Formation of cortical plasticity in older adults following tDCS and motor training

Neurodegeneration accompanies the process of natural aging, reducing the ability to perform functional daily activities. Transcranial direct current stimulation (tDCS) alters neuronal excitability and motor performance; however its beneficial effect on the induction of primary motor cortex (M1) plasticity in older adults is unclear. Moreover, little is known as to whether the tDCS electrode arrangement differentially affects M1 plasticity and motor performance in this population. In a double-blinded, cross-over trial, we compared unilateral, bilateral and sham tDCS combined with visuomotor tracking, on M1 plasticity and motor performance of the non-dominant upper limb, immediately post and 30 min following stimulation. We found (a) unilateral and bilateral tDCS decreased tracking error by 12–22% at both time points; with sham decreasing tracking error by 10% at 30 min only, (b) at both time points, motor evoked potentials (MEPs) were facilitated (38–54%) and short-interval intracortical inhibition was released (21–36%) for unilateral and bilateral conditions relative to sham, (c) there were no differences between unilateral and bilateral conditions for any measure. These findings suggest that tDCS modulated elements of M1 plasticity, which improved motor performance irrespective of the electrode arrangement. The results provide preliminary evidence indicating that tDCS is a safe non-invasive tool to preserve or improve neurological function and motor control in older adults.

Online motor control in children with developmental coordination disorder: chronometric analysis of double-step reaching performance

Background
Although there are a number of plausible accounts to explain movement clumsiness in children [or developmental coordination disorder (DCD)], the cause(s) of the disorder remain(s) an issue of debate. One aspect of motor control that is particularly important to the fluid expression of skill is rapid online control (ROC). Data on DCD have been conflicting. While some recent work using double-step reaching suggests no difficulty in online control, others suggest deficits (e.g. based on sequential pointing). To help resolve this debate, we suggest two things: use of recent neuro-computational models as a framework for investigating motor control in DCD, and more rigorous investigation of double-step reaching. Our working assumption here is that ROC is only viable through the seamless integration of predictive (or forward) models of movement and feedback-based mechanisms.

Aim
The aim of this chronometric study was to explore ROC in children with DCD using a double-step reaching paradigm. We predicted slower online adjustments in DCD based on the argument that these children manifest a core difficulty in predictive control.

Methods
Participants were a group of 17 children with DCD and 27 typically developing children aged between 7 and 12 years. Visual targets were presented on a 17-inch LCD touch screen, inclined to an angle of 15° from horizontal. The children were instructed to press each target as it appeared as quickly and accurately as possible. For 80% of the trials, the central target location remained unchanged for the duration of the movement (non-jump trials), while for the remaining 20% of trials, the target jumped at movement onset to one of the two peripheral locations (jump trials). Reaction time (RT), movement time (MT) and reaching errors were recorded.

Results
For both groups, RT did not vary according to trial condition, while children with DCD were slower to initiate movement. Further, the MT of children with DCD was prolonged to a far greater extent on jump trials relative to controls, with a large effect size. As well, children with DCD committed significantly more errors, notably a reduced ability to inhibit central responses on jump trials.

Conclusion
Our findings help reconcile some disparate findings in the literature using similar tasks. The pattern of performance in children with DCD suggests impairment in the ability to make rapid online adjustments that are based on a predictive (or internal) model of the action. These results pave the way for future kinematic investigation.

Does implicit motor imagery ability predict reaching correction efficiency? A test of recent models of human motor control

Neurocomputational models of reaching indicate that efficient purposive correction of movement midflight (e.g., online control) depends on one's ability to generate and monitor an accurate internal (neural) movement representation. In the first study to test this empirically, the authors investigated the relationship between healthy young adults’ implicit motor imagery performance and their capacity to correct their reaching trajectory. As expected, after controlling for general reaching speed, hierarchical regression demonstrated that imagery ability was a significant predictor of hand correction speed; that is, faster and more accurate imagery performance associated with faster corrections to reaching following target displacement at movement onset. They argue that these findings provide preliminary support for the view that a link exists between an individual's ability to represent movement mentally and correct movement online efficiently.

Online motor fault detection and diagnosis using a hybrid FMM-CART model

In this brief, a hybrid model combining the fuzzy min-max (FMM) neural network and the classification and regression tree (CART) for online motor detection and diagnosis tasks is described. The hybrid model, known as FMM-CART, exploits the advantages of both FMM and CART for undertaking data classification and rule extraction problems. To evaluate the applicability of the proposed FMM-CART model, an evaluation with a benchmark data set pertaining to electrical motor bearing faults is first conducted. The results obtained are equivalent to those reported in the literature. Then, a laboratory experiment for detecting and diagnosing eccentricity faults in an induction motor is performed. In addition to producing accurate results, useful rules in the form of a decision tree are extracted to provide explanation and justification for the predictions from FMM-CART. The experimental outcome positively shows the potential of FMM-CART in undertaking online motor fault detection and diagnosis tasks.