Biology subjects

Guggenberger, R.

Publications and source records attributed to Guggenberger, R..

6 recordsLinked to original sources

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.

neuroscience↗

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.

neuroscience↗

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.

neuroscience↗