Suppression of pathological oscillations with transcranial focused ultrasound in Parkinson’s disease

Domain: biology
Published: 2026-03-25
Editorial outcome: minor_revision

Abstract
Transcranial ultrasound stimulation (TUS) is an emerging method for non-invasive neuromodulation of deep brain structures. However, to date, there is no evidence that TUS can directly modulate disease-related pathological oscillations in the same direction as known therapies. Inspired by clinical deep brain stimulation, in this randomised controlled cross-over study we probed the effects of pallidal TUS pulsed at 130Hz on subthalamic beta-band activity, a biomarker in Parkinson’s Disease (PD) in four male participants with PD. Beta-band power reduced in the ipsilateral subthalamic nucleus (STN) by 10.34% (95% CI:3.81% to 16.87%, p<0.05, false discovery rate (FDR) adjusted). Beta power reduction was correlated between the ipsilateral (R2=0.980, p<0.05, FDR adjusted), but not contralateral, STN and primary motor cortex. Bradykinesia, as measured by change in reaction time, was also reduced by 17.70% (95% CI:8.95% to 26.41%, p<0.05, FDR adjusted). In this proof of concept study, we demonstrate that TUS can suppress pathological oscillations, potentially opening the door for therapeutic TUS (NCT06932185).

Introduction
Parkinson's disease (PD) is a common neurodegenerative disorder characterised by the loss of dopaminergic neurons leading to bradykinesia. Bradykinesia is the pre-requisite for clinical diagnosis when present alongside other symptoms e.g. resting tremor, rigidity or postural instability1. Dopamine depletion, and the subsequent disruption of basal ganglia network dynamics, is thought to be a key mechanism by which symptoms arise2. Therefore, the mainstay of pharmacological treatment is dopamine replacement therapy3. When patients suffer from the side-effects of increasing dosages or motor fluctuations, deep brain stimulation (DBS) is considered4.

DBS, an established therapy for PD, involves implanting electrodes into either the subthalamic nucleus (STN) or the globus pallidus internus (GPi)5. DBS’ adoption has provided an opportunity to record local field potentials (LFPs) from these deep brain nuclei, transforming our understanding of PD circuit pathology and biomarkers of disease6. Beta-band (13-30Hz) power is elevated within both the STN and the GPi in PD, reduced by both dopaminergic medication and DBS7,8, and correlated with symptom severity9. Notably this modulation in beta-band activity can be detected within distributed neurophysiological networks10. Such is the reliability of this pathological oscillation as a biomarker of PD symptom severity, it is the basis for closed-loop DBS, a clinically approved therapy where DBS is only activated when elevated beta-band activity is detected11,12.

Transcranial ultrasound stimulation (TUS) is emerging as an exciting new method of non-invasive neuromodulation due to its ability to target deep brain structures with high spatial resolution13. TUS has therefore been proposed as a potential therapy14. Whilst promising therapeutic effects have been reported after TUS in several conditions including depression15, chronic pain16 and essential tremor17, these studies have either been open label15,17 or relied on subjective self-reporting15,16. To date there has been no evidence that TUS can directly ameliorate pathological oscillations associated with disease. This lack of evidence may be partly explained by technical challenges and a lack of robust biomarkers in the conditions tested, unlike in PD. Furthermore, the optimal parameter set for therapeutic TUS is difficult to identify given the large parameter space available and the differential site-specific effects18. In contrast, DBS for PD has used electrical stimulation pulsed at around 130Hz to improve bradykinesia for over 30 years19.

In this randomised controlled proof-of-concept study, we leverage PD as a model system and show that DBS-inspired 130Hz pulsed GPi-TUS reduces STN beta-band power. We also show that this reduction is coupled to a distributed network that includes the motor cortex and that GPi-TUS improves bradykinesia, as measured by reduction in median reaction time.

Results
Personalised acoustic lenses focus TUS to deep brain nuclei
Target-specific personalised acoustic lenses were designed using treatment planning software to focus ultrasound either to the GPi or to the frontal horn of the left lateral ventricle as an active control site22, whilst avoiding the STN electrode. This allowed us to probe network dynamics whilst minimising the risk of potential artefacts associated with sonicating the electrode23. In each participant, the GPi target was personalised according to the peak structural connectivity from the STN to the posteroventral GPi using established DTI pipelines. Personalised acoustic lenses were able to focus TUS to the entry point within the GPi of these STN-GPi white matter tracts (Fig 2; see Supplementary Materials for full methods). Each lens was validated by comparing measurements in water with the predictions from the treatment planning software (Fig. S2).

130Hz GPi-TUS suppresses beta oscillations in the STN
GPi-TUS led to a reduction in ipsilateral STN mean beta power in all four participants at rest (Fig. 3A). Strikingly, where distinct low (13-20Hz) and high (21-30Hz) beta peaks were present in a participant’s power spectrum, only low-beta power was reduced (Fig. 3B). At the group level, GPi-TUS led to a mean relative reduction in ipsilateral STN beta power of 10.34% (95% CI: 3.81% to 16.87%, p<0.05, false discovery rate (FDR) adjusted) when compared to Ventricle-TUS. Although there was a reduction in contralateral STN beta power, this did not reach statistical significance (Fig. 3B).

GPi-TUS has specific network-level effects
GPi-TUS also led to networked modulation of cortical beta activity, as expected from the literature10, there was no significant reduction in mean beta power in the ipsilateral (left) and contralateral (right) primary motor cortices at the group level (M1, Fig. 3C). However, mean beta power reduction in the ipsilateral M1 region correlated strongly with ipsilateral STN beta reduction (R2= 0.980, p<0.05, FDR adjusted). However, in the contralateral (right) hemisphere, STN beta reduction was not correlated with M1 beta reduction, indicating that GPi-TUS led to a lateralised network-level modulation in beta activity (Fig. 3E). There was no significant change in M1-STN beta coherence at the group level (Fig S3).

