Synchronized neural oscillations and the pathophysiology of Parkinsonʼs disease:

Properties of rhythmogenic currents in spinal Shox2 interneurons across postnatal development

Locomotor behaviors are performed by organisms throughout life, despite developmental changes in cellular properties, neural connectivity, and biomechanics. The basic rhythmic activity in the central nervous system that underlies locomotion is thought to be generated via a complex balance between network and intrinsic cellular properties. Within mature mammalian spinal locomotor circuitry, we have yet to determine which properties of spinal interneurons (INs) are critical to rhythmogenesis and how they change during development. Here, we combined whole cell patch clamp recordings, immunohistochemistry, and RNAscope targeting lumbar Shox2 INs in mice, which are known to be involved in locomotor rhythm generation. We focused on the properties of putatively rhythmogenic ionic currents and the expression of corresponding ion channels across postnatal time points in mice. We show that subsets of Shox2 INs display voltage-sensitive conductances, in addition to respective ion channels, which may contribute to or shape rhythmic bursting. Persistent inward currents, M-type potassium currents, slow afterhyperpolarization, and T-type calcium currents are enhanced with age. In contrast, the hyperpolarization-activated and A-type potassium currents were either found with low prevalence in subsets of neonatal, juvenile, and adult Shox2 INs or did not developmentally change. We show that Shox2 INs become more electrophysiologically diverse by juvenile and adult ages, when locomotor behavior is weight-bearing. These results suggest a developmental shift in the magnitude of rhythmogenic ionic currents and the expression of corresponding ion channels that may be important for mature, weight-bearing locomotor behavior.

Clinico-physiological correlates of Parkinson's disease from multi-resolution basal ganglia recordings

Parkinson's disease (PD) has been associated with pathological neural activity within the basal ganglia. Herein, we analyzed resting-state single-neuron and local field potential (LFP) activities from people with PD who underwent awake deep brain stimulation surgery of the subthalamic nucleus (STN; n = 125) or globus pallidus internus (GPi; n = 44), and correlated rate-based and oscillatory features with UPDRSIII off-medication subscores. Rate-based single-neuron features did not correlate with PD symptoms. STN single-neuron and LFP low-beta (12-21 Hz) power and burst dynamics showed modest correlations with bradykinesia and rigidity severity, while STN spiketrain theta (4-8 Hz) power correlated modestly with tremor severity. GPi low- and high-beta (21-30 Hz) power and burst dynamics correlated moderately with bradykinesia and axial symptom severity. These findings suggest that elevated single-neuron and LFP oscillations may be linked to symptoms, though modest correlations imply that the pathophysiology of PD may extend beyond resting-state beta oscillations.

EEG frequency bands in subjective cognitive decline: A systematic review of resting state studies

As the older population continues to expand, there is a growing prevalence of individuals who experience subjective cognitive decline (SCD), characterized by self-reported failures in cognitive function and an increased risk of cognitive impairment. Recognizing that preventive interventions are typically more effective in preclinical stages, current research endeavors to focus on identifying early biological markers of SCD using resting-state electroencephalogram (rsEEG) methods. To do so, a systematic literature review covering the past 20 years was conducted following PRISMA guidelines, in order to consolidate findings on rsEEG frequency bands in individuals with SCD. Pubmed and Web of Science databases were searched for rsEEG studies of people with SCD. Quality assessments were completed using a modified Newcastle-Ottawa scale. A total of 564 articles published from December 2003 to December 2023 were reviewed, and significant aspects of these papers were analyzed to provide a general overview of the research on this technique. After removing unrelated articles, nine articles were selected for the present study. The review emphasizes patterns in frequency band activity, revealing that individuals classified as SCD exhibited increased theta power than healthy controls, but decreased than MCI. However, findings for the alpha, delta, and beta bands were inconsistent, demonstrating variability across studies and highlighting the need for further research. Although the rsEEG of frequency bands emerges as a promising early biomarker, there is a noteworthy need to establish uniform standards and consistent measurement approaches in order to ensure the reliability and comparability of the results obtained in the research.

The neurophysiological brain-fingerprint of Parkinson's disease

BACKGROUND

Research in healthy young adults shows that characteristic patterns of brain activity define individual "brain-fingerprints" that are unique to each person. However, variability in these brain-fingerprints increases in individuals with neurological conditions, challenging the clinical relevance and potential impact of the approach. Our study shows that brain-fingerprints derived from neurophysiological brain activity are associated with pathophysiological and clinical traits of individual patients with Parkinson's disease (PD).

METHODS

We created brain-fingerprints from task-free brain activity recorded through magnetoencephalography in 79 PD patients and compared them with those from two independent samples of age-matched healthy controls (N = 424 total). We decomposed brain activity into arrhythmic and rhythmic components, defining distinct brain-fingerprints for each type from recording durations of up to 4 min and as short as 30 s.

FINDINGS

The arrhythmic spectral components of cortical activity in patients with Parkinson's disease are more variable over short periods, challenging the definition of a reliable brain-fingerprint. However, by isolating the rhythmic components of cortical activity, we derived brain-fingerprints that distinguished between patients and healthy controls with about 90% accuracy. The most prominent cortical features of the resulting Parkinson's brain-fingerprint are mapped to polyrhythmic activity in unimodal sensorimotor regions. Leveraging these features, we also demonstrate that Parkinson's symptom laterality can be decoded directly from cortical neurophysiological activity. Furthermore, our study reveals that the cortical topography of the Parkinson's brain-fingerprint aligns with that of neurotransmitter systems affected by the disease's pathophysiology.

INTERPRETATION

The increased moment-to-moment variability of arrhythmic brain-fingerprints challenges patient differentiation and explains previously published results. We outline patient-specific rhythmic brain signaling features that provide insights into both the neurophysiological signature and symptom laterality of Parkinson's disease. Thus, the proposed definition of a rhythmic brain-fingerprint of Parkinson's disease may contribute to novel, refined approaches to patient stratification. Symmetrically, we discuss how rhythmic brain-fingerprints may contribute to the improved identification and testing of therapeutic neurostimulation targets.

FUNDING

Data collection and sharing for this project was provided by the Quebec Parkinson Network (QPN), the Pre-symptomatic Evaluation of Novel or Experimental Treatments for Alzheimer's Disease (PREVENT-AD; release 6.0) program, the Cambridge Centre for Aging Neuroscience (Cam-CAN), and the Open MEG Archives (OMEGA). The QPN is funded by a grant from Fonds de Recherche du Québec - Santé (FRQS). PREVENT-AD was launched in 2011 as a $13.5 million, 7-year public-private partnership using funds provided by McGill University, the FRQS, an unrestricted research grant from Pfizer Canada, the Levesque Foundation, the Douglas Hospital Research Centre and Foundation, the Government of Canada, and the Canada Fund for Innovation. The Brainstorm project is supported by funding to SB from the NIH (R01-EB026299-05). Further funding to SB for this study included a Discovery grant from the Natural Sciences and Engineering Research Council of Canada of Canada (436355-13), and the CIHR Canada research Chair in Neural Dynamics of Brain Systems (CRC-2017-00311).

Pathological cell assembly dynamics in a striatal MSN network model

Under normal conditions the principal cells of the striatum, medium spiny neurons (MSNs), show structured cell assembly activity patterns which alternate sequentially over exceedingly long timescales of many minutes. It is important to understand this activity since it is characteristically disrupted in multiple pathologies, such as Parkinson's disease and dyskinesia, and thought to be caused by alterations in the MSN to MSN lateral inhibitory connections and in the strength and distribution of cortical excitation to MSNs. To understand how these long timescales arise we extended a previous network model of MSN cells to include synapses with short-term plasticity, with parameters taken from a recent detailed striatal connectome study. We first confirmed the presence of sequentially switching cell clusters using the non-linear dimensionality reduction technique, Uniform Manifold Approximation and Projection (UMAP). We found that the network could generate non-stationary activity patterns varying extremely slowly on the order of minutes under biologically realistic conditions. Next we used Simulation Based Inference (SBI) to train a deep net to map features of the MSN network generated cell assembly activity to MSN network parameters. We used the trained SBI model to estimate MSN network parameters from ex-vivo brain slice calcium imaging data. We found that best fit network parameters were very close to their physiologically observed values. On the other hand network parameters estimated from Parkinsonian, decorticated and dyskinetic ex-vivo slice preparations were different. Our work may provide a pipeline for diagnosis of basal ganglia pathology from spiking data as well as for the design pharmacological treatments.

Alterations of Cortical Structure and Neurophysiology in Parkinson's Disease Are Aligned with Neurochemical Systems

OBJECTIVE

Parkinson's disease (PD) affects the structural integrity and neurophysiological signaling of the cortex. These alterations are related to the motor and cognitive symptoms of the disease. How these changes are related to the neurochemical systems of the cortex is unknown.

