Non-invasive in vivo acoustoelectric neuromodulation and its contribution to ultrasound stimulation

Non-invasive in vivo acoustoelectric neuromodulation and its contribution to ultrasound stimulation | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Non-invasive in vivo acoustoelectric neuromodulation and its contribution to ultrasound stimulation View ORCID Profile Jean L. Rintoul , View ORCID Profile Christopher Butler , View ORCID Profile Robin O. Cleveland , View ORCID Profile Nir Grossman doi: https://doi.org/10.1101/2025.11.02.685597 Jean L. Rintoul 1 Department of Brain Sciences , Imperial College London, London, UK 2 UK Dementia Research Institute, School of Public Health , Imperial College London, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jean L. Rintoul For correspondence: j.rintoul19{at}alumni.imperial.ac.uk nirg{at}imperial.ac.uk Christopher Butler 1 Department of Brain Sciences , Imperial College London, London, UK 3 The George Institute for Global Health, School of Public Health , Imperial College London, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Christopher Butler Robin O. Cleveland 4 Institute of Biomedical Engineering, University of Oxford , Oxford, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Robin O. Cleveland Nir Grossman 1 Department of Brain Sciences , Imperial College London, London, UK 2 UK Dementia Research Institute, School of Public Health , Imperial College London, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Nir Grossman For correspondence: j.rintoul19{at}alumni.imperial.ac.uk nirg{at}imperial.ac.uk Abstract Full Text Info/History Metrics Supplementary material Preview PDF ABSTRACT Non-invasive brain stimulation offers therapeutic potential without the risks of surgery, yet current electrical approaches lack spatial precision and depth due to the long wavelengths of electric fields. Here we demonstrate that the acoustoelectric interaction—the nonlinear coupling between applied acoustic and electric fields—can overcome these limitations to achieve spatially focused, non-invasive neuromodulation. Using in vitro and in vivo rodent electrophysiology, we show motor-evoked potentials that depend on both the amplitude and frequency of the acoustoelectric field, with artefactual controls excluding purely acoustic or electrical origins. We further identify an acoustoelectric contribution to conventional ultrasound stimulation, arising from the interaction between ultrasound-induced electrical signals and propagating acoustic waves. These findings establish acoustoelectric neuromodulation as a distinct mechanism of neural activation and a significant contributor to how ultrasound stimulation influences brain activity, opening new directions for precise, non-invasive neuromodulation and neurotherapeutic development. INTRODUCTION Non-invasive neuromodulation requires new approaches to achieve its translational potential. Electrical modalities - including electroconvulsive therapy (ECT) 1 , transcranial magnetic stimulation (TMS) 2 , transcranial electrical stimulation (TES) 3 , and temporal interference (TI) 4 , 5 , span a continuum from established practice to rapidly advancing translational innovations, yet all are limited in spatial focality by the long wavelengths of electric fields and their uneven distribution in the brain’s heterogeneous dielectric structure. In contrast, transcranial focused ultrasound stimulation (tFUS) 6 offers superior spatial precision among non-invasive modalities, yet the mechanism by which acoustic fields modulate neuronal activity remains incompletely understood. Clarifying this mechanism is essential for improving specificity and safety. Proposed explanations include activation of mechanosensitive ion channels 7 , modulation of microtubules 8 , thermally mediated effects 9 , indirect auditory pathways 10 , cavitation 11 and the acoustic radiation force 12 . By comparison, electric-field–driven modulation of neuronal excitability is mechanistically described within cable theory and Hodgkin–Huxley 13 models. Ideally, a neuromodulation technique would be non-invasive, safe, elicit predictable responses at relevant amplitudes, and be selectively targetable to cortical and subcortical regions. The acoustoelectric effect—the interaction of acoustic and electric fields in ionic media such as saline or brain tissue—may enable electrically mediated neuromodulation with the spatial precision of ultrasound. First described in 1946 by Fox, Herzfeld, and Rock 14 , the phenomenon arises from conductivity changes produced by compression and rarefaction of ions at the acoustic focus. When an electric field is present, these local conductivity fluctuations induce voltage gradients at the ultrasound focal zone. This principle underlies ultrasound current-source-density imaging 15 , 16 , enabling spatially focused detection of current distributions. We have shown that the acoustoelectric effect is multiplicative (heterodyning) 17 , generating sum- and difference-frequency electric components confined to the acoustic focus. If the difference frequency falls within electrophysiologically relevant frequencies and amplitudes, the resulting focused electric field could drive neuronal activation via a direct electric mechanism while preserving ultrasound’s focality ( Fig. 1 ). This approach offers two independent control axes—acoustic and electric—for optimising amplitude and safety and permits ex vivo calibration by measuring the spatial profile and magnitude of the induced field. Download figure Open in new tab Figure 1. Acoustoelectric neuromodulation and its contribution to ultrasound neuromodulation. a , A focal acoustic field P ( f US ) and a non-focal electric field E ( f E ) mix via the acoustoelectric interaction, producing electrical signals at the difference frequency f US - f E and at the sum frequency f US + f E . If the difference frequency Δ f is in the range to induce electrophysiological responses, then neuromodulation is possible. In parallel, the voltage waveform that powers the ultrasound transducer can capacitively couple to surrounding ionic media (e.g., saline or brain tissue), generating secondary electrical artefacts at the acoustic frequency 18 – 20 , . This coupling is enhanced when a water-filled coupling cone is positioned near the brain. We hypothesised that such artefactual fields can also participate in acoustoelectric mixing, producing a low-frequency component that contributes to tFUS-evoked responses. Here we test whether acoustoelectrically generated electric fields modulate neural activity in vitro (saline phantoms) and in vivo (rodents). We find that evoked responses depend on both the amplitude and frequency of the acoustoelectric field and require the concurrent presence of acoustic and electric fields. Having established that acoustoelectric neuromodulation is genuine rather than artefactual, we further show that mixing between the transducer-driven artefactual field and the propagating acoustic wave is a significant contributor to ultrasound-induced brain stimulation. These findings identify the acoustoelectric effect as a key mechanism in ultrasound neuromodulation and support the development of safer, more precise, and mechanistically grounded non-invasive brain-stimulation therapies. RESULTS Acoustoelectric amplitude characterization First, we confirmed the focality of the acoustoelectrically generated electrical difference frequency in a tank filled with 0.9% saline ( Fig. 2 a ), by using electrodes to generate an electric field and a 500kHz focused transducer to create a focused ultrasound field at 1MPa. A spatial map of the acoustoelectric field at Δ f was measured by translating the stimulating electrode pair and measurement electrode through the acoustic field with 0.5mm steps. It can be seen that the acoustoelectric field in the lateral plane ( Fig. 2 b ) and axial plane ( Fig. 2 c ) is consistent with the focal region ( Supplemental Note 1 ). Download figure Open in new tab Figure 2. Amplitude characterisation of the acoustoelectric signal. a , Free-field phantom measurements using a tank filled with 0.9% saline, a focused ultrasound transducer (500 kHz) generating a focal peak 1MPa, two electrodes separated by 7 mm generating an electric field, and a measurement electrode. b , spatial map of acoustoelectric field (for V E = 12V and f E =8kHz) band-filtered at Δ f =492 kHz. c , spatial maps of acoustoelectric signal at Δ f as in b. d , Setup using a petri dish with 0.9% saline to mimic the small volume of a mouse head, within the electrophysiology set up. e , Measurements of V AE as a function of difference frequency (for V E =10V) with 4 measurements at each frequency. f , In vivo mouse brain measurements (n=3 mice) using an electrode arrangement and settings as in e. i) Representative time series acoustoelectric amplitudes for Δ f =1Hz and Δ f =4Hz. ii) Amplitude spectrum of the acoustoelectric signal for Δ f =1Hz and Δ f =4Hz iii) In vivo acoustoelectric amplitudes as function of Δ f . The acoustoelectric interaction has previously been reported with very small amplitudes 17 , 21 that would be sub-threshold to neural responses 22 . However, it has primarily been explored at higher frequencies close to the transducer frequency 23 , leaving a knowledge gap at the low frequencies (0-100Hz) known to stimulate neurons 24 . To understand acoustoelectric amplitudes at low frequencies, we measured the acoustoelectric voltage amplitudes over a range of low frequencies in a saline filled petri-dish within the intended in vivo electrophysiology apparatus ( Fig. 2 d ) with the instrumentation detailed in Methods . We induced acoustoelectric difference frequencies within the range reported for motor stimulation 22 , 25 , by applying an electric field that was close in frequency to the propagating acoustic field. We found that acoustoelectric amplitudes increased as Δ f decreased ( Fig. 2 e ). Then, a recovery surgery was performed to insert two platinum-iridium electrodes connected to a small plug at the back of the mouse head so that the signal in the brain at the motor cortex could be measured in 3 wild type mice (see Methods for surgery details), using the same platinum-iridium electrodes and spacing as the saline petri-dish experiments. After surgery recovery, each mouse was anaesthetized with ketamine/xylazine and the same electrode and applied fields as the petri-dish were applied to the mouse brain. We show representative acoustoelectric time-series traces for Δf = 1Hz and Δf = 4Hz ( Fig. 2 f i ), where the electrical difference frequency is present alongside a DC offset. The corresponding spectral peaks ( Fig. 2 f ii ) show the relative amplitude at 1Hz was higher than at 4Hz, when the applied electric and acoustic field amplitudes were the same. In vivo acoustoelectric amplitudes decreased with increasing Δ f ( Fig. 2 f iii , n=3 mice), suggesting acoustoelectric neuromodulation would be most likely where the difference frequency between the applied fields was smallest. In vivo acoustoelectric DC neuromodulation The same recovery surgery as described in Fig. 2 was performed in 9 wild type mice, where each mouse was anaesthetized with ketamine/xylazine and the stimulation experiment conducted during the lightening anaesthesia window so that electromyography (EMG) responses could be measured in parallel to the evoked acoustoelectric field in the brain (see Methods for experiment details). Two platinum-iridium ring electrodes (diameter 2cm) applied the bipolar electric field, and the ultrasound transducer was positioned via stereotaxic instrument over the head of the mouse ( Fig. 3 a ) with two electrodes placed in the forepaw to measure neural evoked muscle responses (EMG), with electrically-attenuating acoustically-semi-transparent material (F21 characterized in Supplemental Note 2 ) placed between the transducer cone and gel creating an acoustic connection while reducing the electrical interference from the electrical transducer artefact 26 . Download figure Open in new tab Figure 3. In vivo acoustoelectric amplitude neuromodulation @ Δ f = 0 Hz . a , In vivo arrangement to apply ultrasound while electrically shielding the RF artefact from the ultrasound with F21, with applied bipolar electrical stimulus via external ring electrodes. Acoustic and electric field are applied at 500kHz. b , Example triplet with applied pressure 2.5MPa, voltage output 20V, showing V BRAIN , V EMG and the envelope of V EMG . Acoustoelectric V BRAIN = 9.10 mVpp , pressure only V BRAIN = 4.99 mV and voltage only V BRAIN = 1.11 mV. V EMG software filtered between 100-1000Hz, Envelope of V EMG low-pass filtered at 10Hz. c , triplets applied in randomized order to account for anaesthesia depth changes. V BRAIN comparison between acoustoelectric, pressure only, voltage only and a no stimulation control. ANOVA F(3,27) = 8.50, p = 4.8e-5, post hoc comparisons using the Tukey HSD test, (AE–P, p = 0.02; AE-V, p = 0.0003; P-V, p = 0.52; AE-C, p = 0.00; P-C, p = 0.21; V-C, p = 0.80); over 9 mice, 28 triplets in total. d , Same data set as in c) comparison of Hilbert EMG height normalized across the triplet denoted by . Statistics on the log ratios; ANOVA F(3,27) =252.16, p = 0, post hoc comparisons using the Tukey HSD test, (AE–P, p = 0.002; AE-V, p = 0.0; AE-C, p = 0.0, P-V, p = 0.0, P-C, p = 0.0, V-C, p = 1.0). e , Order analysis showing number of acoustoelectric (AE), pressure (P) and voltage only trials and their experimental order. Chi-squared test X 2 (4, N = 28) = 5.5, p = .23; Bonferroni corrected pairs; AE–P, p = 0.19; AE-V, p = 0.45; P-V, p = 0.54. A series of three tests were performed to determine the effect of applying an acoustic or electric field alone and then both together, referred to as a triplet. Within each triplet the voltage and pressure were the same, and each trial was 6 s long with a 0.25 s onset and offset ramp. The electric potential induced in the brain at Δ f estimates the strength of the neuromodulatory electrical field, and the EMG potential in the paw estimates the strength of the evoked motor response. When both fields were applied together and at the same frequency (500kHz), an acoustoelectric difference frequency Δ f was measurable in the brain at 0Hz (DC). This low frequency electric field typically had a steep gradient at the onset shown by the representative recordings in Fig. 3 b and Video Supplement 1 . Simultaneous EMG recording shows the direct muscle response at the onset and how it differs between the three different stimuli, with the EMG envelope computed via Hilbert transform and then low pass filtered at 10Hz. To overcome the changing sensitivity to the applied stimulus due to the anaesthesia depth changes, we applied each triplet in counterbalanced order (see Supplemental Note 3 on impact of anaesthesia on evoked EMG amplitudes) over 9 mice with 3 or 4 triplets per mouse yielding a total of 28 trials, where the only data selection criterion was that the anaesthesia should be light enough to evoke an EMG response in any trial of the triplet. An important additional question is whether the acoustic or electric fields induced a neuromodulation response alone. We found that the DC electric potential induced in the brain by the acoustoelectric stimulation was larger than that induced by the acoustic or electric stimulation alone, and larger than a no stimulus control (ANOVA F(3,27) = 8.50, p = 4.8e-5; Fig. 3 c ). Similarly, the normalized EMG amplitude 27 induced in the paw by the acoustoelectric stimulation was larger than the those induced by acoustic or electrical stimulation alone (ANOVA F(3,27) =252.16, p = 0; Fig. 3 d ). The EMG response was not dependent on the stimulation order (Chi-squared test X 2 (4, N = 28) = 5.5, p = .23; Bonferroni corrected pairs; AE–P, p = 0.19; AE-V, p = 0.45; P-V, p = 0.54; Fig. 3 e ). These results suggest that applying an acoustic and electric field at the same frequency acoustoelectrically induces a DC electric field that is sufficiently strong to modulate neural activity. In vivo acoustoelectric AC neuromodulation We next tested the neural response to acoustic and electric fields applied at a difference frequency Δ f of 1 Hz, i.e f US =500 kHz, f E =500.001 kHz (n=9 mice, 3-6 trials per mouse, total of 45 trials), where trials that occurred before any EMG response was possible were excluded, and the trial number variability was due to the experiments ending at different times based on when spontaneous mouse movements occurred. The difference frequency (Δ f =1Hz) during acoustoelectric stimulation was present in both the signal recorded in the brain and the envelope EMG, but was not present when the acoustic or electric field was applied alone as