130Hz GPi-TUS improves reaction time
GPi-TUS was also associated with a statistically significant reduction in median reaction time of 17.70% (95% CI: 8.95% to 26.41%, p<0.05, FDR adjusted) compared to ventricle-TUS on the random dot-motion task. This improvement in bradykinesia was seen in all participants (Fig. 3F). Total abbreviated UPDRS-III scores were not significantly reduced at the group level (Fig. 3G).

Beta suppression is not the result of mechanical TUS artefacts
To ensure that beta reduction was not the result of an artefact induced by off target TUS to the DBS electrode tip, we characterised the mechanical artefact associated with direct sonication of a DBS electrode. To do this we inserted a DBS electrode into an agar phantom and sonicated the electrode tip directly using our experimental parameters in a water tank (Fig. S5). We observed no effect of the 130Hz PRF at 600kPa on electrode recordings (Fig 4). Since the PRF used in our study is above the hardware low-pass filter that is set at 100Hz in the Percept® device24, we sought to characterise the mechanical artefact of a PRF below this cut-off in order to confirm that a mechanical artefact could be detected, if present. We found that TUS delivered at 5Hz PRF resulted in a visible signal at 200kPa, with an amplitude above what would be expected for physiological signals (Fig. 4). At 5Hz PRF, an artefact was visible in both the time (Fig. 4A) and frequency (Fig. 4B) domain. To confirm that 130Hz PRF would not interact with the physiological frequency band of interest, we quantified the effect of direct electrode sonication on beta band power, again with 5Hz PRF as a positive control. In the left electrode, beta-band power increased with 5Hz and scaled with peak pressure but this was not true at 130Hz (Fig. 4C). Whilst these results confirm that our findings cannot be explained by mechanical artefacts, we also ensured that the ultrasound focus was >5mm away from the electrode tip to further reduce the risk of polluting the LFP signals and also to minimise artefactual heating in the vicinity of the electrode. Our in-silico simulations indicated a mean maximum temperature rise of 0.47±0.37˚ C, within International Transcranial Ultrasonic Stimulation Safety and Standards (ITRUSST) consortium guidelines (<2˚ C). Mean targeting error was 0.50±0.33mm for the GPi and 0.51±0.37mm for the ventricle (Table S2).

Methods
This study was sponsored by the University of Oxford and conducted after NHS Research Ethics Committee approval (Reference: 23/EM/0165) and registration (trial registration no. NCT06932185), in accordance with the Declaration of Helsinki. The results presented here are from the first four participants recruited to this study. The experimental design is as described in the main text and outlined in Fig. 1C.

Participants
Four consecutive patients were recruited from DBS follow-up clinics (Table 1) and provided informed consent to participate. Patients were eligible for participation if they had PD, DBS targeted to the STN, had a Percept® implanted pulse generator (IPG), and had a measurable beta peak on screening. Potential participants were excluded if they could not tolerate having their DBS turned off for the duration of the experiment, had an extreme language barrier and/or had a neurological/psychiatric comorbidity that may confound the results. Patients were recruited once they were on stable dopaminergic medications and had stable DBS settings. These participants are part of an ongoing study so Data Safety and Monitoring Board (DSMB) approval was gained prior to the publication of this work.

Surgery
The DBS surgery was performed as part of routine clinical practice 3-6 months prior to recruitment of participants into the TUS study. DBS surgery was performed under general anesthesia. A 2.7 mm twist drill craniotomy was made and the electrode lead inserted bilaterally into the STN. All patients had intra-operative imaging to confirm electrode positioning. All electrodes were confirmed using fused pre-operative structural MRI fused with post-operative CT. Target selection was based on anatomical/stereotactic references, for electrode reconstruction in standard space, see Fig. 1D. All participants received a Percept® IPG with B33015 electrodes (Medtronic Inc., Minneapolis, Minnesota, USA).

After implantation, the DBS was programmed by a trained clinician to optimize symptom suppression and control side effects. The day after surgery, the stimulator was turned on to ascertain any immediate effects of stimulation on symptoms and then switched off again. The stimulator was switched on following all wound healing, one month following the operation. DBS was delivered at 125 or 130 Hz and the current was adjusted over subsequent visits to a maximum which was titrated to the maximum tolerated each patient. See Table 1 for the final stimulator settings.

Image acquisition
Pre-operative MRI was performed on a 3T SIGNATM Premier scanner (General Electric Healthcare, Chicago, Illinois, USA). Structural T1-weighted MPRAGE sequences were acquired (repetition time (TR) = 2659ms, echo time (TE) = 2.93ms, inversion time = 1058ms, flip angle (FA) = 8°, field of view = 256 x 256mm, voxel size = 1 x 1 x 1mm3) in addition to diffusion weighted sequences. The multiband diffusion weighted sequence achieved a voxel size of 1.8 x 1.8 x 1.8mm3. Diffusion weighting was applied along 109 non-colinear gradient directions (b = 500s/mm2 for 15 volumes, 1000s/mm2 for 30 volumes, 2600 s/mm2 for 64 volumes), with five non-diffusion (b = 0) weighted volumes (TR = 6233ms, TE = 73.9ms, FA = 90°). An additional b = 0 image was acquired with an opposing phase encoding direction was also acquired for distortion correction. Post-operative CT scans were taken as mentioned above for verification of electrode position (Siemens Naeotom Alpha Version VA50, Siemens Healthineers, Forchheim, Germany). A standard CT brain protocol was used (scan mode: routine spiral adult head quantum plus Xcare; tube voltage: 120 kV; effective mAs 311; acquisition 96 × 0.4 mm volume CT dose index (CTDIvol): 55.7; pitch: 0.35; rotation time (seconds) 0.5; reconstruction slice thickness: 0.6 mm; slice increment: 0.4 mm; reconstruction kernel: HR44; quantum iterative reconstruction [QIR] strength: 3; matrix size: 512 × 512).

