Adaptive Brain Stimulation for Movement Disorders

Deep brain stimulation (DBS) has markedly changed how we treat movement disorders including Parkinson's disease (PD), dystonia, and essential tremor (ET). However, despite its demonstrable clinical benefit, DBS is often limited by side effects and partial efficacy. These limitations may be due in part to the fact that DBS interferes with both pathological and physiological neural activities. DBS could, therefore, be potentially improved were it applied selectively and only at times of enhanced pathological activity. This form of stimulation is known as closed-loop or adaptive DBS (aDBS). An aDBS approach has been shown to be superior to conventional DBS in PD in primates using cortical neuronal spike triggering and in humans employing local field potential biomarkers. Likewise, aDBS studies for essential and Parkinsonian tremor are advancing and show great promise, using both peripheral or central sensing and stimulation. aDBS has not yet been trialed in dystonia and yet exciting and promising biomarkers suggest it could be beneficial here too. In this chapter, we will review the existing literature on aDBS in movement disorders and explore potential biomarkers and stimulation algorithms for applying aDBS in PD, ET, and dystonia

Predicting the effects of deep brain stimulation using a reduced coupled oscillator model

This is the final version. Available on open access from Public Library of Science via the DOI in this recordData Availability: The data analysed in this manuscript is available from MRC BNDU Data Sharing platform at: https://data.mrc.ox.ac.uk/data-set/tremor-data-measured-essential-tremor-patients-subjected-phase-locked-deep-brain DOI: 10.5287/bodleian:xq24eN2KmDeep brain stimulation (DBS) is known to be an effective treatment for a variety of neurological disorders, including Parkinson’s disease and essential tremor (ET). At present, it involves administering a train of pulses with constant frequency via electrodes implanted into the brain. New ‘closed-loop’ approaches involve delivering stimulation according to the ongoing symptoms or brain activity and have the potential to provide improvements in terms of efficiency, efficacy and reduction of side effects. The success of closed-loop DBS depends on being able to devise a stimulation strategy that minimizes oscillations in neural activity associated with symptoms of motor disorders. A useful stepping stone towards this is to construct a mathematical model, which can describe how the brain oscillations should change when stimulation is applied at a particular state of the system. Our work focuses on the use of coupled oscillators to represent neurons in areas generating pathological oscillations. Using a reduced form of the Kuramoto model, we analyse how a patient should respond to stimulation when neural oscillations have a given phase and amplitude, provided a number of conditions are satisfied. For such patients, we predict that the best stimulation strategy should be phase specific but also that stimulation should have a greater effect if applied when the amplitude of brain oscillations is lower. We compare this surprising prediction with data obtained from ET patients. In light of our predictions, we also propose a new hybrid strategy which effectively combines two of the closed-loop strategies found in the literature, namely phase-locked and adaptive DBS

The nature of tremor circuits in parkinsonian and essential tremor

Tremor is a cardinal feature of Parkinson’s disease and essential tremor, the two most common movement disorders. Yet, the mechanisms underlying tremor generation remain largely unknown. We hypothesized that driving deep brain stimulation electrodes at a frequency closely matching the patient’s own tremor frequency should interact with neural activity responsible for tremor, and that the effect of stimulation on tremor should reveal the role of different deep brain stimulation targets in tremor generation. Moreover, tremor responses to stimulation might reveal pathophysiological differences between parkinsonian and essential tremor circuits. Accordingly, we stimulated 15 patients with Parkinson’s disease with either thalamic or subthalamic electrodes (13 male and two female patients, age: 50–77 years) and 10 patients with essential tremor with thalamic electrodes (nine male and one female patients, age: 34–74 years). Stimulation at near-to tremor frequency entrained tremor in all three patient groups (ventrolateral thalamic stimulation in Parkinson’s disease, P = 0.0078, subthalamic stimulation in Parkinson’s disease, P = 0.0312; ventrolateral thalamic stimulation in essential tremor, P = 0.0137; two-tailed paired Wilcoxon signed-rank tests). However, only ventrolateral thalamic stimulation in essential tremor modulated postural tremor amplitude according to the timing of stimulation pulses with respect to the tremor cycle (e.g. P = 0.0002 for tremor amplification, two-tailed Wilcoxon rank sum test). Parkinsonian rest and essential postural tremor severity (i.e. tremor amplitude) differed in their relative tolerance to spontaneous changes in tremor frequency when stimulation was not applied. Specifically, the amplitude of parkinsonian rest tremor remained unchanged despite spontaneous changes in tremor frequency, whereas that of essential postural tremor reduced when tremor frequency departed from median values. Based on these results we conclude that parkinsonian rest tremor is driven by a neural network, which includes the subthalamic nucleus and ventrolateral thalamus and has broad frequency-amplitude tolerance. We propose that it is this tolerance to changes in tremor frequency that dictates that parkinsonian rest tremor may be significantly entrained by low frequency stimulation without stimulation timing-dependent amplitude modulation. In contrast, the circuit influenced by low frequency thalamic stimulation in essential tremor has a narrower frequency-amplitude tolerance so that tremor entrainment through extrinsic driving is necessarily accompanied by amplitude modulation. Such differences in parkinsonian rest and essential tremor will be important in selecting future strategies for closed loop deep brain stimulation for tremor control

