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The World Health Organization proposed the concept of intrinsic capacity (IC; the composite of all the physical and mental capacities of the individual) as central for healthy ageing. However, little research has investigated the interaction and joint associations of IC with cardiovascular disease (CVD) incidence and CVD mortality in middle‐ and older‐aged adults. Using data from 443 130 UK Biobank participants, we analysed seven biomarkers capturing the level of functioning of five domains of IC to calculate a total IC score (ranging from 0 [better IC] to +4 points [poor IC]). Associations between IC score and incidence of six long‐term CVD conditions (hypertension, stroke/transient ischaemic attack stroke, peripheral vascular disease, atrial fibrillation/flutter, coronary artery disease and heart failure), and grouped mortality from these conditions were estimated using Cox proportional models, with a 1‐year landmark analysis to triangulate the findings. Over 10.6 years of follow‐up, CVD morbidity grouped (n= 384 380 participants for the final analytic sample) was associated with IC scores (0 to +4): mean hazard ratio (HR) [95% confidence interval, CI] 1.11 [1.08–1.14], 1.20 [1.16–1.24], 1.29 [1.23–1.36] and 1.56 [1.45–1.59] in men (C‐index = 0.68), and 1.17 [1.13–1.20], 1.30 [1.26–1.36], 1.52 [1.45–1.59] and 1.78 [1.67–1.89] in women (C‐index = 0.70). In regard to mortality, our results indicated that the higher IC score (+4 points) was associated with a significant increase in subsequent CVD mortality (mean HR [95% CI]: 2.10 [1.81–2.43] in men [C‐index = 0.75] and 2.29 [1.85–2.84] in women [C‐index = 0.78]). Results of all sensitivity analyses by full sample, sex and age categories were largely consistent independent of major confounding factors (P< 0.001). IC deficit score is a powerful predictor of functional trajectories and vulnerabilities of the individual in relation to CVD incidence and premature death. Monitoring an individual's IC score may provide an early‐warning system to initiate preventive efforts.

Abstract: Modeling of muscle architecture, which aims to recreate mathematically the physiological structure of the muscle fibers and motor units, is a powerful tool for understanding and modeling the mechanical and electrical behavior of the muscle. Most of the published models are presented in the form of algorithms, without mathematical analysis of mechanisms or outcomes of the model. Through the study of the muscle architecture model proposed by Stashuk, we present the analytical tools needed to better understand these models. We provide a statistical description for the spatial relations between motor units and muscle fibers. We are particularly concerned with two physiological quantities: the motor unit fiber number, which we expect to be proportional to the motor unit territory area; and the motor unit fiber density, which we expect to be constant for all motor units. Our results indicate that the Stashuk model is in good agreement with the physiological evidence in terms of the expectations outlined above. However, the resulting variance is very high. In addition, a considerable ‘edge effect’ is present in the outer zone of the muscle cross-section, making the properties of the motor units dependent on their location. This effect is relevant when motor unit territories and muscle cross-section are of similar size. [Copyright &y& Elsevier]

We studied the effect of stimulus predictability on the alpha and beta changes observed in central regions during stimulus-induced movement paradigms. Six young volunteers were instructed to extend briskly their dominant wrist as soon as possible after hearing a 2000 Hz sound. Two sequences of stimuli were presented to each subject, the first rhythmic at 16 s and the second with random intervals between 5 and 13s. A time-frequency analysis of nonphase-locked activity in the 7–37 Hz range was performed on stimulus-centred EEG sweeps using wavelet filters and Gabor transforms. During the sequence of predictable rhythmic stimuli, stimulus-induced movements were accompanied by a decrease in beta activity that began contralaterally about 1 s prior to the stimulus and extended to both sides later on. This decrease was followed by a rebound after the end of the movement. In the alpha band, the decrease observed started just after the sound. During the sequence of non-predictable, random stimuli, stimulus-induced movements were accompanied by a shorter and smaller alpha and beta-ERD, that started after the stimulus. The presence of a pre-stimulus beta ERD only in the rhythmic predictable paradigm suggests that central pre-movement beta ERD may be an indicator of motor preparation, and could be used for objective evaluation of time estimation and motor timing. The minimal differences observed in the alpha changes in both paradigms suggest that alpha-ERD may not be linked to motor preparation.

Two parameters have been defined for quantifying jiggle: normalized consecutive amplitude differences (CAD) and the cross-correlational coefficient of consecutive discharges (CCC). In real recordings, artifacts from several sources may increase the variability of these parameters as they were originally defined. Two methodological modifications designed to overcome such a limitation are proposed: estimation of baseline fluctuation from segments of the recording free from nearby concurrent motor unit potentials (MUPs), and waveform alignment of consecutive discharges by correlation maximization (CM). The results obtained by the original and modified methods were compared for MUPs from normal subjects and patients with amyotrophic lateral sclerosis and chronic neurogenic diseases. With the modified method, CAD and CCC showed fewer extreme values and less scatter. The number of successfully aligned MUPs with the CM method was 18.8% higher (n = 394; Chi-square = 54.6; P &lt; 0.001), including irregular and unstable MUPs. The proposed modifications improve our capability to quantify the jiggle of real signals and reduce the necessity of manual interventions although low-interference recordings and operator supervision are still required. © 2000 John Wiley & Sons, Inc. Muscle Nerve 23: 1022–1034, 2000