Older adults and individuals with Parkinson's disease control posture along suborthogonal directions that deviate from the traditional anteroposterior and mediolateral directions

Predicting Freezing of Gait in Parkinson's Disease: A Machine-Learning-Based Approach in ON and OFF Medication States

Background: Freezing of gait (FoG) is a debilitating motor symptom of Parkinson's disease (PD), characterized by sudden episodes where patients struggle to initiate or sustain movement, often describing a sensation of their feet being "glued to the ground." This study investigates the potential of machine-learning (ML) models to predict FoG severity in PD patients, focusing on the influence of dopaminergic medication by comparing gait parameters in ON and OFF medication states. Methods: Specifically, this study employed spatiotemporal gait features to develop a predictive model for FoG severity, leveraging a random forest regressor to identify the most influential gait parameters associated with this in each medication state. The results indicate that the model achieved higher predictive performance in the OFF-medication condition (R² = 0.82, MAE = 2.25, MSE = 15.23) compared to the ON-medication condition (R² = 0.52, MAE = 4.16, MSE = 42.00). Results: These findings suggest that dopaminergic treatment alters gait dynamics, potentially reducing the reliability of FoG predictions when patients are medicated. Feature importance analysis revealed distinct gait characteristics associated with FoG severity across medication states. In the OFF condition, step length parameters, particularly left step length mean, were the most dominant predictors, alongside swing time and stride width, indicating the role of spatial and temporal gait control in FoG severity without medication. In contrast, under the ON medication condition, stride width and gait speed emerged as the most influential predictors, followed by stepping frequency, reflecting how medication influences stability and movement rhythm. Conclusions: These findings highlight the need for predictive models that account for medication-induced gait variability, ensuring more reliable FoG detection. By integrating spatiotemporal gait analysis and ML-based prediction, this study contributes to the development of personalized intervention strategies for PD patients experiencing FoG episodes.

Freezing of gait shapes the postural control of individuals with Parkinson's disease: Effect of visual information and medication states

BACKGROUND

Several measures of the center of pressure have been used to describe magnitude and structure of the postural sway in individuals with Parkinson's disease (PD). This study aimed to examine whether both the magnitude and structure of the center of pressure trajectory can differentiate PD individuals with and without freezing of gait in both On- and Off-medication states and with eyes open and closed.

METHODS

Twenty-four individuals with PD (14 without and 10 with freezing of gait) were tested. Participants stood as still as possible on a force plate for 30 s with eyes open and closed and On- and Off-medication. Analyses of variance were used to test the effect of group, medication state, and visual information on the magnitude (area) and structure (shape measured by the ratio between axes length and orientation) of the center of pressure ellipses.

FINDINGS

The center of pressure ellipses were less elongated in On-medication state and for PD with freezing of gait than without freezing of gait. Moreover, the magnitude of the ellipses was larger for PD with freezing than without freezing of gait only in the On-medication state. The orientation of the ellipses was more diagonal for individuals with freezing, in the On-medication state, and with the eyes closed when compared to individuals with PD without freezing under the same conditions.

INTERPRETATION

Magnitude and structure of the postural sway differed between PD individuals with and without freezing of gait, depending on the medication state and the availability of the visual information.

Angular distribution of fractal temporal correlations supports adaptive responses to wobble board instability

Contemporary dynamical models of human postural control propose an intermittent controller regulating the postural centre of pressure (CoP) about a stable saddle-shaped manifold along anatomical anteroposterior (AP) and mediolateral (ML) axes, releasing CoP in an outwards spiral when inactive. Experimental manipulations can evoke this saddle-type topology in fractal temporal correlations along the AP axis and reducing correlations along the ML axis. However, true effects of task demands may often manifest within angular space between anatomical AP and ML axes-a space not typically modelled explicitly. We tested how instability and attentional load influence postural control across the full angular range of fractal variability along the two-dimensional (2D) support surface. Forty-eight healthy young adults performed a suprapostural Trail Making Test (TMT) while standing on a wobble board, inducing continuous perturbations along the ML axis. Stable, quiet standing exhibited classic saddle-like topology, with stronger fractal temporal correlations in CoP displacements along AP axes. The attentional demand of the TMT did not affect angular variation or strength of fractal temporal correlations across the 2Dsupport surface. However, maintaining upright balance on the wobble board reshaped and reoriented the angular distribution of fractal temporal correlations, accentuating saddle-like angular variation and rotating the strongest fractal temporal correlations predominantly along the ML axis. Stabilizing posture in the face of wobble board instability prompted the saddle-type angular distribution of fractal temporal correlations. These findings challenge the traditional dependence of postural control theories exclusively on external force-plate axes and underscore the significance of multifractality in defining control parameters that govern postural stability across the full angular range of the 2D support surface.

Selective engagement of long-latency reflexes in postural control through wobble board training

Long-latency reflexes (LLRs) are critical precursors to intricate postural coordination of muscular adaptations that sustain equilibrium following abrupt disturbances. Both disturbances and adaptive responses reflect excursions of postural control from quiescent Gaussian stability under a narrow bell curve, excursions beyond Gaussianity unfolding at many timescales. LLRs slow with age, accentuating the risk of falls and undermining dexterity, particularly in settings with concurrent additional tasks. We investigated whether the wobble board could cultivate the engagement of LLRs selectively in healthy young participants executing a suprapostural Trail Making Task (TMT). A concurrent additional-task demand constituted visual precision predominantly along the anteroposterior (AP) axis and mechanical instability mainly along the mediolateral (ML) axis. We scrutinized planar center-of-pressure (CoP) trajectories to quantify postural non-Gaussianity across various temporal scales. Wobble board increased engagement of LLRs and decreased engagement of compensatory postural adjustments (CPAs), indicated by the peak in non-Gaussianity of CoP planar displacements over LLR-specific timescales (50-100 ms) and non-Gaussianity of CoP planar displacements progressively diminishing over CPA-specific timescales ([Formula: see text] ms). Engagement with TMT did not show any noticeable influence on non-Gaussian postural sway patterns. Despite aligning the unstable axis of the wobble board with participants' ML axis, thus rendering posture more unstable along the ML axis, the wobble board increased engagement of LLRs significantly more along the AP axis and reduced engagement of CPAs significantly more along the ML axis. These findings offer initial mechanistic insights into how wobble boards may bolster balance and potentially reduce the occurrence of falls by catalyzing the engagement of LLRs selectively.

