Biology subjects

Florin, E.

Publications and source records attributed to Florin, E..

16 recordsLinked to original sources

bioRxiv2026

Role of GABA and NMDA receptors in shaping cortical timescales and large-scale network dynamics

Cortical brain regions integrate information across different timescales, ranging from fast sensory processing to longer integration windows, allowing cognitive functions like working memory. At the large-scale, brain regions organize into transient network states that rapidly switch over time and similarly contribute to cognition. Both cortical timescales and large-scale network dynamics are proposed to be determined by the balance between recurrent synaptic excitation and GABAergic inhibition. Here, we pharmacologically manipulated synaptic transmission at GABAA and NMDA receptors in 60 healthy male participants and acquired resting-state magnetoencephalography. Neuronal timescales followed a hierarchical gradient with shorter timescales in early sensory regions. Increasing GABAergic activity prolonged neuronal timescales across cortical regions. This effect was most prominent in the frontal default mode and in the dorsal attention network. Notably, dynamic network analyses revealed that the occurrence probability of the frontal default mode network increased, whereas the occurrence of the dorsal attention network was reduced. NMDA receptor modulation resulted in no significant changes. Together, these findings provide causal evidence that GABAergic inhibition is a key regulator of cortical temporal organization, linking microscale synaptic mechanisms to neuronal timescales and network dynamics that support diverse cognitive function.

neuroscience↗

bioRxiv2025

Reward-induced endogenous pain inhibition scales with action-outcome certainty in humans

Endogenous pain modulation is thought to encompass a crucial evolutionary purpose in guiding decision-making away from harm. This is well exemplified by the idea that mechanisms of learning through error-correction and endogenous pain modulation are inherently intertwined. However, adaptive behavior requires more than learning through error-correction. Biological environments are volatile, which can cause decision-makers to be uncertain about what actions lead to rewards or punishments. Evidence on how uncertainty in action-outcome distributions impacts endogenous pain modulation is lacking. In the present study, we extend and adapt a well-established paradigm for the study of endogenous pain modulation with the implementation of a reversal learning outcome schedule. Thirty healthy human volunteers took part in this probabilistic gambling task, where they had to gamble for the obtainment of pain relief and the avoidance of pain punishments. Using a computational approach, we show that decision-making in a situation of acute pain is best explained by models that account for uncertainty. Such uncertainty is associated with the observed pain modulation in the task, indicating that endogenous pain modulation may be sensitive to volatility and the perception of uncertainty. Specifically, pain inhibition from winning pain relief increases as a function of certainty in what actions lead to pain relief. Our findings emphasize the importance of considering mechanisms of uncertainty processing in reinforcement learning from painful outcomes and endogenous pain modulation. Those mechanisms could be relevant to understanding behavioral changes in chronic pain, where altered reinforcement learning has already been established.

neuroscience↗

bioRxiv2025

Parameter scalability of multivariate Granger causality

Estimating causal interactions between signals provides unique insights into their dynamics, and causal inference has been widely applied to electrophysiological data to elucidate brain communication. Multivariate autoregressive models (MVAR) form the basis of most causal estimation methods. However, the high dimensionality of whole-brain data renders MVARs difficult to estimate reliably, and reducing the dimensions to a reasonable range affects causal inference. To address these scalability limitations, we develop sparse Multivariate Granger Causality (sMVGC), a novel method premised on the assumption that true causal connections between signals are sparse, thereby constraining the candidate search space and improving scalability. To motivate sMVGC empirically, we simulate electrophysiological data with known causalities and model how the number of samples, signals, and MVAR order affect the performance and computation time of current algorithms. All algorithms scale at least quadratically with these parameters, yet differ in their sensitivity to signal versus sample count, and the sample requirements for accurate inference scale with the number of signals. Guided by these findings, sMVGC improves parameter scalability while preserving estimation accuracy, and we provide practical parameter ranges and model selection guidance for real-world analyses.

neuroscience↗

bioRxiv2025

Beyond beta: Aperiodic broadband power reflects Parkinson's disease severity - a multicenter study

