Cortical beta burst dynamics are altered in Parkinson's disease but normalized by deep brain stimulation

Possible regulatory mechanisms of typical and atypical absence seizures through an equivalent projection from the subthalamic nucleus to the cortex: Evidence in a computational model

The subthalamic nucleus (STN) is an important structure that regulates basal ganglia output and has been involved in the pathophysiology of epilepsy disease. In this paper, we propose an equivalent inhibitory pathway directly projecting from the STN to the cortex and systematically study its regulatory effect on absence seizures. Interestingly, we find that this equivalent inhibitory projection is a key factor for assisting in the development of atypical absence seizures. Through computational simulation and model analysis, we find that the enhancement of coupling strength on this equivalent STN-cortex projection can effectively suppress typical and atypical spike and wave discharges (TSWDs and ASWDs) during absence seizures. Furthermore, altering the activation level of STN through external stimuli can also control seizures, and the presence of equivalent STN-cortex projection makes the control effect more easier to achieve. Several direct and indirect pathways related to the STN can achieve inhibition of SWDs by regulating the activation level of STN, and relevant control strategies have high biological plausibility. Therefore, the STN may be an effective target for the deep brain stimulation (DBS) to control absence seizures. Importantly, we observe that the control effect of DBS-STN on SWDs is significantly superior to other basal ganglia targets in this model. Moreover, we find that the parameter range and value with high biological plausibility for the coupling weight in this equivalent STN-cortex projection can be effectively estimated in this model. Our results imply that the inhibitory effect from the STN to the cortex plays a crucial role in regulating both typical and atypical SWDs, and the STN might be a potential and reasonable DBS target for the treatment of absence epilepsy.

Characteristics of multimodal physiological signal differences in symptom fluctuations in Parkinson's disease

This study investigates the differences in multimodal physiological signals between patients with Parkinson's disease (PD) and healthy individuals, focusing on how symptom fluctuations affect these signals in PD. A total of 35 PD patients and 30 healthy controls participated. The PD patients were further categorized into two groups: those with symptom fluctuations (SF) and those without (NSF). Multimodal physiological signals, including EEG, ECG, respiration, and pulse, were recorded in resting state. Features were extracted from these signals and analyzed using non-parametric statistical tests. The results showed that the SF group had significantly higher absolute power in the β bands in the frontal, parietal, and central regions, as well as increased δ band power in the parietal regions compared to the NSF group. Additionally, several time-domain characteristics of the ECG signal were significantly greater in the SF group. These findings suggest that symptom fluctuations may influence cortical activity and cardiac autonomic function in PD patients. While levodopa-based treatments can alleviate certain symptoms, they may not fully compensate for the functional alterations in brain activity. Further research is needed to explore the effects on other physiological systems.

Pseudo-MRI Engine for MRI-Free Electromagnetic Source Imaging

Structural head MRIs are a crucial ingredient in MEG/EEG source imaging; they are used to define a realistically shaped volume conductor model, constrain the source space, and visualize the source estimates. However, individual MRIs are not always available, or they may be of insufficient quality for segmentation, leading to the use of a generic template MRI, matched MRI, or the application of a spherical conductor model. Such approaches deviate the model geometry from the true head structure and limit the accuracy of the forward solution. Here, we implemented an easy-to-use tool, pseudo-MRI engine, which utilizes the head-shape digitization acquired during a MEG/EEG measurement for warping an MRI template to fit the subject's head. To this end, the algorithm first removes outlier digitization points, densifies the point cloud by interpolation if needed, and finally warps the template MRI and its segmented surfaces to the individual head shape using the thin-plate-spline method. To validate the approach, we compared the geometry of segmented head surfaces, cortical surfaces, and canonical brain regions in the real and pseudo-MRIs of 25 subjects. We also tested the MEG source reconstruction accuracy with pseudo-MRIs against that obtained with the real MRIs from individual subjects with simulated and real MEG data. We found that the pseudo-MRI enables comparable source localization accuracy to the one obtained with the subject's real MRI. The study indicates that pseudo-MRI can replace the need for individual MRI scans in MEG/EEG source imaging for applications that do not require subcentimeter spatial accuracy.

