Thalamocortical dynamics underlying spontaneous transitions in beta power in Parkinsonism

Brain-hemispheric differences in the premotor area for motor planning: An approach based on corticomuscular connectivity during motor decision-making

This study investigates the role of the premotor area (PMA) in motor planning during decision-making, focusing on differences between brain hemispheres. A cross-sectional assessment was conducted involving seventeen right-handed participants who performed tasks requiring responses with either hand to visual stimuli. Motion capture, EEG and EMG signals were collected to analyze corticomuscular coherence (CMC) in the beta and gamma bands across four motor-related cortical areas. Findings revealed significant beta-band CMC between anterior deltoids and contralateral PMA before stimulus onset in simple reaction tasks. Moreover, significant beta-band CMC was observed between the left anterior deltoid and the right PMA during the motor planning phase, prior to the onset of muscle contraction, corresponding with shorter planning times. This connectivity pattern was consistent across both simple and complex reaction tasks, indicating that the PMA plays a crucial role during decision-making. Notably, motor planning for the right hand did not exhibit the same connectivity pattern, suggesting more complex cognitive processes. These results emphasize the distinct functional roles of the left and right hemispheres in motor planning and underscore the importance of CMC in understanding the neural mechanisms underlying motor control. This study contributes to the theoretical framework of motor decision-making and offers insights for future research on motor planning and rehabilitation strategies.

Potential scalp acupuncture and brain stimulation targets for common neurological disorders: evidence from neuroimaging studies

BACKGROUND

Scalp acupuncture is a promising potential therapy for neurological disorders. However, the development of its stimulation targets-both in identifying novel targets and refining the precision of their localization-has advanced slowly, largely due to the inadequate integration of brain science findings. This study leverages advances in brain neuroimaging to identify evidence-based cortical targets, enhancing the potential of scalp acupuncture and other brain stimulation techniques.

METHODS

Using the Neurosynth Compose platform, systematic meta-analyses of neuroimaging studies were conducted to identify potential surface cortical targets for ten neurological conditions: Subjective Cognitive Decline (SCD), Mild Cognitive Impairment (MCI), Alzheimer's Disease (AD), Parkinson's Disease (PD), Multiple System Atrophy (MSA), Post-Stroke Aphasia (PSA), Primary Progressive Aphasia (PPA), Dyslexia, Chronic Pain, and Disorders of Consciousness (DoC). These targets were projected onto the scalp, further localized using scalp acupuncture lines, traditional acupoints and EEG 10-20 system.

RESULTS

We have identified specific cortical targets for scalp acupuncture associated with ten neurological disorders. Our findings are broadly consistent with current scalp acupuncture protocols while introducing additional new stimulation targets, such as the inferior temporal gyrus for memory processing and the angular gyrus for visuospatial attention. Additionally, the identified targets align with evidence from non-invasive brain stimulation, supporting therapeutic strategies for conditions such as movement disorders and cognitive impairments by targeting areas like the dorsolateral prefrontal cortex and primary motor cortex.

CONCLUSION

Our findings provide a foundation for developing a brain imaging-based scalp acupuncture protocol for neurological disorders. The identified targets may also be used as brain stimulation targets for these disorders.

Age-related changes in neural oscillations vary as a function of brain region and frequency band

Advanced aging is associated with robust changes in neural activity. In addition to the well-established age-related slowing of the peak alpha frequency, there is a growing body of evidence showing that older age is also associated with changes in alpha power and beta power. Despite the important progress that has been made, the interacting effects of age and frequency band have not been directly tested in sensor and source space while controlling for aperiodic components. In the current study we address these limitations. We recruited 54 healthy younger and older adults and measured neural oscillations using a high-density electroencephalogram (EEG) system during resting-state with eyes closed. After preprocessing the EEG data and controlling for aperiodic components, we computed alpha and beta power in both sensor and source space. Permutation two-way ANOVAs between frequency band and age group were performed across all electrodes and across all dipoles. Our findings revealed significant interactions in sensorimotor, parietal, and occipital regions. The pattern driving the interaction varied across regions, with older age associated with a progressive decrease in alpha power and a progressive increase in beta power from parietal to sensorimotor regions. Our findings demonstrate that age-related changes in neural oscillations vary as a function of brain region and frequency band. We interpret our findings in the context of clinical and preclinical evidence of age effects on the cholinergic circuit and the Cortico-Basal Ganglia-Thalamo-Cortical (CBGTC) circuit.

