Biology subjects

Gharabaghi, A.

Publications and source records attributed to Gharabaghi, A..

12 recordsLinked to original sources

bioRxiv2025

Novelty-dependent enhancement of sustained attention and electrophysiological signatures via transcutaneous auricular vagus nerve stimulation.

BackgroundSustained attention, the ability to remain focused during a task, relies on Locus coeruleus - Noradrenaline (LC-NA) activity. Noninvasive transcutaneous auricular vagus nerve stimulation (taVNS) is a promising tool to modulate LC-NA activity. We investigated the effects of taVNS on behavioral and electrophysiological markers of sustained attention during a Go-Nogo task. MethodsForty-four healthy participants (36 female) performed the gradual-onset Continuous Performance Task (gradCPT) on two days > 24 hours apart while EEG was recorded. 500 ms trains of taVNS or Sham stimulation was applied with each stimulus in a pseudorandomized fashion. Brain activity during the task was recorded with EEG. Behavioral and electrophysiological parameters of sustained attention were analyzed using (generalized) linear mixed models. ResultsWhen delivered during the first session, taVNS accelerated reaction times and enhanced fronto-central P1-N1 and parietal N1-P2 amplitudes. Larger P1-N1 amplitudes predicted faster responses, linking early cortical gain to behavioral performance. RT improvements persisted into the next-day sham session, whereas taVNS applied only during the familiar second session was ineffective. However, taVNS also increased RT variability, speaking against a general beneficial effect of taVNS on response automation. ConclusionWe show that taVNS modulates behavioral and electrophysiologial parameters of sustained attention and that this modulation depended on the applied stimulation order. These observations have important implications for future taVNS studies where the order of stimulation is counterbalanced across subjects.

neuroscience↗

bioRxiv2025

EEG entropy reflects both intrinsic and stimulation-induced corticospinal excitability

BackgroundCortical excitability fluctuates throughout the day on multiple timescales, ranging from milliseconds to hours. This is reflected in the large variability of the brains response to transcranial magnetic stimulation (TMS). However, robust and interpretable biomarkers of the brains current excitability state are lacking. ObjectiveWe investigated whether entropy derived from singular value decomposition of short electroencephalography (EEG) segments could serve as a biomarker of cortical excitability as probed by TMS. MethodsEntropy was computed from 1-second EEG segments preceding single-pulse TMS applied over the motor cortex. We assessed whether spontaneous fluctuations in pre-pulse entropy predicted trial-by-trial variability in TMS-induced motor-evoked potentials (MEPs). Additionally, we evaluated whether entropy tracked stimulation-induced changes in cortical excitability. ResultsHigher pre-pulse entropy, particularly over frontal regions, was associated with larger MEP amplitudes. TMS locally increased entropy over the motor cortex, while entropy decreased in other regions during the intervention. Participants who showed greater local increases in entropy from pre-to post-intervention also demonstrated larger increases in corticospinal excitability. ConclusionEntropy derived from short EEG segments reflects both intrinsic and stimulation-induced changes in cortical excitability. This marker may help optimize TMS interventions by informing brain state-dependent stimulation strategies and providing an index of intervention efficacy.

neuroscience↗

bioRxiv2024

Real-Time Spatiotemporal Filtering for Artifact-Free EEG during Electrical Neurostimulation

AO_SCPLOWBSTRACTC_SCPLOWCombining electrical neurostimulation with electroencephalography (EEG) for adaptive neurostimulation remains challenging due to the presence of stimulation artifacts in the recorded signal. Interpretation of EEG activity concurrent with stimulation requires real-time filtering of this noisy signal. While traditional frequency domain filters can suppress activity within frequency bands, they fail to differentiate sources in situations when there is a shared frequency characteristic between brain activity and stimulation signal. Here we present a new real-time denoising approach that combines spatial filtering and dynamic filter application. The spatiotemporal filter can suppress stimulation artifacts that share frequency bands with the brain signal in real-time while preserving the full spectral power. The spatial filter is also dynamically updated to account for possible changes in artifact topography. Finally, the filter is robust to changes of stimulation intensity, frequency and duration, enabling reliable denoising performance for inclusion in brain state-based closed-loop stimulation.

neuroscience↗

bioRxiv2024

EEG denoising during transcutaneous auricular vagus nerve stimulation across simulated, phantom and human data

