Computational modeling and minimization of unintended neuronal excitation in a LIFU stimulation

Abstract The neuromodulation effect of low-intensity focused ultrasound (LIFU) is highly target-specific. Unintended off-target neuronal excitation can be elicited when the beam focusing accuracy and resolution are limited, whereas the resulted side effect has not been evaluated quantitatively. Ther...

Full description

Bibliographic Details
Main Authors: Boqiang Fan, Wayne Goodman, Raymond Y. Cho, Sameer A. Sheth, Richard R. Bouchard, Behnaam Aazhang
Format: Article
Language:English
Published: Nature Portfolio 2023-08-01
Series:Scientific Reports
Online Access:https://doi.org/10.1038/s41598-023-40522-w
_version_ 1797453149614637056
author Boqiang Fan
Wayne Goodman
Raymond Y. Cho
Sameer A. Sheth
Richard R. Bouchard
Behnaam Aazhang
author_facet Boqiang Fan
Wayne Goodman
Raymond Y. Cho
Sameer A. Sheth
Richard R. Bouchard
Behnaam Aazhang
author_sort Boqiang Fan
collection DOAJ
description Abstract The neuromodulation effect of low-intensity focused ultrasound (LIFU) is highly target-specific. Unintended off-target neuronal excitation can be elicited when the beam focusing accuracy and resolution are limited, whereas the resulted side effect has not been evaluated quantitatively. There is also a lack of methods addressing the minimization of such side effects. Therefore, this work introduces a computational model of unintended neuronal excitation during LIFU neuromodulation, which evaluates the off-target activation area (OTAA) by integrating an ultrasound field model with the neuronal spiking model. In addition, a phased array beam focusing scheme called constrained optimal resolution beamforming (CORB) is proposed to minimize the off-target neuronal excitation area while ensuring effective stimulation in the target brain region. A lower bound of the OTAA is analytically approximated in a simplified homogeneous medium, which could guide the selection of transducer parameters such as aperture size and operating frequency. Simulations in a human head model using three transducer setups show that CORB markedly reduces the OTAA compared with two benchmark beam focusing methods. The high neuromodulation resolution demonstrates the capability of LIFU to effectively limit the side effects during neuromodulation, allowing future clinical applications such as treatment of neuropsychiatric disorders.
first_indexed 2024-03-09T15:18:41Z
format Article
id doaj.art-8ea75f32d3214e6588de03cfdccdcee7
institution Directory Open Access Journal
issn 2045-2322
language English
last_indexed 2024-03-09T15:18:41Z
publishDate 2023-08-01
publisher Nature Portfolio
record_format Article
series Scientific Reports
spelling doaj.art-8ea75f32d3214e6588de03cfdccdcee72023-11-26T12:55:32ZengNature PortfolioScientific Reports2045-23222023-08-0113111110.1038/s41598-023-40522-wComputational modeling and minimization of unintended neuronal excitation in a LIFU stimulationBoqiang Fan0Wayne Goodman1Raymond Y. Cho2Sameer A. Sheth3Richard R. Bouchard4Behnaam Aazhang5Department of Electrical and Computer Engineering, Rice UniversityDepartment of Electrical and Computer Engineering, Rice UniversityDepartment of Psychiatry and Behavioral Science, Baylor College of MedicineDepartment of Electrical and Computer Engineering, Rice UniversityDepartment of Imaging Physics, University of Texas MD Anderson Cancer CenterDepartment of Electrical and Computer Engineering, Rice UniversityAbstract The neuromodulation effect of low-intensity focused ultrasound (LIFU) is highly target-specific. Unintended off-target neuronal excitation can be elicited when the beam focusing accuracy and resolution are limited, whereas the resulted side effect has not been evaluated quantitatively. There is also a lack of methods addressing the minimization of such side effects. Therefore, this work introduces a computational model of unintended neuronal excitation during LIFU neuromodulation, which evaluates the off-target activation area (OTAA) by integrating an ultrasound field model with the neuronal spiking model. In addition, a phased array beam focusing scheme called constrained optimal resolution beamforming (CORB) is proposed to minimize the off-target neuronal excitation area while ensuring effective stimulation in the target brain region. A lower bound of the OTAA is analytically approximated in a simplified homogeneous medium, which could guide the selection of transducer parameters such as aperture size and operating frequency. Simulations in a human head model using three transducer setups show that CORB markedly reduces the OTAA compared with two benchmark beam focusing methods. The high neuromodulation resolution demonstrates the capability of LIFU to effectively limit the side effects during neuromodulation, allowing future clinical applications such as treatment of neuropsychiatric disorders.https://doi.org/10.1038/s41598-023-40522-w
spellingShingle Boqiang Fan
Wayne Goodman
Raymond Y. Cho
Sameer A. Sheth
Richard R. Bouchard
Behnaam Aazhang
Computational modeling and minimization of unintended neuronal excitation in a LIFU stimulation
Scientific Reports
title Computational modeling and minimization of unintended neuronal excitation in a LIFU stimulation
title_full Computational modeling and minimization of unintended neuronal excitation in a LIFU stimulation
title_fullStr Computational modeling and minimization of unintended neuronal excitation in a LIFU stimulation
title_full_unstemmed Computational modeling and minimization of unintended neuronal excitation in a LIFU stimulation
title_short Computational modeling and minimization of unintended neuronal excitation in a LIFU stimulation
title_sort computational modeling and minimization of unintended neuronal excitation in a lifu stimulation
url https://doi.org/10.1038/s41598-023-40522-w
work_keys_str_mv AT boqiangfan computationalmodelingandminimizationofunintendedneuronalexcitationinalifustimulation
AT waynegoodman computationalmodelingandminimizationofunintendedneuronalexcitationinalifustimulation
AT raymondycho computationalmodelingandminimizationofunintendedneuronalexcitationinalifustimulation
AT sameerasheth computationalmodelingandminimizationofunintendedneuronalexcitationinalifustimulation
AT richardrbouchard computationalmodelingandminimizationofunintendedneuronalexcitationinalifustimulation
AT behnaamaazhang computationalmodelingandminimizationofunintendedneuronalexcitationinalifustimulation