METHODS

We used T1-weighted magnetic resonance imaging (MRI) and magnetoencephalography (MEG) to measure cortical thickness and task-free neurophysiological activity in patients with idiopathic PD (nMEG = 79, nMRI = 65) and matched healthy controls (nMEG = 65, nMRI = 37). Using linear mixed-effects models, we examined the topographical alignment of cortical structural and neurophysiological alterations in PD with cortical atlases of 19 neurotransmitter receptor and transporter densities.

RESULTS

We found that neurophysiological alterations in PD occur primarily in brain regions rich in acetylcholinergic, serotonergic, and glutamatergic systems, with protective implications for cognitive and psychiatric symptoms. In contrast, cortical thinning occurs preferentially in regions rich in noradrenergic systems, and the strength of this alignment relates to motor deficits.

INTERPRETATION

This study shows that the spatial organization of neurophysiological and structural alterations in PD is relevant for nonmotor and motor impairments. The data also advance the identification of the neurochemical systems implicated. The approach uses novel nested atlas modeling methodology that is transferrable to research in other neurological and neuropsychiatric diseases and syndromes. ANN NEUROL 2024;95:802-816.

Neuroscience fundamentals relevant to neuromodulation: Neurobiology of deep brain stimulation in Parkinson's disease

Deep Brain Stimulation (DBS) has become a pivotal therapeutic approach for Parkinson's Disease (PD) and various neuropsychiatric conditions, impacting over 200,000 patients. Despite its widespread application, the intricate mechanisms behind DBS remain a subject of ongoing investigation. This article provides an overview of the current knowledge surrounding the local, circuit, and neurobiochemical effects of DBS, focusing on the subthalamic nucleus (STN) as a key target in PD management. The local effects of DBS, once thought to mimic a reversible lesion, now reveal a more nuanced interplay with myelinated axons, neurotransmitter release, and the surrounding microenvironment. Circuit effects illuminate the modulation of oscillatory activities within the basal ganglia and emphasize communication between the STN and the primary motor cortex. Neurobiochemical effects, encompassing changes in dopamine levels and epigenetic modifications, add further complexity to the DBS landscape. Finally, within the context of understanding the mechanisms of DBS in PD, the article highlights the controversial question of whether DBS exerts disease-modifying effects in PD. While preclinical evidence suggests neuroprotective potential, clinical trials such as EARLYSTIM face challenges in assessing long-term disease modification due to enrollment timing and methodology limitations. The discussion underscores the need for robust biomarkers and large-scale prospective trials to conclusively determine DBS's potential as a disease-modifying therapy in PD.

Transcranial alternating current stimulation improves quality of life in Parkinson's disease: study protocol for a randomized, double-blind, controlled trial

BACKGROUND

The neural cells in the brains of patients with Parkinson's disease (PWP) display aberrant synchronized oscillatory activity within the beta frequency range. Additionally, enhanced gamma oscillations may serve as a compensatory mechanism for motor inhibition mediated by beta activity and also reinstate plasticity in the primary motor cortex affected by Parkinson's disease. Transcranial alternating current stimulation (tACS) can synchronize endogenous oscillations with exogenous rhythms, thereby modulating cortical activity. The objective of this study is to investigate whether the addition of tACS to multidisciplinary intensive rehabilitation treatment (MIRT) can improve symptoms of PWP so as to enhance the quality of life in individuals with Parkinson's disease based on the central-peripheral-central theory.

METHODS

The present study was a randomized, double-blind trial that enrolled 60 individuals with Parkinson's disease aged between 45 and 70 years, who had Hoehn-Yahr scale scores ranging from 1 to 3. Participants were randomly assigned in a 1:1 ratio to either the tACS + MIRT group or the sham-tACS + MIRT group. The trial consisted of a two-week double-blind treatment period followed by a 24-week follow-up period, resulting in a total duration of twenty-six weeks. The primary outcome measured the change in PDQ-39 scores from baseline (T0) to 4 weeks (T2), 12 weeks (T3), and 24 weeks (T4) after completion of the intervention. The secondary outcome assessed changes in MDS-UPDRS III scores at T0, the end of intervention (T1), T2, T3, and T4. Additional clinical assessments and mechanistic studies were conducted as tertiary outcomes.

DISCUSSION

The objective of this study is to demonstrate that tACS can enhance overall functionality and improve quality of life in PWP, based on the framework of MIRT. Additionally, it seeks to establish a potential correlation between these therapeutic effects and neuroplasticity alterations in relevant brain regions. The efficacy of tACS will be assessed during the follow-up period in order to optimize neuroplasticity and enhance its potential impact on rehabilitation efficiency for PWP.

TRIAL REGISTRATION

Chinese Clinical Trial Registry ChiCTR2300071969. Registered on 30 May 2023.

Modulation of DBS-induced cortical responses and movement by the directionality and magnitude of current administered

Subthalamic deep brain stimulation (STN-DBS) is an effective therapy for alleviating motor symptoms in people with Parkinson's disease (PwP), although some may not receive optimal clinical benefits. One potential mechanism of STN-DBS involves antidromic activation of the hyperdirect pathway (HDP), thus suppressing cortical beta synchrony to improve motor function, albeit the precise mechanisms underlying optimal DBS parameters are not well understood. To address this, 18 PwP with STN-DBS completed a 2 Hz monopolar stimulation of the left STN during MEG. MEG data were imaged in the time-frequency domain using minimum norm estimation. Peak vertex time series data were extracted to interrogate the directional specificity and magnitude of DBS current on evoked and induced cortical responses and accelerometer metrics of finger tapping using linear mixed-effects models and mediation analyses. We observed increases in evoked responses (HDP ~ 3-10 ms) and synchronization of beta oscillatory power (14-30 Hz, 10-100 ms) following DBS pulse onset in the primary sensorimotor cortex (SM1), supplementary motor area (SMA) and middle frontal gyrus (MFG) ipsilateral to the site of stimulation. DBS parameters significantly modulated neural and behavioral outcomes, with clinically effective contacts eliciting significant increases in medium-latency evoked responses, reductions in induced SM1 beta power, and better movement profiles compared to suboptimal contacts, often regardless of the magnitude of current applied. Finally, HDP-related improvements in motor function were mediated by the degree of SM1 beta suppression in a setting-dependent manner. Together, these data suggest that DBS-evoked brain-behavior dynamics are influenced by the level of beta power in key hubs of the basal ganglia-cortical loop, and this effect is exacerbated by the clinical efficacy of DBS parameters. Such data provides novel mechanistic and clinical insight, which may prove useful for characterizing DBS programming strategies to optimize motor symptom improvement in the future.

Spatiotemporal signal space separation for regions of interest: Application for extracting neuromagnetic responses evoked by deep brain stimulation

Magnetoencephalography (MEG) recordings are often contaminated by interference that can exceed the amplitude of physiological brain activity by several orders of magnitude. Furthermore, the activity of interference sources may spatially extend (known as source leakage) into the activity of brain signals of interest, resulting in source estimation inaccuracies. This problem is particularly apparent when using MEG to interrogate the effects of brain stimulation on large-scale cortical networks. In this technical report, we develop a novel denoising approach for suppressing the leakage of interference source activity into the activity representing a brain region of interest. This approach leverages spatial and temporal domain projectors for signal arising from prespecified anatomical regions of interest. We apply this denoising approach to reconstruct simulated evoked response topographies to deep brain stimulation (DBS) in a phantom recording. We highlight the advantages of our approach compared to the benchmark-spatiotemporal signal space separation-and show that it can more accurately reveal brain stimulation-evoked response topographies. Finally, we apply our method to MEG recordings from a single patient with Parkinson's disease, to reveal early cortical-evoked responses to DBS of the subthalamic nucleus.

Cognition of Time and Thinking Beyond

A common research protocol in cognitive neuroscience is to train subjects to perform deliberately designed experiments while recording brain activity, with the aim of understanding the brain mechanisms underlying cognition. However, how the results of this protocol of research can be applied in technology is seldom discussed. Here, I review the studies on time processing of the brain as examples of this research protocol, as well as two main application areas of neuroscience (neuroengineering and brain-inspired artificial intelligence). Time processing is a fundamental dimension of cognition, and time is also an indispensable dimension of any real-world signal to be processed in technology. Therefore, one may expect that the studies of time processing in cognition profoundly influence brain-related technology. Surprisingly, I found that the results from cognitive studies on timing processing are hardly helpful in solving practical problems. This awkward situation may be due to the lack of generalizability of the results of cognitive studies, which are under well-controlled laboratory conditions, to real-life situations. This lack of generalizability may be rooted in the fundamental unknowability of the world (including cognition). Overall, this paper questions and criticizes the usefulness and prospect of the abovementioned research protocol of cognitive neuroscience. I then give three suggestions for future research. First, to improve the generalizability of research, it is better to study brain activity under real-life conditions instead of in well-controlled laboratory experiments. Second, to overcome the unknowability of the world, we can engineer an easily accessible surrogate of the object under investigation, so that we can predict the behavior of the object under investigation by experimenting on the surrogate. Third, the paper calls for technology-oriented research, with the aim of technology creation instead of knowledge discovery.

