Simulation of Brain Temperature Dynamics under Varying Pulse Widthsand the Influence of Na+ and K+ Conductance on Neuronal Activity
Bibliographic record
Abstract
Neuron action potentials (APs) represent the transition of neurons from a resting to an active state.While activation via energy deposition and mechanical waves is recognized, the underlying mechanisms remain unclear.Low-Intensity Focused Ultrasound (LIFU) enables non-invasive neuromodulation, yet its interaction with neuronal excitability and AP initiation is not fully understood.This study presents a simulation framework to investigate brain thermal responses at the AP intensity threshold across varying pulse widths (PWs), leveraging thermal modeling's higher detection sensitivity compared to experimental methods.This work investigates thermal effects at the neuronal AP threshold under LIFU stimulation.A single-cell neuron model was used to define activation thresholds, and thermal responses were simulated across three pulse widths (1, 5, and 9 ms), each applied with a 10% duty cycle (DC) and 100 Hz pulse repetition frequency (PRF).An in-lab transducer was experimentally characterized and replicated in simulation to validate acoustic and thermal behavior.The study offers a novel framework linking neuronal excitability with thermal simulation for physiologically grounded LIFU dosimetry.Temperature elevations of approximately 0.010704 °C were observed across all PW inputs when maintaining a 10% DC.For simulations with a PRF of 100 Hz, the temperature increases were 0.010704 °C, 0.010708 °C, and 0.010712 °C for 1 ms, 5 ms, and 9 ms PWs, respectively.These minimal variations suggest that under constant DC, PW duration has negligible influence on thermal elevation.In contrast, PRF had a more noticeable impact on thermal buildup.Additionally, differences in sodium and potassium conduction were observed, indicating the distinct roles of ion channel dynamics in neuronal activation.Collectively, these findings enhance our understanding of LIFU-induced neuromodulation by clarifying the relationship between thermal responses and temporal input parameters.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".