent difference. The measured data - via fitting to the numerical model - allow the derivation of two-layer dielectric dispersive models that represent absorption and reflection at the surface of the skin with known uncertainty. The proposed models can be used to conservatively demonstrate compliance with the APD limits of wireless devices operating at frequencies of up to 110 GHz in any of the 5G and 6G bands defined. HighlightsO_LIbroadband evaluation of the skin reflection coefficient with open waveguide probes; C_LIO_LIcorroboration of increased absorption of millimeter wave radiation in body regions with increased stratum corneum thickness; C_LIO_LIdispersive dielectric models representing reflection and absorption at the skin surface with known population coverage. C_LI

bioengineering↗

bioRxiv2024

Simulation Insights on the Compound Action Potential in Multifascicular Nerves

ObjectiveDevelop an efficient method for simulating evoked compound action potential (eCAP) signals from complex nerves to help optimize and interpret eCAP recordings; validate it through comparison with measured vagus nerve eCAP recordings; elucidate the subtle interplay giving rise to specific eCAP signal shapes and magnitudes. ApproachWe developed an extended reciprocity theorem approach to model neuron signals in heterogeneous environments, and use it to study analytically the single fibre action potential. We then established a semi-analytic model that also uses hybrid electromagnetic-electrophysiological simulations to model eCAP signals from complex nerves populated with heterogeneous fiber populations of fibers. A cuff electrode was used to measure activity induced by vagus nerve stimulation in in vivo porcine experiments; these measurements were compared with signals produced by the model. Main ResultsThe semi-analytic model produces signals that approximate the shape and amplitude of in vivo measurements. Partially activated fascicles contribute substantially to the signal, as eCAP contributions from smoothly varying fiber calibers in fully activated ones partially cancel. As a result, eCAP magnitude does not depend monotonically on the stimulation current and recruitment level. Because the eCAP is sensitive to the degree of activation in individual fascicles, and to the location of the recording electrodes with respect to individual fascicles, the contributions of different fascicles to the recorded eCAP signals vary significantly with changes in the shape and placement of the stimulus and the recording electrodes. SignificanceOur method can be used to rapidly assess new stimulation and recording setups involving complex nerves and neurovascular bundles, e.g., to maximize signal information content, for closed-loop control in bioelectronic medicine applications, and potentially to non-destructively reconstruct structural and functional nerve topologies through inverse problem solving.

neuroscience↗

bioRxiv2024

BlueRecording: A Pipeline for the efficient calculation of extracellular recordings in large-scale neural circuit models

As the size and complexity of network simulations accessible to computational neuroscience grows, new avenues open for research into extracellularly recorded electric signals. Biophysically detailed simulations permit the identification of the biological origins of the different components of recorded signals, the evaluation of signal sensitivity to different anatomical, physiological, and geometric factors, and selection of recording parameters to maximize the signal information content. Simultaneously, virtual extracellular signals produced by these networks may become important metrics for neuro-simulation validation. To enable efficient calculation of extracellular signals from large neural network simulations, we have developed BlueRecording, a pipeline consisting of standalone Python code, along with extensions to the Neurodamus simulation control application, the CoreNEURON computation engine, and the SONATA data format, to permit online calculation of such signals. In particular, we implement a general form of the reciprocity theorem, which is capable of handling non-dipolar current sources, such as may be found in long axons and recordings close to the current source, as well as complex tissue anatomy, dielectric heterogeneity, and electrode geometries. To our knowledge, this is the first application of this generalized (i.e., non-dipolar) reciprocity-based approach to simulate EEG recordings. We use these tools to calculate extracellular signals from an in silico model of the rat somatosensory cortex and hippocampus and to study signal contribution differences between regions and cell types.

neuroscience↗

bioRxiv2024

Safety of Non-invasive Brain Stimulation in Patients with Implants: A Computational Study

ObjectiveNon-invasive brain stimulation (NIBS) methodologies, such as transcranial electric (tES) and magnetic stimulation are increasingly employed for therapeutic, diagnostic, or research purposes. The concurrent presence of active or passive implants can pose safety risks, affect the NIBS delivery, or generate confounding signals. A systematic investigation is required to understand the interaction mechanisms, quantify exposure, assess safety, and establish guidance for NIBS applications. ApproachWe used measurements, simplified generic, and detailed anatomical modeling to: (i) systematically analyze exposure conditions with passive and active implants, considering local field enhancement, exposure dosimetry, tissue heating and neuromodulation, capacitive lead current injection, low-impedance pathways between electrode contacts, and insulation damage; (ii) identify safety metrics and efficient prediction strategies; (iii) quantify these metrics in relevant exposure cases and (iv) identify worst case conditions. Various aspects including implant design, positioning, scar tissue formation, anisotropy, and frequency were investigated. ResultsAt typical tES frequencies, local enhancement of dosimetric exposure quantities can reach up to one order of magnitude for DBS and SEEG implants (more for elongated passive implants), potentially resulting in unwanted neuromodulation that can confound results but is still 2-3 orders of magnitude lower than active DBS. Under worst-case conditions, capacitive current injection in the lead of active implants can produce local exposures of similar magnitude as the passive field enhancement, while capacitive pathways between contacts are negligible. Above 10 kHz, applied current magnitudes increase, necessitating consideration of tissue heating. Furthermore, capacitive effects become more prominent, leading to current injection that can reach DBS-like levels. Adverse effects from abandoned/damaged leads in direct electrode vicinity cannot be excluded. SignificanceSafety related concerns of tES application in the presence of implants are systematically identified and explored, resulting in specific and quantitative guidance and establishing a basis for safety standards. Furthermore, several methods for reducing risks are suggested.

