Gait Stability Measurement by Using Average Entropy

Chronic motor performance following different traumatic brain injury severity-A systematic review

Introduction

Traumatic brain injury (TBI) is now known to be a chronic disease, causing ongoing neurodegeneration and linked to increased risk of neurodegenerative motor diseases, such as Parkinson's disease and amyotrophic lateral sclerosis. While the presentation of motor deficits acutely following traumatic brain injury is well-documented, however, less is known about how these evolve in the long-term post-injury, or how the initial severity of injury affects these outcomes. The purpose of this review, therefore, was to examine objective assessment of chronic motor impairment across the spectrum of TBI in both preclinical and clinical models.

Methods

PubMed, Embase, Scopus, and PsycINFO databases were searched with a search strategy containing key search terms for TBI and motor function. Original research articles reporting chronic motor outcomes with a clearly defined TBI severity (mild, repeated mild, moderate, moderate-severe, and severe) in an adult population were included.

Results

A total of 97 studies met the inclusion criteria, incorporating 62 preclinical and 35 clinical studies. Motor domains examined included neuroscore, gait, fine-motor, balance, and locomotion for preclinical studies and neuroscore, fine-motor, posture, and gait for clinical studies. There was little consensus among the articles presented, with extensive differences both in assessment methodology of the tests and parameters reported. In general, an effect of severity was seen, with more severe injury leading to persistent motor deficits, although subtle fine motor deficits were also seen clinically following repeated injury. Only six clinical studies investigated motor outcomes beyond 10 years post-injury and two preclinical studies to 18-24 months post-injury, and, as such, the interaction between a previous TBI and aging on motor performance is yet to be comprehensively examined.

Conclusion

Further research is required to establish standardized motor assessment procedures to fully characterize chronic motor impairment across the spectrum of TBI with comprehensive outcomes and consistent protocols. Longitudinal studies investigating the same cohort over time are also a key for understanding the interaction between TBI and aging. This is particularly critical, given the risk of neurodegenerative motor disease development following TBI.

The autonomic balance of heart rhythm complexity after renal artery denervation: insight from entropy of entropy and average entropy analysis

Background

The current method to evaluate the autonomic balance after renal denervation (RDN) relies on heart rate variability (HRV). However, parameters of HRV were not always predictive of response to RDN. Therefore, the complexity and disorder of heart rhythm, measured by entropy of entropy (EoE) and average entropy (AE), have been used to analyze autonomic dysfunction. This study evaluated the dynamic changes in autonomic status after RDN via EoE and AE analysis.

Methods

Five patients were prospectively enrolled in the Global SYMPLICITY Registry from 2020 to 2021. 24-h Holter and ambulatory blood pressure monitoring (ABPM) was performed at baseline and 3 months after RDN procedures. The autonomic status was analyzed using the entropy-based AE and EoE analysis and the conventional HRV-based low frequency (LF), high frequency (HF), and LF/HF.

Results

After RDN, the ABPM of all patients showed a significant reduction in blood pressure (BP) and heart rate. Only AE and HF values of all patients had consistent changes after RDN (p < 0.05). The spearman rank-order correlation coefficient of AE vs. HF was 0.86, but AE had a lower coefficient of variation than HF.

Conclusions

Monitoring the AE and EoE analysis could be an alternative to interpreting autonomic status. In addition, a relative change of autonomic tone, especially an increasing parasympathetic activity, could restore autonomic balance after RDN.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12938-022-00999-4.

Gait analysis under the lens of statistical physics

Human gait is a fundamental activity, essential for the survival of the individual, and an emergent property of the interactions between complex physical and cognitive processes. Gait is altered in many situations, due both to external constraints, as e.g. paced walk, and to physical and neurological pathologies. Its study is therefore important as a way of improving the quality of life of patients, but also as a door to understanding the inner working of the human nervous system. In this review we explore how four statistical physics concepts have been used to characterise normal and pathological gait: entropy, maximum Lyapunov exponent, multi-fractal analysis and irreversibility. Beyond some basic definitions, we present the main results that have been obtained in this field, as well as a discussion of the main limitations researchers have dealt and will have to deal with. We finally conclude with some biomedical considerations and avenues for further development.