Image preprocessing
Image pre-processing was carried out using the FMRIB Software Library (FSL; Oxford, UK). Susceptibility-induced off-resonance field was estimated using topup using b = 0 volumes with opposing PE directions. Eddy was then used to correct for motion and eddy currents. BEDPOSTX was used for ball and stick modelling of local diffusion parameters, with up to three crossing fibres per voxel. Pre-operative T1-weighted MRI images were registered to post-operative CT images using FLIRT and registered to Montreal Neurological Institute (MNI) standard space using FNIRT. The inverse warp was then applied to the left STN and left GPi masks in the DISTAL atlas54, and these were then binarised to produce left STN and left GPi masks in native space.

Tractography based targeting
Probabilistic tractography was carried out using PROBTRACX with modified Euler streaming with the native STN mask set as the seed and the GPi set as a waypoint and termination mask. The output (fdt map) was then transformed to CT space and the ‘peak connectivity’ region between in the GPi was set as the single voxel target for onward ultrasound point source simulation. This target was visualised in native T1-weighted imaging to verify target location. An important note is that this ‘peak connectivity’ region represents the highest probability of tract existing between the STN and GPi.

Personalised Acoustic Lens Design
Two personalised acoustic lenses were designed for each participant, one targeting the peak connectivity voxel in the GPi and another targeting the frontal horn of the left lateral ventricle. The design process is described elsewhere15. In brief, each participant’s CT was segmented to bone, soft-tissue, cerebrospinal fluid (CSF) and electrode and assigned acoustic or thermal properties given in Table S1. Each target was then set for a ‘backward’ simulation, where a point source was placed at the target and the acoustic wave propagated out to the receiver/sensor plane. The amplitude map was used to determine the ultrasound transducer placement by maximizing the amplitude over the surface at the transducer. The phase was used to design a phase-conjugate lens mounted to the transducer to account for the aberration due to the propagation path. was then carried out using ultrasound transducer and acoustic lens to determine the acoustic field inside the head (Fig. 3A). The ultrasound focus was then verified in the structural MRI with the co-registered CT to ensure that the ultrasound focus overlapped with the tractography ‘peak connectivity’ region, and that the electrode was avoided.

Numerical simulations of the acoustic wave equation were performed using the GPU-accelerated k-Wave Toolbox55 code on a Quadro RTX 6000 graphics card (NVIDIA, Santa Clara, CA, USA). Simulations were performed at a spatial resolution of 0.5mm in all three dimensions. The Courant-Friedrichs-Lewy number was set to 0.28 based on the highest sound speed in the domain.

Thermal simulations
Thermal simulations of the Pennes’ bioheat equation were performed using the k-Wave Toolbox to ensure that temperature safety limits were met. For each participant the same grid and segmentation was used as in the ultrasound simulations with the thermal properties given in the supplemental date. For each lens the heat source was derived from the predicted acoustic field map and pulsing protocol (Fig. 3B) and the temperature simulated over the entirety of the experiment. Acoustic lenses were only manufactured after confirmation that temperature does not rise above 2˚ C anywhere in the head. Thermal simulations were validated in a porcine ex-vivo model (Fig. S4). Tissue samples were acquired from a local butcher and not subject to ethical approval. The handling and disposing of samples followed standard University of Oxford guidelines for the use of animal-derived tissue in a laboratory setting.

Acoustic Lens Fabrication
Lens positives were 3D printed using a Formlabs Form 3B resin 3D printer (Clear v4 resin, 0.1mm layer height). After curing, lens positives were used to make silicone (Dragon Skin 10 SLOW, Smooth-On), negative moulds. Negative moulds were filled with SYLGARD 184 silicone elastomer polydimethylsiloxane (PDMS), degassed in a centrifuge at 3,000rpm for 1 minute and placed in a near vacuum for 30 minutes. To avoid the bonding of the silicone negative mould to the PDMS lens, negative moulds were plasma treated, deposited with polyvinyl alcohol and sprayed with the a spray release agent (Ease Release 200, Smooth-On). PDMS lenses were cured in a 60°C oven for 12 hours.

Acoustic lens validation
Each lens was visually inspected for abnormalities and validated in degassed water using a needle hydrophone (model HNA-400, ONDA, Sunnydale, CA, USA). Each lens and silicone membrane were mounted to the transducer under degassed water as illustrated in supplementary Fig. 1. 2D pressure maps were measured around the peak pressure region with a fixed axial distance (field of view = 40mm x 40mm, spatial resolution = 1mm). Pressure readings were taken as the peak rarefaction pressure averaged over 10 pulses (500kHz, 20 cycles per pulse, 10ms pulse period). Resultant pressure maps were accepted or rejected based on a visual inspection. All lenses were retested overnight before each experimental session with lenses placed in their final mounted position. Simulated and measured free field acoustic pressure fields for all lenses can be found in the Supplementary Information Fig. 2.