A guide to group effective connectivity analysis, part 2: Second level analysis with PEB

This paper provides a worked example of using Dynamic Causal Modelling (DCM) and Parametric Empirical Bayes (PEB) to characterise inter-subject variability in neural circuitry (effective connectivity). It steps through an analysis in detail and provides a tutorial style explanation of the underlying theory and assumptions (i.e, priors). The analysis procedure involves specifying a hierarchical model with two or more levels. At the first level, state space models (DCMs) are used to infer the effective connectivity that best explains a subject's neuroimaging timeseries (e.g. fMRI, MEG, EEG). Subject-specific connectivity parameters are then taken to the group level, where they are modelled using a General Linear Model (GLM) that partitions between-subject variability into designed effects and additive random effects. The ensuing (Bayesian) hierarchical model conveys both the estimated connection strengths and their uncertainty (i.e., posterior covariance) from the subject to the group level; enabling hypotheses to be tested about the commonalities and differences across subjects. This approach can also finesse parameter estimation at the subject level, by using the group-level parameters as empirical priors. The preliminary first level (subject specific) DCM for fMRI analysis is covered in a companion paper. Here, we detail group-level analysis procedures that are suitable for use with data from any neuroimaging modality. This paper is accompanied by an example dataset, together with step-by-step instructions demonstrating how to reproduce the analyses

Tuning the brakes – Modulatory role of transcranial random noise stimulation on inhibition

Electroencephalogram (EEG) and behavioural data (joystick) was collected from 15 healthy participants who completed a modified version of a Go/No-go task. This dataset consists of raw data, pre-processed EEG and behavioural data, along with impulsivity scores in a .csv file. The pre-processed data is in MATLAB .mat format. Raw Data The EEG and behavioural data (Joystick) along with trigger data was collected using a TMSi Porti amplifier with a sampling rate of 2,048Hz and is in .een format. The raw EEG files contain brain activity recorded in the first 16 channels and last 2 channels (channels 17 and 18) correspond to Joystick and Trigger information (used to identify the type of event – Go/Conflict/NoGo) respectively. The Raw data is segregated into 2 folders- Active and Sham which is further divided into baseline and after stimulation conditions. The main behavioural outcome is the change in NoGo errors (pre-processed folder- Figure 1C in from the article ‘Tuning the brakes – Modulatory role of transcranial random noise stimulation on inhibition,’ Brain Stimulation, 2024), comparing baseline and after-stimulation in sham and active conditions. Metadata corresponding to impulsivity scores and the change in NoGo behaviour are provided in ‘UPPS_nogo.csv’ (used for Figure 1D). The EEG data was recorded while the participants completed the task during baseline and after stimulation, and was used to calculate the spectral power (Figure 1E). The study also presents intermittent bursts from the EEG data, comparing the average burst durations at baseline and after-stimulation (Figure 1F) in sham and active stimulation conditions. Code All data were analysed in MATLAB (2018b) using a combination of EEGLAB, ERP LAB and FieldTrip packages. Installation guides can be found on https://sccn.ucsd.edu/eeglab/index.php https://matlab.mathworks.com/ https://erpinfo.org/erplab  https://www.fieldtriptoolbox.org/download/ The behavioural data plots use the software IOSR toolbox : https://github.com/IoSR-Surrey/MatlabToolbox Code_figure_IC.m: This script plots the NoGo error rates in baseline and after stimulation in sham and active conditions. This script uses the mat file ‘Nogo_behav_pre_post.mat’ Code_figure_1E.m: This script plots the spectral power and grand average of the after-stimulation EEG data with clusters obtained from a non-parametric analysis. This script uses the mat file ‘data_psd_trns_pre_post.mat’. Code_figure_1F.m: This script plots the intermittent burst durations during sham and active conditions and uses the file ‘Nogo_bursts_pre_post.mat

Adaptive deep brain stimulation for Parkinson's disease demonstrates reduced speech side effects compared to conventional stimulation in the acute setting.