bioengineering↗

bioRxiv2022

Safety Recommendations for Temporal Interference Stimulation in the Brain

Temporal interference stimulation (TIS) is a new form of transcranial electrical stimulation (tES) that has been proposed as a method for targeted, non-invasive stimulation of deep brain structures. While TIS holds promise for a variety of clinical and non-clinical applications, little data is yet available regarding its effects in humans. To inform the design and approval of experiments involving TIS, researchers require quantitative guidance regarding exposure limits and other safety concerns. To this end, we sought to delineate a safe range of exposure parameters (voltages and currents applied via external scalp electrodes) for TIS in humans through comparisons with well-established but related brain stimulation modalities. Specifically, we surveyed the literature for adverse events (AEs) associated with transcranial alternating/direct current stimulation (tACS/tDCS), deep brain stimulation (DBS), and TIS to establish known boundaries for safe operating conditions. Drawing on the biophysical mechanisms associated with the identified AEs, we determined appropriate exposure metrics for each stimulation modality. Using these metrics, we conducted an in silico comparison of various exposure scenarios for tACS, DBS, and TIS using multiphysics simulations in an anatomically detailed head model with realistic current strengths. By matching stimulation scenarios in terms of biophysical impact, we inferred the frequency-dependent TIS stimulation parameters that resulted in exposure magnitudes known to be safe for tACS and DBS. Based on the results of our simulations and existing knowledge regarding tES and DBS safety, we propose frequency-dependent thresholds below which TIS voltages and currents are unlikely to pose a risk to humans. Safety-related data from ongoing and future human studies are required to verify and refine the thresholds proposed here.

neuroscience↗

bioRxiv2022

Non-invasive temporal interference electrical stimulation of the human hippocampus

Deep brain stimulation (DBS) via implanted electrodes is used worldwide to treat patients with severe neurological and psychiatric disorders however its invasiveness precludes widespread clinical use and deployment in research. Temporal interference (TI) is a strategy for non-invasive steerable DBS using multiple kHz-range electric fields with a difference frequency within the range of neural activity. Here we report the validation of the non-invasive DBS concept in humans. We used electric field modelling and measurements in a human cadaver to verify that the locus of the transcranial TI stimulation can be steerably focused in the hippocampus with minimal exposure to the overlying cortex. We then used functional magnetic resonance imaging (fMRI) and behaviour experiments to show that TI stimulation can focally modulate hippocampal activity and enhance the accuracy of episodic memories in healthy humans. Our results demonstrate targeted, non-invasive electrical stimulation of deep structures in the human brain.

neuroscience↗

bioRxiv2021

The SPARC DRC: Building a resource for the autonomic nervous system community

The Data and Resource Center (DRC) of the NIH-funded SPARC program is developing databases, connectivity maps and simulation tools for the mammalian autonomic nervous system. The experimental data and mathematical models supplied to the DRC by the SPARC consortium are curated, annotated and semantically linked via a single knowledgebase. A data portal has been developed that allows discovery of data and models both via semantic search and via an interface that includes Google Map-like 2D flatmaps for displaying connectivity, and 3D anatomical organ scaffolds that provide a common coordinate framework for cross-species comparisons. We discuss examples that illustrate the data pipeline, which includes data upload, curation, segmentation (for image data), registration against the flatmaps and scaffolds, and finally display via the web portal, including the link to freely available online computational facilities that will enable neuromodulation hypotheses to be investigated by the autonomic neuroscience community and device manufacturers.

neuroscience↗

bioRxiv2020

Mechanistic modeling suggests that low-intensity focused ultrasound can selectively recruit myelinated or unmyelinated nerve fibers

Low-Intensity Focused Ultrasound Stimulation (LIFUS) holds promise for the remote modulation of neuronal activity, but an incomplete mechanistic characterization hinders its clinical maturation. Here, we developed a computational framework to model intramembrane cavitation in multi-compartmental, morphologically-realistic neuronal representations, and used it to investigate ultrasound neuromodulation of peripheral nerves by spatially-varying pressure fields. Our findings show that LIFUS offers distinct parametric sub-spaces to selectively recruit myelinated or unmyelinated axons and modulate their spiking activity over physiologically relevant regimes and within safe exposure limits. This singular feature, explained by fiber-specific differences in membrane electromechanical coupling, consistently explains recent empirical findings and suggests that LIFUS can preferentially target nociceptive and sensory fibers to enable peripheral therapeutic applications not addressable by electric stimulation. These results open up new opportunities for the development of more selective and effective peripheral neuroprostheses. Our framework can be readily applied to other neural targets to establish application-specific LIFUS protocols.

neuroscience↗