Ultrasound stimulation
Sonications were performed using a custom single element planar US transducer (H294, Sonic Concepts Inc, Bothwell, WA, USA) with a 500kHz fundamental frequency fitted with a personalised lens bespoke to each target. Each acoustic lens was mounted to the transducer while immersed in degassed water, a silicone membrane was attached and filled with degassed water. During the experiment, ultrasound gel was combed into the participants hair and applied to the silicone membrane.

The target order was randomised and each target was sonicated on separate consecutive days. On each day, a sham TUS block preceded an active TUS block and followed the same format (Fig. 3C). Simultaneous online neuronavigation was performed using with Brainsight v2.5.5 (Rogue Research Inc., Montréal, Québec, Canada) using the T1-weighted MR images from each participant.

In each of the five, 5 minute, active TUS blocks, ultrasound was delivered in 800ms pulse trains repeated every 2.5 seconds. Within each pulse train, individual ultrasound pulses had a 90µs pulse duration and a 130Hz pulse repetition frequency (PRF) corresponding to a duty cycle of 1.17% (Fig. 1B). The ultrasound stimulation waveform was digitally generated using Python 3.8.10 at a sampling rate of 10MHz and converted to an analogue voltage with an arbitrary waveform generator (Handyscope HS5, TiePie, WL Sneek, The Netherlands). This voltage was amplified with a 55dB broadband amplifier (1040L, E&I, Rochester, NY, USA) and connected to the transducer through a 50 electrical impedance matching network.

We have previously shown that pulsed TUS results in an auditory artefact that can be masked by playing an audible signal to participants through earphones21.21 Here a 130Hz square-wave auditory masking signal was played through earphones during all blocks (active and sham). This signal was created using the simpleaudio package for Python 3.8.10.

Electrophysiology data recording
LFPs were recorded wirelessly using the Medtronic Percept’s ‘Indefinite Streaming’ mode which streamed to the tablet programmer. Bilateral STN-LFPs were recorded (bipolar left 0-2, 1-3, bipolar right 0-2, 1-3) at a sampling rate of 250 Hz and the data recorded from the left and right hemisphere were considered independently. Electroencephalography (EEG) was recorded using a Saga amplifier (TMS International) at a sampling rate of 4096Hz. Individual electrodes were placed at FP1, FP2, F3, Fz, F4, C3, Cz, C4, P3, Pz and P4 with Ten20 conductive paste. Since we delivered TUS through the temporal window, these midline electrodes were selected to ensure that our EEG electrodes would not interact with our ultrasound field. Electrodes were located using the anatomical landmarks outlined in the 10-20 system for localizing EEG electrodes.

Electrophysiological data recorded from the DBS electrodes was time locked to the task and EEG using transcutaneous electrical nerve stimulation (TENS). Electrical pulses were generated using a biphasic constant current stimulator (DS8R, Digitimer, Hertfordshire, UK). Subthreshold amplitudes was set for each participant to the maximum value that they could not perceive by ramping up the current from 0.1mA in 0.1mA increments until they reported a sensation. After which the current was reduced until they no longer reported this sensation.

Electrophysiology preprocessing
LFP data were then extracted via a tablet programmer and stored in a laboratory computer for offline processing. The Perceive Toolbox (https://github.com/neuromodulation/perceive) was used to convert the stored JSON files to MAT-files for further analysis in MATLAB (MathWorks, Natick, MA). For the recorded EEGs, bipolar signals were constructed offline by differentiating between ‘C3’ and ‘Cz’ (i.e. ‘C3Cz’) or ‘C4’ and ‘Cz’ (i.e. ‘C4Cz’). Only C3Cz and C4Cz bipolar channels were analysed here. EEG was time-locked and down-sampled to a sampling rate of 250Hz to match the LFP sampling rate. LFP and EEG data were then band-pass filtered between 4-45Hz, detrended and DC removed.

Spectral analysis
After preprocessing, resting power spectral density (PSD) was estimated using Welch’s overlapped segment averaging estimator for the LFP, EEG, accelerometry and head tracker recordings of each individual for the entirety of each rest block (approx. 5 minutes) using pwelch, with a Hann window of 1s and a window overlap of 600ms. The PSD was then broadband total power normalised. The difference between the PSD of each TUS and sham block was taken in order to account for fluctuations in beta power across days. The relative beta power (13-30Hz) difference between GPI-TUS and Ventricle-TUS was then computed for each participant in both the LFP and EEG data.

Coherence
Coherence (magnitude-squared coherence) was calculated using Welch's averaged modified periodogram method with a 1s window and a frequency resolution of 1Hz using mscohere. Coherence was analysed from 4 to 45Hz and difference in coherence from baseline was computed and presented.

Behavioral tasks
Participants practiced each task the day before the experiment under guided supervision until they clearly demonstrated task comprehension. In addition, instructions were displayed before each block throughout the experiment. Behavioural tasks were presented on a 1600 x 900 LCD display with participants sat at a comfortable distance. All tasks were created using PyschoPy 2024.2.4 with Python 3.8.10. A photodiode was affixed to the top of the display with opaque black tape to time lock the EEG data to the presentation of new stimuli.

Throughout the experiment participants were encouraged to remain stationary and attend to the display. As shown in Fig. 3C. all tasks were initially presented in a sham block without the application of TUS. An auditory mask was played through earphones throughout, and the participant was blinded throughout. Participants were asked to partake in a series of tasks: rest, stop-signal task and random-dot motion task. A break of five minutes was present between each block to allow participants to relax and to mitigate any potential heating effects from TUS.

Each block started with an initial rest period. During this time a white fixation cross was presented on a black background in the centre of the screen for five minutes which participants were instructed to attend to while remaining stationary.