shown in the representative recording Fig. 4 a . Download figure Open in new tab Figure 4. In vivo acoustoelectric neuromodulation @ Δ f = 1 Hz . a , triplet where the acoustic wave was applied at 500kHz 1.8MPa, and the bipolar voltage output stimulus at 12Vpp, 500.001kHz. Δ f = 1 Hz at V BRAIN is mirrored in the envelope of V EMG and reflected in the spectrum. Using the spectrum to measure amplitude, acoustoelectric = 510 μV , pressure = 80 μV , and voltage = 60 μV . b , i) The spectrum amplitude at 1Hz was calculated for 9 mice, with a total of 45 triplet tests performed when the anaesthesia was light enough to evoke an EMG response. Spectrum was computed on the central 4 seconds to exclude the onset and offset ramps. ANOVA F(3,44) = 6.36, p = 0.0004, post-hoc comparisons using Tukey’s HSD test, (AE–P, p = 0.002; AE-V, p = 0.004; P-V, p = 0.5; AE-C, p = 0.02; P-C, p = 0.9; V-C, p=0.9); ii) The spectrum V EMG amplitude at 1Hz ratio per triplet was compared using the log of the ratio for statistical tests. ANOVA F(3,43) = 370.69, p = 0, post-hoc comparisons using the Tukey HSD test, (AE–P, p = 2.27e-5; AE-V, p = 0.00; P-V, p = 0.00; AE-C, p = 0.00; P-C, p = 0.00; V-C, p=1.00); c , Experimental order analysis. d , Time series V BRAIN were time correlated to the envelope V EMG peaks(red). Shown are 3 time-correlated peaks demonstrating both the correlation and irregularity of the V EMG e , Frequency specificity comparison between V BRAIN at 1Hz and 0Hz, where V BRAIN has selection criteria such that the 1Hz amplitude is larger than the 0.5Hz amplitude eliminating trials where electrochemical or pressure onset effects dominated. i) V BRAIN comparison at Δ f = 0 and 1Hz, with mean and s.d. error bar. ii) T-test comparison between Δ f = 0,1Hz acoustoelectric V BRAIN measurements, two sided t (21) = 4.38, p = 8.5e-5; V BRAIN at 1Hz (mean±s.d. = 491±351 µV); V BRAIN at 0Hz (mean±s.d. = 108.2±130µV). iii) V EMG comparison between 0, 1Hz datasets shows 1Hz peak only in Δ f = 1 Hz data set iv) T-test comparison between Δ f = 0,1 Hz acoustoelectric V EMG , two sided t (21) = 4.11, p = 0.00019; 1Hz V EMG , (mean±s.d. = 2.45±2 µV); 0Hz V EMG , (mean±s.d. = 0.42±0.55µV). Using the same counterbalanced experimental protocol as the acoustoelectric DC amplitude experiments, triplets were performed over the same 9 mice, with 4-5 repeats per mouse yielding a total of 44 triplets. To quantify the amplitude of the acoustoelectric signal at Δ f = 1 Hz , we computed the spectrum using Fourier transformation and extracted the amplitude at the 1 Hz bin. We found that the 1Hz electric potential induced in the brain by the acoustoelectric stimulation was larger than those induced by the acoustic or electrical stimulation, or a no stimulation control (ANOVA F(3,44) = 6.36, p = 0.0004; Fig. 4 b i ). Similarly, the normalized EMG amplitude induced in the paw by the acoustoelectric stimulation was larger than those induced by the acoustic or electrical stimulation (ANOVA F(3,43) = 370.69, p = 0; Fig. 4 b ii ). The EMG response was not dependent on the stimulation order (Chi-squared test X 2 (2, N = 44) = 2.84, p = 0.24; Bonferroni corrected pairs AE–P, P = 0.83; AE-V, P = 0.27; P-V, P = 0.36; Fig. 4 c ). The EMG response at 1 Hz was often irregularly shaped, with significant DC onsets potentially due to; acoustic standing waves in the mouse head (see Supplemental Note 4 ), electrochemical offsets at the electrode interface ( Supplemental Note 5 ), and the onset response induced by the acoustic field alone (see Fig. 3 b , acoustic only signal). In a typical representative signal ( Fig. 4 d ) we found two time-correlated peaks at the beginning and one at the end. Shown in the Video Supplement 2 , the variance in the acoustoelectrically generated amplitude in the measured brain signal V BRAIN can be seen, with the mouse responding via paw movements to only the largest voltage changes in the brain. To validate that the 1Hz spectral amplitude was not confounded by the DC offsets observed in some recordings, we repeated the analysis but with only the acoustoelectric recordings where the DC offset did not dominate the 1Hz acoustoelectric signal. We found an evident peak at the 1 Hz difference frequency in both the electric signal recorded in the brain ( Fig. 4 e i-ii ) and the paw EMG ( Fig. 4 e iii-iv ) that did not exist when the US and electric fields were applied at the same frequency, i.e. DC acoustoelectric stimulation (amplitude spectral density (ASD) at 1Hz vs 0Hz: V BRAIN : t(21)= 4.38, P = 8.5e-5; V EMG : t(21)= 4.11, P = 0.00019). Therefore, the acoustoelectric field generated in the brain affects the frequency of evoked neuromodulation. Acoustoelectric artefact tests To determine if temperature dissipation might be the cause of the response during acoustoelectric neuromodulation, we applied an acoustoelectric 0.5Hz difference frequency where the acoustic field was 500kHz and the applied voltage 500.0005kHz ( Fig. 5 a i, ii ). A temperature probe was placed in the ultrasound gel above the skull and below the end of the ultrasound cone while a continuous 6 second acoustoelectric signal was applied, repeating 6 times for pressure or electric field alone, and electric and pressure field together to generate an acoustoelectric difference frequency at 0.5Hz. While applying continuous ultrasound at 1MPa does increase the temperature ( Fig. 5 b , shown in blue), the acoustoelectric and pressure only trials had a very high mean temperature correlation (0.98; Pearson, Fig. 5 b ), and no significant group difference (AE-P = 0.9, n=36; Fig. 5 c ). This suggests the pressure and acoustoelectric trials are not differentiated by temperature, unlike the EMG and brain signal differences in the acoustoelectric versus pressure only experiments shown in Fig. 4 . Hence, temperature is unlikely to be the cause of acoustoelectric neuromodulation. Download figure Open in new tab Figure 5. Acoustoelectric artefact tests a , 1MPa pressure, 40V pp applied voltage, 6 repeats per AE/P/V group, using a thermometer placed in the ultrasound gel just above the mouse skull and below the ultrasound cone. Δ f = 0.5Hz. i) V US at 500kHz, 1MPa signal applied in each recording shown ii) V E at 20V. iii) V BRAIN filtered <10Hz revealing 0.5Hz acoustoelectric oscillation. b , Temperature change as a function of time since beginning of trial with s.d. error shown for each AE/P/V group. c , ANOVA F(2,35) = 35.6, p = 1.5e-12, post-hoc comparisons using Tukey’s HSD test, (AE–P, p = 0.9; AE-V, p = 9e-10; P-V, p = 5.9e-11); d , Saline petri dish within the electrophysiology set up to induce and measure an acoustoelectric field. e , T-test comparison of the amplitude of the electric field generated by the ultrasound transducer at 500kHz, measured in the medium with and without F21, two sided; (t (20) = 5.72, p = 1.15e-6), note that since the electric field at the carrier was large, these carrier amplitudes are measured after 5kHz −3dB point low pass filtering on the preamplifier front end. f , Varying Δ f with and without F21 with 4 repeats at each frequency. g , T-test comparison of the Δ f acoustoelectric field amplitude with and without F21 using independently applied electric field, two sided; (t (20) = 0.38, p = 0.70). To eliminate electric field only mixing between the transducer artefact electric field ( V US ) and the independently applied electric field ( V E ) as the cause of the difference frequency, we calibrated an acoustoelectric field in a saline petri-dish to have the focal maximum at the measurement electrodes ( Fig. 5 d , with calibration procedure and spatial map described in Supplemental Note 6 ). The electric signal powering the ultrasound created an electric artefact at the pressure frequency which could be measured in the saline petri dish and attenuated by inserting electrically insulating F21 material ( Fig. 5 e ; p = 1.15e-6). An acoustoelectric field was measured with and without the electrically attenuating F21 material, at a series of electric field difference frequencies to induce a spectrum ( Fig. 5 f ). There was no difference in the acoustoelectric amplitudes between the electrically attenuating F21 and no F21 group ( Fig. 5 g ; p = 0.38), suggesting that the induced acoustoelectric signal did not originate from mixing with the electric field artefact from the ultrasound transducer. Non-linearity