The effect of L-dopa and DBS on cortical oscillations in Parkinson's disease analyzed by hidden Markov model algorithm

BACKGROUND

Parkinson's disease (PD) is a movement disorder caused by dopaminergic neurodegeneration. Both Levodopa (L-dopa) and Subthalamic Deep Brain Stimulation (STN-DBS) effectively alleviate symptoms, yet their cerebral effects remain under-explored. Understanding these effects is essential for optimizing treatment strategies and assessing disease severity. Magnetoencephalogram (MEG) data provide a continuous time series signal that reflects the dynamic changes in brain activity. The hidden Markov model (HMM) can capture and model the temporal features and underlying states of the MEG signal to extract potential brain states and monitor dynamic changes. In this study, we employed HMM to investigate the cortical mechanism underlying the treatment of PD patients using MEG recordings.

METHODS

21 PD patients treated with medication underwent MEG recording in both L-dopa medoff and medon conditions. Additionally, 11 PD patients receiving STN-DBS treatment underwent MEG recording in both dbsoff and dbson conditions. The MEG data were segmented into four states by Time-delay embedded Hidden Markov Model (TDE-HMM) algorithm. The state parameters including Fractional Occupancy (FO), Interval Times (IT), and Life Time (LT) for each state and power spectrum of β band were analyzed to study the effects of L-dopa and STN-DBS treatment respectively.

RESULTS

L-dopa significantly increased the motor state of HMM and power in the motor area of both high β (21-35 Hz) and low β (13-20 Hz); the motor state of high β in medoff were correlated with the Unified Parkinson's Disease Rating Scale III (UPDRS III). Conversely, DBS significantly diminishes the motor state of HMM and power in motor area of high β oscillations. The score changes of tremor and limb rigidity after DBS treatment were significantly correlated with the changes of motor state of high β.

CONCLUSIONS

This study demonstrates that L-dopa and STN-DBS exert differing effects on β oscillations in the motor cortex of PD patients, primarily in high β band. Understanding these distinct neurophysiological impacts can provide valuable insights for refining therapeutic approaches in motor control for PD patients.

A framework for translational therapy development in deep brain stimulation

Deep brain stimulation (DBS) is an established treatment for motor disorders like Parkinson's disease, but its mechanisms and effects on neurons and networks are not fully understood, limiting research-driven progress. This review presents a framework that combines neurophysiological insights and translational research to enhance DBS therapy, emphasizing biomarkers, device technology, and symptom-specific neuromodulation. It also examines the role of animal research in improving DBS, while acknowledging challenges in clinical translation.

Deep Brain Stimulation restores information processing in parkinsonian cortical networks

Parkinson's disease (PD) is a neurodegenerative disorder associated with alterations of neural activity and information processing primarily in the basal ganglia and cerebral cortex. Deep brain stimulation (DBS) of the subthalamic nucleus (STN-DBS) is the most effective therapy when patients experience levodopa-induced motor complications. A growing body of evidence points towards a cortical effect of STN-DBS, restoring key electrophysiological markers, such as excessive beta band oscillations, commonly observed in PD. However, the mechanisms of STN-DBS remain elusive. Here, we aim to better characterize the cortical substrates underlying STN-DBS-induced improvement in motor symptoms. We recorded electroencephalograms (EEG) from PD patients and found that, although apparent EEG features were not different with or without therapy, EEG signals could more accurately predict limb movements under STN-DBS. To understand the origins of this enhanced information transmission under STN-DBS in the human EEG data, we investigated the information capacity and dynamics of a variety of computational models of cortical networks. The extent of improvement in decoding accuracy of complex naturalistic inputs under STN-DBS depended on the synaptic parameters of the network as well as its excitability and synchronization levels. Additionally, decoding accuracy could be optimized by adjusting STN-DBS parameters. Altogether, this work draws a comprehensive link between known alterations in cortical activity and the degradation of information processing capacity, as well as its restoration under DBS. These results also offer new perspectives for optimizing STN-DBS parameters based on clinically accessible measures of cortical information processing capacity.