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.

Combined effect of stimulation and electromagnetic induction on absence seizure inhibition in coupled thalamocortical circuits

Deep brain stimulation (DBS) and electromagnetic induction are new techniques that are increasingly used in modern epilepsy treatments; however, the mechanism of action remains unclear. In this study, we constructed a bidirectional-coupled cortico-thalamic model, based on which we proposed three regulation schemes: isolated regulation of DBS, isolated regulation of electromagnetic induction and combined regulation of the previous two. In particular, we introduced DBS with a lower amplitude and considered the influence of electromagnetic induction caused by the transmembrane current on the membrane potential. The most striking finding of this study is that the three therapeutic schemes could effectively control abnormal discharge, and combined regulation could reduce the occurrence of epileptic seizures more effectively. The present study bridges the gap between the bidirectional coupling model and combined control. In this way, the damage induced by electrical stimulation of the patient's brain tissue could be reduced, and the abnormal physiological discharge pattern of the cerebral cortex was simultaneously regulated by different techniques. This work opens new avenues for improving brain dysfunction in patients with epilepsy, expands ideas for promoting the development of neuroscience and is meaningful for improving the health of modern society and developing the field of science.

When do bursts matter in the primary motor cortex? Investigating changes in the intermittencies of beta rhythms associated with movement states

Brain activity exhibits significant temporal structure that is not well captured in the power spectrum. Recently, attention has shifted to characterising the properties of intermittencies in rhythmic neural activity (i.e. bursts), yet the mechanisms that regulate them are unknown. Here, we present evidence from electrocorticography recordings made over the motor cortex to show that the statistics of bursts, such as duration or amplitude, in the beta frequency (14-30 Hz) band, significantly aid the classification of motor states such as rest, movement preparation, execution, and imagery. These features reflect nonlinearities not detectable in the power spectrum, with states increasing in nonlinearity from movement execution to preparation to rest. Further, we show using a computational model of the cortical microcircuit, constrained to account for burst features, that modulations of laminar specific inhibitory interneurons are responsible for the temporal organisation of activity. Finally, we show that the temporal characteristics of spontaneous activity can be used to infer the balance of cortical integration between incoming sensory information and endogenous activity. Critically, we contribute to the understanding of how transient brain rhythms may underwrite cortical processing, which in turn, could inform novel approaches for brain state classification, and modulation with novel brain-computer interfaces.

Functional Interactions Between the Parafascicular Thalamic Nucleus and Motor Cortex Are Altered in Hemiparkinsonian Rat

Parkinson’s disease (PD) is characterized by aberrant discharge patterns and exaggerated oscillatory activity within basal ganglia-thalamocortical circuits. We have previously observed substantial alterations in spike and local field potential (LFP) activities recorded in the thalamic parafascicular nucleus (PF) and motor cortex (M1), respectively, of hemiparkinsonian rats during rest or catching movements. This study explored whether the mutual effects of the PF and M1 depended on the amplitude and phase relationship in their identified neuron spikes or group rhythmic activities. Microwire electrode arrays were paired and implanted in the PF and M1 of rats with unilateral dopaminergic cell lesions. The results showed that the identified PF neurons exhibited aberrant cell type-selective firing rates and preferential and excessive phase-locked firing to cortical LFP oscillations mainly at 12–35 Hz (beta frequencies), consistent with the observation of identified M1 neurons with ongoing PF LFP oscillations. Experimental evidence also showed a decrease in phase-locking at 0.7–12 Hz and 35–70 Hz in the PF and M1 circuits in the hemiparkinsonian rats. Furthermore, anatomical evidence was provided for the existence of afferent and efferent bidirectional reciprocal connectivity pathways between the PF and M1 using an anterograde and retrograde neuroanatomical tracing virus. Collectively, our results suggested that multiple alterations may be present in regional anatomical and functional modes with which the PF and M1 interact, and that parkinsonism-associated changes in PF integrate M1 activity in a manner that varies with frequency, behavioral state, and integrity of the dopaminergic system.