Band-Specific Altered Cortical Connectivity in Early Parkinson's Disease and its Clinical Correlates

BACKGROUND

Functional connectivity (FC) has shown promising results in assessing the pathophysiology and identifying early biomarkers of neurodegenerative disorders, such as Parkinson's disease (PD).

OBJECTIVES

In this study, we aimed to assess possible resting-state FC abnormalities in early-stage PD patients using high-density electroencephalography (EEG) and to detect their clinical relationship with motor and non-motor PD symptoms.

METHODS

We enrolled 26 early-stage levodopa naïve PD patients and a group of 20 healthy controls (HC). Data were recorded with 64-channels EEG system and a source-reconstruction method was used to identify brain-region activity. FC was calculated using the weighted phase-lag index in θ, α, and β bands. Additionally, we quantified the unbalancing between β and lower frequencies through a novel index (β-functional ratio [FR]). Statistical analysis was conducted using a network-based statistical approach.

RESULTS

PD patients showed hypoconnected networks in θ and α band, involving prefrontal-limbic-temporal and frontoparietal areas, respectively, and a hyperconnected network in the β frequency band, involving sensorimotor-frontal areas. The θ FC network was negatively related to Non-Motor Symptoms Scale scores and α FC to the Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale part III gait subscore, whereas β FC and β-FR network were positively linked to the bradykinesia subscore. Changes in θ FC and β-FR showed substantial reliability and high accuracy, precision, sensitivity, and specificity in discriminating PD and HC.

CONCLUSIONS

Frequency-specific FC changes in PD likely reflect the dysfunction of distinct cortical networks, which occur from the early stage of the disease. These abnormalities are involved in the pathophysiology of specific motor and non-motor PD symptoms, including gait, bradykinesia, mood, and cognition. © 2023 International Parkinson and Movement Disorder Society.

The neurophysiological brain-fingerprint of Parkinson's disease

In this study, we investigate the clinical potential of brain-fingerprints derived from electrophysiological brain activity for diagnostics and progression monitoring of Parkinson's disease (PD). We obtained brain-fingerprints from PD patients and age-matched healthy controls using short, task-free magnetoencephalographic recordings. The rhythmic components of the individual brain-fingerprint distinguished between patients and healthy participants with approximately 90% accuracy. The most prominent cortical features of the Parkinson's brain-fingerprint mapped to polyrhythmic activity in unimodal sensorimotor regions. Leveraging these features, we also show that Parkinson's disease stages can be decoded directly from cortical neurophysiological activity. Additionally, our study reveals that the cortical topography of the Parkinson's brain-fingerprint aligns with that of neurotransmitter systems affected by the disease's pathophysiology. We further demonstrate that the arrhythmic components of cortical activity are more variable over short periods of time in patients with Parkinson's disease than in healthy controls, making individual differentiation between patients based on these features more challenging and explaining previous negative published results. Overall, we outline patient-specific rhythmic brain signaling features that provide insights into both the neurophysiological signature and clinical staging of Parkinson's disease. For this reason, the proposed definition of a rhythmic brain-fingerprint of Parkinson's disease may contribute to novel, refined approaches to patient stratification and to the improved identification and testing of therapeutic neurostimulation targets.

Adverse and compensatory neurophysiological slowing in Parkinson's disease

Patients with Parkinson's disease (PD) exhibit multifaceted changes in neurophysiological brain activity, hypothesized to represent a global cortical slowing effect. Using task-free magnetoencephalography and extensive clinical assessments, we found that neurophysiological slowing in PD is differentially associated with motor and non-motor symptoms along a sagittal gradient over the cortical anatomy. In superior parietal regions, neurophysiological slowing reflects an adverse effect and scales with cognitive and motor impairments, while across the inferior frontal cortex, neurophysiological slowing is compatible with a compensatory role. This adverse-to-compensatory gradient is sensitive to individual clinical profiles, such as drug regimens and laterality of symptoms; it is also aligned with the topography of neurotransmitter and transporter systems relevant to PD. We conclude that neurophysiological slowing in patients with PD signals both deleterious and protective mechanisms of the disease, from posterior to anterior regions across the cortex, respectively, with functional and clinical relevance to motor and cognitive symptoms.

Pathological Correlates of Cognitive Decline in Parkinson's Disease: From Molecules to Neural Networks

Parkinson's disease (PD) is a progressive neurodegenerative disorder caused by the death of dopaminergic neurons in the substantia nigra and appearance of protein aggregates (Lewy bodies) consisting predominantly of α-synuclein in neurons. PD is currently recognized as a multisystem disorder characterized by severe motor impairments and various non-motor symptoms. Cognitive decline is one of the most common and worrisome non-motor symptoms. Moderate cognitive impairments (CI) are diagnosed already at the early stages of PD, usually transform into dementia. The main types of CI in PD include executive dysfunction, attention and memory decline, visuospatial impairments, and verbal deficits. According to the published data, the following mechanisms play an essential role demonstrates a crucial importance in the decline of the motor and cognitive functions in PD: (1) changes in the conformational structure of transsynaptic proteins and protein aggregation in presynapses; (2) synaptic transmission impairment; (3) neuroinflammation (pathological activation of the neuroglia); (4) mitochondrial dysfunction and oxidative stress; (5) metabolic disorders (hypometabolism of glucose, dysfunction of glycolipid metabolism; and (6) functional rearrangement of neuronal networks. These changes can lead to the death of dopaminergic cells in the substantia nigra and affect the functioning of other neurotransmitter systems, thus disturbing neuronal networks involved in the transmission of information related to the regulation of motor activity and cognitive functions. Identification of factors causing detrimental changes in PD and methods for their elimination will help in the development of new approaches to the therapy of PD. The goal of this review was to analyze pathological processes that take place in the brain and underlie the onset of cognitive disorders in PD, as well as to describe the impairments of cognitive functions in this disease.

Volitional control of beta activities in Parkinson's disease patients

Patients diagnosed with Parkinson's disease (PD) have difficulty initiating and executing movements due to an acquired imbalance of the basal ganglia thalamocortical circuit secondary to loss of dopaminergic input into the striatum. The unbalanced circuit is hyper-synchronized, presenting as larger and longer bursts of beta-band (13-30 Hz) oscillations in the subthalamic nucleus (STN). As a first step toward a novel PD therapy that aims to improve symptoms through beta desynchronization, we sought to determine if individuals with PD could acquire volitional control of STN beta power in a neurofeedback task. We found a significant difference in STN beta power between task conditions, and relevant brain signal features could be detected and decoded in real time. This demonstration of volitional control of STN beta motivates development of a neurofeedback therapy to modulate PD symptom severity.

Investigating cortico-striatal beta oscillations in Parkinson's disease cognitive decline

This scientific commentary refers to ‘Corticostriatal beta oscillation changes associated with cognitive function in Parkinson’s disease’ by Paulo et al. (https://doi.org/10.1093/brain/awad206).

Reduced thalamic excitation to motor cortical pyramidal tract neurons in parkinsonism

Degeneration of midbrain dopaminergic (DA) neurons alters the connectivity and functionality of the basal ganglia-thalamocortical circuits in Parkinson's disease (PD). Particularly, the aberrant outputs of the primary motor cortex (M1) contribute to parkinsonian motor deficits. However, cortical adaptations at cellular and synaptic levels in parkinsonism remain poorly understood. Using multidisciplinary approaches, we found that DA degeneration induces cell subtype- and input-specific reduction of thalamic excitation to M1 pyramidal tract (PT) neurons. At molecular level, we identified that N-methyl-d-aspartate (NMDA) receptors play a key role in mediating the reduced thalamocortical excitation to PT neurons. At circuit level, we showed that the reduced thalamocortical transmission in parkinsonian mice can be rescued by chemogenetically suppressing basal ganglia outputs. Together, our data suggest that cell subtype- and synapse-specific adaptations in M1 contribute to altered cortical outputs in parkinsonism and are important aspects of PD pathophysiology.

Excessive α-β Oscillations Mark Enlarged Motor Sign Severity and Parkinson's Disease Duration

BACKGROUND

β Oscillations in the subthalamic nucleus (STN) have been proven to contribute to Parkinson's disease (PD), but the exact borders of β subbands vary substantially across the studies, and information regarding heterogeneity of β rhythmic activity is still limited. Recently, α oscillations in the basal ganglia have also become the focus of PD research.