Parkinsons disease is linked to increased beta oscillations in the subthalamic nucleus, which correlate with motor symptoms. However, findings across studies have varied. Our standardized analysis of multicenter datasets shows that small sample sizes contributed to these discrepancies--a challenge we address by pooling datasets into one large cohort (n=119). Moving beyond beta power, we disentangled spectral components reflecting distinct neural processes. Combining aperiodic offset, low beta, and low gamma oscillations explained significantly more variance in symptom severity than beta alone. Moreover, interhemispheric within-patient analyses showed that, unlike beta oscillations, aperiodic broadband power-likely reflecting spiking activity-was increased in the more affected hemisphere. These findings identify aperiodic broadband power as a potential biomarker for adaptive deep brain stimulation and provide novel insights into the relationship between subthalamic hyperactivity and motor symptoms in human Parkinsons disease.

neuroscience↗

bioRxiv2023

Deep brain stimulation device-specific artefacts in MEG recordings

BackgroundDeep brain stimulation (DBS) has strong beneficial effects for treating movement disorders. The related cortical mechanisms can be studied with magnetoencephalography (MEG) during active DBS. However, MEG is prone to artefacts induced by the electrical stimulation and the movement of ferromagnetic DBS components. Although artefacts might vary between DBS devices from different manufacturers, no such comparison has been performed. To date, no combined MEG-DBS studies have been conducted within Yokogawa MEG systems. ObjectiveThe aim of the present study was to compare DBS artefacts in MEG phantom recordings acquired with two MEG systems (Neuromag, Yokogawa) using DBS devices from three different manufacturers (Abbott, Boston Scientifc, Medtronic) and to test whether established cleaning methods can sufficiently reduce artefacts. MethodsDBS devices, electrodes, and extension cables were attached to a gelatine MEG phantom, and data was acquired in a Neuromag and a Yokogawa MEG system. ResultsThere are device-specific differences in movement-related artefacts with weaker artefacts for Boston Scientific (BSC) devices. Stimulation-, movement- and IPG-related artefacts are best cleaned when combining the ICA-MI and Hampel filter across recordings and DBS devices. However, the cleaning of movement-related artefacts can result in artefactual spectral peaks in physiologically relevant frequencies below 20 Hz. ConclusionsDevice-specific IPG-related artefacts have to be considered for MEG-DBS studies and can be cleaned with combinations of published cleaning methods for MEG and EEG data. Critically, cleaning movement-related artefacts can potentially result in spectral peaks, which resemble physiological activity. Finally, combined MEG-DBS recordings are feasible in Yokogawa-MEG systems.

neuroscience↗

bioRxiv2023

Spontaneous network transitions predict somatosensory perception

Sensory perception is essential for transforming incoming information in the brain into targeted behavior. Our brains are everlastingly active, and variations in perception are ubiquitously associated with human behavioral performance. Previous studies indicate that changes in spontaneous neural activity within local sensory areas correlate with the perception of ambiguous stimuli. However, the contribution of whole brain spontaneous networks to perception is not well understood. Using an ambiguous tactile temporal discrimination task, we demonstrate that the interaction between wholebrain networks in the seconds of the spontaneous pre-stimulus period also contributes to perception during the task. Transitions to a frontal and a multi-frequency network across the brain are essential for the correct percept. Conversely, incorrect percepts are mainly preceded by transitions to an alphaparietal network. Brain transitions occur faster during the period before stimulus presentation for correct stimuli detection, suggesting the need for enhanced network flexibility during this phase. Significance statementOur brain is constantly engaged in processing sensory input and translating it into sensory perceptions. When confronted with ambiguous sensory information, individuals do not always have the same perceptual experience. We demonstrate that brain network transitions to frontal areas are essential for the correct percept. Conversely, incorrect percepts are mainly preceded by transitions to an alpha-parietal network. Correct stimuli detections are characterized by faster transitions, suggesting the need for enhanced network flexibility. These results extend our knowledge of perception by pointing to the relevance of whole-brain spontaneous networks and their dynamic properties.

