Biology subjects

Zrenner, C.

Publications and source records attributed to Zrenner, C..

10 recordsLinked to original sources

bioRxiv2026

Spacing theta-burst stimulation enhances synaptic potentiation in the vulnerable prefrontal cortex

Neuromodulation with intermittent theta-burst stimulation (iTBS) is a clinical treatment for major depression. One postulated mechanism of iTBS is to strengthen the synaptic connections which activate the prefrontal cortex to regulate mood. With ex vivo electrical stimulation and neuronal calcium imaging, we demonstrate that a common, clinical iTBS pattern (600-stimuli, ~3-minutes) reliably strengthens the synaptic recruitment of the adult mouse prefrontal cortex. This synaptic potentiation, however, becomes less reliable in the depressive-like mouse model of prolonged social isolation. To better understand this change, we examine the impact of social isolation on the complex pattern of calcium elevation during clinical iTBS. In social isolation, the calcium peaks rise more prominently, but the induction peak no longer predicts the synaptic potentiation outcome. To better regulate calcium dynamics during induction, we test a paradigm with fewer iTBS episodes, separated by longer intervals. This spaced iTBS (90-stimuli, ~10-minutes) in the adult prefrontal cortex limits calcium elevation during induction and yields reliable long-term potentiation (LTP) following either juvenile- or adult-onset social isolation. This research illustrates new strategies to interrogate and to enhance synaptic plasticity in the vulnerable prefrontal cortex.

neuroscience↗

bioRxiv2025

Excitation-Inhibition Balance and Fronto-Limbic Connectivity Drive TMS Treatment Outcomes in Refractory Depression

Depression affects over 350 million people worldwide, with treatment resistance occurring in up to 30% of cases. Intermittent theta burst stimulation (iTBS) targeting the left dorsolateral prefrontal cortex (DLPFC) has emerged as a promising intervention, yet the neurophysiological mechanisms determining which patients will respond remain poorly understood. Here, we combined transcranial magnetic stimulation with electroencephalography and whole-brain computational modeling to uncover the mechanistic basis of treatment efficacy in 90 patients with treatment-resistant depression. We identified two distinct neurophysiological signatures that differentiate responders from non-responders: (1) post-treatment shifts in excitation-inhibition balance toward greater inhibitory control, and (2) a pre-treatment brain state characterized by anticorrelated dynamics between subgenual anterior cingulate cortex and DLPFC. These features were significantly correlated with clinical improvement and could not be explained by non-specific factors. Our findings provide a neurophysiologically-informed framework for developing personalized and optimized neuromodulation approaches in treatment-resistant depression.

neuroscience↗

bioRxiv2024

Pulsed inhibition of corticospinal excitability by the thalamocortical sleep spindle

Thalamocortical sleep spindles, i.e., oscillatory bursts at [~]12-15 Hz of waxing and waning amplitude, are a hallmark feature of non-rapid eye movement (NREM) sleep and believed to play a key role in sleep-dependent memory reactivation and consolidation. Generated in the thalamus and projecting to neocortex and hippocampus, they are phasically modulated by neocortical slow oscillations (<1 Hz) and in turn phasically modulate hippocampal sharp-wave ripples (>80 Hz). This hierarchical cross-frequency nesting may enable phase-dependent plasticity in the neocortex, and spindles have thus been considered windows of plasticity in the sleeping brain. However, the assumed phasic excitability modulation had not yet been demonstrated for spindles. Utilizing a recently developed real-time spindle detection algorithm, we applied spindle phase-triggered transcranial magnetic stimulation (TMS) to the primary motor cortex (M1) hand area and measured motor evoked potentials (MEP) to characterize corticospinal excitability during sleep spindles. We found a net suppression of MEP amplitudes during spindles, driven by selective inhibition during the falling flank of the spindle oscillation, but no inhibition during its peak, rising flank, and trough. Importantly, this phasic inhibition occurred on top of the general sleep-related inhibition observed during spindle-free NREM sleep and did not extend into the immediate refractory post-spindle periods. We conclude that spindles exert asymmetric "pulsed inhibition" of corticospinal excitability, which is assumedly relevant for processes of phase-dependent plasticity. These findings and the developed real-time spindle targeting methods will enable future studies to uncover the causal role of spindles in synaptic plasticity and systems memory consolidation.

