Striatal parvalbumin interneurons are activated in a mouse model of cerebellar dystonia.

Dystonia is thought to arise from abnormalities in the motor loop of the basal ganglia; however, there is an ongoing debate regarding cerebellar involvement. We adopted an established cerebellar dystonia mouse model by injecting ouabain to examine the contribution of the cerebellum. Initially, we examined whether the entopeduncular nucleus (EPN), substantia nigra pars reticulata (SNr), globus pallidus externus (GPe) and striatal neurons were activated in the model. Next, we examined whether administration of a dopamine D1 receptor agonist and dopamine D2 receptor antagonist or selective ablation of striatal parvalbumin (PV, encoded by Pvalb)-expressing interneurons could modulate the involuntary movements of the mice. The cerebellar dystonia mice had a higher number of cells positive for c-fos (encoded by Fos) in the EPN, SNr and GPe, as well as a higher positive ratio of c-fos in striatal PV interneurons, than those in control mice. Furthermore, systemic administration of combined D1 receptor agonist and D2 receptor antagonist and selective ablation of striatal PV interneurons relieved the involuntary movements of the mice. Abnormalities in the motor loop of the basal ganglia could be crucially involved in cerebellar dystonia, and modulating PV interneurons might provide a novel treatment strategy.

Cranial geometry in patients with dystonia and Parkinson's disease.

Abnormal skull shape has been reported in brain disorders. However, no studies have investigated cranial geometry in neurodegenerative disorders. This study aimed to evaluate the cranial geometry of patients with dystonia or Parkinson's disease (PD). Cranial computed tomography images of 36 patients each with idiopathic dystonia (IDYS), PD, and chronic subdural hematoma (CSDH) were analyzed. Those with IDYS had a significantly higher occipital index (OI) than those with CSDH (p = 0.014). When cephalic index (CI) was divided into the normal and abnormal groups, there was a significant difference between those with IDYS and CSDH (p = 0.000, α = 0.017) and between PD and CSDH (p = 0.031, α = 0.033). The age of onset was significantly correlated with the CI of IDYS (τ = - 0.282, p = 0.016). The Burke-Fahn-Marsden Dystonia Rating Scale motor score (BFMDRS-M) showed a significant correlation with OI in IDYS (τ = 0.372, p = 0.002). The cranial geometry of patients with IDYS was significantly different from that of patients with CSDH. There was a significant correlation between age of onset and CI, as well as between BFMDRS-M and OI, suggesting that short heads in the growth phase and skull balance might be related to the genesis of dystonia and its effect on motor symptoms.

Therapeutic Devices for Motor Symptoms in Parkinson's Disease: Current Progress and a Systematic Review of Recent Randomized Controlled Trials.

Background: Pharmacotherapy is the first-line treatment option for Parkinson's disease, and levodopa is considered the most effective drug for managing motor symptoms. However, side effects such as motor fluctuation and dyskinesia have been associated with levodopa treatment. For these conditions, alternative therapies, including invasive and non-invasive medical devices, may be helpful. This review sheds light on current progress in the development of devices to alleviate motor symptoms in Parkinson's disease. Methods: We first conducted a narrative literature review to obtain an overview of current invasive and non-invasive medical devices and thereafter performed a systematic review of recent randomized controlled trials (RCTs) of these devices. Results: Our review revealed different characteristics of each device and their effectiveness for motor symptoms. Although invasive medical devices are usually highly effective, surgical procedures can be burdensome for patients and have serious side effects. In contrast, non-pharmacological/non-surgical devices have fewer complications. RCTs of non-invasive devices, especially non-invasive brain stimulation and mechanical peripheral stimulation devices, have proven effectiveness on motor symptoms. Nearly no non-invasive devices have yet received Food and Drug Administration certification or a CE mark. Conclusion: Invasive and non-invasive medical devices have unique characteristics, and several RCTs have been conducted for each device. Invasive devices are more effective, while non-invasive devices are less effective and have lower hurdles and risks. It is important to understand the characteristics of each device and capitalize on these.

Diagnosis and Treatment of Tremor in Parkinson's Disease Using Mechanical Devices.

BACKGROUND: Parkinsonian tremors are sometimes confused with essential tremors or other conditions. Recently, researchers conducted several studies on tremor evaluation using wearable sensors and devices, which may support accurate diagnosis. Mechanical devices are also commonly used to treat tremors and have been actively researched and developed. Here, we aimed to review recent progress and the efficacy of the devices related to Parkinsonian tremors. METHODS: The PubMed and Scopus databases were searched for articles. We searched for "Parkinson disease" and "tremor" and "device". RESULTS: Eighty-six articles were selected by our systematic approach. Many studies demonstrated that the diagnosis and evaluation of tremors in patients with PD can be done accurately by machine learning algorithms. Mechanical devices for tremor suppression include deep brain stimulation (DBS), electrical muscle stimulation, and orthosis. In recent years, adaptive DBS and optimization of stimulation parameters have been studied to further improve treatment efficacy. CONCLUSIONS: Due to developments using state-of-the-art techniques, effectiveness in diagnosing and evaluating tremor and suppressing it using these devices is satisfactorily high in many studies. However, other than DBS, no devices are in practical use. To acquire high-level evidence, large-scale studies and randomized controlled trials are needed for these devices.

Elucidation of EEG Characteristics of Fuzzy Reasoning-Based Heuristic BCI and Its Application to Patient With Brain Infarction.

Non-invasive brain-computer interfaces (BCIs) based on common electroencephalography (EEG) are limited to specific instrumentation sites and frequency bands. These BCI induce certain targeted electroencephalographic features of cognitive tasks, identify them, and determine BCI's performance, and use machine-learning to extract these electroencephalographic features, which makes them enormously time-consuming. In addition, there is a problem in which the neurorehabilitation using BCI cannot receive ambulatory and immediate rehabilitation training. Therefore, we proposed an exploratory BCI that did not limit the targeted electroencephalographic features. This system did not determine the electroencephalographic features in advance, determined the frequency bands and measurement sites appropriate for detecting electroencephalographic features based on their target movements, measured the electroencephalogram, created each rule (template) with only large "High" or small "Low" electroencephalograms for arbitrarily determined thresholds (classification of cognitive tasks in the imaginary state of moving the feet by the size of the area constituted by the power spectrum of the EEG in each frequency band), and successfully detected the movement intention by detecting the electroencephalogram consistent with the rules during motor tasks using a fuzzy inference-based template matching method (FTM). However, the electroencephalographic features acquired by this BCI are not known, and their usefulness for patients with actual cerebral infarction is not known. Therefore, this study clarifies the electroencephalographic features captured by the heuristic BCI, as well as clarifies the effectiveness and challenges of this system by its application to patients with cerebral infarction.