Event-Related Desynchronization/Synchronization in Spinocerebellar Ataxia Type 3

Upper limb intention tremor assessment: opportunities and challenges in wearable technology

BACKGROUND

Tremors are involuntary rhythmic movements commonly present in neurological diseases such as Parkinson's disease, essential tremor, and multiple sclerosis. Intention tremor is a subtype associated with lesions in the cerebellum and its connected pathways, and it is a common symptom in diseases associated with cerebellar pathology. While clinicians traditionally use tests to identify tremor type and severity, recent advancements in wearable technology have provided quantifiable ways to measure movement and tremor using motion capture systems, app-based tasks and tools, and physiology-based measurements. However, quantifying intention tremor remains challenging due to its changing nature.

METHODOLOGY & RESULTS

This review examines the current state of upper limb tremor assessment technology and discusses potential directions to further develop new and existing algorithms and sensors to better quantify tremor, specifically intention tremor. A comprehensive search using PubMed and Scopus was performed using keywords related to technologies for tremor assessment. Afterward, screened results were filtered for relevance and eligibility and further classified into technology type. A total of 243 publications were selected for this review and classified according to their type: body function level: movement-based, activity level: task and tool-based, and physiology-based. Furthermore, each publication's methods, purpose, and technology are summarized in the appendix table.

CONCLUSIONS

Our survey suggests a need for more targeted tasks to evaluate intention tremors, including digitized tasks related to intentional movements, neurological and physiological measurements targeting the cerebellum and its pathways, and signal processing techniques that differentiate voluntary from involuntary movement in motion capture systems.

Physiological Recordings of the Cerebellum in Movement Disorders

The cerebellum plays an important role in movement disorders, specifically in symptoms of ataxia, tremor, and dystonia. Understanding the physiological signals of the cerebellum contributes to insights into the pathophysiology of these movement disorders and holds promise in advancing therapeutic development. Non-invasive techniques such as electroencephalogram and magnetoencephalogram can record neural signals with high temporal resolution at the millisecond level, which is uniquely suitable to interrogate cerebellar physiology. These techniques have recently been implemented to study cerebellar physiology in healthy subjects as well as individuals with movement disorders. In the present review, we focus on the current understanding of cerebellar physiology using these techniques to study movement disorders.

Strategy-based motor learning decreases the post-movement β power

Motor learning depends on the joint contribution of several processes including cognitive strategies aiming at goal achievement and prediction error-driven implicit adaptation. Understanding this functional interplay and its clinical implications requires insight into the individual learning processes, including at a neural level. Here, we set out to examine the impact of learning a cognitive strategy, over and above implicit adaptation, on the oscillatory post-movement β rebound (PMBR), which typically decreases in power following (visuo)motor perturbations. Healthy participants performed reaching movements towards a target, with online visual feedback replacing the view of their moving hand. The feedback was sometimes rotated, either relative to their movements (visuomotor rotation) or invariant to their movements (and relative to the target; clamped feedback), always for two consecutive trials interspersed between non-rotated trials. In both conditions, the first trial with a rotation was unpredictable. On the second trial, the task was either to re-aim, and thereby compensate for the rotation experienced in the first trial (visuomotor rotation; Compensate condition), or to ignore the rotation and keep on aiming at the target (clamped feedback; Ignore condition). After-effects did not differ between conditions, indicating that the amount of implicit learning was similar, while large differences in movement direction in the second rotated trial between conditions indicated that participants successfully acquired re-aiming strategies. Importantly, PMBR power following the first rotated trial was modulated differently in the two conditions. Specifically, it decreased in both conditions, but this effect was larger when participants had to acquire a cognitive strategy and prepare to re-aim. Our results therefore suggest that the PMBR is modulated by cognitive demands of motor learning, possibly reflecting the evaluation of a behaviourally significant goal achievement error.