Inference of brain networks with approximate Bayesian computation - assessing face validity with an example application in Parkinsonism

This paper describes and validates a novel framework using the Approximate Bayesian Computation (ABC) algorithm for parameter estimation and model selection in models of mesoscale brain network activity. We provide a proof of principle, first pass validation of this framework using a set of neural mass models of the cortico-basal ganglia thalamic circuit inverted upon spectral features from experimental, in vivo recordings. This optimization scheme relaxes an assumption of fixed-form posteriors (i.e. the Laplace approximation) taken in previous approaches to inverse modelling of spectral features. This enables the exploration of model dynamics beyond that approximated from local linearity assumptions and so fit to explicit, numerical solutions of the underlying non-linear system of equations. In this first paper, we establish a face validation of the optimization procedures in terms of: (i) the ability to approximate posterior densities over parameters that are plausible given the known causes of the data; (ii) the ability of the model comparison procedures to yield posterior model probabilities that can identify the model structure known to generate the data; and (iii) the robustness of these procedures to local minima in the face of different starting conditions. Finally, as an illustrative application we show (iv) that model comparison can yield plausible conclusions given the known neurobiology of the cortico-basal ganglia-thalamic circuit in Parkinsonism. These results lay the groundwork for future studies utilizing highly nonlinear or brittle models that can explain time dependant dynamics, such as oscillatory bursts, in terms of the underlying neural circuits.

The computational neurology of movement under active inference

We propose a computational neurology of movement based on the convergence of theoretical neurobiology and clinical neurology. A significant development in the former is the idea that we can frame brain function as a process of (active) inference, in which the nervous system makes predictions about its sensory data. These predictions depend upon an implicit predictive (generative) model used by the brain. This means neural dynamics can be framed as generating actions to ensure sensations are consistent with these predictions-and adjusting predictions when they are not. We illustrate the significance of this formulation for clinical neurology by simulating a clinical examination of the motor system using an upper limb coordination task. Specifically, we show how tendon reflexes emerge naturally under the right kind of generative model. Through simulated perturbations, pertaining to prior probabilities of this model's variables, we illustrate the emergence of hyperreflexia and pendular reflexes, reminiscent of neurological lesions in the corticospinal tract and cerebellum. We then turn to the computational lesions causing hypokinesia and deficits of coordination. This in silico lesion-deficit analysis provides an opportunity to revisit classic neurological dichotomies (e.g. pyramidal versus extrapyramidal systems) from the perspective of modern approaches to theoretical neurobiology-and our understanding of the neurocomputational architecture of movement control based on first principles.

Understanding the Role of Sensorimotor Beta Oscillations

Beta oscillations have been predominantly observed in sensorimotor cortices and basal ganglia structures and they are thought to be involved in somatosensory processing and motor control. Although beta activity is a distinct feature of healthy and pathological sensorimotor processing, the role of this rhythm is still under debate. Here we review recent findings about the role of beta oscillations during experimental manipulations (i.e., drugs and brain stimulation) and their alteration in aging and pathology. We show how beta changes when learning new motor skills and its potential to integrate sensory input with prior contextual knowledge. We conclude by discussing a novel methodological approach analyzing beta oscillations as a series of transient bursting events.

