Zatsiorsky V M, Li Z M, Latash M L
Department of Kinesiology, Pennsylvania State University, University Park 16802, USA.
Biol Cybern. 1998 Aug;79(2):139-50. doi: 10.1007/s004220050466.
During maximal voluntary contraction (MVC) with several fingers, the following three phenomena are observed: (1) the total force produced by all the involved fingers is shared among the fingers in a specific manner (sharing); (2) the force produced by a given finger in a multi-finger task is smaller than the force generated by this finger in a single-finger task (force deficit); (3) the fingers that are not required to produce any force by instruction are involuntary activated (enslaving). We studied involuntary force production by individual fingers (enslaving effects, EE) during tasks when (an)other finger(s) of the hand generated maximal voluntary pressing force in isometric conditions. The subjects (n = 10) were instructed to press as hard as possible on the force sensors with one, two, three and four fingers acting in parallel in all possible combinations. The EE were (A) large, the slave fingers always producing a force ranging from 10.9% to 54.7% of the maximal force produced by the finger in the single-finger task; (B) nearly symmetrical; (C) larger for the neighboring fingers; and (D) non-additive. In most cases, the EE from two or three fingers were smaller than the EE from at least one finger (this phenomenon was coined occlusion). The occlusion cannot be explained only by anatomical musculo-tendinous connections. Therefore, neural factors contribute substantially to the EE. A neural network model that accounts for all the three effects has been developed. The model consists of three layers: the input layer that models a central neural drive; the hidden layer modeling transformation of the central drive into an input signal to the muscles serving several fingers simultaneously (multi-digit muscles); and the output layer representing finger force output. The output of the hidden layer is set inversely proportional to the number of fingers involved. In addition, direct connections between the input and output layers represent signals to the hand muscles serving individual fingers (uni-digit muscles). The network was validated using three different training sets. Single digit muscles contributed from 25% to 50% of the total finger force. The master matrix and the enslaving matrix were computed; they characterize the ability of a given finger to enslave other fingers and its ability to be enslaved. Overall, the neural network modeling suggests that no direct correspondence exists between neural command to an individual finger and finger force. To produce a desired finger force, a command sent to an intended finger should be scaled in accordance with the commands sent to the other fingers.
在几个手指进行最大自主收缩（MVC）时，会观察到以下三种现象：（1）所有参与的手指产生的总力以特定方式在各手指间分配（分配）；（2）在多手指任务中，特定手指产生的力小于该手指在单手指任务中产生的力（力亏缺）；（3）被指示不产生任何力的手指会被非自主激活（附属）。我们研究了在手部其他手指在等长条件下产生最大自主按压力的任务过程中，单个手指的非自主力产生情况（附属效应，EE）。受试者（n = 10）被要求用一、二、三、四个手指以所有可能的组合方式并行作用，尽可能用力按压力传感器。EE表现为：（A）较大，附属手指产生的力始终在该手指在单手指任务中产生的最大力的10.9%至54.7%范围内；（B）几乎对称；（C）相邻手指的EE更大；（D）非累加性。在大多数情况下，来自两个或三个手指的EE小于来自至少一个手指的EE（这种现象被称为遮挡）。这种遮挡不能仅用解剖学上的肌肉肌腱连接来解释。因此，神经因素对EE有很大贡献。已经开发了一个解释所有这三种效应的神经网络模型。该模型由三层组成：模拟中枢神经驱动的输入层；将中枢驱动转换为同时为几个手指服务的肌肉（多手指肌肉）的输入信号的隐藏层；以及表示手指力输出的输出层。隐藏层的输出设置为与参与的手指数成反比。此外，输入层和输出层之间的直接连接表示对手部为单个手指服务的肌肉（单手指肌肉）的信号。该网络使用三种不同的训练集进行了验证。单手指肌肉对总手指力的贡献为25%至50%。计算了主矩阵和附属矩阵；它们表征了给定手指附属其他手指的能力及其被附属的能力。总体而言，神经网络建模表明，对单个手指的神经指令与手指力之间不存在直接对应关系。为了产生所需的手指力，发送到目标手指的指令应根据发送到其他手指的指令进行缩放。