After a five minute break, participants were presented the stop-signal task (SST). Participants were instructed to click the left or right arrow key in accordance with the direction of the arrows presented on screen. 24 go trials were presented at random time intervals between 2.6 and 3.4 seconds, the median reaction time to these stimuli were recorded. After this, participants were presented with instructions informing them that in subsequent trials they must withhold their response if a red cross appeared. 78 go trials, and 26 no-go trials were presented, if a participant responded more than 100ms slower than their median reaction time during the initial go trials a “Too slow” message appeared. The stop signal delay (SSD), the time delay before the presentation of the stop signal (red cross) was adjusted on a trial-by-trial basis to maintain a stop success of 50%. SST data has not yet been analyzed due to omission rates and violations of the race model that cannot be accommodated in this small sample size56.

Participants were then presented with the random-dot motion (RDM) task which was used to assess rection times. Prior to each block participants were instructed to respond to the coherent movement of dots by clicking the corresponding left or right mount buttons as soon as they knew the answer. At random time intervals between 1.25 and 1.75 seconds dots moved coherently in the left or right direction. These coherent movements included either 8% of dots (low coherence) or 50% of dots (high coherence). Reaction times between 0.1 and 2 seconds from stimulus onset were recorded. The RDM task was repeated twice in approximate 5 minute blocks with a 5 minute break between each task. Reaction times were min/max normalised for all 240 trials completed by that participant on any day. Median reaction time of each block was taken for onward analysis. Data from each for each active TUS block was normalised to the sham block on the same day.

Clinical measures
An abbreviated Unified Parkinson's Disease Rating Scale part III (UPDRS-III) assessment was completed with each participant at baseline each morning and at the end of each sham and active block. Given that participants were seated and connected to our EEG amplifier for all assessments, only a subset of the UPDRS could be recorded. Walking and pull-back tests were not completed. Assessments were recorded and scored offline by a blinded assessor. Rigidity was assessed during each UPDRS assessment by a blinded assessor. It was not possible to deliver TUS during UPDRS-III assessments so these assessments can be considered ‘off-line’, i.e. after TUS had stopped.