in recording hardware could also generate an electric difference frequency. To test it, we fed driving voltage signals of the electric and acoustic stimulation to the input of the recording preamplifier via an isolation transformer. In this case, there were no recordings at the difference frequency, suggesting the acoustoelectric signal did not artefactually originate from frequency mixing in the recording hardware ( Supplemental Note 7 ). In vivo transcranial ultrasound stimulation is impacted by the electrical artefact powering the transducer Now that we have evidence that acoustoelectric neuromodulation is possible when an independent electric field and a propagating acoustic field are applied to an ionic medium, we investigated the impact of the electric field artefact generated by powering the ultrasound transducer, to determine if it may also interact with the propagating acoustic field in ultrasound stimulation. To determine the origin of the electric signal present in the mouse brain at the acoustic frequency, we first placed an acoustically transparent and electrically insulating material (2mm thick F21, Precision Acoustics, UK; characterized in Supplemental Note 2 ) between the end of the ultrasound cone and the mouse. We found the electric signal at the acoustic frequency was attenuated ( Supplemental Note 8 a-c ). We then electrically isolated the transducer from ground by inserting a switch into the cable connecting the transducer to the RF amplifier, which massively increased the amplitude of the electric signal transmitted into the mouse ( Supplemental Note 8 d-f ) as the energy injected into the transducer needed to find a path to ground via nearby ionic media such as the mouse. These results suggest the electrical artefact from powering the transducer capacitively transmits electrical energy into nearby ionic media, similar to an electrical antenna 27 . Using a saline phantom, we then verified that electrical sum and difference frequencies emerge with the focality of the ultrasound when a pulsed pressure signal is applied alone ( Supplemental Note 9 a-b ). The amplitude of these focal frequency mixing products was attenuated when electrically shielding, acoustically transparent F21 was placed between the transducer cone and the saline ( Supplemental Note 9 c-d ). To decouple the ultrasound acoustic field and electric artefact in vivo , we performed an F21 electrical shielding experiment ( Fig. 6 a ) with a pulsed ultrasound stimulation protocol where the pulse was on 0.5s and off for 1.5s (25% duty cycle) at 500kHz as these parameters have been reported to be highly effective at eliciting an evoked ultrasound stimulation responses previously 28 . During the lightening anaesthesia window 2mm thick F21 was inserted into the gel below the ultrasound cone to shield the electrical artefact coming from the ultrasound transducer, decreasing the amplitude of the electric field present in the medium, and then removed and repeated 1-2 times per mouse until spontaneous movements started, alternating the experimental order to minimize anaesthesia depth confounds (see Supplemental Note 3 ). To overcome the small acoustic attenuation induced from the F21 ( Supplemental Note 2 ), 15% was added to the pressure output when the F21 was in place in all experiments. Download figure Open in new tab Figure 6. In vivo ultrasound brain stimulation with attenuation of the electrical artefact from the ultrasound transducer. a , Mouse undergoing pulsed ultrasound stimulation with F21 electrical attenuation material in place. Mice performed the with and without F21 test twice. b , Representative with (red) and without (black) F21 experiment, with V US shown 1kHz low pass filtered at V BRAIN , V EMG and envelope V EMG . Pressure without F21 = 2MPa; pressure with F21 = 2.4MPa. c , Representative amplitude spectrum at V BRAIN comparison between electric artefact with and without F21. d , i) carrier amplitude comparison; t-test, two sided t (01) = 4.65, p = 1.47e-5; F21 group (mean±s.d. = 2895 ± 1253 μV ); without F21 group (mean±s.d. = 4601 ± 2116 μV ) n=7mice, 36 trials, during light ketamine/xylazine anaesthesia. ii) V BRAIN comparison log normalized to take pair based pressure differences into account; t-test, two sided t (01) = 3.52, p = 7e-4; F21 group (mean±s.d. = 755 ± 630 μV ) No F21 group (mean±s.d. = 1572 ± 1217 μV ) using data as in d. iii) V EMG normalized per pair to account for changing anaesthesia; t-test, two sided on log normalized pair; t (01) = 4.65, p = 1.4e-5; F21 group (mean±s.d. = 0. 34 ± 0.11 μV ); No F21 group (mean±s.d. = 0. 665 ± 0.11 μV ) using data as in d. iv) Experimental order analysis. In Fig. 6 b we show a typical representative example, where we applied a pulsed ultrasound signal without F21 and saw it produced a larger amplitude low frequency brain signal than the group with F21 in place at 2.4MPa. The resulting EMG signal measured without F21 has larger spikes, while electrically shielding the transducer with F21 yielded smaller EMG responses (shown in red in Fig. 6 b , shown with simultaneous video and measurements in Video Supplement 3 ). EMG responses also reduced on the third pulse as compared to the first pulse, and the low frequency electrical signal measured in the brain ( V BRAIN ) was not always the same amplitude per pulse, despite the pressure output being the same amplitude for each pulse in a trial. Looking at the spectral amplitude of the electrical artefact from the ultrasound in the same representative sample ( Fig. 6 c ), shows F21 successfully attenuated this signal in the mouse brain. At the group level, with 7 mice yielding a total of 36 pulses with F21/noF21 pairings, the electrical insulation properties of F21 attenuate the electrical carrier artefact from the transducer ( Fig. 6 d i , p = 1.47e-5). This trend is mirrored in the pair normalized brain signal amplitude comparison ( Fig. 6 d ii , p = 7e-4), as well as the EMG signal amplitudes ( Fig. 6 d iii , p = 1.4e-5). Finally, to ensure that the order of trials was not responsible for the increased amplitude when no F21 was present we performed a Chi-squared test on the counterbalanced data showing there was no significant relationship between the order of trials and the amplitude of the EMG response X 2 (3, N = 35) = 1.32, p = 0.24 ( Fig. 6 d iv ). Hence in vivo ultrasound stimulation responses can be attenuated by reducing the amplitude of the electric artefact in the medium, independent of time-based anaesthesia depth changes. In vivo ultrasound stimulation is not due to electric field only mixing To determine if the stimulation effect may be due to the electric field artefact from the 500kHz electric field applied by the ultrasound transducer mixing with itself at the measurement electrodes, we performed an acoustic disconnection test where the electric field remained connected via gel connection with the ultrasound cone, but the acoustic field was removed by leaving an air gap below the end of the cone ( Fig. 8 a ). To overcome a slight reduction in the amplitude of the electric field when a smaller volume of conductive gel was present, we added 15% to the output pressure. The representative plots ( Fig. 7 b ii ) show the voltage recorded at the measurement electrodes in the brain ( V BRAIN ), where no EMG was evoked in the electric artefact only case while the acoustic field did induce a response ( Fig. 7 b iii, iv , and Video Supplement 4 ). This was repeated across 6 mice, with 2 trials of each case recorded for each mouse, each with 3 pulses recorded. The carrier frequency measured in the brain was similar between acoustically connected (no gap) and disconnected (gap) groups, though the larger surface area gel connection enabled better electrical transmission ( Fig. 7 c ; p=0.0018). There were no DC offsets in the brain when there was an air gap blocking the acoustic signal ( Fig. 7 d , p=8.8e-6), and no EMG responses were evoked in any trial ( Fig. 7 e , p=6.0e-18). The difference between the measurement with and without the air gap was not affected by the measurement order ( Fig. 7 f ). Download figure Open in new tab Figure 7. Ultrasound stimulation is not due to electric mixing. a , Instrumentation arrangement with acoustically disconnected measurement in mouse brain such that the acoustic wave is blocked by an air gap and the transducer remains electrically connected to the brain via conductive gel. 6 second recordings with 0.5second 500kHz 1MPa acoustic pulses. b , i) RF output signal for 1MPa pressure showing acoustically connected amplitude (black) 20% lower than acoustically disconnected amplitude. ii) low frequency brain signal V BRAIN , (<1kHz) comparing no gap/acoustically connected (black) against electrical connection only/air gap (red). iii) EMG trace V EMG (100Hz-1kHz) for both no gap (black) and gap (red) cases. iv) Hilbert EMG traces (<10Hz) for no gap (black) and gap. c , Carrier amplitude as measured via the brain electrodes for gap (red) and no gap (black). t (01) = −3.23, p = 0.0018; gap (mean±s.d. = 3066±2302); no gap (mean±s.d. = 5967±4773) n=6 mice, with 2 trials each with 3 pulses for each mouse yielding total 36 pulses. d , Brain height comparison between gap (red) and no gap (black), t (01) = −4.79, p = 8.81e-6; gap (mean±s.d. = 174.2±117.2); no gap (mean±s.d. = 771.2±726.9) n=6 mice. e , Comparison between np gap and gap EMG across n=6 mice, with 2-3 trials per mouse, normalized across each trial to account for time based variation, statistics performed on log on normalized triplet; t (01) = −9.06, P = 2e-13; gap (mean±s.d. = 0. 