Neurophysiology of cerebellar ataxias and gait disorders

There are numerous forms of cerebellar disorders from sporadic to genetic diseases. The aim of this chapter is to provide an overview of the advances and emerging techniques during these last 2 decades in the neurophysiological tests useful in cerebellar patients for clinical and research purposes. Clinically, patients exhibit various combinations of a vestibulocerebellar syndrome, a cerebellar cognitive affective syndrome and a cerebellar motor syndrome which will be discussed throughout this chapter. Cerebellar patients show abnormal Bereitschaftpotentials (BPs) and mismatch negativity. Cerebellar EEG is now being applied in cerebellar disorders to unravel impaired electrophysiological patterns associated within disorders of the cerebellar cortex. Eyeblink conditioning is significantly impaired in cerebellar disorders: the ability to acquire conditioned eyeblink responses is reduced in hereditary ataxias, in cerebellar stroke and after tumor surgery of the cerebellum. Furthermore, impaired eyeblink conditioning is an early marker of cerebellar degenerative disease. General rules of motor control suggest that optimal strategies are needed to execute voluntary movements in the complex environment of daily life. A high degree of adaptability is required for learning procedures underlying motor control as sensorimotor adaptation is essential to perform accurate goal-directed movements. Cerebellar patients show impairments during online visuomotor adaptation tasks. Cerebellum-motor cortex inhibition (CBI) is a neurophysiological biomarker showing an inverse association between cerebellothalamocortical tract integrity and ataxia severity. Ataxic gait is characterized by increased step width, reduced ankle joint range of motion, increased gait variability, lack of intra-limb inter-joint and inter-segmental coordination, impaired foot ground placement and loss of trunk control. Taken together, these techniques provide a neurophysiological framework for a better appraisal of cerebellar disorders.

Sleep Alterations in a Mouse Model of Spinocerebellar Ataxia Type 3

Spinocerebellar ataxia type 3 (SCA3) is a neurodegenerative disorder showing progressive neuronal loss in several brain areas and a broad spectrum of motor and non-motor symptoms, including ataxia and altered sleep. While sleep disturbances are known to play pathophysiologic roles in other neurodegenerative disorders, their impact on SCA3 is unknown. Using spectrographic measurements, we sought to quantitatively characterize sleep electroencephalography (EEG) in SCA3 transgenic mice with confirmed disease phenotype. We first measured motor phenotypes in 18-31-week-old homozygous SCA3 YACMJD84.2 mice and non-transgenic wild-type littermate mice during lights-on and lights-off periods. We next implanted electrodes to obtain 12-h (zeitgeber time 0-12) EEG recordings for three consecutive days when the mice were 26-36 weeks old. EEG-based spectroscopy showed that compared to wild-type littermates, SCA3 homozygous mice display: (i) increased duration of rapid-eye movement sleep (REM) and fragmentation in all sleep and wake states; (ii) higher beta power oscillations during REM and non-REM (NREM); and (iii) additional spectral power band alterations during REM and wake. Our data show that sleep architecture and EEG spectral power are dysregulated in homozygous SCA3 mice, indicating that common sleep-related etiologic factors may underlie mouse and human SCA3 phenotypes.

Influence of the number of trials on evoked motor cortical activity in EEG recordings

OBJECTIVE

Improvements in electroencephalography enable the study of the localization of active brain regions during motor tasks. Movement-related cortical potentials (MRCPs), and event-related desynchronization (ERD) and synchronization (ERS) are the main motor-related cortical phenomena/neural correlates observed when a movement is elicited. When assessing neurological diseases, averaging techniques are commonly applied to characterize motor related processes better. In this case, a large number of trials is required to obtain a motor potential that is representative enough of the subject's condition. This study aimed to assess the effect of a limited number of trials on motor-related activity corresponding to different upper limb movements (elbow flexion/extension, pronation/supination and hand open/close).

APPROACH

An open dataset consisting on 15 healthy subjects was used for the analysis. A Monte Carlo simulation approach was applied to analyse, in a robust way, different typical time- and frequency-domain features, topography, and low-resolution tomography (LORETA).