neuroscience↗

bioRxiv2023

Combining Magnetoencephalography with Telemetric Streaming of Intracranial Recordings and Deep Brain Stimulation - a Feasibility Study

The combination of subcortical Local Field Potential (LFP) recordings and stimulation with Magnetoencephalography (MEG) in Deep Brain Stimulation (DBS) patients enables the investigation of cortico-subcortical communication patterns and provides insights into DBS mechanisms. Until now, these recordings have been carried out in post-surgical patients with externalised leads. However, a new generation of telemetric stimulators makes it possible to record and stream LFP data in chronically implanted patients. Nevertheless, whether such streaming can be combined with MEG has not been tested. In the present study, we tested the most commonly implanted telemetric stimulator - Medtronic Percept PC with a phantom in three different MEG systems: two cryogenic scanners (CTF and MEGIN) and an experimental Optically Pumped Magnetometry (OPM)-based system. We found that when used in combination with the new SenSight segmented leads, Percept PC telemetric streaming only generates band-limited interference in the MEG at 123Hz and harmonics. However, the legacy streaming mode used with older lead models generates multiple, dense artefact peaks in the physiological range of interest (below 50Hz). The effect of stimulation on MEG critically depends on whether it is done in bipolar (between two contacts on the lead) or monopolar (between a lead contact and the stimulator case) mode. Monopolar DBS creates severe interference in the MEG as previously reported. However, we found that the OPM system is more resilient to this interference and could provide artefact-free measurements at least for limited frequency ranges. A resting measurement in the MEGIN system from a Parkinsons patient implanted with Percept PC and subthalamic SenSight leads revealed artefact patterns consistent with our phantom recordings. Moreover, analysis of LFP-MEG coherence in this patient showed oscillatory coherent networks consistent in their frequency and topography with those described in published group studies done with externalised leads. In conclusion, Percept PC telemetric streaming with SenSight leads is compatible with MEG. Furthermore, OPM sensors could provide additional new opportunities for studying DBS effects.

neuroscience↗

bioRxiv2022

Subthalamic stimulation evoked cortical responses relate to motor performance in Parkinson's disease

Subthalamic deep brain stimulation is effective in alleviating motor symptoms in Parkinsons disease. Establishing the clinically best stimulation settings often requires time-consuming test sessions and creates a need for biomarkers to optimize this process. While stimulation-evoked cortical responses have been proposed as such a neurophysiological marker, their relationship to motor performance has not yet been studied systematically. For this aim, we recorded finger-tapping movements and cortical responses evoked by different stimulation amplitudes of 22 patients with Parkinsons disease using magnetoencephalography. The motor cortex amplitude was a significant predictor of a higher finger tap frequency and a more regular tapping profile. In addition, subthalamic stimulation evoked responses in the inferior and middle frontal gyrus, and the supplementary motor area. While earlier studies relied on a limited cortical coverage, we reveal a cortical distribution of responses that aligns with the basal ganglia-thalamo-cortical network. Our study sheds light on the relationship between cortical responses evoked by subthalamic stimulation and motor performance based on objective quantitative parameters. Stimulation-evoked responses could guide clinical programming in the future.

neuroscience↗

bioRxiv2022

Whole-brain dynamical modeling for classification of Parkinson's disease

Simulated whole-brain connectomes demonstrate an enhanced inter-individual variability depending on data processing and modeling approach. By considering the human brain connectome as an individualized attribute, we investigate how empirical and simulated whole-brain connectome-derived features can be utilized to classify patients with Parkinsons disease against healthy controls in light of varying data processing and model validation. To this end, we applied simulated blood oxygenation level-dependent signals derived by a whole-brain dynamical model simulating electrical signals of neuronal populations to reveal differences between patients and controls. In addition to the widely used model validation via fitting the dynamical model to empirical neuroimaging data, we invented a model validation against behavioral data, such as subject classes, which we refer to as behavioral model fitting and show that it can be beneficial for Parkinsonian patient classification. Furthermore, the results of machine-learning reported in this study also demonstrated that performance of the patient classification can be improved when the empirical data are complemented by the simulation results. We also showed that temporal filtering of blood oxygenation level-dependent signals influences the prediction results, where the filtering in the low-frequency band is advisable for Parkinsonian patient classification. In addition, composing the feature space of empirical and simulated data from multiple brain parcellation schemes provided complementary features that improve prediction performance. Based on our findings, we suggest including the simulation results with empirical data is effective for inter-individual research and its clinical application.