neuroscience↗

bioRxiv2023

Intrinsic motor network connectivity predicts corticospinal excitability

State-dependent non-invasive brain stimulation (NIBS) informed by electroencephalography (EEG) has contributed to the understanding of NIBS inter-subject and inter-session variability. While these approaches focused on local EEG characteristics, it is acknowledged that the brain exhibits an intrinsic long-range dynamic organization in networks. This proof-of-concept study explores whether EEG connectivity of the primary motor cortex (M1) in the pre-stimulation period aligns with the motor network (MN) and how MN state affects responses to transcranial magnetic stimulation (TMS) of M1. One thousand suprathreshold TMS pulses were delivered to left M1 in 8 subjects at rest, with simultaneous EEG. Motor evoked potentials (MEPs) were measured from the right hand. Source-space functional connectivity of left M1 to the whole-brain was assessed using the imaginary part of the Phase Locking Value at the frequency of the sensorimotor {micro}-rhythm in a 1-second window before the pulse. Group-level connectivity revealed functional links between left M1, left supplementary motor area, and right M1. Also, pulses delivered at high MN connectivity states result in a greater MEP amplitude compared to low connectivity states. At single-subject level, this relation is more expressed in subjects that feature an overall high cortico-spinal excitability. In conclusion, this study paves the way for MN connectivity based NIBS. HighlightsO_LIEEG pre-stimulus connectivity of left M1 largely corresponds to the motor network C_LIO_LIStronger motor network (MN) connectivity corresponds to greater MEP amplitudes C_LIO_LILinear regression models based on MN connectivity predicts MEP amplitude C_LI

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bioRxiv2021

Artifacts in EEG-based BCI therapies: friend or foe?

EEG-based brain-computer interfaces (BCI) have promising therapeutic potential beyond traditional neurofeedback training, such as enabling personalized and optimized virtual reality (VR) neurorehabilitation paradigms where the timing and parameters of the visual experience is synchronized with specific brain-states. While BCI algorithms are often designed to focus on whichever portion of a signal is most informative, in these brain-state-synchronized applications, it is of critical importance that the resulting decoder is sensitive to physiological brain activity representative of various mental states, and not to artifacts, such as those arising from naturalistic movements. In this study, we compare the relative classification accuracy with which different motor tasks can be decoded from both extracted brain activity and artifacts contained in the EEG signal. EEG data was collected from 17 chronic stroke patients while performing six different head, hand, and arm movements in a realistic VR-based neurorehabilitation paradigm. Results show that the artifactual component of the EEG signal is significantly more informative than brain activity with respect to classification accuracy. This finding is consistent across different feature extraction methods and classification pipelines. While informative brain signals can be recovered with suitable cleaning procedures, we recommend that features should not be designed solely to maximize classification accuracy, as this could select for remaining artifactual components. We also propose the use of machine learning approaches that are interpretable to verify that classification is driven by physiological brain-states. In summary, whereas informative artifacts are a helpful friend in BCI-based communication applications, they can be a problematic foe in the estimation of physiological brain states.

neuroscience↗

bioRxiv2021

Real-time decoding of 5 finger movements from 2 EMG channels for mixed reality human-computer interaction

The search for optimized forms of human-computer interaction (HCI) has intensified alongside the growing potential for the combination of biosignals with virtual reality (VR) and augmented reality (AR) to enable the next generation of personal computing. At the core, this requires decoding the users biosignals into digital commands. Electromyography (EMG) is a biosensor of particular interest due to the ease of data collection, the relatively high signal-to-noise-ratio, its non-invasiveness, and the ability to interpret the signal as being generated by (intentional) muscle activity. Here, we investigate the potential of using data taken from a simple 2-channel EMG setup to differentiate 5 distinct movements. In particular, EMG was recorded from two bipolar sensors over small hand muscles (extensor digitorum, flexor digitorum profundus) while a subject performed 50 trials of dorsal extension and return for each of the five digits. The maximum and the mean values across the trial were determined for each channel and used as features. A k-nearest neighbors (kNN) classification was performed and overall 5-class classification accuracy reached 94% when using the full trials time window, while simulated real-time classification reached 90.4% accuracy when using the constructed kNN model (k=3) with a 280ms sliding window. Additionally, unsupervised learning was performed and a homogeneity of 85% was achieved. This study demonstrates that reliable decoding of different natural movements is possible with fewer than one channel per class, even without taking into account temporal features of the signal. The technical feasibility of this approach in a real-time setting was validated by sending real-time EMG data to a custom Unity3D VR application through a Lab Streaming Layer to control a user interface. Further use-cases of gamification and rehabilitation were also examined alongside the integration of eye-tracking and gesture recognition for a sensor fusion approach to HCI and user intent.

neuroscience↗

bioRxiv2019

TMS-EEG signatures of glutamatergic neurotransmission in human cortex

Neuronal activity in the brain is regulated by an excitation-inhibition balance. Glutamate is the main excitatory neurotransmitter. Transcranial magnetic stimulation (TMS) evoked electroencephalographic (EEG) potentials (TEPs) represent a novel way to quantify pharmacological effects on neuronal activity in the human cortex. Here we tested TEPs under the influence of a single oral dose of two anti-glutamatergic drugs, perampanel, an AMPA-receptor antagonist, and dextromethorphan, an NMDA-receptor antagonist, and nimodipine, an L-type voltage-gated calcium channel blocker in 16 healthy adults in a pseudorandomized, double-blinded, placebo-controlled, crossover design. Single-pulse TMS was delivered to the left motor cortex and TEPs were obtained pre-and post-drug intake. Dextromethorphan specifically increased the amplitude of the N45, a negative potential around 45 ms after the TMS pulse, while perampanel reduced the P70 amplitude in the non-stimulated hemisphere. Nimodipine and placebo had no effect on TEPs. These data extend previous pharmaco-TMS-EEG studies by demonstrating that the N45 is regulated by a balance of GABAAergic inhibition and NMDA-receptor-mediated glutamatergic excitation. In contrast, AMPA-receptor-mediated glutamatergic neurotransmission contributes to interhemispherically propagated activity reflected in the P70. These data are important to understand the physiology of TEPs as markers of excitability and propagated activity in the human cortex in health and disease.