OBJECTIVES

The aim was to study rhythmic oscillations in the STN in PD patients to identify different subbands with stable oscillatory peaks within a broad α-β range and to establish their associations with motor symptoms.

METHODS

Local field potentials inside the STN were recorded during deep brain stimulation (DBS) surgeries. After calculating power spectra and extracting an aperiodic component, oscillatory peaks in the 8- to 35-Hz range with amplitude exceeding 90th percentile were clustered into three bands. Peak parameters were estimated for two lower subbands. Clinical features were compared in patients with and without oscillation peaks in the lowest α-β subband.

RESULTS

We isolated α-β (8-15 Hz), β (15-25 Hz), and β-γ (25-35 Hz) subbands within the 8- to 35-Hz spectral range using oscillatory parameters and Ward's hierarchical clustering. Additional α-β oscillatory peaks were found in about half of patients with β peaks; they were located more ventrally compared to β. We have found a significant increase in disease duration, bradykinesia, and rigidity scores in the group with additional α-β peaks.

CONCLUSIONS

Increased α-β oscillations may emerge as additional phenomena complementing β oscillations; they may mark disease progression in PD and affect DBS stimulation setup. © 2023 International Parkinson and Movement Disorder Society.

Cross-Frequency Coupling and Intelligent Neuromodulation

Cross-frequency coupling (CFC) reflects (nonlinear) interactions between signals of different frequencies. Evidence from both patient and healthy participant studies suggests that CFC plays an essential role in neuronal computation, interregional interaction, and disease pathophysiology. The present review discusses methodological advances and challenges in the computation of CFC with particular emphasis on potential solutions to spurious coupling, inferring intrinsic rhythms in a targeted frequency band, and causal interferences. We specifically focus on the literature exploring CFC in the context of cognition/memory tasks, sleep, and neurological disorders, such as Alzheimer's disease, epilepsy, and Parkinson's disease. Furthermore, we highlight the implication of CFC in the context and for the optimization of invasive and noninvasive neuromodulation and rehabilitation. Mainly, CFC could support advancing the understanding of the neurophysiology of cognition and motor control, serve as a biomarker for disease symptoms, and leverage the optimization of therapeutic interventions, e.g., closed-loop brain stimulation. Despite the evident advantages of CFC as an investigative and translational tool in neuroscience, further methodological improvements are required to facilitate practical and correct use in cyborg and bionic systems in the field.

Balance between pallidal neural oscillations correlated with dystonic activity and severity

BACKGROUND AND OBJECTIVE

The balance between neural oscillations provides valuable insights into the organisation of neural oscillations related to brain states, which may play important roles in dystonia. We aim to investigate the relationship of the balance in the globus pallidus internus (GPi) with the dystonic severity under different muscular contraction conditions.

METHODS

Twenty-one patients with dystonia were recruited. All of them underwent bilateral GPi implantation, and local field potentials (LFPs) from the GPi were recorded via simultaneous surface electromyography. The power spectral ratio between neural oscillations was computed as the measure of neural balance. This ratio was calculated under high and low dystonic muscular contraction conditions, and its correlation with the dystonic severity was assessed using clinical scores.

RESULTS

The power spectral of the pallidal LFPs peaked in the theta and alpha bands. Within participant comparison showed that the power spectral of the theta oscillations significantly increased during high muscle contraction compared with that during low contraction. The power spectral ratios between the theta and alpha, theta and low beta, and theta and high gamma oscillations were significantly higher during high contraction than during low contraction. The total score and motor score were associated with the power spectral ratio between the low and high beta oscillations, which was correlated with the dystonic severity both during high and low contractions. The power spectral ratios between the low beta and low gamma and between the low beta and high gamma oscillations showed a significantly positive correlation with the total score during both high and low contractions; a correlation with the motor scale score was found only during high contraction. Meanwhile, the power spectral ratio between the theta and alpha oscillations during low contraction showed a significantly negative correlation with the total score. The power spectral ratios between the alpha and high beta, alpha and low gamma, and alpha and high gamma oscillations were significantly correlated with the dystonic severity only during low contraction.

CONCLUSION

The balance between neural oscillations, as quantified by the power ratio between specific frequency bands, differed between the high and low muscular contraction conditions and was correlated with the dystonic severity. The balance between the low and high beta oscillations was correlated with the dystonic severity during both conditions, making this parameter a new possible biomarker for closed-loop deep brain stimulation in patients with dystonia.

Case report: Bilateral double beta peak activity is influenced by stimulation, levodopa concentrations, and motor tasks, in a Parkinson's disease patient on chronic deep brain stimulation

Introduction

Subthalamic (STN) local field potentials (LFPs) in the beta band are considered potential biomarkers for closed-loop deep brain stimulation (DBS) in Parkinson's disease (PD). The beta band is further dissected into low-and high-frequency components with somewhat different functions, although their concomitance and association in the single patient is far to be defined. We present a 56-year-old male PD patient undergoing DBS showing a double-beta peak activity on both sides. The aim of the study was to investigate how low-and high-beta peaks were influenced by plasma levodopa (L-dopa) levels, stimulation, and motor performances.

Methods

A systematic evaluation of raw LFPs, plasma L-dopa levels, and motor tasks was performed in the following four conditions: OFF medications/ON stimulation, OFF medications/OFF stimulation, ON medications/OFF stimulation, and ON medications/ON stimulation.

Results

The analysis of the LFP spectra suggests the following results: (1) the high-beta peak was suppressed by stimulation, while the low-beta peak showed a partial and not consistent response to stimulation; (2) the high-beta peak is also influenced by plasma L-dopa concentration, showing a progressive amplitude increment concordant with plasma L-dopa levels, while the low-beta peak shows a different behavir; and (3) motor performances seem to impact beta peaks behavior.

Conclusion

This single exploratory case study illustrates a complex behavior of low-and high-beta peaks in a PD patient, in response to stimulation, L-dopa plasma levels, and motor performances. Our results suggest the importance to investigate patient-specific individual LFP patterns in view of upcoming closed-loop stimulation.

Short-term plasticity of the motor cortex compensates for bradykinesia in Parkinson's disease

Patients with Parkinson's disease (PD) show impaired short-term potentiation (STP) mechanisms in the primary motor cortex (M1). However, the role played by this neurophysiological abnormality in bradykinesia pathophysiology is unknown. In this study, we used a multimodal neuromodulation approach to test whether defective STP contributes to bradykinesia. We evaluated STP by measuring motor-evoked potential facilitation during 5 Hz-repetitive transcranial magnetic stimulation (rTMS) and assessed repetitive finger tapping movements through kinematic techniques. Also, we used transcranial alternating current stimulation (tACS) to drive M1 oscillations and experimentally modulate bradykinesia. STP was assessed during tACS delivered at beta (β) and gamma (γ) frequency, and during sham-tACS. Data were compared to those recorded in a group of healthy subjects. In PD, we found that STP was impaired during sham- and γ-tACS, while it was restored during β-tACS. Importantly, the degree of STP impairment was associated with the severity of movement slowness and amplitude reduction. Moreover, β-tACS-related improvements in STP were linked to changes in movement slowness and intracortical GABA-A-ergic inhibition during stimulation, as assessed by short-interval intracortical inhibition (SICI). Patients with prominent STP amelioration had greater SICI reduction (cortical disinhibition) and less slowness worsening during β-tACS. Dopaminergic medications did not modify β-tACS effects. These data demonstrate that abnormal STP processes are involved in bradykinesia pathophysiology and return to normal levels when β oscillations increase. STP changes are likely mediated by modifications in GABA-A-ergic intracortical circuits and may represent a compensatory mechanism against β-induced bradykinesia in PD.

Aberrant neurophysiological signaling associated with speech impairments in Parkinson's disease

Difficulty producing intelligible speech is a debilitating symptom of Parkinson's disease (PD). Yet, both the robust evaluation of speech impairments and the identification of the affected brain systems are challenging. Using task-free magnetoencephalography, we examine the spectral and spatial definitions of the functional neuropathology underlying reduced speech quality in patients with PD using a new approach to characterize speech impairments and a novel brain-imaging marker. We found that the interactive scoring of speech impairments in PD (N = 59) is reliable across non-expert raters, and better related to the hallmark motor and cognitive impairments of PD than automatically-extracted acoustical features. By relating these speech impairment ratings to neurophysiological deviations from healthy adults (N = 65), we show that articulation impairments in patients with PD are associated with aberrant activity in the left inferior frontal cortex, and that functional connectivity of this region with somatomotor cortices mediates the influence of cognitive decline on speech deficits.

