PDF(Pubmed)
Abstract：
The cerebellum contributes to a diverse array of motor conditions, including ataxia, dystonia, and tremor. The neural substrates that encode this diversity are unclear. Here, we tested whether the neural spike activity of cerebellar output neurons is distinct between movement disorders with different impairments, generalizable across movement disorders with similar impairments, and capable of causing distinct movement impairments. Using in vivo awake recordings as input data, we trained a supervised classifier model to differentiate the spike parameters between mouse models for ataxia, dystonia, and tremor. The classifier model correctly assigned mouse phenotypes based on single-neuron signatures. Spike signatures were shared across etiologically distinct but phenotypically similar disease models. Mimicking these pathophysiological spike signatures with optogenetics induced the predicted motor impairments in otherwise healthy mice. These data show that distinct spike signatures promote the behavioral presentation of cerebellar diseases.
Intentional movement is fundamental to achieving many goals, whether they are as complicated as driving a car or as routine as feeding ourselves with a spoon. The cerebellum is a key brain area for coordinating such movement. Damage to this region can cause various movement disorders: ataxia (uncoordinated movement); dystonia (uncontrolled muscle contractions); and tremor (involuntary and rhythmic shaking). While abnormal electrical activity in the brain associated with movement disorders has been recorded for decades, previous studies often explored one movement disorder at a time. Therefore, it remained unclear whether the underlying brain activity is similar across movement disorders. Van der Heijden and Brown et al. analyzed recordings of neuron activity in the cerebellum of mice with movement disorders to create an activity profile for each disorder. The researchers then used machine learning to generate a classifier that could separate profiles associated with manifestations of ataxia, dystonia, and tremor based on unique features of their neural activity. The ability of the model to separate the three types of movement disorders indicates that abnormal movements can be distinguished based on neural activity patterns. When additional manifestations of these abnormal movements were considered, multiple mouse models of dystonia and tremor tended to show similar profiles. Ataxia models had several different types of neural activity that were all distinct from the dystonia and tremor profiles. After identifying the activity associated with each movement disorder, Van der Heijden and Brown et al. induced the same activity in the cerebella of healthy mice, which then caused the corresponding abnormal movements. These findings lay an important groundwork for the development of treatments for neurological disorders involving ataxia, dystonia, and tremor. They identify the cerebellum, and specific patterns of activity within it, as potential therapeutic targets. While the different activity profiles of ataxia may require more consideration, the neural activity associated with dystonia and tremor appears to be generalizable across multiple manifestations, suggesting potential treatments could be broadly applicable for these disorders.
Intentional movement is fundamental to achieving many goals, whether they are as complicated as driving a car or as routine as feeding ourselves with a spoon. The cerebellum is a key brain area for coordinating such movement. Damage to this region can cause various movement disorders: ataxia (uncoordinated movement); dystonia (uncontrolled muscle contractions); and tremor (involuntary and rhythmic shaking). While abnormal electrical activity in the brain associated with movement disorders has been recorded for decades, previous studies often explored one movement disorder at a time. Therefore, it remained unclear whether the underlying brain activity is similar across movement disorders. Van der Heijden and Brown et al. analyzed recordings of neuron activity in the cerebellum of mice with movement disorders to create an activity profile for each disorder. The researchers then used machine learning to generate a classifier that could separate profiles associated with manifestations of ataxia, dystonia, and tremor based on unique features of their neural activity. The ability of the model to separate the three types of movement disorders indicates that abnormal movements can be distinguished based on neural activity patterns. When additional manifestations of these abnormal movements were considered, multiple mouse models of dystonia and tremor tended to show similar profiles. Ataxia models had several different types of neural activity that were all distinct from the dystonia and tremor profiles. After identifying the activity associated with each movement disorder, Van der Heijden and Brown et al. induced the same activity in the cerebella of healthy mice, which then caused the corresponding abnormal movements. These findings lay an important groundwork for the development of treatments for neurological disorders involving ataxia, dystonia, and tremor. They identify the cerebellum, and specific patterns of activity within it, as potential therapeutic targets. While the different activity profiles of ataxia may require more consideration, the neural activity associated with dystonia and tremor appears to be generalizable across multiple manifestations, suggesting potential treatments could be broadly applicable for these disorders.