Why so slow? Models of parkinsonian bradykinesia

Bradykinesia, or slowness of movement, is a defining feature of Parkinson disease (PD) and a major contributor to the negative effects on quality of life associated with this disorder and related conditions. A dominant pathophysiological model of bradykinesia in PD has existed for approximately 30 years and has been the basis for the development of several therapeutic interventions, but accumulating evidence has made this model increasingly untenable. Although more recent models have been proposed, they also appear to be flawed. In this Perspective, I consider the leading prior models of bradykinesia in PD and argue that a more functionally related model is required, one that considers changes that disrupt the fundamental process of accurate information transmission. In doing so, I review emerging evidence of network level functional connectivity changes, information transfer dysfunction and potential motor code transmission error and present a novel model of bradykinesia in PD that incorporates this evidence. I hope that this model may reconcile inconsistencies in its predecessors and encourage further development of therapeutic interventions.

Dysfunction of motor cortices in Parkinson's disease

The cerebral cortex has long been thought to be involved in the pathophysiology of motor symptoms of Parkinson's disease. The impaired cortical function is believed to be a direct and immediate effect of pathologically patterned basal ganglia output, mediated to the cerebral cortex by way of the ventral motor thalamus. However, recent studies in humans with Parkinson's disease and in animal models of the disease have provided strong evidence suggesting that the involvement of the cerebral cortex is much broader than merely serving as a passive conduit for subcortical disturbances. In the present review, we discuss Parkinson's disease-related changes in frontal cortical motor regions, focusing on neuropathology, plasticity, changes in neurotransmission, and altered network interactions. We will also examine recent studies exploring the cortical circuits as potential targets for neuromodulation to treat Parkinson's disease.

Beta: bursts of cognition

Beta oscillations are linked to the control of goal-directed processing of sensory information and the timing of motor output. Recent evidence demonstrates they are not sustained but organized into intermittent high-power bursts mediating timely functional inhibition. This implies there is a considerable moment-to-moment variation in the neural dynamics supporting cognition. Beta bursts thus offer new opportunities for studying how sensory inputs are selectively processed, reshaped by inhibitory cognitive operations and ultimately result in motor actions. Recent method advances reveal diversity in beta bursts that provide deeper insights into their function and the underlying neural circuit activity motifs. We propose that brain-wide, spatiotemporal patterns of beta bursting reflect various cognitive operations and that their dynamics reveal nonlinear aspects of cortical processing.

Auditory cues modulate the short timescale dynamics of STN activity during stepping in Parkinson's disease

BACKGROUND

Gait impairment has a major impact on quality of life in patients with Parkinson's disease (PD). It is believed that basal ganglia oscillatory activity at β frequencies (15-30 Hz) may contribute to gait impairment, but the precise dynamics of this oscillatory activity during gait remain unclear. Additionally, auditory cues are known to lead to improvements in gait kinematics in PD. If the neurophysiological mechanisms of this cueing effect were better understood they could be leveraged to treat gait impairments using adaptive Deep Brain Stimulation (aDBS) technologies.

OBJECTIVE

We aimed to characterize the dynamics of subthalamic nucleus (STN) oscillatory activity during stepping movements in PD and to establish the neurophysiological mechanisms by which auditory cues modulate gait.

METHODS

We studied STN local field potentials (LFPs) in eight PD patients while they performed stepping movements. Hidden Markov Models (HMMs) were used to discover transient states of spectral activity that occurred during stepping with and without auditory cues.

RESULTS

The occurrence of low and high β bursts was suppressed during and after auditory cues. This manifested as a decrease in their fractional occupancy and state lifetimes. Interestingly, α transients showed the opposite effect, with fractional occupancy and state lifetimes increasing during and after auditory cues.