24 ± 0.18); no gap (mean±s.d. = 0. 75 ± 0.18); n=6 mice. f , Order analysis X 2 (3, N = 35) = 0.16, p = 0.68. These results support the evidence that the DC electric field present in the brain tissue is generated by an acoustoelectric interaction between the ultrasound and its electric artefact. DISCUSSION Non-invasive electrical brain stimulation shows promise as an alternative to invasive neurosurgical approaches for treating epilepsy 29 , obsessive compulsive disorder 30 (OCD), depression 31 , Alzheimer’s disease 32 , and for inducing neurogenesis after 33 , while avoiding the risks of surgical intervention. However, clinical translation requires techniques that deliver better targeting, controllability and safety. Ultrasound stimulation offers high spatial specificity and depth, yet uncertainty about its mechanism persists 34 , 35 . A modality that harnesses the well-characterised electrical models of neural signalling while retaining the spatial focality of ultrasound would enable independent optimisation of amplitude and safety through acoustic and electric control axes. Acoustoelectric neuromodulation achieves this via a non-linear interaction between an applied electric field and a focused ultrasound field, generating a focal difference-frequency electric field that can drive neuromodulation. The observed amplitude and frequency dependence of responses on the acoustoelectrically generated difference frequency argues against pressure-only mechanisms as the primary driver. For example, acoustic radiation force 36 (ARF) can be dissociated from the difference-frequency response by varying the applied electric-field frequency: when a 1 Hz difference frequency is generated and evokes stimulation at 1 Hz, ARF alone cannot account for the effect because the response also requires the independently applied high-frequency electric field. Taken together, the results argue against pressure-only accounts - including non-linear acoustics 37 , acoustically induced thermal dissipation 9 , 38 , cavitation 39 , or auditory pathways 10 , 40 - as sufficient explanations. Acoustoelectric neuromodulation is also not explained by electric mixing only from the transducer drive and an independent applied electric field. The artefactual electric field produced by powering the transducer is not directly proportional to the acoustoelectric difference-frequency signal, based on testing with an electrically attenuating material. Pressure-induced vibration of recording electrodes, which could generate non-linear electrochemical artefacts 41 , is likewise unlikely: free-field saline phantom experiments showed that acoustoelectric amplitude depends on the geometry of the applied fields in the medium 17 , inconsistent with a simple vibration artefact. Because the ultrasound drive voltage can capacitively couple into surrounding ionic media, an artefactual electric field coexists with the propagating acoustic field during ultrasound stimulation. Since both occur at the same frequency, their interaction yields a difference frequency at 0 Hz (DC). The dependence of response amplitude on the electric-field magnitude present in the medium when acoustic and electric frequencies are matched ( Fig. 3 ) indicates that acoustic-only mechanisms - non-linear acoustics 37 , thermal dissipation 9 , 38 , cavitation 39 , and auditory pathways 10 , 40 - cannot solely account for ultrasound-evoked neuronal activation. Conversely, electric-field–only mixing produced by the transducer drive did not elicit stimulation, arguing against purely electrical or electrochemical origins. Together, these observations indicate that both acoustic and electric fields are required to generate a focal difference-frequency electric field and that the acoustoelectric interaction makes a substantial contribution to ultrasound brain stimulation. Limitations include the relationship between mouse head size and acoustic wavelength, which promoted non-focal standing waves ( Supplemental Note 4 ). Using higher-frequency transducers or larger heads (e.g., humans) should reduce standing-wave artefacts and yield more focal acoustoelectric fields 42 . We also exceeded the recommended thermal index for human use 43 , 44 ; translating this approach will require waveform optimisation and careful consideration of skull attenuation and aberration 45 . Acoustoelectric neuromodulation offers advantages over ultrasound alone. It leverages the extensive literature on how electric fields influence neurons 46 and allows rapid in vitro optimisation of focused electrical parameters. With acoustic and electric inputs as independent control axes, safety and efficacy can be tuned separately. Future work should further isolate and quantify the respective acoustic and electric contributions in vivo . Ultrasound responses depend on the electrical environment, including grounding, transducer geometry, and the electric field necessary to drive the transducer (e.g., focused transducers generally require lower drive voltages than planar transducers to achieve equivalent pressures 47 ); endogenous electric fields may also shape the net field inside neural tissue. More broadly, acoustoelectric interactions are likely to influence related modalities that combine acoustic and electromagnetic fields, including magneto-acoustic neuromodulation with static fields 48 , MR-guided tFUS 34 , and conventional ultrasound brain stimulation. Our results demonstrate that acoustoelectric mixing can directly elicit neuromodulation and constitutes a key mechanism underlying ultrasound-induced brain stimulation. By clarifying how acoustic and electric fields interact within neural tissue, this work advances spatially precise, non-invasive control of brain activity. METHODS In-vivo mouse experiment Animals . Mice were wild type C57BL/6 male and female mice, aged between 3-6 months. Mice were housed in standard cages in Imperial College London animal facility, with ad libitum food and water in a controlled light-dark cycle environment, with standard monitoring by veterinary staff. The Imperial College of London’s Animal Welfare and Ethical Review Body approved all animal procedures, and all experiments were performed in accordance with relevant regulations/according to the United Kingdom Animals (Scientific Procedures) Act 1986. Surgery . Mice were anaesthetized using 1-3% (vol/vol) isoflurane in oxygen and subcutaneously administered Vetergesic (0.1mg/kg) and Carprofen (5mg/kg) to inhibit pain and inflammation and saline for hydration (10ml/kg/hour). A Neurotar compatible head plate was attached to the mouse using clear dental cement (PalaXpress Dental Cement, AgnTho’s AB, Sweden) and a craniotomy performed with a drill (diameter 0.5mm) at the intended electrophysiological measurement sites. The position of the craniotomy relative to bregma was anteroposterior 0.0 mm, mediolateral −2 mm for the motor cortex electrode and anteroposterior −3.5mm, mediolateral 2.25mm for the visual cortex reference electrode used to measure electrical amplitudes within the motor cortex. The recording electrodes were 98% platinum-iridium wire segments (0.5mm diameter wire, VWR, Lutterworth, UK) that