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bioRxiv2018

TMS-evoked EEG potentials from prefrontal and parietal cortex: reliability, site specificity, and effects of dextromethorphan

Measuring the brains response to transcranial magnetic stimulation (TMS) with electroencephalography (EEG) offers a unique insight into the local cortical circuits and networks activated following stimulation, particularly in non-motor regions where less is known about TMS physiology. However, the mechanisms underlying TMS-evoked EEG potentials (TEPs) remain largely unknown. We assessed TEP reliability, site-specificity, and sensitivity to changes in excitatory neurotransmission mediated by n-methyl-d-aspartate (NMDA) receptors following stimulation of non-motor regions. In fourteen male volunteers, resting EEG and TEPs from prefrontal (PFC) and parietal (PAR) cortex were measured before and after administration of either dextromethorphan (an NMDA receptor antagonist) or placebo across two sessions separated by at least a week in a double-blinded pseudo-randomised crossover design. At baseline, TEPs showed lower within-than between-subject variability for both stimulation sites across sessions, demonstrating the reliability of non-motor TEPs within individuals. There were differences in amplitude between PFC and PAR TEPs across a wide time range (15-250 ms), however the signals were correlated after ~80 ms, suggesting that early peaks reflect site-specific activity, whereas late peaks reflect activity patterns less dependent on the stimulated sites. TEPs were not altered following dextromethorphan compared to placebo, however low frequency resting oscillations were reduced in power. Our findings suggest that TEPs from PFC and PAR: 1) are reliable within and variable between individuals; 2) reflect stimulation site specific activity across early time periods (<80 ms); and 3) are not sensitive to changes in NMDA receptor-mediated neurotransmission.

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bioRxiv2018

μ-rhythm extracted with personalized EEG filters correlates with corticospinal excitability in real-time phase-triggered EEG-TMS

Ongoing brain activity has been implicated in the modulation of cortical excitability. The combination of electroencephalography (EEG) and transcranial magnetic stimulation (TMS) in a real-time triggered setup is a novel method for testing hypotheses about the relationship between spontaneous neuronal oscillations, cortical excitability, and synaptic plasticity. For this method, a reliable real-time extraction of the neuronal signal of interest from scalp EEG with high signal-to-noise ratio (SNR) is of crucial importance. Here we compare individually tailored spatial filters as computed by spatial-spectral decomposition (SSD), which maximizes SNR in a frequency band of interest, against established local C3-centered Laplacian filters for the extraction of the sensorimotor -rhythm. Single-pulse TMS over the left primary motor cortex was synchronized with the surface positive or negative peak of the respective extracted signal, and motor evoked potentials (MEP) were recorded with electromyography (EMG) of a contralateral hand muscle. Both extraction methods led to a comparable degree of MEP amplitude modulation by phase of the sensorimotor -rhythm at the time of stimulation. This could be relevant for targeting other brain regions with no working benchmark such as the local C3-centered Laplacian filter, as sufficient SNR is an important prerequisite for reliable real-time single-trial detection of EEG features.

neuroscience↗

bioRxiv2018

EEG-triggered TMS reveals stronger brain state-dependent modulation of motor evoked potentials at weaker stimulation intensities

BackgroundCorticospinal excitability depends on the current brain state. The recent development of real-time EEG-triggered transcranial magnetic stimulation (EEG-TMS) allows studying this relationship in a causal fashion. Specifically, it has been shown that corticospinal excitability is higher during the scalp surface negative EEG peak compared to the positive peak of {micro}-oscillations in sensorimotor cortex, as indexed by larger motor evoked potentials (MEPs) for fixed stimulation intensity.\n\nObjectiveWe further characterize the effect of {micro}-rhythm phase on the MEP input-output (IO) curve by measuring the degree of excitability modulation across a range of stimulation intensities. We furthermore seek to optimize stimulation parameters to enable discrimination of functionally relevant EEG-defined brain states.\n\nMethodsA real-time EEG-TMS system was used to trigger MEPs during instantaneous brain-states corresponding to {micro}-rhythm surface positive and negative peaks with five different stimulation intensities covering an individually calibrated MEP IO curve in 15 healthy participants.\n\nResultsMEP amplitude is modulated by {micro}-phase across a wide range of stimulation intensities, with larger MEPs at the surface negative peak. The largest relative MEP-modulation was observed for weak intensities, the largest absolute MEP-modulation for intermediate intensities. These results indicate a leftward shift of the MEP IO curve during the {micro}-rhythm negative peak.\n\nConclusionThe choice of stimulation intensity influences the observed degree of corticospinal excitability modulation by {micro}-phase. Lower stimulation intensities enable more efficient differentiation of EEG {micro}-phase-defined brain states.

neuroscience↗