ObjectiveThe acquisition of electroencephalogram (EEG) data during neurostimulation, particularly concurrent transcutaneous electrical stimulation of the auricular vagus nerve, introduces unique challenges for data preprocessing and analysis due to the presence of significant stimulation artifacts. This study evaluates various denoising techniques to address these challenges effectively. MethodsA variety of denoising techniques were investigated, including interpolation methods, spectral filtering, and spatial filtering techniques. The techniques evaluated included low-pass and notch filtering, spectrum interpolation, average artifact subtraction, the Zapline algorithm, and advanced methods such as independent component analysis (ICA), signal-space projection (SSP), and generalized eigendecomposition with stimulation artifact source separation (GED/SASS). The efficacy of these algorithms was evaluated across three distinct datasets: simulated data, data from a gelatin phantom model, and real human subject data. ResultsOur findings indicate that GED (SASS) and SSP significantly outperformed other methods in reducing artifacts while preserving the integrity of the EEG signal. ICA and Zapline were effective too, but came with important limitations. These methods demonstrated robustness across different data types and conditions, providing effective artifact mitigation with minimal disruption to other essential signal components. ConclusionThis comprehensive analysis demonstrates the efficacy of advanced spatial filtering techniques in the preprocessing of EEG data during auricular vagus nerve stimulation. These methods offer promising avenues for enhancing the quality and reliability of neurostimulation-associated EEG data, facilitating a deeper understanding and wider applications in clinical and research settings.

neuroscience↗

bioRxiv2024

Brain signal complexity and aperiodicity predict human corticospinal excitability

BackgroundTranscranial magnetic stimulation (TMS) holds promise for brain modulation with relevant scientific and therapeutic applications, but it is limited by response variability. Targeting state-dependent EEG features such as phase and power shows potential, but uncertainty remains about the suitable brain states. ObjectiveThis study evaluated broadband EEG measures (BEMs), including the aperiodic exponent (AE) and entropy measures (CTW, LZ), as alternatives to band-limited features, such as power and phase, for predicting corticospinal excitability (CSE). MethodsTMS was delivered with randomly applied single pulses targeting the left primary motor cortex in 34 healthy participants while simultaneously recording EEG and EMG signals. Broadband and band-limited EEG features were evaluated for their ability to predict CSE using motor evoked potentials (MEPs) from the right extensor digitorum communis muscle as the outcome measure. ResultsBEMs (AE, CTW) significantly predicted CSE, comparable to beta-band power and phase, the most predictive and spatially specific band-limited markers of motor cortex CSE. Unlike these localized CSE markers at the site of stimulation, BEMs captured more global brain states and greater within-subject variability, indicating sensitivity to dynamic state changes. Notably, CTW was associated with high CSE, while AE was linked to low CSE. ConclusionThis study reveals BEMs as robust predictors of CSE that circumvent challenges of band-limited EEG features, such as narrowband filtering and phase estimation. They may reflect more general markers of brain excitability. With their slower timescale and broader sensitivity, BEMs are promising biomarkers for state-dependent TMS applications, particularly in therapeutic contexts.

neuroscience↗

bioRxiv2023

Phase-specific stimulation reveals consistent sinusoidal modulation of human corticospinal excitability along the oscillatory beta cycle

The responsiveness of neuronal populations to incoming information fluctuates. Retrospective analyses of randomly applied stimuli reveal a neural input-output relationship along the intrinsic oscillatory cycle. Prospectively harnessing this biological mechanism would necessitate frequency- and phase-specificity, intra- and inter-individual consistency, and instantaneous access to the oscillatory cycle. We used a novel real-time approach to electroencephalography-triggered transcranial magnetic stimulation to precisely target 8 equidistant phases of the oscillatory cycle in the human motor cortex of male and female healthy participants. The phase-dependency of corticospinal excitability was investigated in ten different intrinsic frequencies (4, 8, 12, 16, 20, 24, 28, 32, 36, and 40Hz) and indexed by motor-evoked potentials (MEP) in the corresponding forearm muscle. On both the individual and group level, we detected a consistent sinusoidal MEP modulation along the oscillatory cycle at 24Hz ({chi}22 =9.2, p=.01), but not at any other target frequency (all {chi}22 <5, all p>.08). Moreover, cross-validations showed also at 24Hz the highest consistency of the optimal phase between prospective (real-time) and retrospective (out-of-sample) testing (r=.605, p<.001), and across experimental sessions on three different days (r[≥].45). The optimal corticospinal signal transmission was at the transition from the trough to the rising flank of the oscillatory 24Hz cycle. Integrating real-time measurement and brain stimulation revealed that the sinusoidal input-output relationship of corticospinal signal transmission is frequency- and phase specific, and consistent within and across individuals and sessions. In future, this approach allows to selectively and repetitively target windows of increased responsiveness, and to thereby investigate potential cumulative effects on plasticity induction.