were positioned over the drill holes so that the end of the wire was touching the brain, and dental cement secured it to the head bar. Nail polish was then applied to the electrodes to electrically insulate the platinum-iridium wire, so that only the portion of wire located in the brain was exposed. Low toxicity silicone sealant (Kwik-Cast, World Precision Instruments) was then poured around the electrodes to seal moisture around the craniotomy site and over the exposed skull, whilst also stabilizing the electrodes in position. The mouse was then recovered in a warming chamber and administered Carprofen (5mg/kg) for three days after surgery with daily weight checks, then allowed to recover for one week before experiments commenced. Ultrasound fields application The acoustic field was applied using a curved ceramic (PZT) 500 kHz ultrasound transducer (60mm diameter, 63.5 mm acoustic path length; Precision Acoustics Ltd, UK). The ultrasound transducer was driven by an arbitrary function generator (Handyscope HS5, TiePie engineering, Netherlands) and a 40W linear power amplifier (240 L, Electronics & Innovation Ltd). The ultrasound transducer was mounted in a cone and filled with water characterized in Supplemental Note 1 . Non-conductive Parafilm was stretched over the end of the cone to seal the water in, while the transducer was positioned via stereotaxic instrument above the mouse head. F21 was inserted in the gel to attenuate the electrical signal in the mouse brain emanating from the ultrasound transducer. Ultrasound gel (Anagel, UK) was applied on top of the mouse head, surrounding all electrodes, and providing an acoustic connection between the end of the ultrasound cone and the top of the mouse skull. Electric fields application The electric field was applied using a pair of 98% platinum-iridium wire electrodes (Alfa Aesar, 0.25mm diameter), that were formed into loops to increase the exposed surface area, further decreasing electrochemical induced offsets when the bipolar electric field was applied. One loop was gently inserted into the mouth (loop diameter 1cm), and the second located above the head (loop diameter 2cm), placed between the ultrasound cone and mouse head within the acoustic gel interface. The electrodes were driven by an arbitrary function generator (WiFiScope WS5, TiePie engineering, Netherlands) and a custom-made high-frequency voltage source made of glass core transformers (Hitachi Metals) with a maximum ±20 V amplitude at an effective 3 dB bandwidth range of 5 kHz to 2 MHz. To output voltages which surpassed the 12V function generator range, a 20W linear power amplifier was used (325LA, Electronics & Innovation Ltd). The transformer’s isolation prevented extraneous tissue charging and minimised total harmonic distortion. Electrophysiology recordings The electrode implant was connected to an SR560 preamplifier (Stanford Research Systems, USA) via a 1.27mm Dupont header which was cemented onto the back of the head bar. A low pass filter (5kHz −3dB frequency with circuit diagram in Fig. S12 ) was added whenever a high-frequency electric field was applied to prevent overloading the preamplifier. Electromyography (EMG) recordings EMG was measured using stainless steel EMG electrodes (0.3mm 622 diameter, Arbitter, UK) that were placed in the front paw, with the second electrode at the muscle innervation point on the forearm. The EMG electrodes were then connected to a differential amplifier (SR560, Stanford Research Systems) with a band pass filter (100-1kHz) to isolate just the EMG signal. Data acquisition All applied and monitored signals were recorded at 5MS/s, and were time synced using a combined multiple instrument (CMI) interface between the function generators and oscilloscopes with trigger inputs. The applied voltages, monitor channels and electric potentials were logged using data acquisition hardware (WiFiScope WS5, WiFiScope WS6 DIFF and Handyscope HS5, TiePie engineering, Netherlands). The logged signals were streamed from the data acquisition hardware to a workstation PC using custom C code which utilized the Tiepie SDK to control the triggers, channels, and function generator output. The compiled C code which called the oscilloscope commands was controlled through a Python wrapper script. An instrumentation diagram and connection details available in Supplemental Note 11 . The low-pass filter described in Supplemental Note 12 , was used in only the measurements with a large amplitude independently applied electric field ( Fig. 5 and below). Experimental procedure Anaesthesia of mice was achieved via subcutaneous injection of 75 mg/kg Ketamine and 10 mg/kg of Xylazine. Eye lubricant (Vaseline) was placed on the eyes to prevent them from drying out. The anaesthetized mouse was placed onto a thermal mat (Thermostar, RWD, China) to maintain homeostasis at 37°C and head position stabilized with the installed head clamp (Neurotar standard clamp, Finland) enabling precise stereotaxic localization of the ultrasound transducer over the head. The intracranial recording electrodes were connected to the recording amplifier, and the EMG electrodes were placed in the front paw and forearm and connected to the recording amplifier. Ultrasound gel (Anagel, UK) was applied on top of the mouse head, surrounding all electrodes to provide an acoustic connection between the end of the ultrasound cone and the top of the mouse skull. The stimulating loop electrodes were placed above the head and in the mouth as described above. The US transducer was positioned above the mouse head using a stereotaxic instrument. A F21 material was inserted into the gel to attenuate the US electrical artefact. Acoustic and electric fields were then applied according to the instrumentation description (see Supplemental Note 11 and 12 ). Each experimental block included the three conditions (i.e., E, US, E+US), applied for 6 seconds with a 0.25-second ramp up and down periods in a counterbalanced order. Measurements of EMG could only be made when the anaesthesia depth lightened approximately 45 minutes to an hour after administration. When small spontaneous movements started to occur, the experiment was ended, and the mouse was given Antisedan (1 mg/kg) subcutaneously to hasten Xylazine recovery and moved to a warming chamber for recovery. After recovering in the warming chamber under observation, the mouse was given water saturated food pellets and returned to their home cage. Pressure amplitude ranged between 0.5-2.5MPa. Typical in vivo rodent experiments performed to date were done when the mouse is fully awake 49 – 52 , requiring lower pressure thresholds to evoke EMG and often with spontaneous movements also present. Here, experimental measurements were performed when the anaesthesia was light, and there were no spontaneous movements which required pressure magnitudes that were slightly higher than those measured in awake mice. Phantom measurements Petri dish phantom . A small plastic petri dish (4cm diameter and 2cm depth) filled with 0.9% saline located where the mouse would be positioned within the Faraday cage used for in vivo electrophysiology experiments. Tank phantom A custom-built acrylic tank (50 cm× 20 cm × 20 cm) filled with 0.9% saline. An acoustic damping material (Aptflex-48) was attached to the back of the tank to minimise acoustic reflections. The phantom tank was covered with a Faraday cage. US fields application Similar to the in vivo experiment. Electric fields application Similar to the in vivo electric field application, but with the wire electrode forming short pins placed with a 10mm spacing around one of the recording electrodes. Electrical recording Similar to the in vivo intracranial electric field recording, with the electrode implant used for measurement and the same spacing as the in vivo experiments. Pressure recordings The acoustic field was measured in the tank phantom using a 0.2 mm needle hydrophone with a calibration of 52mV/kPa at 500 kHz provided by the manufacturer, Precision Acoustics Ltd and a DC-coupled preamplifier (Precision Acoustics Ltd). Data acquisition Similar to the in vivo data acquisition. Procedure to identify US focus using the AE effect To locate the acoustic focus in a saline phantom, an electric field was applied at 8kHz and the ultrasound transducer moved in 0.5mm increments to find the