MAIN RESULTS

Grand average potentials, and topographic and tomographic maps showed few differences when using fewer trials, but shifts in the localization of motor-related activity were found for several individuals. MRCP and beta ERD features were more robust to a limited number of trials, yielding differences lower than 20% for cases with 50 trials or more. Strong correlations between features were obtained for subsets above 50 trials. However, the inter-subject variability increased as the number of trials decreased. The elbow flexion/extension movement showed a more robust performance for a limited number of trials, both in population and in individual-based analysis.

SIGNIFICANCE

Our findings suggested that 50 trials can be an appropriate number to obtain stable motor-related features in terms of differences in the averaged motor features, correlation, and changes in topography and tomography.

Decoding Brain Signals to Classify Gait Direction Anticipation

The use of brain-computer interface (BCI) technology has emerged as a promising rehabilitation approach for patients with motor function and motor-related disorders. BCIs provide an augmentative communication platform for controlling advanced assistive robots such as a lower-limb exoskeleton. Brain recordings collected by an electroencephalography (EEG) system have been employed in the BCI platform to command the exoskeleton. To date, the literature on this topic is limited to the prediction of gait intention and gait variations from EEG signals. This study, however, aims to predict the anticipated gait direction using a stream of EEG signals collected from the brain cortex. Three healthy participants (age range: 29-31, 2 female) were recruited. While wearing the EEG device, the participants were instructed to initiate gait movement toward the direction of the arrow triggers (pointing forward, backward, left, or right) being shown on a screen with a blank white background. Collected EEG data was then epoched around the trigger timepoints. These epochs were then converted to the time-frequency domain using event- related synchronization (ERS) and event-related desynchronization (ERD) methods. Finally, the classification pipeline was constructed using logistic regression (LR), support vector machine (SVM), and convolutional neural network (CNN). A ten-fold cross-validation scheme was used to evaluate the classification performance. The results revealed that the CNN classifier outperforms the other two classifiers with an accuracy of 0.75. Clinical Relevance - The outcome of this study has the potential to be ultimately used for interactive navigation of the lower-limb exoskeletons during robotic rehabilitation therapy and enhance neurodegeneration and neuroplasticity in a wide range of individuals with lower-limb motor function disabilities.

“Sound” Decisions: The Combined Role of Ambient Noise and Cognitive Regulation on the Neurophysiology of Food Cravings

Our ability to evaluate long-term goals over immediate rewards is manifested in the brain’s decision circuit. Simplistically, it can be divided into a fast, impulsive, reward “system 1” and a slow, deliberate, control “system 2.” In a noisy eating environment, our cognitive resources may get depleted, potentially leading to cognitive overload, emotional arousal, and consequently more rash decisions, such as unhealthy food choices. Here, we investigated the combined impact of cognitive regulation and ambient noise on food cravings through neurophysiological activity. Thirty-seven participants were recruited for an adapted version of the Regulation of Craving (ROC) task. All participants underwent two sessions of the ROC task; once with soft ambient restaurant noise (∼50 dB) and once with loud ambient restaurant noise (∼70 dB), while data from electroencephalography (EEG), electrodermal activity (EDA), and self-reported craving were collected for all palatable food images presented in the task. The results indicated that thinking about future (“later”) consequences vs. immediate (“now”) sensations associated with the food decreased cravings, which were mediated by frontal EEG alpha power. Likewise, “later” trials also increased frontal alpha asymmetry (FAA) —an index for emotional motivation. Furthermore, loud (vs. soft) noise increased alpha, beta, and theta activity, but for theta activity, this was solely occurring during “later” trials. Similarly, EDA signal peak probability was also higher during loud noise. Collectively, our findings suggest that the presence of loud ambient noise in conjunction with prospective thinking can lead to the highest emotional arousal and cognitive load as measured by EDA and EEG, respectively, both of which are important in regulating cravings and decisions. Thus, exploring the combined effects of interoceptive regulation and exteroceptive cues on food-related decision-making could be methodologically advantageous in consumer neuroscience and entail theoretical, commercial, and managerial implications.