EEG, MEG and neuromodulatory approaches to explore cognition: Current status and future directions

Neural oscillations and their association with brain states and cognitive functions have been object of extensive investigation over the last decades. Several electroencephalography (EEG) and magnetoencephalography (MEG) analysis approaches have been explored and oscillatory properties have been identified, in parallel with the technical and computational advancement. This review provides an up-to-date account of how EEG/MEG oscillations have contributed to the understanding of cognition. Methodological challenges, recent developments and translational potential, along with future research avenues, are discussed.

Interactions Between Motor Thalamic Field Potentials and Single-Unit Spiking Are Correlated With Behavior in Rats

Field potential (FP) oscillations are believed to coordinate brain activity over large spatiotemporal scales, with specific features (e.g., phase and power) in discrete frequency bands correlated with motor output. Furthermore, complex correlations between oscillations in distinct frequency bands (phase-amplitude, amplitude-amplitude, and phase-phase coupling) are commonly observed. However, the mechanisms underlying FP-behavior correlations and cross-frequency coupling remain unknown. The thalamus plays a central role in generating many circuit-level neural oscillations, and single-unit activity in motor thalamus (Mthal) is correlated with behavioral output. We, therefore, hypothesized that motor thalamic spiking coordinates motor system FPs and underlies FP-behavior correlations. To investigate this possibility, we recorded wideband motor thalamic (Mthal) electrophysiology as healthy rats performed a two-alternative forced-choice task. Delta (1–4 Hz), beta (13–30 Hz), low gamma (30–70 Hz), and high gamma (70–200 Hz) power were strongly modulated by task performance. As in the cortex, the delta phase was correlated with beta/low gamma power and reaction time. Most interestingly, subpopulations of Mthal neurons defined by their relationship to the behavior exhibited distinct relationships with FP features. Specifically, neurons whose activity was correlated with action selection and movement speed were entrained to delta oscillations. Furthermore, changes in their activity anticipated power fluctuations in beta/low gamma bands. These complex relationships suggest mechanisms for commonly observed FP-FP and spike-FP correlations, as well as subcortical influences on motor output.

Deep brain stimulation has state-dependent effects on motor connectivity in Parkinson's disease

Subthalamic nucleus deep brain stimulation is an effective treatment for advanced Parkinson's disease; however, its therapeutic mechanism is unclear. Previous modelling of functional MRI data has suggested that deep brain stimulation has modulatory effects on a number of basal ganglia pathways. This work uses an enhanced data collection protocol to collect rare functional MRI data in patients with subthalamic nucleus deep brain stimulation. Eleven patients with Parkinson's disease and subthalamic nucleus deep brain stimulation underwent functional MRI at rest and during a movement task; once with active deep brain stimulation, and once with deep brain stimulation switched off. Dynamic causal modelling and Bayesian model selection were first used to compare a series of plausible biophysical models of the cortico-basal ganglia circuit that could explain the functional MRI activity at rest in an attempt to reproduce and extend the findings from our previous work. General linear modelling of the movement task functional MRI data revealed deep brain stimulation-associated signal increases in the primary motor and cerebellar cortices. Given the significance of the cerebellum in voluntary movement, we then built a more complete model of the motor system by including cerebellar-basal ganglia interactions, and compared the modulatory effects deep brain stimulation had on different circuit components during the movement task and again using the resting state data. Consistent with previous results from our independent cohort, model comparison found that the rest data were best explained by deep brain stimulation-induced increased (effective) connectivity of the cortico-striatal, thalamo-cortical and direct pathway and reduced coupling of subthalamic nucleus afferent and efferent connections. No changes in cerebellar connectivity were identified at rest. In contrast, during the movement task, there was functional recruitment of subcortical-cerebellar pathways, which were additionally modulated by deep brain stimulation, as well as modulation of local (intrinsic) cortical and cerebellar circuits. This work provides in vivo evidence for the modulatory effects of subthalamic nucleus deep brain stimulation on effective connectivity within the cortico-basal ganglia loops at rest, as well as further modulations in the cortico-cerebellar motor system during voluntary movement. We propose that deep brain stimulation has both behaviour-independent effects on basal ganglia connectivity, as well as behaviour-dependent modulatory effects.