peak acoustoelectric amplitude at 492kHz. The acoustoelectric mixing amplitudes reached a maximum when the ultrasound focus was positioned over the voltage stimulation source (see Fig. S6 ). In the saline phantom, detailed calibration maps could be made like the ones shown ( Fig. S6 b i , ii ). Spatial measurement procedure For the 2-dimensional acoustoelectric free field maps recorded in a biomimicking tank phantom (20cmx20cmx50cms) filled with 0.9% saline, a recording was made for each pixel consisting of a ramped electric field at the beginning and end to avoid voltage spike transients in the preamplifier. To time sync individual recordings to one another, a half-amplitude 2 wavelength duration acoustic field was applied at the beginning of the signal. Once all measurements were made, each file was time aligned to the embedded acoustic field marker, after filtering to isolate the sum and difference frequencies, the individual files were extracted into a large three-dimensional array including time and two dimensions. This enabled the recovery of ϕ ae ( t ), such that we can obtain the time evolution of the generated and views. Signal processing and data analysis Signal processing and data analysis were performed using custom Python scripts that utilised functions from the NumPy, SciPy, and Pandas Python libraries. Data exclusion criterion The only criterion for exclusion was when the anaesthesia was too deep to evoke any EMG response, despite the Δ f frequency being present in the brain (see Supplemental Note 3 ). All files were included in the dataset, even if there was no difference frequency sinusoid present due to poor data quality due to electrochemical offsets ( Supplemental Note 5 ). Computation of EMG envelope EMG envelope was computed by performing a Hilbert transform and extracting the amplitude of the resulting analytical signal. A low-pass filter with a cutoff frequency of 10 Hz was applied to the resulting envelope. Frequency domain analysis A 1-D discrete Fourier Transform with Flat Top window to optimize amplitude accuracy was computed on the central 4 seconds of each recording, as we report amplitude spectral density (ASD) instead of power spectral density (PSD) to easily read the average amplitude of each measurement. After the Fourier transform is computed, the two-sided amplitude spectrum is multiplied by 2, and half the array is taken, converting it into its single-sided form. EMG amplitude normalisation The EMG amplitude (i.e., ASD at Δ f ) at each stimulation condition was normalised to the total amplitude across the conditions at each triplet block to account for inter-block variation in responsiveness due to, for example, changes in anaesthetic level. Statistical tests were performed on the log values. Peak alignment analysis Peaks in the brain electric potential signal and EMG envelope signal were identified using the Python-based find_peaks function. An alignment between the peaks was defined as occurring when the EMG peak coincided with a peak of the brain signal, provided the time difference was within 1/5 of the period (i.e., 0.2 seconds for 1Hz stimulation). REPORTING SUMMARY Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article. DATA AVAILABILITY The data supporting the results in this study are available within the paper and its supplementary information. The raw and analysed datasets generated during the study will be made publicly available before publication of the manuscript. CODE AVAILABILITY Further information is available from the corresponding author upon request. Code supporting the findings in this article will be made publicly available before publication of the manuscript. AUTHOR CONTRIBUTIONS J.L.R. conceived and developed the hardware, simulations and experiments and wrote and revised the paper. C.B. revised the paper. R.C. edited and revised the paper. N.G. edited and revised the paper and supervised experiments. DECLARATION OF INTERESTS A patent application WIPO(PCT): WO2024175933A1 was filed by Imperial Innovations with Jean Rintoul and Nir Grossman as named inventors. ACKNOWLEDGEMENTS N.G. was supported by the UK Dementia Research Institute (UK DRI)—an initiative funded by the Medical Research Council, Engineering and Physical Sciences Research Council (EPSRC) UK, Science & PINS Award for Neuromodulation, the NIHR IBRC Confident in Concept Award, and the American Alzheimer’s Association. We thank all the members of CBS, Hammersmith at Imperial College London for their tireless care of the mice who live there, Dr. David Bono and Dr. Jon Howard for assisting with experimental set up, Dr. Patrycja Dzialecka for advice on mouse recovery surgeries, Dr. Hazael Montanaro for advice on acoustic finite element modelling simulation using Sim4Life, and Dr. Nawal Zabouri for initial rodent surgery training. REFERENCES 1. ↵ Suleman , R. A Brief History of Electroconvulsive Therapy . American Journal of Psychiatry Residents’ Journal 16 , 6 – 6 ( 2020 ). OpenUrl 2. ↵ Zangen , A. , Roth , Y. , Voller , B. & Hallett , M. Transcranial magnetic stimulation of deep brain regions: evidence for efficacy of the H-Coil . Clinical Neurophysiology 116 , 775 – 779 ( 2005 ). OpenUrl CrossRef PubMed Web of Science 3. ↵ Herrmann , C. S. , Rach , S. , Neuling , T. & Strüber , D. Transcranial alternating current stimulation: A review of the underlying mechanisms and modulation of cognitive processes . Front Hum Neurosci 7 , 46064 ( 2013 ). OpenUrl 4. ↵ Grossman , N. et al. Noninvasive Deep Brain Stimulation via Temporally Interfering Electric Fields . Cell 169 , 1029 – 1041.e16 ( 2017 ). OpenUrl CrossRef PubMed 5. ↵ Violante , I. R. et al. Non-invasive temporal interference electrical stimulation of the human hippocampus . Nat Neurosci 26 , 1994 – 2004 ( 2023 ). OpenUrl CrossRef PubMed 6. ↵ Fry , F. J. , Ades , H. W. & Fry , W. J. Production of reversible changes in the central nervous system by ultrasound . Science (1979) 127 , 83 – 84 ( 1958 ). OpenUrl FREE Full Text 7. ↵ Yoo , S. , Mittelstein , D. R. , Hurt , R. C. , Lacroix , J. & Shapiro , M. G. Focused ultrasound excites cortical neurons via mechanosensitive calcium accumulation and ion channel amplification . Nature Communications 2022 13:1 13 , 1 – 13 ( 2022 ). OpenUrl PubMed 8. ↵ Sanguinetti , J. L. et al. Transcranial Focused Ultrasound to the Right Prefrontal Cortex Improves Mood and Alters Functional Connectivity in Humans . Front Hum Neurosci 14 , 52 ( 2020 ). OpenUrl CrossRef PubMed 9. ↵ Truong , D. , Guillen , A. & Datta , A. Comparison of thermal effects induced by transcranial focused ultrasound and transcranial pulse stimulation . Brain Stimul 16 , 9 – 10 ( 2023 ). OpenUrl 10. ↵ Sato , T. , Shapiro , M. G. & Tsao , D. Y. Ultrasonic Neuromodulation Causes Widespread Cortical Activation via an Indirect Auditory Mechanism . Neuron 98 , 1031 – 1041.e5 ( 2018 ). OpenUrl CrossRef PubMed 11. ↵ Naor , O. , Krupa , S. & Shoham , S. Ultrasonic neuromodulation . J Neural Eng 13 , 31003 ( 2016 ). OpenUrl 12. ↵ Blackmore , J. , Shrivastava , S. , Sallet , J. , Butler , C. R. & Cleveland , R. O. Ultrasound Neuromodulation: A Review of Results, Mechanisms and Safety . Ultrasound Med Biol 45 , 1509 – 1536 ( 2019 ). OpenUrl CrossRef PubMed 13. ↵ Hodgkin , A. L. & Huxley , A. F. Action potentials recorded from inside a nerve fibre [8] . Nature 144 , 710 – 712 ( 1939 ). OpenUrl CrossRef Web of Science 14. ↵ Fox , F. E. , Herzfeld , K. F. & Rock , G. D. The Effect of Ultrasonic Waves on the Conductivity of Salt Solutions . Physical Review 70 , 329 ( 1946 ). OpenUrl 15. ↵ Olafsson , R. et al. Cardiac activation mapping using ultrasound current source density imaging (UCSDI) . IEEE Trans Ultrason Ferroelectr Freq Control 56 , 565 – 574 ( 2009 ). OpenUrl PubMed 16. ↵ Alvarez , A. , Preston , C. , Trujillo , T. & Witte , R. S. Acoustoelectric imaging for beat-to-beat cardiac activation wave mapping in an in vivo swine model . IEEE International Ultrasonics Symposium, IUS 2020-September , ( 2020 ). 