Deep brain stimulation (DBS) for Parkinson's disease (PD) is currently limited by costs, partial efficacy and surgical and stimulation-related side effects. This has motivated the development of adaptive DBS (aDBS) whereby stimulation is automatically adjusted according to a neurophysiological biomarker of clinical state, such as β oscillatory activity (12–30 Hz). aDBS has been studied in parkinsonian primates and patients and has been reported to be more energy efficient and effective in alleviating motor symptoms than conventional DBS (cDBS) at matched amplitudes

Thalamocortical dynamics underlying spontaneous transitions in beta power in Parkinsonism

Parkinson's disease (PD) is a neurodegenerative condition in which aberrant oscillatory synchronization of neuronal activity at beta frequencies (15-35 Hz) across the cortico-basal ganglia-thalamocortical circuit is associated with debilitating motor symptoms, such as bradykinesia and rigidity. Mounting evidence suggests that the magnitude of beta synchrony in the parkinsonian state fluctuates over time, but the mechanisms by which thalamocortical circuitry regulates the dynamic properties of cortical beta in PD are poorly understood. Using the recently developed generic Dynamic Causal Modelling (DCM) framework, we recursively optimized a set of plausible models of the thalamocortical circuit (n = 144) to infer the neural mechanisms that best explain the transitions between low and high beta power states observed in recordings of field potentials made in the motor cortex of anesthetized Parkinsonian rats. Bayesian model comparison suggests that upregulation of cortical rhythmic activity in the beta-frequency band results from changes in the coupling strength both between and within the thalamus and motor cortex. Specifically, our model indicates that high levels of cortical beta synchrony are mainly achieved by a delayed (extrinsic) input from thalamic relay cells to deep pyramidal cells and a fast (intrinsic) input from middle pyramidal cells to superficial pyramidal cells. From a clinical perspective, our study provides insights into potential therapeutic strategies that could be utilized to modulate the network mechanisms responsible for the enhancement of cortical beta in PD. Specifically, we speculate that cortical stimulation aimed to reduce the enhanced excitatory inputs to either the superficial or deep pyramidal cells could be a potential non-invasive therapeutic strategy for PD

Phase-Dependent Suppression of Beta Oscillations in Parkinson's Disease Patients

Synchronized oscillations within and between brain areas facilitate normal processing, but are often amplified in disease. A prominent example is the abnormally sustained beta-frequency (∼20 Hz) oscillations recorded from the cortex and subthalamic nucleus of Parkinson's disease patients. Computational modeling suggests that the amplitude of such oscillations could be modulated by applying stimulation at a specific phase. Such a strategy would allow selective targeting of the oscillation, with relatively little effect on other activity parameters. Here, activity was recorded from 10 awake, parkinsonian patients (6 male, 4 female human subjects) undergoing functional neurosurgery. We demonstrate that stimulation arriving on a particular patient-specific phase of the beta oscillation over consecutive cycles could suppress the amplitude of this pathophysiological activity by up to 40%, while amplification effects were relatively weak. Suppressive effects were accompanied by a reduction in the rhythmic output of subthalamic nucleus (STN) neurons and synchronization with the mesial cortex. While stimulation could alter the spiking pattern of STN neurons, there was no net effect on firing rate, suggesting that reduced beta synchrony was a result of alterations to the relative timing of spiking activity, rather than an overall change in excitability. Together, these results identify a novel intrinsic property of cortico-basal ganglia synchrony that suggests the phase of ongoing neural oscillations could be a viable and effective control signal for the treatment of Parkinson's disease. This work has potential implications for other brain diseases with exaggerated neuronal synchronization and for probing the function of rhythmic activity in the healthy brain