CONCLUSIONS

We show that STN oscillatory activity in the α and β frequency bands are differentially modulated by gait-promoting oscillatory cues. These findings suggest that the enhancement of α rhythms may be an approach for ameliorating gait impairments in PD.

Post-task responses following working memory and movement are driven by transient spectral bursts with similar characteristics

The post-movement beta rebound has been studied extensively using magnetoencephalography (MEG) and is reliably modulated by various task parameters as well as illness. Our recent study showed that rebounds, which we generalise as "post-task responses" (PTRs), are a ubiquitous phenomenon in the brain, occurring across the cortex in theta, alpha, and beta bands. Currently, it is unknown whether PTRs following working memory are driven by transient bursts, which are moments of short-lived high amplitude activity, similar to those that drive the post-movement beta rebound. Here, we use three-state univariate hidden Markov models (HMMs), which can identify bursts without a priori knowledge of frequency content or response timings, to compare bursts that drive PTRs in working memory and visuomotor MEG datasets. Our results show that PTRs across working memory and visuomotor tasks are driven by pan-spectral transient bursts. These bursts have very similar spectral content variation over the cortex, correlating strongly between the two tasks in the alpha (R2 = .89) and beta (R2 = .53) bands. Bursts also have similar variation in duration over the cortex (e.g., long duration bursts occur in the motor cortex for both tasks), strongly correlating over cortical regions between tasks (R2 = .56), with a mean over all regions of around 300 ms in both datasets. Finally, we demonstrate the ability of HMMs to isolate signals of interest in MEG data, such that the HMM probability timecourse correlates more strongly with reaction times than frequency filtered power envelopes from the same brain regions. Overall, we show that induced PTRs across different tasks are driven by bursts with similar characteristics, which can be identified using HMMs. Given the similarity between bursts across tasks, we suggest that PTRs across the cortex may be driven by a common underlying neural phenomenon.

Oscillatory and non-oscillatory features of the magnetoencephalic sensorimotor rhythm in Parkinson's disease

Parkinson's disease (PD) is associated with changes in neural activity in the sensorimotor alpha and beta bands. Using magnetoencephalography (MEG), we investigated the role of spontaneous neuronal activity within the somatosensory cortex in a large cohort of early- to mid-stage PD patients (N = 78) on Parkinsonian medication and age- and sex-matched healthy controls (N = 60) using source reconstructed resting-state MEG. We quantified features of the time series data in terms of oscillatory alpha power and central alpha frequency, beta power and central beta frequency, and 1/f broadband characteristics using power spectral density. Furthermore, we characterised transient oscillatory burst events in the mu-beta band time-domain signals. We examined the relationship between these signal features and the patients' disease state, symptom severity, age, sex, and cortical thickness. PD patients and healthy controls differed on PSD broadband characteristics, with PD patients showing a steeper 1/f exponential slope and higher 1/f offset. PD patients further showed a steeper age-related decrease in the burst rate. Out of all the signal features of the sensorimotor activity, the burst rate was associated with increased severity of bradykinesia, whereas the burst duration was associated with axial symptoms. Our study shows that general non-oscillatory features (broadband 1/f exponent and offset) of the sensorimotor signals are related to disease state and oscillatory burst rate scales with symptom severity in PD.

Human Sensorimotor Beta Event Characteristics and Aperiodic Signal Are Highly Heritable

