COORDINATED FORCE PRODUCTION IN MULTI-FINGER TASKS - FINGER INTERACTION AND NEURAL-NETWORK MODELING

During maximal voluntary contraction (MVC) with several fingers, the f ollowing three phenomena are observed: (1) the total force produced by all the involved fingers is shared among the fingers in a specific ma nner (sharing); (2) the force produced by a given finger in a multi-fi nger task is smaller than the force generated by this finger in a sing le-finger task (force deficit); (3) the fingers that are not required to produce any force by instruction are involuntary activated (enslavi ng). We studied involuntary force production by individual fingers (en slaving effects, EE) during tasks when (an)other finger(s) of the hand generated maximal voluntary pressing force in isometric conditions. T he subjects (n = 10) were instructed to press as hard as possible on t he force sensors with one, two, three and four fingers acting in paral lel in all possible combinations. The EE were (A) large, the slave fin gers always producing a force ranging from 10.9% to 54.7% of the maxim al force produced by the finger in the single-finger task; (B) nearly symmetrical; (C) larger for the neighboring fingers; and (D) non-addit ive. 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-tendin ous connections. Therefore, neural factors contribute substantially to the EE. A neural network model that accounts for all the three effect s has been developed. The model consists;of three layers: the input la yer that models a central neural drive; the hidden layer modeling tran sformation of the central drive into an input signal to the muscles se rving several fingers simultaneously (multi-digit muscles); and the ou tput 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 r epresent signals to the hand muscles serving individual fingers (uni-d igit muscles). The network was validated using three different trainin g 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 fin gers and its ability to be enslaved. Overall, the neural network model ing suggests that no direct correspondence exists between neural comma nd to an individual finger and finger force. To produce a desired fing er force, a command sent to an intended finger should be scaled in acc ordance with the commands sent to the other fingers.