Multi-Finger Interaction and Synergies in Finger Flexion and Extension Force Production

Abstract

The aim of this study was to discover finger interaction indices during single-finger ramp tasks and multi-finger coordination during a steady state force production in two directions, flexion, and extension. Furthermore, the indices of anticipatory adjustment of elemental variables (i.e., finger forces) prior to a quick pulse force production were quantified. It is currently unknown whether the organization and anticipatory modulation of stability properties are affected by force directions and strengths of in multi-finger actions. We expected to observe a smaller finger independency and larger indices of multi-finger coordination during extension than during flexion due to both neural and peripheral differences between the finger flexion and extension actions. We also examined the indices of the anticipatory adjustment between different force direction conditions. The anticipatory adjustment could be a neural process, which may be affected by the properties of the muscles and by the direction of the motions. The maximal voluntary contraction (MVC) force was larger for flexion than for extension, which confirmed the fact that the strength of finger flexor muscles (e.g., flexor digitorum profundus) was larger than that of finger extensor (e.g., extensor digitorum). The analysis within the uncontrolled manifold (UCM) hypothesis was used to quantify the motor synergy of elemental variables by decomposing two sources of variances across repetitive trials, which identifies the variances in the uncontrolled manifold (VUCM) and that are orthogonal to the UCM (VORT). The presence of motor synergy and its strength were quantified by the relative amount of VUCM and VORT. The strength of motor synergies at the steady state was larger in the extension condition, which suggests that the stability property (i.e., multi-finger synergies) may be a direction specific quantity. However, the results for the existence of anticipatory adjustment; however, no difference between the directional conditions suggests that feed-forward synergy adjustment (changes in the stability property) may be at least independent of the magnitude of the task-specific apparent performance variables and its direction (e.g., flexion and extension forces).

Age Effects in Postural Control Analyzed via a Principal Component Analysis of Kinematic Data and Interpreted in Relation to Predictions of the Optimal Feedback Control Theory

“…Movement control in this context is usually assessed by quantifying movement variability, with low variability indicating tight and high variability suggesting less tight control. This prediction of the OFC is referred to as “minimal intervention principle” (Todorov and Jordan, 2002 ) and corresponds to earlier concepts in motor control theory such as the uncontrolled manifold hypothesis (Scholz and Schöner, 1999 ; Latash et al, 2001 ; Friedman et al, 2009 ; Arpinar-Avsar et al, 2013 ; Park and Xu, 2017 ). However, if different movement dimensions are controlled differently, then one could hypothesize that aging effects may emerge differently in different dimensions or that only specific dimensions, for instance only the most task-relevant ones, reveal aging effects.…”

“…The current study built on the concepts from the OFC theory (Todorov and Jordan, 2002 ) and the uncontrolled manifold hypothesis (Scholz and Schöner, 1999 ; Latash et al, 2001 ; Friedman et al, 2009 ; Arpinar-Avsar et al, 2013 ; Park and Xu, 2017 ), however, contrary to these concepts, the approach taken in the current study is fully data-driven and does not require the a priori postulation of a cost function or a task variable. Further, the current study is also based on the concept of kinematics in posture space, a novel concept that has recently been validated biomechanically by demonstrating that the PP k (t) and PA k (t) predict the center of pressure COP trajectory (Federolf, 2016 ).…”

Frequency-Dependent Effects on Coordination and Prefrontal Hemodynamics During Finger Force Production Tasks

“…The mediolateral distance between slots was fixed at 3.0 cm (Figure 1B). The 3.0 cm value was a typical distance reported in the previous hand/finger pressing experiments, which possibly ensured a comfortable hand posture during pressing with fingers (Park and Xu, 2017;Kim et al, 2018;Kong et al, 2019). The experimental frame attached to the transducers was mechanically fixed on an immovable table.…”

“…Finger Force Data Analysis Customized analysis codes (MATLAB, MathWorks, Natick, MA, USA) were written for the finger force data analyses during both the auxiliary and main tasks. Prior to the variable computation, individual finger force data were low-pass filtered using a 4th-order Butterworth filter with a 10 Hz cut-off (Park and Xu, 2017). The force data collected during the main task, that is, the cyclic force production task, were divided into half-cycles of force increase (F UP ) and force decrease (F DW ; Park et al, 2012b).…”

“…Recent studies on the coordination of multi-finger forces showed that performance errors with respect to the constrained target values and performance reproducibility were highly correlated with the stability indices of the net outcome variable by means of co-varied adjustment of an abundant set of elements (Park et al, 2010 ; Park et al, 2012a ; Park and Xu, 2017 ; Kim et al, 2018 ). Such co-varied organization of elemental variables has been termed “ synergy ” (Latash, 2008a ).…”