Individuals' phenotypes, including the brain's structure and function, are largely determined by genes and their interplay. The resting brain generates salient rhythmic patterns that can be characterized noninvasively using functional neuroimaging such as magnetoencephalography (MEG). One of these rhythms, the somatomotor (rolandic) beta rhythm, shows intermittent high amplitude "events" that predict behavior across tasks and species. Beta rhythm is altered in neurological disease. The aperiodic (1/f) signal present in electrophysiological recordings is also modulated by some neurological conditions and aging. Both sensorimotor beta and aperiodic signal could thus serve as biomarkers of sensorimotor function. Knowledge about the extent to which these brain functional measures are heritable could shed light on the mechanisms underlying their generation. We investigated the heritability and variability of human spontaneous sensorimotor beta rhythm events and aperiodic activity in 210 healthy male and female adult siblings' spontaneous MEG activity. The most heritable trait was the aperiodic 1/f signal, with a heritability of 0.87 in the right hemisphere. Time-resolved beta event amplitude parameters were also highly heritable, whereas the heritabilities for overall beta power, peak frequency, and measures of event duration remained nonsignificant. Human sensorimotor neural activity can thus be dissected into different components with variable heritability. We postulate that these differences partially reflect different underlying signal-generating mechanisms. The 1/f signal and beta event amplitude measures may depend more on fixed, anatomical parameters, whereas beta event duration and its modulation reflect dynamic characteristics, guiding their use as potential disease biomarkers.

The Swedish National Facility for Magnetoencephalography Parkinson's disease dataset

Parkinson's disease (PD) is characterised by a loss of dopamine and dopaminergic cells. The consequences hereof are widespread network disturbances in brain function. It is an ongoing topic of investigation how the disease-related changes in brain function manifest in PD relate to clinical symptoms. We present The Swedish National Facility for Magnetoencephalography Parkinson's Disease Dataset (NatMEG-PD) as an Open Science contribution to identify the functional neural signatures of Parkinson's disease and contribute to diagnosis and treatment. The dataset contains whole-head magnetoencephalographic (MEG) recordings from 66 well-characterised PD patients on their regular dose of dopamine replacement therapy and 68 age- and sex-matched healthy controls. NatMEG-PD contains three-minute eyes-closed resting-state MEG, MEG during an active movement task, and MEG during passive movements. The data includes anonymised MRI for source analysis and clinical scores. MEG data is rich in nature and can be used to explore numerous functional features. By sharing these data, we hope other researchers will contribute to advancing our understanding of the relationship between brain activity and disease state or symptoms.

Diverse beta burst waveform motifs characterize movement-related cortical dynamics

Classical analyses of induced, frequency-specific neural activity typically average band-limited power over trials. More recently, it has become widely appreciated that in individual trials, beta band activity occurs as transient bursts rather than amplitude-modulated oscillations. Most studies of beta bursts treat them as unitary, and having a stereotyped waveform. However, we show there is a wide diversity of burst shapes. Using a biophysical model of burst generation, we demonstrate that waveform variability is predicted by variability in the synaptic drives that generate beta bursts. We then use a novel, adaptive burst detection algorithm to identify bursts from human MEG sensor data recorded during a joystick-based reaching task, and apply principal component analysis to burst waveforms to define a set of dimensions, or motifs, that best explain waveform variance. Finally, we show that bursts with a particular range of waveform motifs, ones not fully accounted for by the biophysical model, differentially contribute to movement-related beta dynamics. Sensorimotor beta bursts are therefore not homogeneous events and likely reflect distinct computational processes.

Bio-Inspired Design of Superconducting Spiking Neuron and Synapse

The imitative modelling of processes in the brain of living beings is an ambitious task. However, advances in the complexity of existing hardware brain models are limited by their low speed and high energy consumption. A superconducting circuit with Josephson junctions closely mimics the neuronal membrane with channels involved in the operation of the sodium-potassium pump. The dynamic processes in such a system are characterised by a duration of picoseconds and an energy level of attojoules. In this work, two superconducting models of a biological neuron are studied. New modes of their operation are identified, including the so-called bursting mode, which plays an important role in biological neural networks. The possibility of switching between different modes in situ is shown, providing the possibility of dynamic control of the system. A synaptic connection that mimics the short-term potentiation of a biological synapse is developed and demonstrated. Finally, the simplest two-neuron chain comprising the proposed bio-inspired components is simulated, and the prospects of superconducting hardware biosimilars are briefly discussed.