摘要：
小脑有助于各种各样的运动条件，包括共济失调,肌张力障碍,和震颤。编码这种多样性的神经底物尚不清楚。这里,我们测试了小脑输出神经元的神经尖峰活动在不同损伤的运动障碍之间是否不同，可在具有类似损伤的运动障碍中推广，并且能够引起明显的运动障碍。使用体内清醒记录作为输入数据，我们训练了一个有监督的分类器模型来区分共济失调小鼠模型之间的尖峰参数，肌张力障碍,和震颤。分类器模型基于单神经元签名正确地分配小鼠表型。在病因学上不同但表型相似的疾病模型中共享了尖峰特征。将这些病理生理尖峰特征与光遗传学模拟在其他健康小鼠中诱导了预测的运动障碍。这些数据表明，不同的尖峰特征促进了小脑疾病的行为表现。
有意运动是实现许多目标的基础，他们是否像开车一样复杂，或者像用勺子喂自己一样常规。小脑是协调这种运动的关键大脑区域。对该区域的损害可导致各种运动障碍：共济失调（不协调的运动）;肌张力障碍（不受控制的肌肉收缩）;和震颤（不自主和有节奏的摇动）。虽然与运动障碍相关的大脑异常电活动已经记录了几十年，以前的研究经常一次探索一种运动障碍。因此,目前尚不清楚不同运动障碍的潜在大脑活动是否相似。VanderHeijden和Brown等人。分析了患有运动障碍的小鼠小脑中神经元活动的记录，以创建每种疾病的活动概况。然后，研究人员使用机器学习来生成一个分类器，该分类器可以分离与共济失调表现相关的配置文件。肌张力障碍,和基于神经活动的独特特征的震颤。该模型将三种运动障碍分开的能力表明，可以根据神经活动模式来区分异常运动。当考虑到这些异常运动的其他表现时，肌张力障碍和震颤的多个小鼠模型倾向于显示相似的轮廓。共济失调模型具有几种不同类型的神经活动，均与肌张力障碍和震颤特征不同。在确定与每种运动障碍相关的活动后，VanderHeijden和Brown等人。在健康小鼠的小脑中诱导相同的活动，然后导致相应的异常运动。这些发现为涉及共济失调的神经系统疾病的治疗方法的发展奠定了重要的基础。肌张力障碍,和震颤。他们识别小脑，以及其中特定的活动模式，作为潜在的治疗靶点。虽然共济失调的不同活动特征可能需要更多的考虑，与肌张力障碍和震颤相关的神经活动似乎可以在多种表现中推广，提示潜在的治疗方法可能广泛适用于这些疾病。
有意运动是实现许多目标的基础，他们是否像开车一样复杂，或者像用勺子喂自己一样常规。小脑是协调这种运动的关键大脑区域。对该区域的损害可导致各种运动障碍：共济失调（不协调的运动）;肌张力障碍（不受控制的肌肉收缩）;和震颤（不自主和有节奏的摇动）。虽然与运动障碍相关的大脑异常电活动已经记录了几十年，以前的研究经常一次探索一种运动障碍。因此,目前尚不清楚不同运动障碍的潜在大脑活动是否相似。VanderHeijden和Brown等人。分析了患有运动障碍的小鼠小脑中神经元活动的记录，以创建每种疾病的活动概况。然后，研究人员使用机器学习来生成一个分类器，该分类器可以分离与共济失调表现相关的配置文件。肌张力障碍,和基于神经活动的独特特征的震颤。该模型将三种运动障碍分开的能力表明，可以根据神经活动模式来区分异常运动。当考虑到这些异常运动的其他表现时，肌张力障碍和震颤的多个小鼠模型倾向于显示相似的轮廓。共济失调模型具有几种不同类型的神经活动，均与肌张力障碍和震颤特征不同。在确定与每种运动障碍相关的活动后，VanderHeijden和Brown等人。在健康小鼠的小脑中诱导相同的活动，然后导致相应的异常运动。这些发现为涉及共济失调的神经系统疾病的治疗方法的发展奠定了重要的基础。肌张力障碍,和震颤。他们识别小脑，以及其中特定的活动模式，作为潜在的治疗靶点。虽然共济失调的不同活动特征可能需要更多的考虑，与肌张力障碍和震颤相关的神经活动似乎可以在多种表现中推广，提示潜在的治疗方法可能广泛适用于这些疾病。