Statistics & Reproducibility
Statistical analysis was completed in MATLAB (MathWorks, Natick, MA). Owing to the small sample size, only a limited number of comparisons were made and these were defined a priori. Lillifors test was used to test for normality and paired t-tests were used and correlations were tested accordingly. Adjustment for multiple comparisons was completed using the false discovery rate approach described by Benjamini and Hochberg57. Note that both the participant and the assessor was blinded to condition during the study.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This randomized controlled cross-over study presents the first evidence that transcranial ultrasound stimulation (TUS) pulsed at 130Hz can non-invasively suppress pathological beta-band oscillations in the subthalamic nucleus (STN) and improve bradykinesia in patients with Parkinson’s Disease (PD). By recruiting four PD patients with implanted sensing-enabled DBS electrodes, the authors successfully demonstrated that GPi-TUS reduces ipsilateral STN beta power by approximately 10.34% and reaction time by 17.70%.\nWhile the study provides groundbreaking evidence for the modulation of deep brain biomarkers, the sample size of four participants is a significant limitation for generalizability. Although the authors acknowledge this limitation, it is recommended to explicitly frame the manuscript as a \"pilot study\" or \"proof-of-concept\" investigation. If possible, including a post-hoc power analysis would help validate the statistical strength of the observed effects despite the small N.\nThe results show a significant improvement in reaction time (17.70%) during the \"online\" task, yet there was no significant reduction in the UPDRS-III scores at the group level. This discrepancy requires a deeper discussion regarding the \"washout\" period of TUS effects. If the therapeutic benefits are strictly limited to the stimulation period (online effects), the discussion should address the implications for TUS as a clinical therapy and potential strategies to induce neuroplasticity or longer-lasting (offline) effects.\nThe study demonstrates that 130Hz TUS mimics the effects of 130Hz electrical DBS. While the \"indirect pathway\" hypothesis is mentioned, the manuscript would benefit from a more detailed theoretical discussion on how the mechanical effects of ultrasound at this specific pulse repetition frequency (PRF) translate to neuronal modulation (inhibition vs. excitation) in the GPi, as opposed to the electrical mechanisms of DBS.", "reviewer_2": "This manuscript investigates the impact of low-frequency transcranial ultrasound stimulation (TUS) targeting the GPi in Parkins’s disease patients with implanted STN DBS leads. By recording STN LFP activity intraoperatively during active and sham sonication conditions, the authors demonstrate that GPi-TUS can transiently suppress pathological beta oscillations in the ipsilateral STN.\nOverall this is a very interesting study that fills a major gap in the low frequency FUS field, in which the stimulation parameters and the effects on the target structures are unknown. This study provides one of the first evidence that low frequency FUS alters pathological network oscillations which has similar effects as high frequency DBS.\nThe strengths of the study include: multisite multimodal network perturbation and recording to uncover potential mechanisms of FUS; personalized acoustic lens to maximize accuracy and focality of proposed stimulation field as demonstrated in supplementary figures, well-characterized ultrasound-induced artifacts on DBS electrodes.\nHowever, a number of concerns should be addressed to strengthen the conclusions and generalizability of the study:\n- The study has a very small sample size and one of the study subjects (3rd panel in figure 3A) did not show much, if any, beta suppressions with GPi-TUS compared to the other sham conditions. Can the authors elaborate on why they think this subject did not show corresponding beta STN suppression?\n- Does GPi-TUS suppress STN beta beyond the duration of TUS? There are several studies and lines of evidence that GPi stimulation can trigger neuroplasticity and effects of stimulation can last beyond cessation of stimulation.\n- What is the duration for each condition (sham/active GPi-TUS or ventricle TUS). How long of a resting period was there between blocks?\n- Did patients take their dopamine medication during the testing or in the middle of a block? Can the authors comment on the effect of medication on their STN beta if they did take medicine in between the blocks.\n- Was sham TUS always applied before active TUS? The order of GPi vs ventricle is randomized, but if the order of sham vs active is not counterbalanced or randomized, this could confound the results.\n- What is the effect of TUS on M1-STN coherence?\n- The authors picked 130 Hz PRF to mimic the frequency of DBS stimulation, have they used another PRF (such as 30 Hz or 60 Hz), where it should have minimal effect on STN beta as a control?", "reviewer_3": "The manuscript provides with a novel application of TUS, demonstrating the possibility of obtaining similar results to DBS for PD through a non-invasive technique, which seems highly relevant to the field. The authors also demonstrate that the same biomarkers used for DBS can be used under a TUS procedure. The authors provide a clear and concise description of the protocol and analysis followed. Although some relevant information regarding the experiment setup is missing from the main manuscript, it is included in the supplementary materials.\nHowever, the manuscript would benefit from the following suggestions to improve its clarity and further validate some of the claims from the study.\nMajor points:\n- Always have the sham session before the active session could lead to a training effect improving outcomes in the active session. The authors might want to clarify that they avoided this problem by normalising all their results with the active ventricle (rather than with the sham).\n- For the change in reaction time, to what extent have outliers influenced the results? In other words, what is the reaction time difference observed for the mean (compared to the median)?\n- In this manuscript, the presented DBS-inspired TUS protocol can modulate disease-related pathological oscillations in the same direction as known therapies such as DBS for PD. The same biomarker (beta-band power) as for DBS is analysed and a reduction in beta-band power is reported. However, to further validate that the proposed protocol can provide similar outcomes as stablished DBS protocols, it would be good to include some comparison between the results obtained in the study and the results (e.i., common beta-band power reduction) obtained in stablished DBS studies. Is the beta-band power reduction obtained from TUS comparable to the beta-band power reduction commonly obtained with DBS?\n- In the discussion, using acoustic lenses is mentioned as an advantage of the current study. Could the authors discuss how lenses enabled the current study? What would have happened without lenses (e.g. stimulating adjacent tissue)?\nMinor points:\n- p. 6: (right -> (right)\n- Fig. 3A: it is difficult to see differences at the moment. Maybe the colour scheme could be changed so that active is in dark colours and sham in lighter shades of the same colours (e.g. GPi in dark/light red and Ventricle in dark/light blue).\n- The placement of Figures 1 and 2, and Table 1, could be improved to make the manuscript clearer. It is recommended to locate the Figures and Tables in the order that they are referred to in the manuscript (e.g., Table 1 is referred to first, so it should be located before Figure 1 and 2 instead of after).\n- On the supplementary materials, for easier reproducibility of the study, it would be good to specify if the EEG electrodes used are water or gel based, since both options seem to be available for the described amplifier.\n- Fig. S3B is too small to be read correctly. Please, increase the size of the individual plots.