Biological regulatory capacity is commonly represented by a scalar: a summed biomarker index, standardized composite, risk count, or single recovery parameter. Such measures can be useful, but they do not identify capacity independently of the conditions under which they are constructed. This article argues that regulatory capacity is not an intrinsic scalar property of an organism. It is a conditional relation among the architecture and current state of a regulatory system, the perturbations that reach it, the metric or cost functional used to evaluate its response, the viability criterion, and the time horizon. Near an operating state, systems with identical eigenvalue spectra can incur different integrated burdens and reverse their apparent ranking when the perturbation channel or burden metric changes. For a declared quadratic burden in a stable local linearization, the observability Gramian associated with a virtual cost output represents accumulated weighted recovery burden, and the measurement map, burden-evaluation map, and latent state representation must be distinguished. For network physiology, allostasis, and allostatic-load measurement, routine choices about biomarkers, scaling, thresholds, and challenge paradigms are therefore constitutive assumptions rather than neutral preprocessing. Biological adaptation is likewise not equated with stability, compensation, recovery, or continued viability: it is indexed to a specified level of organization, protected function, reference or counterfactual, time horizon, and outcome or cost criterion. Regulatory Bandwidth is developed as a conditional perturbation-response profile rather than a universal scalar. Its contribution is a measurement architecture that requires the regulatory system, perturbation ecology, burden criterion, viability endpoint, and horizon to be declared before scalarization. The primary falsifiable comparison is between a flexible scalar-capacity model, which predicts one preserved ordering across declared perturbation classes, and a conditional-profile model tested on held-out sessions, contexts, or perturbations. Rejection of the scalar model supports conditional structure without establishing architectural heterogeneity, which requires a further shared-architecture comparison; this distinction bears directly on neurodiversity and person-environment fit. A worked orthostatic-challenge specification shows how measured outputs, constructed burden, viability, measurement error, and mechanistic interpretation can be separated in practice. The framework preserves the usefulness of scalar summaries while restricting the claims they can support.
IntroductionAgeing is associated with impaired gait adaptability, which leads to an increased fall risk in older adults. The study of the temporal structure of stride‐to‐stride fluctuations may help mitigate these inherent risks, as it has been proposed as a marker of locomotor functionality and motor control. However, the underlying mechanisms responsible for the progressive deterioration of stride‐to‐stride fluctuations in older adults are yet to be fully unravelled. Recent research suggests several central and peripheral factors can influence the walking adaptation process with the corticospinal tract playing a key role in motor control during gait. Thus, the purpose of this study was to investigate whether lower‐leg corticospinal drive significantly explains the temporal structure of gait stride-to-stride fluctuations.MethodsThirty‐four older adults underwent transcranial magnetic stimulation (TMS) assessments of the tibialis anterior in one experimental session, where corticospinal excitability (CSE), silent period duration (SP), and short‐interval intracortical inhibition (SICI) were quantified. Immediately after the TMS intervention, they also performed a 12‐min treadmill walking trial at preferred walking speed, during which inter‐stride intervals were extracted and analysed using detrended fluctuation analysis to obtain the fractal scaling exponent (αISIs). Multiple linear regression analysis was used to examine whether CSE, SP and SICI predicted αISIs.ResultsThe results of the linear regression model indicated that none of the corticospinal measures significantly predicted gait’s temporal dynamics (F(3,28) = 0.20, p = 0.896, R2 = 0.021).DiscussionThese findings suggest that gait adaptability does not seem to be explained by corticospinal function, likely emerging from distributed, multilevel neural control processes involving spinal, supraspinal, and peripheral mechanisms rather than corticospinal contributions alone.
