Changes in the theta band coherence during motor task after hand immobilization

Pallidal and motor cortical interactions determine gait initiation dynamics in Parkinson's disease

Gait initiation is a fundamental human task, requiring one or more anticipatory postural adjustments (APA) prior to stepping. Deviations in amplitude and timing of APAs exist in Parkinson's disease (PD), causing dysfunctional postural control which increases the risk of falls. The motor cortex and basal ganglia have been implicated in the regulation of postural control, however, their dynamics during gait initiation, relationship to APA metrics, and response to pharmacotherapy such as levodopa are unknown. To address these questions, we streamed electrocorticography (ECoG) potentials from the premotor and primary motor cortices, as well as local field potentials (LFPs) from the globus pallidus in five people with PD exhibiting gait and balance dysfunction during a cued gait initiation task. Amplitude and timing of APA were evaluated with force plates and synchronized to the neural data. Subjects performed gait initiation trials under ON and LOW levodopa conditions to assess effects of medication on APA metrics and underlying neural dynamics. All subjects demonstrated pallidal and cortical oscillatory changes during different phases of gait initiation. Grouped analysis revealed that from quiet standing to the first foot step, pallidal beta power showed stepwise decrease and broadband gamma power increases, whereas cortical potentials showed low frequency (theta, alpha, beta) power decrease during gait initiation, regardless of medication state. The pallidum and motor cortices also became increasingly coherent during gait initiation compared to quiet standing prior to APA onset. Using linear mixed models, we found that while pallidal gamma powers are predictive of APA scaling, pallidal-cortical coherence (theta, alpha, beta) and premotor-M1 gamma coherence are predictive of APA timing. Our study is the first detailed characterization of basal-ganglia cortical circuit dynamics during human gait initiation. We identified significant pallidal motor cortical power and coherence changes that underlie the amplitude and timing of APA which appear to be independent of medication states of the study subjects. Our results provide evidence for a model where synchronized premotor and motor cortical activities transiently couple with the globus pallidus to regulate the timing of postural responses, and local pallidal activity regulate the amplitude of postural changes during gait initiation. It suggests that abnormal pallidal outflow and synchronization between the pallidum and motor cortices may be a pathophysiological mechanism underlying disordered postural response in Parkinson's disease.

Alterations of PAC-based resting state networks in Parkinson's disease are partially alleviated by levodopa medication

Introduction

Parkinson's disease (PD) is a neurodegenerative disorder affecting the whole brain, leading to several motor and non-motor symptoms. In the past, it has been shown that PD alters resting state networks (RSN) in the brain. These networks are usually derived from fMRI BOLD signals. This study investigated RSN changes in PD patients based on maximum phase-amplitude coupling (PAC) throughout the cortex. We also tested the hypothesis that levodopa medication shifts network activity back toward a healthy state.

Methods

We recorded 23 PD patients and 24 healthy age-matched participants for 30 min at rest with magnetoencephalography (MEG). PD patients were measured once in the dopaminergic medication ON and once in the medication OFF state. A T1-MRI brain scan was acquired from each participant for source reconstruction. After correcting the data for artifacts and performing source reconstruction using a linearly constrained minimum variance beamformer, we extracted visual, sensorimotor (SMN), and frontal RSNs based on PAC.

Results

We found significant changes in all networks between healthy participants and PD patients in the medication OFF state. Levodopa had a significant effect on the SMN but not on the other networks. There was no significant change in the optimal PAC coupling frequencies between healthy participants and PD patients.

Discussion

Our results suggest that RSNs, based on PAC in different parts of the cortex, are altered in PD patients. Furthermore, levodopa significantly affects the SMN, reflecting the clinical alleviation of motor symptoms and leading to a network normalization compared to healthy controls.

Cross-frequency phase coupling of brain oscillations and relevance attribution as saliency detection in abstract reasoning

reasoning is associated with the ability to detect relations among objects, ideas, events. It underlies the understanding of other individuals' thoughts and intentions. In natural settings, individuals have to infer relevant associations that have proven to be reliable or precise predictors. Salience theory suggests that the attribution of meaning to stimulus depends on their contingency, saliency, and relevance to adaptation. So far, subjective estimates of relevance have mostly been explored in motivation and implicit learning. Mechanisms underlying formation of associations in abstract thinking with regard to their subjective relevance, or salience, are not clear. Applying novel computational methods, we investigated relevance detection in categorization tasks in 17 healthy individuals. Two models of relevance detection were developed: a conventional one with nouns from the same semantic category, an aberrant one based on an insignificant common feature. Control condition introduced non-related words. The participants were to detect either a relevant principle or an insignificant feature to group presented words. In control condition they inferred that the stimuli were irrelevant to any grouping idea. Cross-frequency phase coupling analysis revealed statistically distinct patterns of synchronization representing search and decision in the models of normal and aberrant relevance detection. Significantly distinct frontotemporal functional networks with central and parietal components in the theta and alpha frequency bands may reflect differences in relevance detection.

Motor Imagery Training During Arm Immobilization Prevents Corticomotor Idling: An EEG Resting-State Analysis

Limb disuse causes overt, measurable alterations in motor functions. Motor imagery (MI) practice has been used as a behavioral strategy to prevent motor impairments due to limb disuse or immobilization. Yet, how MI operates at the neural level in the context of short-term limb immobilization remains understudied. We hypothesized that MI treatment applied during 12 h of arm immobilization prevents immobilization-related changes in resting-state electroencephalographic (rsEEG) power and functional connectivity. Fourteen participants first underwent rsEEG after 12 h of normal motor activity (without immobilization). Then, rsEEG recording was performed after 12 h of arm immobilization either with MI treatment or without, each condition separated by 1 week, according to a randomized within-subjects design. MI treatment consisted in performing varied visual and kinaesthetic MI exercises (5 sessions of 15 min every two hours). The results showed that in the delta, theta, alpha and beta frequency bands, interhemispheric difference in sensors power over the motor cortex (i.e. C3 vs. C4) was reduced after arm immobilization, while it did not change when MI treatment was delivered during the immobilization period. Moreover, functional connectivity across the sensors-network in the delta (1-4 Hz) and alpha (8-12 Hz) frequency bands decreased after immobilization while it was restored by MI treatment. To conclude, MI counteracts functional neural changes within and between motor regions in the context of limb immobilization. Practical applications for motor rehabilitation strategies, particularly in stroke patients, are also discussed.

EEG coherence as a diagnostic tool to measure the initial stages of Parkinson Disease

Although Parkinson Disease was described a long time ago by James Parkinson and several biomarkers were used to predict the symptoms of PD, there is no accepted tool to distinguish the initial stages of this pathology. The present hypothesis discusses the Coherence Function, an Electroencephalography measure which could be used as a simple, and low-cost tool to describe the onset of cardinal signals of PD. Our hypothesis is based on three factors: beta frequency related to movement, motor action over particular cortical regions, and cortical coupling between cortical areas involved in the execution of voluntary movement. We believe that these factors support our hypothesis pointing out coherence function as an interesting measure to detect initial stages of PD.