A Tensor Decomposition Uncovers the Effect of Ageing on Muscle and Grip-Load Force Couplings During Grasping

Abstract

Do motor patterns of grasp-to-lift movements change as a result of ageing? Previous studies often relied on simple temporal and kinetic variables to unveil differences caused by ageing, yet their neuromuscular origins remain largely unknown. Here we employed a bimanual grasping protocol with younger and older adults and combined measurements of muscle activity with grip and load forces to provide a window into the neuromuscular strategies underlying effective grasping. We introduced a tensor decomposition to identify patterns of muscle activity and grip-load force ratios while also characterising their temporal profiles and relative activation across object weights and participants of different age groups. This approach extracted the motor components underpinning object grasping across participants. We then probed age-induced changes in these components. A classification analysis revealed two motor components that are differentially recruited between the two age groups. Linear regression analyses further showed that advanced age and poorer manual dexterity can be predicted by the coupled activation of forearm and hand muscles which is associated with high levels of grip force. Our findings suggest that ageing may induce stronger muscle couplings in distal aspects of the upper limbs, and a less economic grasping strategy to overcome age-related decline in manual dexterity.

Quantifying the diverse contributions of hierarchical muscle interactions to motor function

“…(b) Dataset 2: Healthy adult participants performed 10 trials of balancing on a balance board while EMG was captured among the bilateral lower-limb flexors and extensors simultaneously to the horizontal angular displacement of the balance board. (c) Dataset 3: Healthy younger and older adults performed a reach-grasp-lift-hold and replace task of both light and heavy objects while EMG from the arm musculature bilaterally were captured along with load and grip forces on the grasped object [35]. For full details on the experimental setup of these datasets, see ‘Data acquisition and experimental conditions’ section of the Materials and methods.…”

“…In the motor control literature, older adults have been shown to exhibit greater motor variability compared to young cohorts [41], leading to behavioral inconsistency. To answer this question, we applied our proposed approach to Dataset 3 [35], consisting of EMG recordings (8 upper-limb muscles bilaterally) from healthy young (N=14) and older (N=18) participants performing a bimanual reach-grasp-hold task of both light and heavy objects (see Fig. 3(c) and ‘ Data acquisition and experimental conditions’ section of the Materials and methods).…”

“…For dataset 3 (fig. 4(c)) [35], 14 young adults (22.1±2.4 years old, two left-handed, two males) and 18 older adults (71.6±6.9 years old, two left-handed, 8 males) performed a bimanual grasp-lift-hold-replace task of both a light (0.2kg) and heavy (0.4kg) object (two manipulanda made from carbon-filled nylon) while in a seated position in front of a table. The objects were placed on the table 75% of shoulder width and 70% of maximum reach for each participant who were instructed to grasp the object and lift it to a target height in front of them (300mm height) and to hold the object as still as possible at this position for 10 seconds.…”

“…3(E)). Healthy younger and older adults performed a reach-grasp-lift-hold and replace task of both light and heavy objects while EMG from the arm musculature bilaterally were captured along with load and grip forces on the grasped object [35]. For full details on the experimental setup of these datasets, see 'Data acquisition and experimental conditions' section of the Materials and methods.…”

“…Extrapolation of the kinematic data for dataset 1 and 2 to align with the corresponding EMG signals was carried out using a cubic spline method. For dataset 3, the signals were processed as described in [35]. More specifically, the rectified EMG signals were low-pass filtered (filter: 4 th order Butterworth with zero-phase distortion, cut-off: 10Hz) and down-sampled to 200Hz to align with kinetic and kinematic datapoints.…”