\n- On the supplementary materials, it would be good to describe how were the EEG electrodes located. Di you make use of a commercially available EEG cap as is common with the described amplifier or did you place the electrodes directly on the patients' head? Did the placement of the electrodes interfere in any case with the placement of the transducer?", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "We thank the reviewer for their kind words. We agree that this work represents a “proof-of-concept”, demonstrating that biomarkers of disease can be modulated by TUS in a small number of patients with Parkinson’s disease. We have therefore reframed the study as a “proof-of concept” study in the abstract, main text and discussion of this manuscript.\n‘In this randomised controlled proof-of-concept study, we leverage PD as a model system and show, for the first time that DBS-inspired 130Hz pulsed GPi-TUS reduces STN beta-band power.’\n‘In this proof-of-concept work, we have not obtained sufficient ‘off-line’ electrophysiological data to quantify the duration of beta power suppression but this is likely to vary across participants given the heterogeneous nature of PD45’\nPage 2 (38), Page 3 (69), Page 10 (251)\nAs suggested, we have also completed a post-hoc power analysis. This analysis suggests that a sample size of 4 would provide a power of ≥0.9 for both suppression of STN beta and reduction reaction time during a behavioural task. This post-hoc power analysis has been included in the supplementary information provided in this manuscript (Figure S5).\n‘Finally, post-hoc power calculations demonstrate that despite the small sample size, this study is adequately powered to detect a true difference of the magnitude observed (Fig. S7)’\nPage 11 (291-293), Supplementary information, figure S7, page 17 (333-334)\nThe results show a significant improvement in reaction time (17.70%) during the \"online\" task, yet there was no significant reduction in the UPDRS-III scores at the group level. This discrepancy requires a deeper discussion regarding the \"washout\" period of TUS effects. If the therapeutic benefits are strictly limited to the stimulation period (online effects), the discussion should address the implications for TUS as a clinical therapy and potential strategies to induce neuroplasticity or longer-lasting (offline) effects.\nWe thank the reviewer for this suggestion and agree that this is a very important question for the field.\nTo address this we have included an additional paragraph in the discussion which discusses this in some detail, with a focus on possible explanations for our observations. In this paragraph we also discuss the heterogeneity of response observed in our participants and its potential relevance to the heterogeneity in UPDRS-III scores (which were assessed ‘off-line’) as this also answers a question raised by reviewer 2.\nPage 10 (244-260)\nThe study demonstrates that 130Hz TUS mimics the effects of 130Hz electrical DBS. While the \"indirect pathway\" hypothesis is mentioned, the manuscript would benefit from a more detailed theoretical discussion on how the mechanical effects of ultrasound at this specific pulse repetition frequency (PRF) translate to neuronal modulation (inhibition vs. excitation) in the GPi, as opposed to the electrical mechanisms of DBS.\nWe thank the reviewer for this suggestion, we have now included a more detailed explanation in the discussion section.\nPage 9 (207-235)\nWe thank the reviewer for this kind summary.\nHowever, a number of concerns should be addressed to strengthen the conclusions and generalizability of the study:\n- The study has a very small sample size and one of the study subjects (3rd panel in figure 3A) did not show much, if any, beta suppressions with GPi-TUS compared to the other sham conditions. Can the authors elaborate on why they think this subject did not show corresponding beta STN suppression?\nWe thank the reviewer for this astute observation. Anecdotally, during the experiments we noted that this participant had a very severe tremor, unlike the other participants in this study.\nThe reviewer’s comment prompted us to look at this more closely; we have now quantified tremor magnitude in both the accelerometery data and the head tracker data available. The power spectral density of both analyses demonstrate that the is a clear 4-5Hz resting tremor in the participant’s right arm (as measured with an accelerometer) and that this translates to a head tremor (measured as a rotational movement; see Fig S6).\nWe think there are two possible consequences of this. Firstly, the participant’s movement may have meant that participant 3 received less TUS to target. Secondly, a single large movement can uncouple the ultrasound transducer from the scalp – the likelihood of this occurring increases significantly with increased movement. Once uncoupled from the scalp, TUS will no longer be delivered to target.\nWe have now highlighted this in the manuscript in the discussion section discussing heterogeneity of response.\n‘We observe, for example, that the absolute reduction in beta power in our third participant (Fig 3A, third panel from the left) is visibly lower than that of the other three participants in this study. Post-hoc analysis of accelerometery and head tracker data revealed that this participant had significantly greater tremor amplitudes than the other participants in this study (Fig. S6).’\nPage 10 (253-257), Supplementary information, figure S6, page 16 (319-321)\n- Does GPi-TUS suppress STN beta beyond the duration of TUS? There are several studies and lines of evidence that GPi stimulation can trigger neuroplasticity and effects of stimulation can last beyond cessation of stimulation.\nWe thank the reviewer for this question. It is a similar question to that raised by reviewer 1, specifically discussing the potential ‘off-line’ effects and the duration that they might last.\nIn this initial dataset we do not have off-line LFP data to confidently answer this question and recognise this as a limitation of our study.\nWe have therefore elaborated on this limitation in the discussion and highlighted the need for off-line data collection in future.\nPage 10 (244-260)\n- What is the duration for each condition (sham/active GPi-TUS or ventricle TUS). How long of a resting period was there between blocks?\nEach condition is lasts 5 minutes and each rest period between blocks is also 5 minutes. This is now clarified in the Supplementary Information.\nSupplementary Information, page 5 (149), page 7 (211, 216), page 8 (233)\n- Did patients take their dopamine medication during the testing or in the middle of a block? Can the authors comment on the effect of medication on their STN beta if they did take medicine in between the blocks.\nWe thank the reviewer for this suggested clarification.\nAll experiments were conducted between medication doses and timings were fixed on each day so to avoid the potential confound associated with medication timings.\nWe have now added a sentence to clarify this in the manuscript.\n‘All sham and TUS experiments were completed between dopamine replacement therapy doses.’\nPage 6 (114-115)\n- Was sham TUS always applied before active TUS? The order of GPi vs ventricle is randomized, but if the order of sham vs active is not counterbalanced or randomized, this could confound the results.\nWe thank the reviewer for this comment. This was also raised as a point of clarification by reviewer 3.\nIt is indeed true that sham experiments are always conducted before active experiments but this is done to allow for baseline normalisation on each day. The only comparisons made are between baseline (sham) normalised ventricle data and baseline (sham) normalised PD data.\nWe did this in order to reduce the risk of confounds from variations in baseline beta power.\n‘since all comparisons presented are baseline (sham) normalised GPI-TUS vs baseline (sham) normalised Ventricle-TUS, any potential fluctuations in baseline beta power are both recorded and accounted for which reduces the risk of confounds associated with these known fluctuations20.’\nPage 11 (286-289)\n- What is the effect of TUS on M1-STN coherence?\nWe thank the reviewer for this question as it has prompted us to investigate this.