Age-related changes in the basic functional unit of the neuromuscular system, the motor unit, and its neural inputs have a profound effect on motor function, especially among the expanding number of old (older than ∼60 yr) and very old (older than ∼80 yr) adults. This review presents evidence that age-related changes in motor unit morphology and properties lead to impaired motor performance that includes 1) reduced maximal strength and power, slower contractile velocity, and increased fatigability; and 2) increased variability during and between motor tasks, including decreased force steadiness and increased variability of contraction velocity and torque over repeat contractions. The age-related increase in variability of motor performance with aging appears to involve reduced and more variable synaptic inputs that drive motor neuron activation, fewer and larger motor units, less stable neuromuscular junctions, lower and more variable motor unit action potential discharge rates, and smaller and slower skeletal muscle fibers that coexpress different myosin heavy chain isoforms in the muscle of older adults. Physical activity may modify motor unit properties and function in old men and women, although the effects on variability of motor performance are largely unknown. Many studies are of cross-sectional design, so there is a tremendous opportunity to perform high-impact and longitudinal studies along the continuum of aging that determine 1) the influence and cause of the increased variability with aging on functional performance tasks, and 2) whether lifestyle factors such as physical exercise can minimize this age-related variability in motor performance in the rapidly expanding numbers of very old adults.

/pdf/the-aging-neuromuscular-system-and-motor-performance-5a5udn40ru.pdf

The aging neuromuscular system and motor performance

This work presents a survey of the current state of the art of visualization techniques for parallel coordinates. It covers geometric models for constructing parallel coordinates and reviews methods for creating and understanding visual representations of parallel coordinates. The classification of these methods is based on a taxonomy that was established from the literature and is aimed at guiding researchers to find existing techniques and identifying white spots that require further research. The techniques covered in this survey are further related to an established taxonomy of knowledge-discovery tasks to support users of parallel coordinates in choosing a technique for their problem at hand. Finally, we discuss the challenges in constructing and understanding parallel-coordinates plots and provide some examples from different application domains.

State of the Art of Parallel Coordinates

The parallel coordinates technique is widely used for the analysis of multivariate data. During recent decades significant research efforts have been devoted to exploring the applicability of the technique and to expand upon it, resulting in a variety of extensions. Of these many research activities, a surprisingly small number concerns user-centred evaluations investigating actual use and usability issues for different tasks, data and domains. The result is a clear lack of convincing evidence to support and guide uptake by users as well as future research directions. To address these issues this paper contributes a thorough literature survey of what has been done in the area of user-centred evaluation of parallel coordinates. These evaluations are divided into four categories based on characterization of use, derived from the survey. Based on the data from the survey and the categorization combined with the authors' experience of working with parallel coordinates, a set of guidelines for future research directions is proposed.

Evaluation of Parallel Coordinates: Overview, Categorization and Guidelines for Future Research

https://pure.rug.nl/ws/files/2696534/2009BrainResLorist.pdf

The influence of mental fatigue and motivation on neural network dynamics; an EEG coherence study

A typical data-driven visualization of electroencephalography (EEG) coherence is a graph layout, with vertices representing electrodes and edges representing significant coherences between electrode signals. A drawback of this layout is its visual clutter for multichannel EEG. To reduce clutter, we define a functional unit (FU) as a data-driven region of interest (ROI). An FU is a spatially connected set of electrodes recording pairwise significantly coherent signals, represented in the coherence graph by a spatially connected clique. Earlier, we presented two methods to detect FUs: a maximal clique-based (MCB) method (time complexity O(3n/3), with n being the number of vertices) and a more efficient watershed-based (WB) method (time complexity O(n2 logn)). To reduce the potential oversegmentation of the WB method, we introduce an improved WB (IWB) method (time complexity O(n2 log n)). The IWB method merges basins representing FUs during the segmentation if they are spatially connected and if their union is a clique. The WB and IWB methods are both up to a factor of 100,000 faster than the MCB method for a typical multichannel setting with 128 EEG channels, thus making interactive visualization of multichannel EEG coherence possible. Results show that considering the MCB method as the gold standard, the difference between IWB and MCB FU maps is smaller than between WB and MCB FU maps. We also introduce two novel group maps for data-driven group analysis as extensions of the IWB method. First, the group mean coherence map preserves dominant features from a collection of individual FU maps. Second, the group FU size map visualizes the average FU size per electrode across a collection of individual FU maps. Finally, we employ an extensive case study to evaluate the IWB FU map and the two new group maps for data-driven group analysis. Results, in accordance with conventional findings, indicate differences in EEG coherence between younger and older adults. However, they also suggest that an initial selection of hypothesis-driven ROIs could be extended with additional data-driven ROIs.

/pdf/data-driven-visualization-and-group-analysis-of-multichannel-5aonfq7gtn.pdf

Data-Driven Visualization and Group Analysis of Multichannel EEG Coherence with Functional Units

The field of visualization assists data interpretation in many areas, but some types of data are not manageable by existing visualization techniques This holds in particular for time-varying multichannel EEG data No existing technique can simultaneously visualize information from all channels in use and all time steps To address this problem, a new visualization technique is presented, based on the parallel coordinate method and making use of a tiled organization This tiled organization employs a two-dimensional row-column representation, rather than a one-dimensional arrangement in columns as used for the classical parallel coordinates The usefulness of the new method, referred to as tiled parallel coordinates, is demonstrated by one particular type of EEG data It can be applied to an arbitrary number of time steps, for the maximum number of channels currently in use The general setup of the method makes it widely applicable to other time-varying multivariate data types

/pdf/tiled-parallel-coordinates-for-the-visualization-of-time-3ucdvagwk7.pdf

Tiled parallel coordinates for the visualization of time-varying multichannel EEG data

Synchronous electrical activity in di erent brain regions is generally assumed to imply functional relationships between these regions. A measure for this synchrony is electroencephalography (EEG) coherence. Recently, we developed a new method for data-driven visualization of high-density EEG coherence, avoiding the visual clutter of conventional data-driven methods. It uses the concept of maximal clique detection, having time complexity O(3) with n the number of vertices. Here, a more e cient clustering method is used with time complexity O(n log n), based on a watershed algorithm which is modi ed to detect cliques in a greedy way. Here, it obtains a speedup of factor 400 while results are similar, making interactive visualization of high-density EEG coherence feasible.

/pdf/watershed-based-visualization-of-high-density-eeg-coherence-39tyncs8b3.pdf

Watershed-Based Visualization of High-Density EEG Coherence

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Data-Driven Visualization and Group Analysis of Multichannel EEG Coherence with Functional Units

Tiled parallel coordinates for the visualization of time-varying multichannel EEG data

Watershed-Based Visualization of High-Density EEG Coherence