Multifractal perturbations to multiplicative cascades promote multifractal nonlinearity with asymmetric spectra

Biological and psychological processes have been conceptualized as emerging from intricate multiplicative interactions among component processes across various spatial and temporal scales. Among the statistical models employed to approximate these intricate nonlinear interactions across scales, one prominent framework is that of cascades. Despite decades of empirical work using multifractal formalisms, several fundamental questions persist concerning the proper interpretations of multifractal evidence of nonlinear cross-scale interactivity. Does multifractal spectrum width depend on multiplicative interactions, constituent noise processes participating in those interactions, or both? We conducted numerical simulations of cascade time series featuring component noise processes characterizing a range of nonlinear temporal correlations: nonlinearly multifractal, linearly multifractal (obtained via the iterative amplitude adjusted wavelet transform of nonlinearly multifractal), phase-randomized linearity (obtained via the iterative amplitude adjustment Fourier transform of nonlinearly multifractal), and phase and amplitude randomized (obtained via shuffling of nonlinearly multifractal). Our findings show that the multiplicative interactions coordinate with the nonlinear temporal correlations of noise components to dictate emergent multifractal properties. Multiplicative cascades with stronger nonlinear temporal correlations make multifractal spectra more asymmetric with wider left sides. However, when considering multifractal spectral differences between the original and surrogate time series, even multiplicative cascades produce multifractality greater than in surrogate time series, even with linearized multifractal noise components. In contrast, additivity among component processes leads to a linear outcome. These findings provide a robust framework for generating multifractal expectations for biological and psychological models in which cascade dynamics flow from one part of an organism to another.

Postural control in gymnasts: anisotropic fractal scaling reveals proprioceptive reintegration in vestibular perturbation

Dexterous postural control subtly complements movement variability with sensory correlations at many scales. The expressive poise of gymnasts exemplifies this lyrical punctuation of release with constraint, from coarse grain to fine scales. Dexterous postural control upon a 2D support surface might collapse the variation of center of pressure (CoP) to a relatively 1D orientation-a direction often oriented towards the focal point of a visual task. Sensory corrections in dexterous postural control might manifest in temporal correlations, specifically as fractional Brownian motions whose differences are more and less correlated with fractional Gaussian noises (fGns) with progressively larger and smaller Hurst exponent H. Traditional empirical work examines this arrangement of lower-dimensional compression of CoP along two orthogonal axes, anteroposterior (AP) and mediolateral (ML). Eyes-open and face-forward orientations cultivate greater variability along AP than ML axes, and the orthogonal distribution of spatial variability has so far gone hand in hand with an orthogonal distribution of H, for example, larger in AP and lower in ML. However, perturbing the orientation of task focus might destabilize the postural synergy away from its 1D distribution and homogenize the temporal correlations across the 2D support surface, resulting in narrower angles between the directions of the largest and smallest H. We used oriented fractal scaling component analysis (OFSCA) to investigate whether sensory corrections in postural control might thus become suborthogonal. OFSCA models raw 2D CoP trajectory by decomposing it in all directions along the 2D support surface and fits the directions with the largest and smallest H. We studied a sample of gymnasts in eyes-open and face-forward quiet posture, and results from OFSCA confirm that such posture exhibits the classic orthogonal distribution of temporal correlations. Head-turning resulted in a simultaneous decrease in this angle Δθ, which promptly reversed once gymnasts reoriented their heads forward. However, when vision was absent, there was only a discernible negative trend in Δθ, indicating a shift in the angle's direction but not a statistically significant one. Thus, the narrowing of Δθ may signify an adaptive strategy in postural control. The swift recovery of Δθ upon returning to a forward-facing posture suggests that the temporary reduction is specific to head-turning and does not impose a lasting burden on postural control. Turning the head reduced the angle between these two orientations, facilitating the release of postural degrees of freedom towards a more uniform spread of the CoP across both dimensions of the support surface. The innovative aspect of this work is that it shows how fractality might serve as a control parameter of adaptive mechanisms of dexterous postural control.

Unraveling the threads of stability: A review of the neurophysiology of postural control in Parkinson's disease

Postural instability is a detrimental and often treatment-refractory symptom of Parkinson's disease. While many existing studies quantify the biomechanical deficits among various postural domains (static, anticipatory, and reactive) in this population, less is known regarding the neural network dysfunctions underlying these phenomena. This review will summarize current studies on the cortical and subcortical neural activities during postural responses in healthy subjects and those with Parkinson's disease. We will also review the effects of current therapies, including neuromodulation and feedback-based wearable devices, on postural instability symptoms. With recent advances in implantable devices that allow chronic, ambulatory neural data collection from patients with Parkinson's disease, combined with sensors that can quantify biomechanical measurements of postural responses, future work using these devices will enable better understanding of the neural mechanisms of postural control. Bridging this knowledge gap will be the critical first step towards developing novel neuromodulatory interventions to enhance the treatment of postural instability in Parkinson's disease.