ObjectiveWe report two family and one sporadic case with dyssynergia cerebellaris myoclonica, investigate the clinical and neural electrophysiological features. MethodsThe proband and sporadic patient was examined by clinical, neuroimaging, video-EEG and synchronous electromyography. ResultsThere were 6 patients with dyssynergia cerebellaris myoclonica of the 27 family members in the first family(3 male and 3 female). There were 4 patients with dyssynergia cerebellaris myoclonica of the 20 family members in the second family(2 male and 2 female). All patiens had disproportionately myoclonus, epilepsy and progressive cerebellar ataxia. EEG showed bursts of spike-slow wave, polyspilke-slow wave distributing in the bilateral brain both in ictal and interictal period, sometimes it is especially in central, parietal and frontal area. EEG showed bursts of spike-slow wave, polyspilke-slow wave distributing in the central, parietal and frontal area in interictal period. Pathology of the skin and muscles are normal. ConclusionThe diagnosis of dyssynergia cerebellaris myoclonica was mainly based on typical clinical manifestations, brain MRI and EEG changes.Long time video EEG and synchronous EMG is important for the diagnosis. Skin and muscles pathology can be normal.
目的:通过对滑车神经行经小脑幕侧方区域的应用解剖学研究,寻找小脑幕侧方区域手术时避免损伤滑车神经的临床解剖标志。方法:对15例（男10例,女5例）防腐固定无畸形、无病变的成人头颅标本用红色乳胶灌注后,10倍手术显微镜下观察滑车神经在小脑幕侧方区域的行径,及其与周围重要神经、血管结构的毗邻关系,测量滑车神经长度、宽度、厚度及其与周围标志点的距离,并对所得结果进行统计学分析。结果:滑车神经在小脑上动脉和大脑后动脉之间向前行,进入小脑幕侧方区域,在动眼神经三角的后部穿越游离缘硬膜,其长度为（6.78±1.87）mm,宽度为（1.09±0.21）mm,厚度为（0.78±0.11）mm。滑车神经进入小脑幕侧方区域的入口处位于前床突、颈内动脉床突上段起始部、动眼神经入口后方,位于后床突后外方；距离前床突（23.24±3.18）mm、颈内动脉床突上段起始部（17.57±3.26）mm、动眼神经入口（11.42±3.32）mm；距离后床突（14.21±3.25）mm。结论:行小脑幕侧方区域手术时,为避免损伤滑车神经,前床突、后床突、颈内动脉床突上段起始部和动眼神经入口可以作为寻找滑车神经入口的重要标志,同时注意区分小脑上动脉和大脑后动脉。
Objective To explore the correlation between body mass index (BMI) and disease severity in patients with spinocerebellar ataxia type 3 (SCA3). Methods Patients who visited the Department of Neurology of the First Affiliated Hospital of Fujian Medical University with a confirmed diagnosis of SCA3 between July 2022 and August 2023 were selected as a case group, and healthy individuals between June 2024 and October 2024 were selected as a control group, and the BMI levels of the two groups were compared. Patient demographics and clinical statistics were collected, the severity of ataxia in SCA3 patients was assessed using the Scale for the Assessment and Rating of Ataxi, and the relationship between BMI and disease severity was evaluated. Results A total of 101 patients and 101 healthy individuals were included. The BMI levels of SCA3 patients were significantly lower than those of normal controls (t=−2.370, P=0.019). The results of the multiple linear regression model showed that the BMI, disease duration and smoking history had an effect on the disease severity of SCA3 patients (P<0.05), and disease duration and disease severity had a significant effect on the disease progression in SCA3 patients (P<0.05). Conclusion There may be a correlation between BMI and disease severity in SCA3 patients, and controlling the BMI level may help to control the disease in SCA3 patients.
How to realize the control of limb movement and apply it to intelligent robot systems at the level of cerebellar cortical neurons is a hot topic in the fields of artificial intelligence and rehabilitation medicine. At present, the cerebellar model usually used is only for the purpose of controlling the effect, borrowing from the functional mode of the cerebellum, but it ignores the structural characteristics of the cerebellum. In fact, in addition to being used for controlling purposes, the cerebellar model should also have the interpretability of the control process and be able to analyze the consequences of cerebellar lesions. Therefore, it is necessary to establish a bionic cerebellar model which could better express the characteristics of the cerebellum. In this paper, the process that the cerebellum processes external input information and then generates control instructions at the neuron level was explored. By functionally segmenting the cerebellum into homogeneous structures, a novel bionic cerebellar motion control model incorporating all major cell types and connections was established. Simulation experiments and force feedback device control experiments show that the bionic cerebellar motion control model can achieve better control effect than the currently widely used cerebellar model articulation controller, which verifies the effectiveness of the bionic cerebellar motion control model. It has laid the foundation for real brain-like artificial intelligence control.