neuroscience↗

bioRxiv2023

Phase-specific stimulation of the human brain with real-time measurement instead of prediction

BackgroundThe responsiveness of the human brain to external input fluctuates. Timing the external perturbation with regard to the oscillatory brain state may improve the intended stimulation effects. However, current brain state-dependent interventions targeting phases of the oscillatory cycle need to apply prediction algorithms to compensate for latencies between measurement and stimulation, and are therefore imprecise. ObjectiveWe investigated the phase-specific precision of a novel non-predictive approach on the basis of integrated real-time measurement and brain stimulation. MethodsApplying a simulation, we estimated the circular standard deviation (SD) to hit 2, 4, 8, 16 or 32 equidistant phase bins of the oscillatory cycle with high precision. Furthermore, we used electroencephalography-triggered transcranial magnetic stimulation in healthy subjects to empirically determine the precision of hitting the targeted phase of the oscillatory cycle for 10 different frequencies from 4Hz to 40Hz using our approach. ResultsThe simulation revealed that SDs of less than 17.6{degrees}, 9.7{degrees}, 5.1{degrees}, 2.5{degrees}, and 1.3{degrees} were necessary to precisely hit 2, 4, 8, 16, and 32 distinct phase bins of the oscillatory cycle. By completing measurement, signal-processing and stimulation with a round-time of 1ms, our empirical approach achieved SDs of 0.4{degrees} at 4Hz to 4.3{degrees} at 40Hz. This facilitates selective targeting of 32 phases (at 4Hz), 16 phases (at 8, 12, 16, 20, 24Hz) and 8 phases (at 28, 32, 36, 40Hz), respectively. ConclusionIntegrated real-time measurement and stimulation circumvents the need for prediction and results in more precise phase-specific brain stimulation than with state-of-the-art procedures.

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bioRxiv2022

The complexity of event-related MEG signals decreases with maturation in human fetuses and newborns

The complexity of neural activity is a commonly used read-out of healthy functioning in cortical circuits. Prior work has linked neural complexity to the level of maternal care in preterm infants at risk for developing mental disorders, yet the evolution of neural complexity in early human development is largely unknown. We hypothesized that cortical dynamics would evolve to optimize information processing as birth approaches, thereby increasing the complexity of cortical activity. To test this hypothesis, we conducted the first ever study relating prenatal neural complexity to maturation. MEG recordings were obtained from a sample of fetuses and newborns, including longitudinal data before and after birth. Using cortical responses to auditory irregularities, we computed several entropy measures which reflect the complexity of the MEG signal. Despite our hypothesis, neural complexity significantly decreased with maturation in both fetuses and newborns. Furthermore, we found that complexity decreased significantly faster in male fetuses for most entropy measures. Our surprising results lay the groundwork for the first ever mapping of how neural complexity evolves in early human development, with important implications for future efforts to develop predictive biomarkers of psychiatric disorders based on the complexity of perinatal MEG signals.