Structural and neurophysiological alterations in Parkinson's disease are aligned with cortical neurochemical systems

Parkinson's disease (PD) affects cortical structures and neurophysiology. How these deviations from normative variants relate to the neurochemical systems of the cortex in a manner corresponding to motor and cognitive symptoms is unknown. We measured cortical thickness and spectral neurophysiological alterations from structural magnetic resonance imaging and task-free magnetoencephalography in patients with idiopathic PD (NMEG = 79; NMRI = 65), contrasted with similar data from matched healthy controls (NMEG = 65; NMRI = 37). Using linear mixed-effects models and cortical atlases of 19 neurochemical systems, we found that the structural and neurophysiological alterations of PD align with several receptor and transporter systems (acetylcholine, serotonin, glutamate, and noradrenaline) albeit with different implications for motor and non-motor symptoms. Some neurophysiological alignments are protective of cognitive functions: the alignment of broadband power increases with acetylcholinergic systems is related to better attention function. However, neurochemical alignment with structural and other neurophysiological alterations is associated with motor and psychiatric impairments, respectively. Collectively, the present data advance understanding of the association between the nature of neurophysiological and structural cortical alterations in PD and the symptoms that are characteristic of the disease. They also demonstrate the value of a new nested atlas modeling approach to advance research on neurological and neuropsychiatric diseases.

Decoupling of interacting neuronal populations by time-shifted stimulation through spike-timing-dependent plasticity

The synaptic organization of the brain is constantly modified by activity-dependent synaptic plasticity. In several neurological disorders, abnormal neuronal activity and pathological synaptic connectivity may significantly impair normal brain function. Reorganization of neuronal circuits by therapeutic stimulation has the potential to restore normal brain dynamics. Increasing evidence suggests that the temporal stimulation pattern crucially determines the long-lasting therapeutic effects of stimulation. Here, we tested whether a specific pattern of brain stimulation can enable the suppression of pathologically strong inter-population synaptic connectivity through spike-timing-dependent plasticity (STDP). More specifically, we tested how introducing a time shift between stimuli delivered to two interacting populations of neurons can effectively decouple them. To that end, we first used a tractable model, i.e., two bidirectionally coupled leaky integrate-and-fire (LIF) neurons, to theoretically analyze the optimal range of stimulation frequency and time shift for decoupling. We then extended our results to two reciprocally connected neuronal populations (modules) where inter-population delayed connections were modified by STDP. As predicted by the theoretical results, appropriately time-shifted stimulation causes a decoupling of the two-module system through STDP, i.e., by unlearning pathologically strong synaptic interactions between the two populations. Based on the overall topology of the connections, the decoupling of the two modules, in turn, causes a desynchronization of the populations that outlasts the cessation of stimulation. Decoupling effects of the time-shifted stimulation can be realized by time-shifted burst stimulation as well as time-shifted continuous simulation. Our results provide insight into the further optimization of a variety of multichannel stimulation protocols aiming at a therapeutic reshaping of diseased brain networks.

Combined EEG and immersive virtual reality unveil dopaminergic modulation of error monitoring in Parkinson's Disease

Detecting errors in your own and others' actions is associated with discrepancies between intended and expected outcomes. The processing of salient events is associated with dopamine release, the balance of which is altered in Parkinson's disease (PD). Errors in observed actions trigger various electrocortical indices (e.g. mid-frontal theta, error-related delta, and error positivity [oPe]). However, the impact of dopamine depletion to observed errors in the same individual remains unclear. Healthy controls (HCs) and PD patients observed ecological reach-to-grasp-a-glass actions performed by a virtual arm from a first-person perspective. PD patients were tested under their dopaminergic medication (on-condition) and after dopaminergic withdrawal (off-condition). Analyses of oPe, delta, and theta-power increases indicate that while the formers were elicited after incorrect vs. correct actions in all groups, the latter were observed in on-condition but altered in off-condition PD. Therefore, different EEG error signatures may index the activity of distinct mechanisms, and error-related theta power is selectively modulated by dopamine depletion. Our findings may facilitate discovering dopamine-related biomarkers for error-monitoring dysfunctions that may have crucial theoretical and clinical implications.

Parkinsonian daytime sleep-wake classification using deep brain stimulation lead recordings

Excessive daytime sleepiness is a recognized non-motor symptom that adversely impacts the quality of life of people with Parkinson's disease (PD), yet effective treatment options remain limited. Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective treatment for PD motor signs. Reliable daytime sleep-wake classification using local field potentials (LFPs) recorded from DBS leads implanted in STN can inform the development of closed-loop DBS approaches for prompt detection and disruption of sleep-related neural oscillations. We performed STN DBS lead recordings in three nonhuman primates rendered parkinsonian by administrating neurotoxin 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP). Reference sleep-wake states were determined on a second-by-second basis by video monitoring of eyes (eyes-open, wake and eyes-closed, sleep). The spectral power in delta (1-4 Hz), theta (4-8 Hz), low-beta (8-20 Hz), high-beta (20-35 Hz), gamma (35-90 Hz), and high-frequency (200-400 Hz) bands were extracted from each wake and sleep epochs for training (70% data) and testing (30% data) a support vector machines classifier for each subject independently. The spectral features yielded reasonable daytime sleep-wake classification (sensitivity: 90.68 ± 1.28; specificity: 88.16 ± 1.08; accuracy: 89.42 ± 0.68; positive predictive value; 88.70 ± 0.89, n = 3). Our findings support the plausibility of monitoring daytime sleep-wake states using DBS lead recordings. These results could have future clinical implications in informing the development of closed-loop DBS approaches for automatic detection and disruption of sleep-related neural oscillations in people with PD to promote wakefulness.

Binaural acoustic stimulation in patients with Parkinson's disease

Acoustic stimulation can improve motor symptoms in Parkinson's disease (PD) and might therefore represent a potential non-invasive treatment option. Scalp electroencephalography studies in healthy subjects indicate that specifically binaural beat stimulation (BBS) in the gamma frequency range is associated with synchronized cortical oscillations at 40 Hertz (Hz). Several studies suggest that oscillations in the gamma-frequency range (>30 Hz) serve a prokinetic function in PD. In this double-blind, randomized study, 25 PD patients were recruited. The study was conducted with (ON) and without dopaminergic medication (OFF). Each drug condition consisted of two phases (no stimulation and acoustic stimulation). The acoustic stimulation phase was divided into two blocks including BBS and conventional acoustic stimulation (CAS) as a control condition. For BBS, a modulated frequency of 35 Hz was used (left: 320 Hz; right: 355 Hz) and for CAS 340 Hz on both sides. We assessed effects on motor performance using Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) and two validated commercially available portable devices (Kinesia ONE™ and Kinesia 360™) measuring motor symptoms such as dyskinesia, bradykinesia, and tremor. Repeated measures ANOVA revealed that BBS improved resting tremor on the side of the more affected limb in the OFF condition, as measured by wearables (F_(2,48) = 3.61, p = 0.035). However, BBS did not exert a general positive effect on motor symptoms as assessed via MDS-UPDRS (F_(2,48) = 1.00, p = 0.327). For CAS, we did not observe an improvement in specific symptoms but rather an overall beneficial effect on motor performance (MDS-UPDRS total score OFF medication: F_(2,48) = 4.17, p = 0.021; wearable scores: F_(2,48) = 2.46, p = 0.097). In this study, we found an improvement of resting tremor when applying BBS in the gamma frequency band OFF medication. Moreover, the positive effects of CAS underline the general positive potential for improvement of motor function by acoustically supported therapeutic approaches. However, more studies are needed to fully characterize the clinical relevance of BBS and to further optimize its ameliorating effects.

Assessing the interaction between L-dopa and γ-transcranial alternating current stimulation effects on primary motor cortex plasticity in Parkinson's disease

L-dopa variably influences transcranial magnetic stimulation (TMS) parameters of motor cortex (M1) excitability and plasticity in Parkinson's disease (PD). In patients OFF dopaminergic medication, impaired M1 plasticity and defective GABA-A-ergic inhibition can be restored by boosting gamma (γ) oscillations via transcranial alternating current stimulation (tACS) during intermittent theta-burst stimulation (iTBS). However, it is unknown whether L-dopa modifies the beneficial effects of iTBS-γ-tACS on M1 in PD. In this study, a PD patients group underwent combined iTBS-γ-tACS and iTBS-sham-tACS, each performed both OFF and ON dopaminergic therapy (four sessions in total). Motor evoked potentials (MEPs) elicited by single TMS pulses and short-interval intracortical inhibition (SICI) were assessed before and after iTBS-tACS. We also evaluated possible SICI changes during γ-tACS delivered alone in OFF and ON conditions. The amplitude of MEP elicited by single TMS pulses and the degree of SICI inhibition significantly increased after iTBS-γ-tACS. The amount of change produced by iTBS-γ-tACS was similar in patients OFF and ON therapy. Finally, γ-tACS (delivered alone) modulated SICI during stimulation and this effect did not depend on the dopaminergic condition of patients. In conclusion, boosting cortical γ oscillatory activity via tACS during iTBS improved M1 plasticity and enhanced GABA-A-ergic transmission in PD patients to the same extent regardless of dopaminergic state. These results suggest a lack of interaction between L-dopa and γ-tACS effects at the M1 level. The possible neural substrate underlying iTBS-γ tACS effects, that is, γ-resonant GABA-A-ergic interneurons activity, may explain our findings.