\nWe find no significant difference in M1-STN coherence which is perhaps unsurprising since we are applying TUS to an external node to this particular structural connection (the GPi). Whilst there may be a trend towards a relative increase in high beta coherence, this does not pass statistical significance after correction.\nWe now have included this finding in the results, discussion and supplementary information.\n‘There was no significant change in M1-STN beta coherence at the group level (Fig S3).’\n‘bypassed by the hyperdirect pathway, a monosynaptic cortico-subthalamic projection. Similarly, we found a correlated reduction in ipsilateral M1-STN beta power without significant modulation in M1-STN coherence’\nPage 6 (141-142), Page 9 (216-219), Supplementary information, figure S3, page 12 (272-276), page 6 (196-200)\n- The authors picked 130 Hz PRF to mimic the frequency of DBS stimulation, have they used another PRF (such as 30 Hz or 60 Hz), where it should have minimal effect on STN beta as a control?\nMany thanks for raising this very important limitation. In our study, we did not explore the parameter space as our primary research question focused on whether TUS, when applied at DBS parameter sets (130Hz PRF), would have comparable effects to electrical stimulation with implanted electrodes. To confirm these effects are site specific we used an active control (ventricle) but do not have any alternative frequencies as controls. Previous studies of TUS to the same target would indicate that alternative PRFs may increase beta power (the opposite of our finding) which we have now highlighted in the limitations section of our discussion:\n‘Finally, this study does not investigate the effect of alternative parameter sets on beta power or bradykinesia, although previous work suggests that alternative PRFs may have opposing effects to those observed in this study41.’\nPage 11 (283-285)\nWe thank the reviewer for their kind words and their interpretation of our results.\nHowever, the manuscript would benefit from the following suggestions to improve its clarity and further validate some of the claims from the study.\nMajor points:\n- Always have the sham session before the active session could lead to a training effect improving outcomes in the active session. The authors might want to clarify that they avoided this problem by normalising all their results with the active ventricle (rather than with the sham).\nWe thank the reviewer for this helpful suggestion, this point of clarification was also raised by reviewer 2. This has been clarified in the manuscript\nPage 11 (286-289)\n- For the change in reaction time, to what extent have outliers influenced the results? In other words, what is the reaction time difference observed for the mean (compared to the median)?\nWe thank the reviewer for this suggested sensitivity analysis. We have now also calculated the mean difference in reaction time and find that there is a similar magnitude of reaction time reduction for the mean (18.6%) as for the median (17.7%).\nSince the reaction time data are not normally distributed, we have kept the results for the median in the main body of the text but include the results for the mean in the supplementary information and describe it as a sensitivity analysis.\nSupplementary information, figure S8, page 17 (326-328)\n- In this manuscript, the presented DBS-inspired TUS protocol can modulate disease-related pathological oscillations in the same direction as known therapies such as DBS for PD. The same biomarker (beta-band power) as for DBS is analysed and a reduction in beta-band power is reported. However, to further validate that the proposed protocol can provide similar outcomes as stablished DBS protocols, it would be good to include some comparison between the results obtained in the study and the results (e.i., common beta-band power reduction) obtained in stablished DBS studies. Is the beta-band power reduction obtained from TUS comparable to the beta-band power reduction commonly obtained with DBS?\nWe thank the reviewer for this comment and agree that this comparison with published DBS literature was lacking in our discussion.\nTo address this we have now included a paragraph outlining the common beta-band reduction seen in the literature for both STN and GPi DBS, as well as STN beta-band suppression when applying DBS to the GPi.\nPage 10 (261-273)\n- In the discussion, using acoustic lenses is mentioned as an advantage of the current study. Could the authors discuss how lenses enabled the current study? What would have happened without lenses (e.g. stimulating adjacent tissue)?\nWe have now elaborated on this statement and referenced the evidence behind the use of acoustic lenses when compared to alternatives.\nSince we used an unfocused transducer, an acoustic lens is necessary to create a TUS focus at target.\n‘Additional strengths include the double blinding to condition and the use of personalised acoustic lenses which allow standardisation of peak pressures and bespoke participant-specific steering of the ultrasound focus in all three axes (medial-lateral/anterior-posterior/superior-inferior)51,52’\nPage 11 (289-292)\nMinor points:\n- p. 6: (right -> (right)\nMany thanks, this has been corrected.\nPage 6 (137)\n- Fig. 3A: it is difficult to see differences at the moment. Maybe the colour scheme could be changed so that active is in dark colours and sham in lighter shades of the same colours (e.g. GPi in dark/light red and Ventricle in dark/light blue).\nMany thanks for this suggestion, we have now implemented this in the revised Fig 3A.\nPage 6 (148)\n- The placement of Figures 1 and 2, and Table 1, could be improved to make the manuscript clearer. It is recommended to locate the Figures and Tables in the order that they are referred to in the manuscript (e.g., Table 1 is referred to first, so it should be located before Figure 1 and 2 instead of after).\nMany thanks, this has been amended.\nPage 3 (72)\n- On the supplementary materials, for easier reproducibility of the study, it would be good to specify if the EEG electrodes used are water or gel based, since both options seem to be available for the described amplifier.\nMany thanks, this has now been clarified.\n‘Individual electrodes were placed at FP1, FP2, F3, Fz, F4, C3, Cz, C4, P3, Pz and P4 with Ten20 conductive paste. Since we delivered TUS through the temporal window, these midline electrodes were selected to ensure that our EEG electrodes would not interact with our ultrasound field. Electrodes were located using the anatomical landmarks outlined in the 10-20 system for localizing EEG electrodes.’\nSupplementary Information, page 6 (167-171)\n- Fig. S3B is too small to be read correctly. Please, increase the size of the individual plots.\nMany thanks, these plots have now been increased in size with an increase in font size.\nSupplementary Information, figure S4, page 13 (280)\n- On the supplementary materials, it would be good to describe how were the EEG electrodes located. Di you make use of a commercially available EEG cap as is common with the described amplifier or did you place the electrodes directly on the patients' head? Did the placement of the electrodes interfere in any case with the placement of the transducer?\nMany thanks, this has now been clarified.\n‘Individual electrodes were placed at FP1, FP2, F3, Fz, F4, C3, Cz, C4, P3, Pz and P4 with Ten20 conductive paste. Since we delivered TUS through the temporal window, these midline electrodes were selected to ensure that our EEG electrodes would not interact with our ultrasound field. Electrodes were located using the anatomical landmarks outlined in the 10-20 system for localizing EEG electrodes.’\nSupplementary Information, page 6 (167-171)\nWe thank the reviewer for their comments above."}, {"round": 2, "reviewer_comments": {"reviewer_1": "I am satisfied with the authors' responses. Thanks.", "reviewer_2": "The authors have adequately addressed my questions and provided clarification and details that have strengthened their manuscript.", "reviewer_3": "The authors have successfully addressed all points.", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": ""}]

Decision letter