neuroscience↗

bioRxiv2022

Slowing of Frontocentral Beta Oscillations in Atypical Parkinsonism

Diagnosis of atypical parkinsonian syndromes mostly relies on clinical presentation as well as structural and molecular brain imaging. It has not been investigated whether cortical oscillatory activity exhibits features distinguishing these syndromes. Therefore, we measured resting-state magnetoencephalography in 13 patients with corticobasal syndrome, 10 patients with progressive supranuclear palsy, 23 patients with idiopathic Parkinsons disease and 23 healthy controls. We compared spectral power as well as amplitude and frequency of power peaks between the groups. Atypical Parkinsonism was associated with spectral slowing, distinguishing both corticobasal syndrome and progressive supranuclear palsy from age-matched healthy controls and Parkinsons disease. Patients with atypical Parkinsonism showed a shift of beta peaks (13-30 Hz) towards lower frequencies in frontal and central areas bilaterally. A concomitant increase in theta/alpha power relative to controls was observed in both atypical parkinsonian syndromes and in Parkinsons disease. Our results demonstrate that slowing of frontocentral beta oscillations is characteristic of atypical Parkinsonism. Spectral slowing with a different topography has previously been observed in other neurodegenerative disorders, such as Alzheimers disease, suggesting that spectral slowing might be an electrophysiological marker of cortical neurodegeneration. As such, it might support differential diagnosis of parkinsonian syndromes in the future. HighlightsO_LISlowing of beta oscillations distinguishes atypical parkinsonian syndromes from healthy controls and idiopathic Parkinsons disease C_LIO_LISpectral slowing is most pronounced in frontocentral areas C_LIO_LIBeta oscillations are shifted towards lower frequencies independent of their amplitude C_LI

neuroscience↗

bioRxiv2021

Separating neural oscillations from aperiodic 1/f activity: challenges and recommendations

Electrophysiological power spectra typically consist of two components: An aperiodic part usually following an 1/f power law P{propto}1/f{beta}and periodic components appearing as spectral peaks. While the investigation of the periodic parts, commonly referred to as neural oscillations, has received considerable attention, the study of the aperiodic part has only recently gained more interest. The periodic part is usually quantified by center frequencies, powers, and bandwidths, while the aperiodic part is parameterized by the y-intercept and the 1/f exponent {beta}. For investigation of either part, however, it is essential to separate the two components. In this article, we scrutinize two frequently used methods, FOOOF (Fitting Oscillations & One-Over-F) and IRASA (Irregular Resampling Auto-Spectral Analysis), that are commonly used to separate the periodic from the aperiodic component. We evaluate these methods using diverse spectra obtained with electroencephalography (EEG), magnetoencephalography (MEG), and local field potential (LFP) recordings relating to three independent research datasets. Each method and each dataset poses distinct challenges for the extraction of both spectral parts. The specific spectral features hindering the periodic and aperiodic separation are highlighted by simulations of power spectra emphasizing these features. Through comparison with the simulation parameters defined a priori, the parameterization error of each method is quantified. Based on the real and simulated power spectra, we evaluate the advantages of both methods, discuss common challenges, note which spectral features impede the separation, assess the computational costs, and propose recommendations on how to use them.

neuroscience↗

bioRxiv2021

Evaluation of data-driven MEG analysis approaches for the extraction of fMRI resting state networks

