Biology subjects

Mahmoudi, B.

Publications and source records attributed to Mahmoudi, B..

3 recordsLinked to original sources

bioRxiv2025

Impact of Slow Wave Abnormalities and Impaired Coordination of Pyloric Closure and Antral Contraction on Gastric Emptying: A Compartmental Modeling Study

PurposeTo develop a computationally efficient gastric compartmental model that simulates diseased stomach function by altering antral-pyloric coordination and "slow wave" properties. The model evaluates motility, gastric emptying and mixing. The computational efficiency of the model enables broad parameter sweeps to simulate various pathological conditions, offering an alternative to computationally expensive finite element or finite volume approaches. MethodsWe developed an extended compartmental model to simulate gastric function under dysrhythmic conditions. Building on prior work, this framework incorporates enhanced fluid flow equations and improved inter-compartment connectivity. These modifications enable simulation of impaired antro-pyloric coordination, regional variations in "slow wave" frequency (bradygastria, tachygastria), and reduced amplitude to mimic quiescent activity. Model outputs included gastric emptying rates, mixing efficiency, and transpyloric flow events across healthy and impaired conditions. ResultsSimulations demonstrated that abnormal "slow wave" frequency or amplitude delays gastric emptying and diminishes mixing efficiency. Bradygastria induced retrograde transpyloric flow, reflecting backflow from intestine to stomach, a pathological marker. These disruptions were most pronounced when antro-pyloric coordination was impaired. Predictions aligned with prior experimental and computational findings, while model execution was ~ 50-fold faster than real-time gastric dynamics, highlighting computational efficiency. ConclusionThis physiologically inspired compartmental model captures the impact of "slow wave" abnormalities on gastric motility. By reproducing impaired flow and mixing patterns characteristic of diseased states, it provides a valuable tool for probing mechanisms of gastric dysfunction. Importantly, its computational efficiency positions the model for use in developing and rapid testing of model-based, closed-loop neurostimulation therapies for gastrointestinal disorders.

biophysics↗

bioRxiv2024

Refining Brain Stimulation Therapies: An Active Learning Approach to Personalization

Brain stimulation holds promise for treating brain disorders, but personalizing therapy remains challenging. Effective treatment requires establishing a functional link between stimulation parameters and brain response, yet traditional methods like random sampling (RS) are inefficient and costly. To overcome this, we developed an active learning (AL) framework that identifies optimal relationships between stimulation parameters and brain response with fewer experiments. We validated this framework through three experiments: (1) in silico modeling with synthetic data from a Parkinsons disease model, (2) in silico modeling with real data from a non-human primate, and (3) in vivo modeling with a real-time rat optogenetic stimulation experiment. In each experiment, we compared AL models to RS models, using various query strategies and stimulation parameters (amplitude, frequency, pulse width). AL models consistently outperformed RS models, achieving lower error on unseen test data in silico (p<0.0056, N=1,000) and in vivo (p=0.0036, N=20). This approach represents a significant advancement in brain stimulation, potentially improving both research and clinical applications by making them more efficient and effective. Our findings suggest that AL can substantially reduce the cost and time required for developing personalized brain stimulation therapies, paving the way for more effective and accessible treatments for brain disorders.

neuroscience↗

bioRxiv2022

Data Driven Control of Vagus Nerve Stimulation for the Cardiac System

1Vagus nerve stimulation is an emerging therapy that seeks to offset pathological conditions by electrically stimulating the vagus nerve through cuff electrodes, where an electrical pulse is defined by several parameters such as pulse amplitude, pulse width, and pulse frequency. This electroceutical therapy has been approved for epilepsy, and treatment resistant depression. Currently, vagus nerve stimulation is under investigation for the treatment of heart failure, heart arrhythmia, hypertension, and gastric motility disorders. Through several clinical trials that sought to assess vagus nerve stimulation for the treatment of heart failure, stimulation parameters were determined heuristically and the results were left inconclusive, which has led to the suggestion of using a closed-loop approach to optimize the stimulation parameters. A recent investigation has demonstrated highly specific control of cardiac physiology by selectively activating different fibers in the vagus nerve. When multiple locations and multiple stimulation parameters are considered for optimization, the design of closed-loop control becomes considerably more challenging. To address this challenge, we investigated a data-driven control scheme for both modeling and controlling the rat cardiac system. Using an existing in silico physiological model of a rat heart to generate synthetic input-output data, we trained a long short-term memory network (LSTM) to map the effect of stimulation on the heart rate and the blood pressure. The trained LSTM was utilized in a model predictive control framework to optimize the vagus nerve stimulation parameters for set point tracking of the heart rate and the blood pressure in closed-loop simulations. Additionally, we altered the underlying in silico physiological model to consider intra-patient variability, and diseased dynamics from increased sympathetic tone in designing closed-loop VNS strategies. Throughout the different simulation scenarios, we leveraged the design of the controller to demonstrate alternative clinical objectives. Our results show the controller can optimize stimulation parameters to achieve set-point tracking with nominal offset while remaining computationally efficient. Furthermore, we show a controller formulation that compensates for mismatch due to intra-patient variabilty, and diseased dynamics. This study demonstrates the first application and a proof-of-concept for using a purely data-driven approach for the optimization of vagus nerve stimulation parameters in closed-loop control of the cardiac system.

bioengineering↗