Role of the subthalamic nucleus in perceiving and estimating the passage of time

Sense of time (temporal sense) is believed to be processed by various brain regions in a complex manner, among which the basal ganglia, including the striatum and subthalamic nucleus (STN), play central roles. However, the precise mechanism for processing sense of time has not been clarified. To examine the role of the STN in temporal processing of the sense of time by directly manipulating STN function by switching a deep brain stimulation (DBS) device On/Off in 28 patients with Parkinson's disease undergoing STN-DBS therapy. The test session was performed approximately 20 min after switching the DBS device from On to Off or from Off to On. Temporal sense processing was assessed in three different tasks (time reproduction, time production, and bisection). In the three temporal cognitive tasks, switching STN-DBS to Off caused shorter durations to be produced compared with the switching to the On condition in the time production task. In contrast, no effect of STN-DBS was observed in the time bisection or time reproduction tasks. These findings suggest that the STN is involved in the representation process of time duration and that the role of the STN in the sense of time may be limited to the exteriorization of memories formed by experience.

Association between Beta Oscillations from Subthalamic Nucleus and Quantitative Susceptibility Mapping in Deep Gray Matter Structures in Parkinson's Disease

This study aimed to investigate the association between beta oscillations and brain iron deposition. Beta oscillations were filtered from the microelectrode recordings of local field potentials (LFP) in the subthalamic nucleus (STN), and the ratio of the power spectral density of beta oscillations (PSDXb) to that of the LFP signals was calculated. Iron deposition in the deep gray matter (DGM) structures was indirectly assessed using quantitative susceptibility mapping (QSM). The Unified Parkinson's Disease Rating Scale (UPDRS), part III, was used to assess the severity of symptoms. Spearman correlation coefficients were applied to assess the associations of PSDXb with QSM values in the DGM structures and the severity of symptoms. PSDXb showed a significant positive correlation with the average QSM values in DGM structures, including caudate and substantia nigra (SN) (p = 0.008 and 0.044). Similarly, the PSDXb showed significant negative correlations with the severity of symptoms, including axial symptoms and the gait in the medicine-off state (p = 0.006 for both). The abnormal iron metabolism in the SN and striatum pathways may be one of the underlying mechanisms for the occurrence of abnormal beta oscillations in the STN, and beta oscillations may serve as important pathophysiological biomarkers of PD.

FSTL1-knockdown improves neural oscillation via decreasing neuronal-inflammation regulating apoptosis in Aβ1-42 induced AD model mice

Follistatin like protein 1 (FSTL1) is a famous growth regulatory protein. FSTL1 has been noticed in many diseases, including heart and lung ischemia, cerebral ischemia, glioma, schizophrenia, and Autism. The role of FSTL1 has been declared in the genetics and development of the central nervous system. Therefore, we designed this study to investigate the function and the role of FSTL1 in Alzheimer's disease. Firstly, we noticed upregulated expression level of FSTL1 among four to six-month-old 5XFAD AD mice. Accordingly, we hypothesized that FSTL1-Knockdown improved AD model mice's cognitive function and recover from Alzheimer's disease. Thus, AD model mice were made by single intracerebroventricular injections of Aβ_1-42 peptides in FSTL1^+/- and CON mice. Next, our results concluded that FSTL1-knockdown effectively improved cognitive functions. FSTL1-knockdown enhanced the pattern of neural oscillations, and synaptic plasticity in Aβ_1-42 treated FSTL1-Knockdown mice compared to Aβ_1-42 induced AD model mice. Next, FSTL1-Knockdown inhibited the activation of microglia and binding of TLR-4 with microglia. Further, inactivated microglia stopped the formation of MyD88. Thus, our data revealed that FSTL1-Knockdown is slowing down the caspase/BAX/Bcl-2/TLR-4 regulating apoptosis pathway, and the expression of inflammatory cytokines in the hippocampus of Aβ_1-42 inserted FSTL1-Knockdown mice. Overall, all these data illuminate the clinical significance role of down-regulated FSTL1. FSTL1-Knockdown reduced the amyloid-beta by affecting microglia, neural-inflammation and apoptosis in AD-like model mice. Finally, down regulation of FSTL1 improved synaptic plasticity, neural oscillations, and cognitive behaviours in the Aβ_1-42 induced AD model mice.

Loss of the Synuclein Family Members Differentially Affects Baseline- and Apomorphine-Associated EEG Determinants in Single-, Double- and Triple-Knockout Mice

Synucleins comprise a family of small proteins highly expressed in the nervous system of vertebrates and involved in various intraneuronal processes. The malfunction of alpha-synuclein is one of the key events in pathogenesis of Parkinson disease and certain other neurodegenerative diseases, and there is a growing body of evidence that malfunction of other two synucleins might be involved in pathological processes in the nervous system. The modulation of various presynaptic mechanisms of neurotransmission is an important function of synucleins, and therefore, it is feasible that their deficiency might affect global electrical activity detected of the brain. However, the effects of the loss of synucleins on the frequency spectra of electroencephalograms (EEGs) have not been systematically studied so far. In the current study, we assessed changes in such spectra in single-, double- and triple-knockout mice lacking alpha-, beta- and gamma-synucleins in all possible combinations. EEGs were recorded from the motor cortex, the putamen, the ventral tegmental area and the substantia nigra of 78 3-month-old male mice from seven knockout groups maintained on the C57BL/6J genetic background, and 10 wild-type C57BL/6J mice for 30 min before and for 60 min after the systemic injection of a DA receptor agonist, apomorphine (APO). We found that almost any variant of synuclein deficiency causes multiple changes in both basal and APO-induced EEG oscillation profiles. Therefore, it is not the absence of any particular synuclein but rather a disbalance of synucleins that causes widespread changes in EEG spectral profiles.

Interplay of different synchronization modes and synaptic plasticity in a system of class I neurons

We analyze the effect of spike-timing-dependent plasticity (STDP) on a system of pulse-coupled class I neurons. Our research begins with a system of two mutually connected quadratic integrate-and-fire (QIF) neurons, which are canonical representatives of class I neurons. Along with various asymptotic modes previously observed in other neuronal models with plastic synapses, we found a stable synchronous mode characterized by unidirectional link from a slower neuron to a faster neuron. In this frequency-locked mode, the faster neuron emits multiple spikes per cycle of the slower neuron. We analytically obtain the Arnold tongues for this mode without STDP and with STDP. We also consider larger plastic networks of QIF neurons and show that the detected mode can manifest itself in such a way that slow neurons become pacemakers. As a result, slow and fast neurons can form large synchronous clusters that generate low-frequency oscillations. We demonstrate the generality of the results obtained with two connected QIF neurons using Wang-Buzsáki and Morris-Lecar biophysically plausible class I neuron models.

Contribution of the sensorimotor beta oscillations and the cortico-basal ganglia-thalamic circuitry during value-based decision making: A simultaneous EEG-fMRI investigation

In decision neuroscience, the motor system has primarily been considered to be involved in executing choice actions. However, a competing perspective suggests its engagement in the evaluation of options, traditionally considered to be performed by the brain's valuation system. Here, we investigate the role of the motor system in value-based decision making by determining the neural circuitries associated with the sensorimotor beta oscillations previously identified to encode decision options. In a simultaneous EEG-fMRI study, participants evaluated reward and risk associated with a forthcoming action. A significant sensorimotor beta desynchronization was identified prior to and independent of response. The level of beta desynchronization showed evidence of encoding the reward levels. This beta desynchronization covaried, on a trial-by-trial level, with BOLD activity in the cortico-basal ganglia-thalamic circuitry. In contrast, there was only a weak covariation within the valuation network, despite significant modulation of its BOLD activity by reward levels. These results suggest that the way in which decision variables are processed differs in the valuation network and in the cortico-basal ganglia-thalamic circuitry. We propose that sensorimotor beta oscillations indicate incentive motivational drive towards a choice action computed from the decision variables even prior to making a response, and it arises from the cortico-basal ganglia-thalamic circuitry.