IntroductionAtrial arrhythmias, notably atrial fibrillation (AF), exhibit complex spatiotemporal patterns that remain incompletely characterized. Based on topological constraints requiring counter-rotating reentries in two-dimensional manifolds, we present a framework classifying all atrial wavefronts into two fundamental archetypes: open and closed waves. Open waves are line-like structures bounded by endpoints that tend to self-perpetuate; closed waves are ring-like structures lacking endpoints that tend toward self-termination.Methods and ResultsWe demonstrate that complex wavefront interactions reduce to five elementary processes: (1) Open wave creation, (2) Open wave annihilation, (3) Closed wave creation, (4) Closed wave annihilation and (5) Open wave collision. Validation within a cohort of 600 patient-specific AF simulations showed that all observed events adhered to these constructed topological rules, suggesting that complexity of fibrillatory conduction within the studied computational framework arises from simple underlying principles. Furthermore, closing all open waves, achieved a 100% success rate when electrical silence was used as the primary criteria for efficacy.DiscussionThis unified framework reconciles existing electrophysiological theories, including the multiple wavelet hypothesis, the mother rotor hypothesis, meandering rotors, and focal sources, while providing a foundation for optimizing clinical interventions.
Nephrons receive blood from a tree-shaped network of arteries and arterioles and regulate demand by actions that affect the diameters of their afferent arterioles. The network structure is asymmetric. The flow of blood requires a gradient of hydrostatic pressure so that the blood pressure at the origins of different afferent arterioles must differ from each other. Nephron mechanisms that act on afferent arterioles include a myogenic mechanism, a common feature of smooth muscle cells throughout the body, and tubuloglomerular feedback (TGF), a negative feedback mechanism that senses concentrations of common electrolytes in tubular fluid as it passes from the thick ascending limb into the distal tubule. The myogenic mechanism generates an autonomous oscillation in the electrical potential difference of the plasma membrane. The TGF signal also oscillates, albeit at a lower frequency, and modulates the frequency and amplitude of the myogenic mechanism’s potential difference oscillation. The interaction of TGF with the myogenic mechanism generates a bimodal electrical signal that enters the adjacent arterial segment. The arterial segments of the network are electrically conductive, providing a channel for nephrons to interact with each other. In addition, the oscillation of nephron blood flow caused by the myogenic-TGF interaction leads to changes in local arterial blood pressure, providing a hemodynamic interaction among nephrons. We developed a mathematical model that incorporates these features. The model demonstrates renal autoregulation and the oscillations in tubular fluid flow and pressure found in experiments. The results illustrate the effects of interactions on the network. In this paper we estimate Lyapunov exponents to establish the dynamic state of each nephron, and mutual information to provide quantitative evaluations of the importance of each of the information streams on nephron dynamics. The results indicate that all nephrons operate in a state of weak chaotic dynamics and that electrical signaling through the arteries exerts a greater influence on nephron dynamics than do hemodynamic interactions. Chaotic attractors are sensitive to initial conditions, and we suggest that the nephron cluster is therefore sensitive to the information streams in the system, enabling the cluster to adapt to changing external conditions.
Hypoxic-ischemic encephalopathy (HIE) remains a critical challenge in neonatal intensive care, requiring precise bedside monitoring to mitigate long-term neurological morbidity. While individual assessment of cerebral and cardiovascular activity is standard, the dynamic interaction between these systems provides untapped diagnostic value. This pilot study investigates neuro-cardiac coupling using synchronized long-term EEG and ECG recordings, focusing primarily on a core subset of 61 neonates classified by clinical HIE severity (Sarnat Stage 1 vs. Stage 2). We employed cross-modal analysis to evaluate the relationship between the amplitude-integrated EEG (aEEG) envelope and heart rate variability (HRV) metrics. Our results demonstrate that neuro-cardiac interaction is significantly modulated by HIE severity and electrocortical background patterns. Neonates with healthy continuous normal voltage (CNV) patterns exhibited robust neuro-cardiac synchronization (median Spearman’s r=0.22), which was significantly reduced by approximately 60% in patients with abnormal EEG backgrounds (median Spearman’s r=0.09, p=0.015). To ensure robust translation, machine learning models were evaluated across diverse parameter grids. While isolated hyperparameter configurations yielded peak performance metrics–such as an overall maximum accuracy of 90.22% using combined cardiocycle and spectral ECG features with XGBoost–the feature space demonstrated strong, un-biased baseline stability, yielding reliable average accuracies across window lengths (e.g., 77.27% mean F1-score for EEG-based Logistic Regression and 72.73% mean F1 for ECG-based XGBoost). These findings suggest that multi-modal coupling metrics offer enhanced physiological insight for automated HIE grading in a pilot setting.