Low-frequency oscillations in cortical level to help diagnose task-specific dystonia

Task-specific dystonia is a neurological movement disorder that abnormal contractions of muscles result in the twisting of fixed postures or muscle spasm during specific tasks. Due to the rareness and the pathophysiology of the disease, there is no test to confirm the diagnosis of task-specific dystonia, except comprehensive observations by the experts. Evidence from neural electrophysiological data suggests that enhanced low frequency (4-12 Hz) oscillations in the subcortical structure of the globus pallidus were associated with the pathological abnormalities concerning β and γ rhythms in motor areas and motor cortical network in patients with task-specific dystonia. However, whether patients with task-specific dystonia have any low-frequency abnormalities in motor cortical areas remains unclear. In this study, we hypothesized that low-frequency abnormalities are present in core motor areas and motor cortical networks in patients with task-specific dystonia during performing the non-symptomatic movements and those low-frequency abnormalities can help the diagnosis of this disease. We tested this hypothesis by using EEG, effective connectivity analysis, and a machine learning method. Fifteen patients with task-specific dystonia and 15 healthy controls were recruited. The machine learning method identified 8 aberrant movement-related network connections concerning low frequency, β and γ frequencies, which enabled the separation of the data of patients from those of controls with an accuracy of 90%. Importantly, 7 of the 8 aberrant connections engaged the premotor area contralateral to the affected hand, suggesting an important role of the premotor area in the pathological abnormities. The patients exhibited significantly lower low frequency activities during the movement preparation and significantly lower β rhythms during movements compared with healthy controls in the core motor areas. Our findings of low frequency- and β-related abnormalities at the cortical level and aberrant motor network could help diagnose task-specific dystonia in the clinical setting, and the importance of the contralesional premotor area suggests its diagnostic potential for task-specific dystonia.

A Novel Time-Incremental End-to-End Shared Neural Network with Attention-Based Feature Fusion for Multiclass Motor Imagery Recognition

In the research of motor imagery brain-computer interface (MI-BCI), traditional electroencephalogram (EEG) signal recognition algorithms appear to be inefficient in extracting EEG signal features and improving classification accuracy. In this paper, we discuss a solution to this problem based on a novel step-by-step method of feature extraction and pattern classification for multiclass MI-EEG signals. First, the training data from all subjects is merged and enlarged through autoencoder to meet the need for massive amounts of data while reducing the bad effect on signal recognition because of randomness, instability, and individual variability of EEG data. Second, an end-to-end sharing structure with attention-based time-incremental shallow convolution neural network is proposed. Shallow convolution neural network (SCNN) and bidirectional long short-term memory (BiLSTM) network are used to extract frequency-spatial domain features and time-series features of EEG signals, respectively. Then, the attention model is introduced into the feature fusion layer to dynamically weight these extracted temporal-frequency-spatial domain features, which greatly contributes to the reduction of feature redundancy and the improvement of classification accuracy. At last, validation tests using BCI Competition IV 2a data sets show that classification accuracy and kappa coefficient have reached 82.7 ± 5.57% and 0.78 ± 0.074, which can strongly prove its advantages in improving classification accuracy and reducing individual difference among different subjects from the same network.

Changes in Balance, Gait and Electroencephalography Oscillations after Robot-Assisted Gait Training: An Exploratory Study in People with Chronic Stroke

Robot-assisted gait training (RAGT) systems offer the advantages of standard rehabilitation and provide precise and quantifiable control of therapy. We examined the clinical outcome of RAGT and analyzed the correlations between gait analysis data and event-related desynchronization (ERD) and event-related synchronization (ERS) in patients with chronic stroke. We applied the Berg balance scale (BBS) and analyzed gait parameters and the ERD and ERS of self-paced voluntary leg movements performed by patients with chronic stroke before and after undergoing RAGT. A significant change was observed in BBS (p = 0.011). We also showed preliminary outcomes of changes in gait cycle duration (p = 0.015) and in ipsilesional ERS in the low-beta (p = 0.033) and high-beta (p = 0.034) frequency bands before and after RAGT. In addition, correlations were observed between BBS and ipsilesional ERS in the alpha and low-beta bands (r = −0.52, p = 0.039; r = −0.52, p = 0.040). The study demonstrated that RAGT can improve balance and provided an idea of the possible role of brain oscillation and clinical outcomes in affecting stroke rehabilitation.