A Thalamo-Cortex Microcircuit Model of Beta Oscillations in the Parkinsonian Motor Cortex

Exaggerated beta oscillations (~13-30 Hz) observed in the cortical areas of the brain is one of the characteristics of disrupted information flow in the primary motor cortex in Parkinson's disease (PD). However, the mechanism underlying the generation of these enhanced beta rhythms remains unclear. The thalamo-cortex microcircuit (TCM) contains reciprocal synaptic connections that generate low frequency oscillations in the microcircuit in healthy conditions. Recent studies suggest that alterations in synaptic connections both within and between the cortex and thalamus play a critical role in the generation of pathological beta rhythms in PD. In this study, we examine this hypothesis in a spiking neuronal network model of the TCM. The model is compared and validated against neural firing patterns recorded in rodent models of PD from the literature.

Basal ganglia oscillations as biomarkers for targeting circuit dysfunction in Parkinson's disease

Oscillations are a naturally occurring phenomenon in highly interconnected dynamical systems. However, it is thought that excessive synchronized oscillations in brain circuits can be detrimental for many brain functions by disrupting neuronal information processing. Because synchronized basal ganglia oscillations are a hallmark of Parkinson's disease (PD), it has been suggested that aberrant rhythmic activity associated with symptoms of the disease could be used as a physiological biomarker to guide pharmacological and electrical neuromodulatory interventions. We here briefly review the various manifestations of basal ganglia oscillations observed in human subjects and in animal models of PD. In this context, we also review the evidence supporting a pathophysiological role of different oscillations for the suppression of voluntary movements as well as for the induction of excessive motor activity. In light of these findings, it is discussed how oscillations could be used to guide a more precise targeting of dysfunctional circuits to obtain improved symptomatic treatment of PD.

Beta-Band Resonance and Intrinsic Oscillations in a Biophysically Detailed Model of the Subthalamic Nucleus-Globus Pallidus Network

Increased beta-band oscillatory activity in the basal ganglia network is associated with Parkinsonian motor symptoms and is suppressed with medication and deep brain stimulation (DBS). The origins of the beta-band oscillations, however, remains unclear with both intrinsic oscillations arising within the subthalamic nucleus (STN)-external globus pallidus (GPe) network and exogenous beta-activity, originating outside the network, proposed as potential sources of the pathological activity. The aim of this study was to explore the relative contribution of autonomous oscillations and exogenous oscillatory inputs in the generation of pathological oscillatory activity in a biophysically detailed model of the parkinsonian STN-GPe network. The network model accounts for the integration of synaptic currents and their interaction with intrinsic membrane currents in dendritic structures within the STN and GPe. The model was used to investigate the development of beta-band synchrony and bursting within the STN-GPe network by changing the balance of excitation and inhibition in both nuclei, and by adding exogenous oscillatory inputs with varying phase relationships through the hyperdirect cortico-subthalamic and indirect striato-pallidal pathways. The model showed an intrinsic susceptibility to beta-band oscillations that was manifest in weak autonomously generated oscillations within the STN-GPe network and in selective amplification of exogenous beta-band synaptic inputs near the network's endogenous oscillation frequency. The frequency at which this resonance peak occurred was determined by the net level of excitatory drive to the network. Intrinsic or endogenously generated oscillations were too weak to support a pacemaker role for the STN-GPe network, however, they were considerably amplified by sparse cortical beta inputs and were further amplified by striatal beta inputs that promoted anti-phase firing of the cortex and GPe, resulting in maximum transient inhibition of STN neurons. The model elucidates a mechanism of cortical patterning of the STN-GPe network through feedback inhibition whereby intrinsic susceptibility to beta-band oscillations can lead to phase locked spiking under parkinsonian conditions. These results point to resonance of endogenous oscillations with exogenous patterning of the STN-GPe network as a mechanism of pathological synchronization, and a role for the pallido-striatal feedback loop in amplifying beta oscillations.