Review: Subjective Time Perception, Dopamine Signaling, and Parkinsonian Slowness

The association between idiopathic Parkinson's disease, a paradigmatic dopamine-deficiency syndrome, and problems in the estimation of time has been studied experimentally for decades. I review that literature, which raises a question about whether and if dopamine deficiency relates not only to the motor slowness that is an objective and cardinal parkinsonian sign, but also to a compromised neural substrate for time perception. Why does a clinically (motorically) significant deficiency in dopamine play a role in the subjective perception of time's passage? After a discussion of a classical conception of basal ganglionic control of movement under the influence of dopamine, I describe recent work in healthy mice using optogenetics; the methodology visualizes dopaminergic neuronal firing in very short time intervals, then allows for correlation with motor behaviors in trained tasks. Moment-to-moment neuronal activity is both highly dynamic and variable, as assessed by photometry of genetically defined dopaminergic neurons. I use those animal data as context to review a large experimental experience in humans, spanning decades, that has examined subjective time perception mainly in Parkinson's disease, but also in other movement disorders. Although the human data are mixed in their findings, I argue that loss of dynamic variability in dopaminergic neuronal activity over very short intervals may be a fundamental sensory aspect in the pathophysiology of parkinsonism. An important implication is that therapeutic response in Parkinson's disease needs to be understood in terms of short-term alterations in dynamic neuronal firing, as has already been examined in novel ways—for example, in the study of real-time changes in neuronal network oscillations across very short time intervals. A finer analysis of a treatment's network effects might aid in any effort to augment clinical response to either medications or functional neurosurgical interventions in Parkinson's disease.

Subthalamic low beta bursts differ in Parkinson's disease phenotypes

OBJECTIVE

Parkinson's disease (PD) patients may be categorized into tremor-dominant (TD) and postural-instability and gait disorder (PIGD) motor phenotypes, but the dynamical aspects of subthalamic nucleus local field potentials (STN-LFP) and the neural correlates of this phenotypical classification remain unclear.

METHODS

35 STN-LFP (20 PIGD and 15 TD) were investigated through continuous wavelet transform and machine-learning-based methods. The beta oscillation - the main band associated with motor impairment in PD - dynamics was characterized through beta burst parameters across phenotypes and burst intervals under specific proposed criteria for optimal burst threshold definition.

RESULTS

Low-frequency (13-22 Hz) beta burst probability was the best predictor for PD phenotypes (75% accuracy). PIGD patients presented higher average burst duration (p = 0.018), while TD patients exhibited higher burst probability (p = 0.014). Categorization into shorter and longer than 400 ms bursts led to significant interaction between burst length categories and the phenotypes (p < 0.050) as revealed by mixed-effects models. Long burst durations and short bursts probability positively correlated, respectively, with rigidity-bradykinesia (p = 0.029) and tremor (p = 0.038) scores.

CONCLUSIONS

Subthalamic low-frequency beta bursts differed between TD and PIGD phenotypes and correlated with motor symptoms.

SIGNIFICANCE

These findings improve the PD phenotypes' electrophysiological characterization and may define new criteria for adaptive deep brain stimulation.

Spatio-temporal dynamics of large-scale electrophysiological networks during cognitive action control in healthy controls and Parkinson's disease patients

Among the cognitive symptoms that are associated with Parkinson's disease (PD), alterations in cognitive action control (CAC) are commonly reported in patients. CAC enables the suppression of an automatic action, in favor of a goal-directed one. The implementation of CAC is time-resolved and arguably associated with dynamic changes in functional brain networks. However, the electrophysiological functional networks involved, their dynamic changes, and how these changes are affected by PD, still remain unknown. In this study, to address this gap of knowledge, 10 PD patients and 10 healthy controls (HC) underwent a Simon task while high-density electroencephalography (HD-EEG) was recorded. Source-level dynamic connectivity matrices were estimated using the phase-locking value in the beta (12-25 Hz) and gamma (30-45 Hz) frequency bands. Temporal independent component analyses were used as a dimension reduction tool to isolate the task-related brain network states. Typical microstate metrics were quantified to investigate the presence of these states at the subject-level. Our results first confirmed that PD patients experienced difficulties in inhibiting automatic responses during the task. At the group-level, we found three functional network states in the beta band that involved fronto-temporal, temporo-cingulate and fronto-frontal connections with typical CAC-related prefrontal and cingulate nodes (e.g., inferior frontal cortex). The presence of these networks did not differ between PD patients and HC when analyzing microstates metrics, and no robust correlations with behavior were found. In the gamma band, five networks were found, including one fronto-temporal network that was identical to the one found in the beta band. These networks also included CAC-related nodes previously identified in different neuroimaging modalities. Similarly to the beta networks, no subject-level differences were found between PD patients and HC. Interestingly, in both frequency bands, the dominant network at the subject-level was never the one that was the most durably modulated by the task. Altogether, this study identified the dynamic functional brain networks observed during CAC, but did not highlight PD-related changes in these networks that might explain behavioral changes. Although other new methods might be needed to investigate the presence of task-related networks at the subject-level, this study still highlights that task-based dynamic functional connectivity is a promising approach in understanding the cognitive dysfunctions observed in PD and beyond.

Troubleshooting Gait Disturbances in Parkinson's Disease With Deep Brain Stimulation

Deep brain stimulation (DBS) of the subthalamic nucleus or the globus pallidus is an established treatment for Parkinson's disease (PD) that yields a marked and lasting improvement of motor symptoms. Yet, DBS benefit on gait disturbances in PD is still debated and can be a source of dissatisfaction and poor quality of life. Gait disturbances in PD encompass a variety of clinical manifestations and rely on different pathophysiological bases. While gait disturbances arising years after DBS surgery can be related to disease progression, early impairment of gait may be secondary to treatable causes and benefits from DBS reprogramming. In this review, we tackle the issue of gait disturbances in PD patients with DBS by discussing their neurophysiological basis, providing a detailed clinical characterization, and proposing a pragmatic programming approach to support their management.

Low-Intensity Transcranial Ultrasound Stimulation: Mechanisms of Action and Rationale for Future Applications in Movement Disorders

Low-intensity transcranial ultrasound stimulation (TUS) is a novel non-invasive brain stimulation technique that uses acoustic energy to induce changes in neuronal activity. However, although low-intensity TUS is a promising neuromodulation tool, it has been poorly studied as compared to other methods, i.e., transcranial magnetic and electrical stimulation. In this article, we first focus on experimental studies in animals and humans aimed at explaining its mechanisms of action. We then highlight possible applications of TUS in movement disorders, particularly in patients with parkinsonism, dystonia, and tremor. Finally, we highlight the knowledge gaps and possible limitations that currently limit potential TUS applications in movement disorders. Clarifying the potential role of TUS in movement disorders may further promote studies with therapeutic perspectives in this field.

Towards therapeutic non-invasive electrical modulation of brain circuits in Parkinson's disease

This scientific commentary refers to ‘Driving motor cortex oscillations modulates bradykinesia in Parkinson’s disease’ by Guerra et al. (doi: 10.1093/brain/awab257).

Combined Subthalamic and Nigral Stimulation Modulates Temporal Gait Coordination and Cortical Gait-Network Activity in Parkinson's Disease

Background

Freezing of gait (FoG) is a disabling burden for Parkinson's disease (PD) patients with poor response to conventional therapies. Combined deep brain stimulation of the subthalamic nucleus and substantia nigra (STN+SN DBS) moved into focus as a potential therapeutic option to treat the parkinsonian gait disorder and refractory FoG. The mechanisms of action of DBS within the cortical-subcortical-basal ganglia network on gait, particularly at the cortical level, remain unclear.

Methods

Twelve patients with idiopathic PD and chronically-implanted DBS electrodes were assessed on their regular dopaminergic medication in a standardized stepping in place paradigm. Patients executed the task with DBS switched off (STIM OFF), conventional STN DBS and combined STN+SN DBS and were compared to healthy matched controls. Simultaneous high-density EEG and kinematic measurements were recorded during resting-state, effective stepping, and freezing episodes.

Results

Clinically, STN+SN DBS was superior to conventional STN DBS in improving temporal stepping variability of the more affected leg. During resting-state and effective stepping, the cortical activity of PD patients in STIM OFF was characterized by excessive over-synchronization in the theta (4-8 Hz), alpha (9-13 Hz), and high-beta (21-30 Hz) band compared to healthy controls. Both active DBS settings similarly decreased resting-state alpha power and reduced pathologically enhanced high-beta activity during resting-state and effective stepping compared to STIM OFF. Freezing episodes during STN DBS and STN+SN DBS showed spectrally and spatially distinct cortical activity patterns when compared to effective stepping. During STN DBS, FoG was associated with an increase in cortical alpha and low-beta activity over central cortical areas, while with STN+SN DBS, an increase in high-beta was prominent over more frontal areas.

Conclusions

STN+SN DBS improved temporal aspects of parkinsonian gait impairment compared to conventional STN DBS and differentially affected cortical oscillatory patterns during regular locomotion and freezing suggesting a potential modulatory effect on dysfunctional cortical-subcortical communication in PD.