Electroencephalography Mu Rhythm Changes and Decreased Spasticity After Repetitive Peripheral Magnetic Stimulation in Patients Following Stroke

Background: Spasticity is common among patients with stroke. Repetitive peripheral magnetic stimulation (rPMS) is a painless and noninvasive therapy that is a promising approach to reducing spasticity. However, the central mechanism of this therapy remains unclear. Changes in cortical activity and decreased spasticity after rPMS intervention require further exploration. The aim of this study was to explore the electroencephalography (EEG) mu rhythm change and decrease in spasticity after rPMS intervention in patients with stroke. Materials and methods: A total of 32 patients with spasticity following stroke were recruited in this study and assigned to the rPMS group (n = 16) or sham group (n = 16). The modified Ashworth scale, modified Tardieu scale, and Fugl-Meyer assessment of the upper extremity were used to assess changes in upper limb spasticity and motor function. Before and after the rPMS intervention, EEG evaluation was performed to detect EEG mu rhythm changes in the brain. Results: After one session of rPMS intervention, spasticity was reduced in elbow flexors (p < 0.05) and wrist flexors (p < 0.05). Upper limb motor function measured according to the Fugl-Meyer assessment was improved (p < 0.05). In the rPMS group, the power of event-related desynchronization decreased in the mu rhythm band (8-12 Hz) in the contralesional hemisphere (p < 0.05). Conclusions: The results indicate that rPMS intervention reduced spasticity. Cortical activity changes may suggest this favorable change in terms of its neurological effects on the central nervous system.

Collective almost synchronization-based model to extract and predict features of EEG signals

Understanding the brain is important in the fields of science, medicine, and engineering. A promising approach to better understand the brain is through computing models. These models were adjusted to reproduce data collected from the brain. One of the most commonly used types of data in neuroscience comes from electroencephalography (EEG), which records the tiny voltages generated when neurons in the brain are activated. In this study, we propose a model based on complex networks of weakly connected dynamical systems (Hindmarsh–Rose neurons or Kuramoto oscillators), set to operate in a dynamic regime recognized as Collective Almost Synchronization (CAS). Our model not only successfully reproduces EEG data from both healthy and epileptic EEG signals, but it also predicts EEG features, the Hurst exponent, and the power spectrum. The proposed model is able to forecast EEG signals 5.76 s in the future. The average forecasting error was 9.22%. The random Kuramoto model produced the outstanding result for forecasting seizure EEG with an error of 11.21%.

Cortical network dysfunction revealed by magnetoencephalography in carriers of spinocerebellar ataxia 1 or 2 mutation

OBJECTIVE

In patients with spinocerebellar ataxia type 1 or 2 (SCA1 or SCA2) and in their asymptomatic gene-positive relatives (AsyRs) we investigated the event-related desynchronization and synchronisation (ERD/ERS) on magnetoencephalographic signals to assess the changes occurring before manifest ataxia, by comparing the results obtained in AsyRs and in their gene-negative healthy relatives (HRs).

METHODS

Twenty-four patients (12 SCA1, 12 SCA2), 24 AsyRs (13 SCA1, 11 SCA2) and 17 HRs performed a visually cued Go/No-go task. We evaluated the ERD/ERS in regions of interest corresponding to the frontal, central and parietal cortices.

RESULTS

In the SCA patients the main findings were a loss of side predominance for alpha and beta ERD and significantly weakened beta ERS. In AsyRs the main finding was a significantly enhanced alpha ERD, namely in those who were approaching the estimated time of symptom onset.

CONCLUSIONS

In ataxic patients, the loss of ERD lateralisation and the significantly reduction of beta ERS suggest defective bilateral processes that are involved in ending the movement. In AsyRs, enhanced alpha ERD proposes the presence of preclinical marker closely preceding symptom onset.

SIGNIFICANCE

Movement-related ERD/ERS can detect the defective sensorimotor integration in ataxic patients, and reveals possible compensatory mechanisms in their AsyRs.