Biology subjects

Kuhn, A. A.

Publications and source records attributed to Kuhn, A. A..

3 recordsLinked to original sources

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

Interrogating basal ganglia circuit function in Parkinson's disease and dystonia

BackgroundThe dichotomy between the hypo-versus hyperkinetic nature of Parkinsons disease (PD) and dystonia, respectively, is thought to be reflected in the underlying basal ganglia pathophysiology. ObjectiveInvestigate differences in globus pallidus internus (GPi) neuronal activity, and short- and long-term plasticity of direct pathway projections. ResultsGPi neurons were slower, burstier, and less regular in dystonia. In PD, symptom severity positively correlated with the power of low-beta frequency spiketrain oscillations. In dystonia, symptom severity negatively correlated with firing rate, and positively correlated with neuronal variability and the power of theta frequency spiketrain oscillations. Dystonia was moreover associated with less long-term plasticity and slower synaptic depression. ConclusionWe substantiated claims of hyper-versus hypofunctional GPi output in PD versus dystonia, and provided cellular-level validation of the pathological nature of theta and low-beta oscillations in respective disorders. Such circuit changes may be underlain by disease-related differences in plasticity of striato-pallidal synapses.

neuroscience↗

bioRxiv2020

Average beta burst duration profiles provide a signature of dynamical changes between the ON and OFF medication states in Parkinson's disease

Parkinsons disease motor symptoms are associated with an increase in subthalamic nucleus beta band oscillatory power. However, these oscillations are phasic, and there is a growing body of evidence suggesting that beta burst duration may be of critical importance to motor symptoms. This makes insights into the dynamics of beta bursting generation valuable, in particular to refine closed-loop deep brain stimulation in Parkinsons disease. In this study, we ask the question "Can average burst duration reveal how dynamics change between the ON and OFF medication states?". Our analysis of local field potentials from the subthalamic nucleus demonstrates using linear surrogates that the system generating beta oscillations is more likely to act in a non-linear regime OFF medication and that the change in a non-linearity measure is correlated with motor impairment. In addition, we pinpoint the simplest dynamical changes that could be responsible for changes in the temporal patterning of beta oscillations between medication states by fitting to data biologically inspired models, and simpler beta envelope models. Finally, we show that the non-linearity can be directly extracted from average burst duration profiles under the assumption of constant noise in envelope models. This reveals that average burst duration profiles provide a window into burst dynamics, which may underlie the success of burst duration as a biomarker. In summary, we demonstrate a relationship between average burst duration profiles, dynamics of the system generating beta oscillations, and motor impairment, which puts us in a better position to understand the pathology and improve therapies such as deep brain stimulation. Author summaryIn Parkinsons disease, motor impairment is associated with abnormal oscillatory activity of neurons in deep motor regions of the brain. These oscillations come in the shape of bursts, and the duration of these bursts has recently been shown to be of importance to motor symptoms. To better understand the disease and refine therapies, we relate the duration of these bursts to properties of the system generating them in the pathological state and in a proxy of the healthy state. The data suggest that the system generating bursts involves more complexity in the pathological state, and we show that a measure of this complexity is associated with motor impairment. We propose biologically inspired models and simpler models that can generate the burst patterns observed in the pathological and healthy state. The models confirm what was observed in data, and tell us how burst generation mechanisms could differ in the disease. Finally, we identify a mathematical link allowing us to infer properties of the burst generating system from burst duration measurements in patient recordings. This sheds some light on the significance of burst duration as a marker of pathology.

neuroscience↗