Closed-Loop Adaptive Deep Brain Stimulation in Parkinson's Disease: Procedures to Achieve It and Future Perspectives

Parkinson's disease (PD) is a neurodegenerative disease with a heavy burden on patients, families, and society. Deep brain stimulation (DBS) can improve the symptoms of PD patients for whom medication is insufficient. However, current open-loop uninterrupted conventional DBS (cDBS) has inherent limitations, such as adverse effects, rapid battery consumption, and a need for frequent parameter adjustment. To overcome these shortcomings, adaptive DBS (aDBS) was proposed to provide responsive optimized stimulation for PD. This topic has attracted scientific interest, and a growing body of preclinical and clinical evidence has shown its benefits. However, both achievements and challenges have emerged in this novel field. To date, only limited reviews comprehensively analyzed the full framework and procedures for aDBS implementation. Herein, we review current preclinical and clinical data on aDBS for PD to discuss the full procedures for its achievement and to provide future perspectives on this treatment.

Association of subthalamic beta frequency sub-bands to symptom severity in patients with Parkinson's disease: A systematic review

OBJECTIVE

Local field potentials (LFP), specifically beta (13-30Hz) frequency measures, have been found to be associated with motor dysfunction in people with Parkinson's disease (PwPD). A consensus on beta subband (low- and high-beta) relationships to clinical state or therapy response has yet to be determined. The objective of this review is to synthesize literature reporting the association of low- and high-beta characteristics to clinical ratings of motor symptoms in PwPD.

METHODS

A systematic search of existing literature was completed using EMBASE. Articles which collected subthalamic nucleus (STN) LFPs using macroelectrodes in PwPD, analyzed low- (13-20 Hz) and high-beta (21-35 Hz) bands, collected UPDRS-III, and reported correlational strength or predictive capacity of LFPs to UPDRS-III scores.

RESULTS

The initial search yielded 234 articles, with 11 articles achieving inclusion. Beta measures included power spectral density, peak characteristics, and burst characteristics. High-beta was a significant predictor of UPDRS-III responses to therapy in 5 (100%) articles. Low-beta was significantly associated with UPDRS-III total score in 3 (60%) articles. Low- and high-beta associations to UPDRS-III subscores were mixed.

CONCLUSION

This systematic review reinforces previous reports that beta band oscillatory measures demonstrate a consistent relationship to Parkinsonian motor symptoms and ability to predict motor response to therapy. Specifically, high-beta, demonstrated a consistent ability to predict UPDRS-III responses to common PD therapies, while low-beta measures were associated with general Parkinsonian symptom severity. Continued research is needed to determine which beta subband demonstrates the greatest association to motor symptom subtypes and potentially offers clinical utility toward LFP-guided DBS programming and adaptive DBS.

Dual-task walking improvement with enhanced kinesthetic awareness in Parkinson's disease with mild gait impairment: EEG connectivity and clinical implication

Due to basal ganglia dysfunction, short step length is a common gait impairment in Parkinson's disease (PD), especially in a dual-task walking. Here, we use electroencephalography (EEG) functional connectivity to investigate neural mechanisms of a stride awareness strategy that could improve dual-task walking in PD. Eighteen individuals with PD who had mild gait impairment walked at self-paced speed while keeping two interlocking rings from touching each other. During the dual-task walking trial, the participants received or did not receive awareness instruction to take big steps. Gait parameters, ring-touching time, and EEG connectivity in the alpha and beta bands were analyzed. With stride awareness, individuals with PD exhibited greater gait velocity and step length, along with a significantly lower mean EEG connectivity strength in the beta band. The awareness-related changes in the EEG connectivity strength of the beta band positively correlated with the awareness-related changes in gait velocity, cadence, and step length, but negatively correlated with the awareness-related change in step-length variability. The smaller reduction in beta connectivity strength was associated with greater improvement in locomotion control with stride awareness. This study is the first to reveal that a stride awareness strategy modulates the beta band oscillatory network and is related to walking efficacy in individuals with PD in a dual-task condition.