Serotonin 1A receptor agonist modulation of motor deficits and cortical oscillations by NMDA receptor interaction in parkinsonian rats

Although serotonin 1A (5-HT1A) receptor agonists are widely used as the additive compound to reduce l-dopa-induced dyskinesia in Parkinson's disease (PD), few studies focused on the effect and mechanism of 5-HT1A receptor agonist on the motor symptoms of PD. Unilateral 6-hydroxydopamine (6-OHDA)-lesioned rats were used and implantation of electrodes was performed in the motor cortex of these rats. So the effect of 5-HT1A receptor agonist 8-OH-DPAT on motor behaviors and oscillatory activities were evaluated. In addition, 8-OH-DPAT combined with D2 receptor antagonist raclopride, NMDA receptor antagonist MK-801, or its agonist d-cycloserine (DCS) were co-administrated. 8-OH-DPAT administration significantly improved spontaneous locomotor activity and asymmetric forepaw function in 6-OHDA-lesioned rats. Meanwhile, 8-OH-DPAT identified selective modulation of the abnormal high beta oscillations (25-40 Hz) in the motor cortex of 6-OHDA-lesioned rats, without inducing pathological finely tuned gamma around 80 Hz. Different from 8-OH-DPAT, l-dopa treatment produced a prolonged improvement on motor performances and differential regulation of high beta and gamma oscillations. However, dopamine D2 receptor antagonist had no influence on the 8-OH-DPAT-mediated-motor behaviors and beta oscillations in 6-OHDA-lesioned rats. In contrast, subthreshold NMDA receptor antagonist MK-801 obviously elevated the 8-OH-DPAT-mediated-motor behaviors, while NMDA receptor agonist DCS partially impaired the 8-OH-DPAT-mediated symptoms in 6-OHDA-lesioned rats. This study suggests that 5-HT1A receptor agonist 8-OH-DPAT improves motor activity and modulates the oscillations in the motor cortex of parkinsonian rats. Different from l-dopa, 8-OH-DPAT administration ameliorates motor symptoms of PD through glutamatergic rather than the dopaminergic pathway.

Dopamine depletion selectively disrupts interactions between striatal neuron subtypes and LFP oscillations

Dopamine degeneration in Parkinson’s disease (PD) dysregulates the striatal neural network and causes motor deficits. However, it is unclear how altered striatal circuits relate to dopamine-acetylcholine chemical imbalance and abnormal local field potential (LFP) oscillations observed in PD. We perform a multimodal analysis of the dorsal striatum using cell-type-specific calcium imaging and LFP recording. We reveal that dopamine depletion selectively enhances LFP beta oscillations during impaired locomotion, supporting beta oscillations as a biomarker for PD. We further demonstrate that dynamic cholinergic interneuron activity during locomotion remains unaltered, even though cholinergic tone is implicated in PD. Instead, dysfunctional striatal output arises from elevated coordination within striatal output neurons, which is accompanied by reduced locomotor encoding of parvalbumin interneurons and transient pathological LFP high-gamma oscillations. These results identify a pathological striatal circuit state following dopamine depletion where distinct striatal neuron subtypes are selectively coordinated with LFP oscillations during locomotion.

Zemel et al. demonstrate that dopamine loss disrupts striatal neural network and enhances local field potential beta oscillations during impaired locomotion. Specifically, striatal projecting neuron activation is abnormally coordinated and accompanied by pathological high-gamma oscillations. While parvalbumin interneurons reduce locomotor encoding, cholinergic interneurons strengthen their interactions with projecting neurons.

The Entropy of Adaptively Segmented Beta Oscillations Predict Motor Improvement in Patients with Parkinsons Disease

OBJECTIVE

Beta bursts of local fields potentials (LFPs) recorded from subthalamic nucleus (STN) have been recently proposed as a new temporal feature for patients with Parkinsons disease (PD). We introduce a new technique for the adaptive time-domain segmentation of STN-LFP recordings such that the constructed time segments are proportional to the duration of stationary beta activity. We investigated whether the spectral entropy of the adaptively captured beta oscillations can describe the improvement in motor signs following dopaminergic medication.

METHODS

STN-LFP recordings from externalized chronic deep brain stimulation (DBS) leads were obtained in 9 PD patients. During this monitoring, each patient underwent 3 medication intake cycles where short acting agents (L-DOPA equivalent dose) were administered. We analyzed 2-minute resting state LFP data in each OFF and L-DOPA-induced ON medication states and constructed time domain segmentation of LFP signal in which the length segmentations are adapted to time-varying nature of the oscillatory activity.

RESULTS

Adaptively constructed segments were noted to be significantly longer in OFF- and shorter in ON-state (p<0.001). Interestingly, in the OFF state, the peak frequency of long beta bursts (>375ms) was in the low range (12-23Hz) of the beta spectrum, whereas shorter beta bursts (<375ms) were widespread in the 13-30Hz band. Measured clinical improvement was highly correlated with the difference in the spectral entropy of beta bursts between OFF and ON states (r=-0.83, p<0.01).

CONCLUSION AND SIGNIFICANCE

Our findings suggest that beta oscillations can be adaptively segmented without the use of a predetermined amplitude threshold, thereby allowing for objective quantification of burst itself. Compared to the shorter ones, longer oscillations with duration 375ms were highly correlated with the clinical improvement, supporting a pathological role for them. Overall, these findings coupled with our adaptive approach could enable the quantitative use of temporal dynamics of beta activity in assessing severity of PD and improvements in motor features.

Identification of an early-stage Parkinson's disease neuromarker using event-related potentials, brain network analytics and machine-learning

OBJECTIVE

The purpose of this study is to explore the possibility of developing a biomarker that can discriminate early-stage Parkinson's disease from healthy brain function using electroencephalography (EEG) event-related potentials (ERPs) in combination with Brain Network Analytics (BNA) technology and machine learning (ML) algorithms.

BACKGROUND

Currently, diagnosis of PD depends mainly on motor signs and symptoms. However, there is need for biomarkers that detect PD at an earlier stage to allow intervention and monitoring of potential disease-modifying therapies. Cognitive impairment may appear before motor symptoms, and it tends to worsen with disease progression. While ERPs obtained during cognitive tasks performance represent processing stages of cognitive brain functions, they have not yet been established as sensitive or specific markers for early-stage PD.

METHODS

Nineteen PD patients (disease duration of ≤2 years) and 30 healthy controls (HC) underwent EEG recording while performing visual Go/No-Go and auditory Oddball cognitive tasks. ERPs were analyzed by the BNA technology, and a ML algorithm identified a combination of features that distinguish early PD from HC. We used a logistic regression classifier with a 10-fold cross-validation.

RESULTS

The ML algorithm identified a neuromarker comprising 15 BNA features that discriminated early PD patients from HC. The area-under-the-curve of the receiver-operating characteristic curve was 0.79. Sensitivity and specificity were 0.74 and 0.73, respectively. The five most important features could be classified into three cognitive functions: early sensory processing (P50 amplitude, N100 latency), filtering of information (P200 amplitude and topographic similarity), and response-locked activity (P-200 topographic similarity preceding the motor response in the visual Go/No-Go task).

CONCLUSIONS

This pilot study found that BNA can identify patients with early PD using an advanced analysis of ERPs. These results need to be validated in a larger PD patient sample and assessed for people with premotor phase of PD.

Predictive accuracy of CNN for cortical oscillatory activity in an acute rat model of parkinsonism

In neurological and neuropsychiatric disorders neuronal oscillatory activity between basal ganglia and cortical circuits are altered, which may be useful as biomarker for adaptive deep brain stimulation. We investigated whether changes in the spectral power of oscillatory activity in the motor cortex (MCtx) and the sensorimotor cortex (SMCtx) of rats after injection of the dopamine (DA) receptor antagonist haloperidol (HALO) would be similar to those observed in Parkinson disease. Thereafter, we tested whether a convolutional neural network (CNN) model would identify brain signal alterations in this acute model of parkinsonism. A sixteen channel surface micro-electrocorticogram (ECoG) recording array was placed under the dura above the MCtx and SMCtx areas of one hemisphere under general anaesthesia in rats. Seven days after surgery, micro ECoG was recorded in individual free moving rats in three conditions: (1) basal activity, (2) after injection of HALO (0.5 mg/kg), and (3) with additional injection of apomorphine (APO) (1 mg/kg). Furthermore, a CNN-based classification consisting of 23,530 parameters was applied on the raw data. HALO injection decreased oscillatory theta band activity (4-8 Hz) and enhanced beta (12-30 Hz) and gamma (30-100 Hz) in MCtx and SMCtx, which was compensated after APO injection (P ¡ 0.001). Evaluation of classification performance of the CNN model provided accuracy of 92%, sensitivity of 90% and specificity of 93% on one-dimensional signals. The CNN proposed model requires a minimum of sensory hardware and may be integrated into future research on therapeutic devices for Parkinson disease, such as adaptive closed loop stimulation, thus contributing to more efficient way of treatment.