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bioRxiv2022

FiNN: A toolbox for neurophysiological network analysis

Recently, neuroscience has seen a shift from localist approaches to network-wide investigations of brain function. Neurophysiological signals across different spatial and temporal scales provide insight into neural communication. However, additional methodological considerations arise when investigating network-wide brain dynamics rather than local effects. Specifically, larger amounts of data, investigated across a higher dimensional space, are necessary. Here, we present FiNN (Find Neurophysiological Networks), a novel toolbox for the analysis of neurophysiological data with a focus on functional and effective connectivity. FiNN provides a wide range of data processing methods, and statistical and visualization tools to facilitate inspection of connectivity estimates and the resulting metrics of brain dynamics. The Python toolbox (https://github.com/neurophysiological-analysis/FiNN) and its documentation (https://neurophysiological-analysis.github.io/FiNN/) are freely available. We evaluated FiNN against a number of established frameworks on both a conceptual and an implementation level. We found FiNN to require much less processing time and memory than other toolboxes. In addition, FiNN adheres to a design philosophy of easy access and modifiability, while providing efficient data processing implementations. Since the investigation of network-level neural dynamics is experiencing increasing interest, we place FiNN at the disposal of the neuroscientific community as open-source software.

neuroscience↗

bioRxiv2022

Directional Absolute Coherence: a phase-based measure of effective connectivity for neurophysiology data

Communication between neural structures is a topic of much clinical and scientific interest and has been linked to a variety of behavioural, cognitive, and psychiatric measures. Here, we introduce a novel effective connectivity measure, termed the directional absolute coherence (DAC). Combining aspects of magnitude squared coherence, imaginary coherence, and phase slope index, DAC provides an estimate of connectivity that is resistant to volume conduction, encapsulates the directionality of neural communication, and is bound to the interval of -1 and 1. To highlight the properties of this newly proposed method, we compare DAC to a number of established connectivity methods using data recorded from the subthalamic nucleus of patients with Parkinsons disease with deep brain stimulation electrodes. By applying a combination of real and simulated data, we demonstrate that DAC provides a reliable estimate of the magnitude and direction of connectivity, independent of the phase difference between brain signals. As such, DAC facilitates a reliable investigation of inter-regional neural communication, rendering it a valuable tool for gaining a deeper understanding of the functional architecture of the brain and its relationship to behaviour and cognition. A Python implementation of DAC is freely available at https://github.com/neurophysiological-analysis/FiNN. Highlights- Volume conduction and limited interpretability affect many connectivity methods. - DAC is a combined approach to overcome these pitfalls. - DAC augments information content and interpretability in comparison to other methods. - DAC allows for reliable estimation of effective connectivity.

neuroscience↗

bioRxiv2022

Direct Modulation Index: a measure of phase amplitude coupling for neurophysiology data

Neural communication across different spatial and temporal scales is a topic of great interest in clinical and basic science. Phase-amplitude coupling (PAC) has attracted particular interest due to its functional role in a wide range of cognitive and motor functions. Here, we introduce a novel measure termed the direct modulation index (dMI). Based on the classical modulation index, dMI provides an estimate of PAC that is bound to an absolute interval between 0 and +1, resistant against noise, and reliable even for small amounts of data. To highlight the properties of this newly-proposed measure, we evaluated dMI by comparing it to the classical modulation index, mean vector length, and phase-locking value using simulated data. We ascertained that dMI provides a more accurate estimate of PAC than the existing methods and that is resilient to varying noise levels and signal lengths. As such, dMI permits a reliable investigation of PAC, which may reveal insights crucial to our understanding of functional brain architecture in key contexts such as behaviour and cognition. A Python toolbox that implements dMI and other measures of PAC is freely available at https://github.com/neurophysiological-analysis/FiNN. Highlights- Neural coupling measures are sensitive to higher harmonics of the target oscillation. - dMI achieves frequency-specificity by sine-fitting the phase-amplitude histogram. - Increased robustness to noise and signal duration in comparison to other measures. - dMI allows for reliable estimation of phase-amplitude coupling.

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bioRxiv2018

Risks in signal processing pipelines influencing the estimation of phase dependency for EEG-TMS

Phase-dependency of cortico-spinal excitability can be researched using TMS-EEG. Due to the large artifact, non-causal filters can smear the TMS artifact and distort the phase. However, causal filters can become biased by too high filter orders or uneven pass-bands. We explored the influence of different signal processing pipelines on the estimation of the optimal phase. This exploration involved performing two simulation studies. In the first, we simulated two different phase-dependencies (uni-versus bimodal) and sought to recover them with two distinct approaches that have previously been described. In the second, we specifically explored how filter parameters (e.g., order, pass-band) biased the phase estimation. On the basis of these findings, we propose using up-to-date toolboxes, re-running scripts after software updates and performing simulation studies in parallel to safeguard the analysis pipeline of empirical studies.

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