17. ↵ Rintoul , J. L. , Neufeld , E. , Butler , C. , Cleveland , R. O. & Grossman , N. Remote focused encoding and decoding of electric fields through acoustoelectric heterodyning . Communications Physics 2023 6:1 6 , 1 – 11 ( 2023 ). OpenUrl 18. ↵ Xing , L. , Huang , Y. , Alja’Afreh , S. S. & Boyes , S. J. A monopole water antenna . LAPC 2012 - 2012 Loughborough Antennas and Propagation Conference https://doi.org/10.1109/LAPC.2012.6402985 ( 2012 ) doi: 10.1109/LAPC.2012.6402985 . OpenUrl CrossRef 19. Eves , E. E. Beamed Microwave Power Transmission and its Application to Space . IEEE Trans Microw Theory Tech 40 , 1239 – 1250 ( 1992 ). OpenUrl 20. ↵ He , M. , Fic , K. , Frackowiak , E. , Novák , P. & Berg , E. J. Influence of aqueous electrolyte concentration on parasitic reactions in high-voltage electrochemical capacitors . Energy Storage Mater 5 , 111 – 115 ( 2016 ). OpenUrl 21. ↵ Li , Q. , Olafsson , R. , Ingram , P. , Wang , Z. & Witte , R. Measuring the acoustoelectric interaction constant using ultrasound current source density imaging . Phys Med Biol 57 , 5929 – 5941 ( 2012 ). OpenUrl PubMed 22. ↵ Vöröslakos , M. et al. Direct effects of transcranial electric stimulation on brain circuits in rats and humans . Nat Commun 9 , 1 – 17 ( 2018 ). OpenUrl CrossRef PubMed 23. ↵ Olafsson , R. , Witte , R. S. , Huang , S. W. & O’Donnell , M. Ultrasound current source density imaging . IEEE Trans Biomed Eng 55 , 1840 – 1848 ( 2008 ). OpenUrl CrossRef PubMed 24. ↵ Alhawwash , A. , Horn , M. R. , Lazorchak , N. & Yoshida , K. Characterization of motor nerve stimulation using sinusoidal low frequency alternating currents and cuff electrodes . J Neural Eng 22 , 016035 ( 2025 ). OpenUrl 25. ↵ Zandieh , S. , Hopf , R. , Redl , H. & Schlag , M. G. The effect of ketamine/xylazine anesthesia on sensory and motor evoked potentials in the rat . Spinal Cord 41 , 16 – 22 ( 2003 ). OpenUrl CrossRef PubMed Web of Science 26. ↵ Quien , M. M. & Saric , M. Ultrasound imaging artifacts: How to recognize them and how to avoid them . Echocardiography 35 , 1388 – 1401 ( 2018 ). OpenUrl PubMed 27. ↵ Motovilova , E. & Huang , S. Y. A review on reconfigurable liquid dielectric antennas . Materials 13 , ( 2020 ). 28. ↵ Murphy , K. R. et al. Optimized ultrasound neuromodulation for non-invasive control of behavior and physiology . Neuron 112 , 3252 – 3266.e5 ( 2024 ). OpenUrl CrossRef PubMed 29. ↵ Jobst , B. C. & Cascino , G. D. Resective Epilepsy Surgery for Drug-Resistant Focal Epilepsy: A Review . JAMA 313 , 285 – 293 ( 2015 ). OpenUrl CrossRef PubMed 30. ↵ Luyten , L. , Hendrickx , S. , Raymaekers , S. , Gabriëls , L. & Nuttin , B. Electrical stimulation in the bed nucleus of the stria terminalis alleviates severe obsessive-compulsive disorder . Molecular Psychiatry 2016 21:9 21 , 1272 – 1280 ( 2015 ). OpenUrl PubMed 31. ↵ Holtzheimer , P. E. & Nemeroff , C. B. Advances in the treatment of depression . NeuroRx 3 , 42 – 56 ( 2006 ). OpenUrl CrossRef PubMed 32. ↵ Chang , C. H. , Lane , H. Y. & Lin , C. H. Brain stimulation in Alzheimer’s disease . Front Psychiatry 9 , 329705 ( 2018 ). OpenUrl CrossRef 33. ↵ Sharififar , S. , Shuster , J. J. & Bishop , M. D. Adding electrical stimulation during standard rehabilitation after stroke to improve motor function. A systematic review and meta-analysis . Ann Phys Rehabil Med 61 , 339 – 344 ( 2018 ). OpenUrl PubMed 34. ↵ Martin , E. et al. Ultrasound system for precise neuromodulation of human deep brain circuits . Nature Communications 2025 16:1 16 , 1 – 14 ( 2025 ). OpenUrl PubMed 35. ↵ Tyler , W. J. Noninvasive neuromodulation with ultrasound? A continuum mechanics hypothesis . Neuroscientist 17 , 25 – 36 ( 2011 ). OpenUrl CrossRef PubMed Web of Science 36. ↵ Menz , M. D. et al. Radiation Force as a Physical Mechanism for Ultrasonic Neurostimulation of the Ex Vivo Retina . The Journal of Neuroscience 39 , 6251 ( 2019 ). OpenUrl Abstract / FREE Full Text 37. ↵ Fenlon , F. H. An Extension of the Bessel-Fubini Series for a Multiple-Frequency CW Acoustic Source of Finite Amplitude . J Acoust Soc Am 51 , 284 – 289 ( 1972 ). OpenUrl 38. ↵ Fu , B. et al. A Systematic Investigation of Thermal Effects of High-Intensity Focused Ultrasound Therapy for Ultrasound Neuromodulation . IEEE Trans Instrum Meas 73 , 1 – 12 ( 2024 ). OpenUrl CrossRef 39. ↵ Lemaire , T. , Neufeld , E. , Kuster , N. & Micera , S. Understanding ultrasound neuromodulation using a computationally efficient and interpretable model of intramembrane cavitation . J Neural Eng 16 , ( 2019 ). 40. ↵ Braun , V. , Blackmore , J. , Cleveland , R. O. & Butler , C. R. Transcranial ultrasound stimulation in humans is associated with an auditory confound that can be effectively masked . Brain Stimul 13 , 1527 – 1534 ( 2020 ). OpenUrl PubMed 41. ↵ McAdams , E. T. , Lackermeier , A. , McLaughlin , J. A. , Macken , D. & Jossinet , J. The linear and non-linear electrical properties of the electrode-electrolyte interface . Biosens Bioelectron 10 , 67 – 74 ( 1995 ). OpenUrl CrossRef 42. ↵ Martin , E. et al. Ultrasound system for precise neuromodulation of human deep brain circuits . bioRxiv 2024.06.08.597305-2024.06.08.597305 ( 2024 ) doi: 10.1101/2024.06.08.597305 . OpenUrl Abstract / FREE Full Text 43. ↵ Duck , F. A. The Meaning of Thermal Index (TI) and Mechanical Index (MI) Values . Ultrasound 5 , 36 – 40 ( 1997 ). OpenUrl CrossRef 44. ↵ Retz , K. et al. Measured acoustic intensities for clinical diagnostic ultrasound transducers and correlation with thermal index . Ultrasound in Obstetrics & Gynecology 50 , 236 – 241 ( 2017 ). OpenUrl PubMed 45. ↵ Tsai , P. C. , Gougheri , H. S. & Kiani , M. Skull Impact on the Ultrasound Beam Profile of Transcranial Focused Ultrasound Stimulation . Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 5188-5191 ( 2019 ) doi: 10.1109/EMBC.2019.8857269 . OpenUrl CrossRef 46. ↵ Vöröslakos , M. et al. Direct effects of transcranial electric stimulation on brain circuits in rats and humans . Nat Commun 9 , 1 – 17 ( 2018 ). OpenUrl CrossRef PubMed 47. ↵ Wu , Q. , You , B. , Zhang , X. & Tu , J. Investigation of an Active Focusing Planar Piezoelectric Ultrasonic Transducer . Sensors 2024, Vol. 24 , Page 4082 24, 4082 ( 2024 ). OpenUrl PubMed 48. ↵ Li , X. , Yu , K. & He , B. Magnetoacoustic tomography with magnetic induction (MAT-MI) for imaging electrical conductivity of biological tissue: A tutorial review . Phys Med Biol 61 , R249 – R270 ( 2016 ). OpenUrl PubMed 49. ↵ King , R. L. , Brown , J. R. & Pauly , K. B. Localization of Ultrasound-Induced In Vivo Neurostimulation in the Mouse Model . Ultrasound Med Biol 40 , 1512 – 1522 ( 2014 ). OpenUrl CrossRef PubMed 50. King , R. L. , Brown , J. R. , Newsome , W. T. & Pauly , K. B. Effective Parameters for Ultrasound-Induced In Vivo Neurostimulation . Ultrasound Med Biol 39 , 312 – 331 ( 2013 ). OpenUrl CrossRef PubMed 51. Brunner , C. , Montaldo , G. & Urban , A. Functional ultrasound imaging of stroke in awake rats . Elife 12 , ( 2023 ). 52. ↵ Murphy , K. R. et al. Optimized ultrasound neuromodulation for non-invasive control of behavior and physiology . Neuron https://doi.org/10.1016/J.NEURON.2024.07.002 ( 2024 ) doi: 10.1016/J.NEURON.2024.07.002 . OpenUrl CrossRef View the discussion thread. Back to top Previous Next Posted November 04, 2025. Download PDF Supplementary Material Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Non-invasive in vivo acoustoelectric neuromodulation and its contribution to ultrasound stimulation Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Non-invasive in vivo acoustoelectric neuromodulation and its contribution to ultrasound stimulation Jean L. Rintoul , Christopher Butler , Robin O. Cleveland , Nir Grossman bioRxiv 2025.11.02.685597; doi: https://doi.org/10.1101/2025.11.02.685597 Share This Article: Copy Citation Tools Non-invasive in vivo acoustoelectric neuromodulation and its contribution to ultrasound stimulation Jean L. Rintoul , Christopher Butler , Robin O. Cleveland , Nir Grossman bioRxiv 2025.11.02.685597; doi: https://doi.org/10.1101/2025.11.02.685597 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Neuroscience Subject Areas All Articles Animal Behavior and Cognition (7624) Biochemistry (17651) Bioengineering (13873) Bioinformatics (41887) Biophysics (21424) Cancer Biology (18566) Cell Biology (25465) Clinical Trials (138) Developmental Biology (13365) Ecology (19871) Epidemiology (2067) Evolutionary Biology (24293) Genetics (15591) Genomics (22478) Immunology (17715) Microbiology (40331) Molecular Biology (17150) Neuroscience (88492) Paleontology (666) Pathology (2828) Pharmacology and Toxicology (4817) Physiology (7635) Plant Biology (15114) Scientific Communication and Education (2044) Synthetic Biology (4286) Systems Biology (9817) Zoology (2268)