The electrophysiological basis of resting state networks (RSN) is still under debate. In particular, no principled mechanism has been determined that is capable of explaining all RSN equally well. While magnetoencephalography (MEG) and electroencephalography (EEG) are the methods of choice to determine the electrophysiological basis of RSN, no standard analysis pipeline of RSN yet exists. In this paper, we compare the two main existing data-driven analysis strategies for extracting resting state networks from MEG data. The first approach extracts RSN through an independent component analysis (ICA) of the Hilbert envelope in different frequency bands. The second approach uses phase -amplitude coupling to determine the RSN. To evaluate the performance of these approaches, we compare the MEG-RSN to the functional magnetic resonance imaging (fMRI)-RSN from the same subjects. Overall, it was possible to extract the canonical fMRI RSN with MEG. The approach based on phase-amplitude coupling yielded the best correspondence to the fMRI-RSN. The Hilbert envelope-ICA produced different dominant frequency-bands underlying RSN for different ICA runs, suggesting the absence of a single dominant frequency underlying the RSN. Our results also suggest that individual RSN are not characterized by one single dominant frequency. Instead, the resting state networks seem to be based on a combination of the delta/theta phase and gamma amplitude.

neuroscience↗

bioRxiv2020

Neurophysiological network dynamics of pitch change detection.

The detection of pitch changes is crucial to sound localization, music appreciation and speech comprehension, yet the brain network oscillatory dynamics involved remain unclear. We used time-resolved cortical imaging in a pitch change detection task. Tone sequences were presented to both typical listeners and participants affected with congenital amusia, as a model of altered pitch change perception. Our data show that tone sequences entrained slow (2-4 Hz) oscillations in the auditory cortex and inferior frontal gyrus, at the pace of tone presentations. Inter-regional signaling at this slow pace was directed from auditory cortex towards the inferior frontal gyrus and motor cortex. Bursts of faster (15-35Hz) oscillations were also generated in these regions, with directed influence from the motor cortex. These faster components occurred precisely at the expected latencies of each tone in a sequence, yielding a form of local phase-amplitude coupling with slower concurrent activity. The intensity of this coupling peaked dynamically at the moment of anticipated pitch changes. We clarify the mechanistic relevance of these observations in relation to behavior as, by task design, typical listeners outperformed amusic participants. Compared to typical listeners, inter-regional slow signaling toward motor and inferior frontal cortices was depressed in amusia. Also, the auditory cortex of amusic participants over-expressed tonic, fast-slow phase-amplitude coupling, pointing at a possible misalignment between stimulus encoding and internal predictive signaling. Our study provides novel insight into the functional architecture of polyrhythmic brain activity in auditory perception and emphasizes active, network processes involving the motor system in sensory integration.

neuroscience↗

bioRxiv2020

Differential dopaminergic modulation of spontaneous cortico-subthalamic activity in Parkinson's disease

Pathological oscillations including elevated beta activity in the subthalamic nucleus (STN) and between STN and cortical areas are a hallmark of neural activity in Parkinsons disease (PD). Oscillations also play an important role in normal physiological processes and serve distinct functional roles at different points in time. We characterised the effect of dopaminergic medication on oscillatory whole-brain networks in PD in a time-resolved manner by employing a hidden Markov model on combined STN local field potentials and magnetoencephalography (MEG) recordings from 17 PD patients. Dopaminergic medication led to coherence within the medial and orbitofrontal cortex in the delta/theta frequency range. This is in line with known side effects of dopamine treatment such as deteriorated executive functions in Parkinsons disease. In addition, dopamine caused the beta band activity to switch from an STN-mediated motor network to a frontoparietal-mediated one. In contrast, dopamine did not modify local STN-STN coherence in PD. STN-STN synchrony emerged both on and off medication. By providing electrophysiological evidence for the differential effects of dopaminergic medication on the discovered networks, our findings open further avenues for electrical and pharmacological interventions in PD.

neuroscience↗

bioRxiv2017

Imaging of neural oscillations with embedded inferential and group prevalence statistics.

