Evidence map›Paper›PMID 40175469›Full record

ArticleScientific reports2025

Computational sensitivity evaluation of ultrasound neuromodulation resolution to brain tissue sound speed with robust beamforming.

Boqiang Fan, Wayne Goodman, Sameer A Sheth, Richard R Bouchard, Behnaam Aazhang

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Boqiang FanDepartment of Electrical and Computer Engineering, Rice University, Houston, TX, 77005, USA. Boqiang.Fan@rice.edu.
Wayne GoodmanDepartment of Electrical and Computer Engineering, Rice University, Houston, TX, 77005, USA.
Sameer A ShethDepartment of Electrical and Computer Engineering, Rice University, Houston, TX, 77005, USA.
Richard R BouchardDepartment of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
Behnaam AazhangDepartment of Electrical and Computer Engineering, Rice University, Houston, TX, 77005, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Low-intensity focused ultrasound (LIFU) neuromodulation requires precise targeting and high resolution enabled by phased array transducers and beamforming. However, focusing optimization usually relies on phantom measurements or simulations with inaccurate acoustic properties to degrade neuromodulation resolution. Therefore, this work analyzes the sensitivity of neuromodulation resolution, measured by off-target activation area (OTAA), to brain tissue sound speed. A Robust Optimal Resolution (ROR) beamforming method is proposed to minimize the worst-case OTAA with restricted sound speed inaccuracy and propagation information estimated with deviated sound speed. The propagation estimation model utilizes equivalent source method (ESM) to map sound field between different acoustic parameter sets. Simulation in a human head model validates the effectiveness of the proposed propagation estimation model, and shows that ROR beamforming method can significantly reduce the worst-case OTAA compared to benchmark methods by [Formula: see text] on average and up to [Formula: see text], improving the robustness of stimulation and addressing the sensitivity issue. This allows reliable high-resolution neuromodulation in potential clinical applications with reduced invasive acquisition of propagation measurements for focusing optimization.

Indexed as

BrainComputer SimulationHumansPhantoms, ImagingSoundUltrasonography

Identifiers

PMID40175469
PMCPMC11965375

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.