Biology subjects

Elfouly, M. A. A.

Publications and source records attributed to Elfouly, M. A. A..

3 recordsLinked to original sources

bioRxiv2025

Early Diagnosis of Parkinson via Transient Beta Frequencies in a Delayed Van der Pol Model

Biological motor circuits are shaped by pathway-specific delays that generate history-dependent oscillations and short-lived transients not captured by memoryless models. We develop a delayed Van der Pol in which the delay ratio r acts as a control parameter for Hopf bifurcation and internal resonance and we pair it with an auditable, short-window signal-processing pipeline tailored to early beta activity. The pipeline combines Short-Time Fourier Transform (STFT), Continuous Wavelet Transform (CWT) ridge tracking, kernel-density estimates (KDE) of the dominant frequency, and algorithmic beta-burst detection under fixed, shared parameters. Transient burden is quantified by a metric triplet with a software-implementable attenuation rule and complemented by an early-detection index-the Transient Persistence Time. Across methods, we observe a monotonic attenuation of short-lived beta transients as r approaches a critical band. A reliable observation window of 0 - 0.35 s captures compact early packets at low r, progressive ridge stabilization in CWT, KDE narrowing over time, and near-elimination of bursts for near critical r, consistent with a transition to sustained narrowband rhythm. Clinically, these measures enable stage inference, a bedside Move-Stop protocol for rapid readout, and actionable policies for adaptive DBS and medication titration that minimize transient burden without inducing rigid locking. The fixed parameterization and audit-ready outputs support reproducibility and multi-center deployment. This framework advances data-driven, precision neuromodulation by targeting early transients before pathological synchrony becomes entrenched.

neuroscience↗

bioRxiv2025

Internal Resonance Dynamics in a Delayed van der Pol Oscillator Modeling Basal Ganglia Oscillations

Movement disorders, like Parkinsons disease, happen because of unusual patterns in the connections between the cortex and basal ganglia, often caused by timing issues in feedback pathways. This study uses a two-delay nonlinear dynamic model, based on the delayed the van der Pol oscillator, to examine how delays in the feedback loops of the direct and indirect basal ganglia pathways lead to unusual movement patterns and resonance. A thorough analysis of resonance looks at how the ratios of delays and the time it takes to respond affect the systems frequency, showing when internal resonance occurs and how frequency stabilizes under different conditions. We examine how stable the system is by looking at changes in its behavior and measuring the Lyapunov exponent across distinct types of nonlinear feedback setups. Simulations demonstrate transitions from stable oscillations to chaos with varying delays and saturation strength. Our results reproduce symptoms of Parkinsons disease, such as resting tremor, dyskinesia, and freezing, demonstrating how delayed inhibition or hyperactivity destabilizes motor function. The discussion supports these findings, indicating that early problems start in the striatum, with complex effects in the globus pallidus that worsen motor symptoms. This model explains the temporal evolution of Parkinsons disease symptoms and highlights the timing of feedback and saturation as key therapeutic targets. Overall, this research offers a biological explanation for motor problems caused by delays and supports novel approaches for brain stimulation using flexible methods.

neuroscience↗

bioRxiv2025

Integration of Nanomaterials and DBS to Improve Basal Ganglia Oscillations Using Delayed Van der Pol Model

Basal ganglia play a crucial role in motor control and the challenges posed by pathological oscillations, especially in Parkinsons disease. Although deep brain stimulation (DBS) is effective in attenuating pathological frequencies, it often leads to side effects. This study presents a comprehensive framework for understanding and improving neural oscillation dynamics by integrating nanomaterials with DBS. Nanomaterials with their controllable frequencies through length, width, and structure could stabilize oscillations and maintain normal neural frequencies. This study introduces three mathematical models: the DBS model, the nanomaterials model, and a combined model of both, which use the Van der Pol delay model to demonstrate how the periodic force and nanomaterials collaborate to stabilize brain activity. Numerical simulations indicate that DBS by itself lowers harmful frequencies but might make the motor cortex unstable because it increases the strength of signals. In contrast, the nanomaterial models reduce the amplitude and activate the motor cortex. The combination of DBS and nanomaterials significantly improves stability, reduces pathological oscillations, and decreases hyperactivity. The use of nanomaterials must be carefully monitored to ensure that they do not suppress normal neural activity, highlighting the need for experimental validation. This study provides a promising direction for the development of personalized therapeutic technologies that balance the suppression of pathological activity with the preservation of normal neural function.

neuroscience↗