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猫脊髓小脑活动中被动后肢姿势的表现。

Representation of passive hindlimb postures in cat spinocerebellar activity.

作者信息

Bosco G, Rankin A, Poppele R

机构信息

Department of Physiology, University of Minnesota, Minneapolis 55455, USA.

出版信息

J Neurophysiol. 1996 Aug;76(2):715-26. doi: 10.1152/jn.1996.76.2.715.

DOI:10.1152/jn.1996.76.2.715
PMID:8871193
Abstract
  1. We report here about the modulation of dorsal spinocerebellar tract (DSCT) activity by limb posture. In principle, DSCT activity could represent limb position in one of several ways. According to a classical notion of DSCT function, DSCT activity might be expected to correlate with changes in individual joint angles. However, given the evidence for extensive polysynaptic convergence onto DSCT units, it is reasonable to propose that DSCT activity represents more global variables such as the orientation of limb segments or the length and orientation of the whole limb. 2. In six anesthetized cats we recorded the activity of 96 antidromically identified DSCT neurons while a robot arm passively positioned the left hindfoot in 20 positions distributed in the sagittal plane, holding each position for 8 s. For each position we measured the joint angles, limb segment angles, and the length and orientation of the limb axis (defined as the line connecting the hip joint to the hindpaw). We used regression statistics to quantify 1) possible relationships among geometric variables of the hindlimb and 2) relationships between DSCT firing rate and limb variables. 3. First, we found a statistically significant relationship among the joint angles that could be described by a covariance plane accounting for approximately 70 percent of the total variance. Thus the 3 degrees of freedom represented by the joint angles in the sagittal plane are effectively reduced to only 2 by the coupling between joints. This finding resembles that described for the behaving cat during stance. However, the correlation between the hip and ankle angles in the passively displaced hindlimb was just the opposite of that observed during active stance. Moreover, we observed that the length and the orientation of the limb axis is determined simply by a linear combination of the three joint angles. 4. Most of the DSCT neurons (82 of 96) were significantly modulated by changes in foot position (1-way analysis of variance, P < 0.001). For those cells, we explored systematically how their activity was related to limb geometric variables. We found mostly linear relationships between individual joint or limb segments angles and DSCT firing rates. However, although these relationships were statistically significant, the random variance was often quite high. Moreover, approximately 70% of the cells were modulated by more than one joint or limb segment angle, suggesting that a model incorporating global geometric variables might explain a larger fraction of the variance in the neural data. 5. Consequently we tested how well DSCT activity was modulated by the length and the orientation of the limb axis with the use of a linear regression model with length and orientation (or the equivalent linear combination of joint angles) as predictors. We found that this model explained a larger fraction of the variability in the firing pattern of nearly every modulated cell than did any of the single joint models tested. 6. We also attempted to account for the effect of the mechanical joint covariance on this result by accounting for correlated independent variables in the analysis. We used a regression model incorporating all three joint or limb segment angles and performed a backward elimination of insignificant or redundant variables. The result was that 67% of the neurons were independently modulated by at least two joint angles, indicating that the modulation did not necessarily reflect the biomechanical constraint of joint angle covariation, but rather a central convergence of sensory information from more than a single joint. 7. From these results we conclude that the firing rates of a majority of DSCT neurons encode the position of the hindfoot relative to the hip joint.(ABSTRACT TRUNCATED)
摘要
  1. 我们在此报告肢体姿势对背侧脊髓小脑束(DSCT)活动的调节作用。原则上,DSCT活动可以通过几种方式之一来表示肢体位置。根据DSCT功能的经典概念,DSCT活动可能与各个关节角度的变化相关。然而,鉴于有证据表明存在广泛的多突触汇聚到DSCT神经元上,合理的推测是DSCT活动代表更全局的变量,如肢体节段的方向或整个肢体的长度和方向。2. 在六只麻醉的猫中,我们记录了96个经逆向鉴定的DSCT神经元的活动,同时一个机械臂将左后足被动定位在矢状面内分布的20个位置上,每个位置保持8秒。对于每个位置,我们测量了关节角度、肢体节段角度以及肢体轴的长度和方向(定义为连接髋关节和后爪的线)。我们使用回归统计来量化1)后肢几何变量之间的可能关系,以及2)DSCT放电率与肢体变量之间的关系。3. 首先,我们发现关节角度之间存在统计学上显著的关系,这种关系可以用一个协方差平面来描述,该平面占总方差的约70%。因此,矢状面内关节角度所代表的3个自由度通过关节之间的耦合有效地减少到仅2个。这一发现类似于在站立时行为猫的情况。然而,被动移位后肢中髋关节和踝关节角度之间的相关性与主动站立时观察到的情况正好相反。此外,我们观察到肢体轴的长度和方向仅由三个关节角度的线性组合决定。4. 大多数DSCT神经元(96个中的82个)受到足部位置变化的显著调节(单因素方差分析,P < 0.001)。对于这些细胞,我们系统地探究了它们的活动与肢体几何变量之间的关系。我们发现单个关节或肢体节段角度与DSCT放电率之间大多呈线性关系。然而,尽管这些关系具有统计学显著性,但随机方差通常相当高。此外,约70%的细胞受到不止一个关节或肢体节段角度的调节,这表明一个包含全局几何变量的模型可能解释神经数据中更大比例的方差。5. 因此,我们使用一个以长度和方向(或等效的关节角度线性组合)作为预测因子的线性回归模型,测试DSCT活动受肢体轴长度和方向调节的程度。我们发现,与任何测试的单个关节模型相比,这个模型解释了几乎每个受调节细胞放电模式中更大比例的变异性。6. 我们还试图通过在分析中考虑相关的自变量来解释机械关节协方差对这一结果的影响。我们使用一个包含所有三个关节或肢体节段角度的回归模型,并对不显著或冗余的变量进行向后消除。结果是67%的神经元至少由两个关节角度独立调节,这表明这种调节不一定反映关节角度协变的生物力学约束,而是来自不止一个关节的感觉信息的中枢汇聚。7. 从这些结果我们得出结论,大多数DSCT神经元的放电率编码后足相对于髋关节的位置。(摘要截断)

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