Magnetoencephalography and electroencephalography (MEG, EEG) are essential techniques for studying distributed signal dynamics in the human brain. In particular, the functional role of neural oscillations remains to be clarified. Imaging methods need to identify distinct brain regions that concurrently generate oscillatory activity, with adequate separation in space and time. Yet, spatial smearing and inhomogeneous signal-to-noise are challenging factors to source reconstruction from external sensor data. The detection of weak sources in the presence of stronger regional activity nearby is a typical complication of MEG/EEG source imaging. We propose a novel, hypothesis-driven source reconstruction approach to address these methodological challenges1. The imaging with embedded statistics (iES) method is a subspace scanning technique that constrains the mapping problem to the actual experimental design. A major benefit is that, regardless of signal strength, the contributions from all oscillatory sources, which activity is consistent with the tested hypothesis, are equalized in the statistical maps produced. We present extensive evaluations of iES on group MEG data, for mapping 1) induced oscillations using experimental contrasts, 2) ongoing narrow-band oscillations in the resting-state, 3) co-modulation of brain-wide oscillatory power with a seed region, and 4) co-modulation of oscillatory power with peripheral signals (pupil dilation). Along the way, we demonstrate several advantages of iES over standard source imaging approaches. These include the detection of oscillatory coupling without rejection of zero-phase coupling, and detection of ongoing oscillations in deeper brain regions, where signal-to-noise conditions are unfavorable. We also show that iES provides a separate evaluation of oscillatory synchronization and desynchronization in experimental contrasts, which has important statistical advantages. The flexibility of iES allows it to be adjusted to many experimental questions in systems neuroscience.\n\nAuthor summaryThe oscillatory activity of the brain produces a repertoire of signal dynamics that is rich and complex. Noninvasive recording techniques such as scalp magnetoencephalography and electroencephalography (MEG, EEG) are key methods to advance our comprehension of the role played by neural oscillations in brain functions and dysfunctions. Yet, there are methodological challenges in mapping these elusive components of brain activity that have remained unresolved. We introduce a new mapping technique, called imaging with embedded statistics (iES), which alleviates these difficulties. With iES, signal detection is constrained explicitly to the operational hypotheses of the study design. We show, in a variety of experimental contexts, how iES emphasizes the oscillatory components of brain activity, if any, that match the experimental hypotheses, even in deeper brain regions where signal strength is expected to be weak in MEG. Overall, the proposed method is a new imaging tool to respond to a wide range of neuroscience questions concerning the scaffolding of brain dynamics via anatomically-distributed neural oscillations.

neuroscience↗

bioRxiv2016

Phase-coherence classification: a new wavelet-based method to separate local field potentials into local (in)coherent and volume-conducted components

Local field potentials (LFP) reflect the integrated electrophysiological activity of a large group of neurons. To minimize influence of external activity on the analysis, conventionally bipolar recordings are used to eliminate volume-conducted signals. Here we introduce a novel method, called phase-coherence classification (PCC), to separate LFP in time-frequency domain into a volume-conducted, a local incoherent and local coherent signal. The PCC allows to compute the power spectral densities of each signal and to associate each class with possible locations of electro-physiological activity. In order to test the resolution properties and accuracy of the method we generate composite and non-stationary synthetic time series with similar statistical characteristics as measured LFP. The PCC identifies volume-conducted signals with a phase threshold that is determined from probability density functions of non-phase-shifted synthetic time series. We estimate optimal PCC parameters for the analysis of beta band oscillations in LFP and apply the PCC to a test data set obtained from within the subthalamic nucleus of eight patients with Parkinsons disease (PD). We show that PCC can identify activity of multiple local clusters during a tremor episode and quantify the relative power of local and volume-conducted signals. We further analyze the electrophysiological response to an apomorphine injection during rest and show that incoherent activity in the low beta band shows a significant medication-induced decrease. We further find significant movement-induced changes on medication of the local coherent signal, which increased during an isometric hold task and decreased during phasic wrist movement. This indicates a different role of incoherent and coherent signals possibly related to physiologically different networks. This new PCC method can potentially also be applied to EEG and MEG data in order to minimize the influence of spatial leakage on power spectra and coherence estimates.

neuroscience↗