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Systematic examination of low-intensity ultrasound parameters on human motor cortex excitability and behavior

  1. Anton Fomenko  Is a corresponding author
  2. Kai-Hsiang Stanley Chen
  3. Jean-François Nankoo
  4. James Saravanamuttu
  5. Yanqiu Wang
  6. Mazen El-Baba
  7. Xue Xia
  8. Shakthi Sanjana Seerala
  9. Kullervo Hynynen
  10. Andres M Lozano  Is a corresponding author
  11. Robert Chen  Is a corresponding author
  1. Krembil Research Institute, University Health Network, Canada
  2. Department of Neurology, National Taiwan University Hospital Hsin-Chu Branch, Taiwan
  3. Division of Neurology, Department of Medicine, University of Toronto, Canada
  4. Sunnybrook Research Institute, Canada
  5. Division of Neurosurgery, Department of Surgery, Toronto Western Hospital, University of Toronto, Canada
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Cite this article as: eLife 2020;9:e54497 doi: 10.7554/eLife.54497

Abstract

Low-intensity transcranial ultrasound (TUS) can non-invasively modulate human neural activity. We investigated how different fundamental sonication parameters influence the effects of TUS on the motor cortex (M1) of 16 healthy subjects by probing cortico-cortical excitability and behavior. A low-intensity 500 kHz TUS transducer was coupled to a transcranial magnetic stimulation (TMS) coil. TMS was delivered 10 ms before the end of TUS to the left M1 hotspot of the first dorsal interosseous muscle. Varying acoustic parameters (pulse repetition frequency, duty cycle, and sonication duration) on motor-evoked potential amplitude were examined. Paired-pulse measures of cortical inhibition and facilitation, and performance on a visuomotor task was also assessed. TUS safely suppressed TMS-elicited motor cortical activity, with longer sonication durations and shorter duty cycles when delivered in a blocked paradigm. TUS increased GABAA-mediated short-interval intracortical inhibition and decreased reaction time on visuomotor task but not when controlled with TUS at near-somatosensory threshold intensity.

Introduction

There is a pressing need to develop precise and effective methods of non-invasive brain stimulation, both as tools to investigate neurophysiology and as potential therapeutic modalities for circuitopathies such as Parkinson’s disease. Low-intensity transcranial ultrasound (TUS) is a promising noninvasive brain stimulation technique actively being studied for its ability to reversibly modulate mammalian brain activity (Fomenko et al., 2018; Naor et al., 2016; Tyler et al., 2008). By focusing the propagation of acoustic waves through the skull, a higher degree of spatial specificity and deeper targeting can be achieved over other noninvasive stimulation methods such as transcranial magnetic stimulation (TMS) and transcranial direct-current stimulation (Fomenko and Lozano, 2019; Pasquinelli et al., 2019).

Proof-of-concept studies using TUS on healthy human volunteers have elicited a broad range of suppressive and inhibitory effects on cortical and subcortical target areas, although it remains unknown how varying the sonication parameters (i.e.: sonication duration, duty cycle, frequency) affects the magnitude and direction of neural activity within human brain circuits (Ai et al., 2018; Lee et al., 2016b; Legon et al., 2014; Sanguinetti et al., 2014). To date, published human experiments have demonstrated electrophysiological suppression of sensory-evoked potentials, task performance alteration, modulation of cortical oscillatory dynamics, and corresponding activation on fMRI (Lee et al., 2016a; Legon et al., 2014; Mueller et al., 2014). Self-reported outcomes induced by TUS have included mood improvement (Hameroff et al., 2013), phosphene detection (Lee et al., 2016b; Schimek et al., 2020), and tactile limb sensations (Lee et al., 2015; Lee et al., 2016a), when frontal, somatosensory, and occipital cortices were targeted, respectively. However, each study examined only a single set of stimulation parameters, and it is not known whether human neural circuits are more sensitive to titration of a particular parameter, nor whether a dose-response effect exists. Furthermore, studies reported heterogeneous effects of TUS on targets such as the motor cortex, with some studies showing suppression of motor-evoked potentials (MEPs) (Legon et al., 2018a), and others demonstrating increased cortical excitability after prolonged sonication (Gibson et al., 2018).

Here, we describe the first double-blinded study examining the effects of systematically varying sonication parameters of TUS applied to the primary motor cortex (M1) of healthy human participants (Figure 1). We tested the M1 because measurement of MEPs from TMS provides an objective and readily quantifiable measure of M1 excitability. Using a combined TMS-TUS stimulation approach (Legon et al., 2018a), we hypothesize that some parameters have a linear suppressive dose-response effect, and others may be less important. We then examine the effects of TUS on several intracortical circuits using paired-pulse TMS. Finally, we study the effects of applying TUS to M1 in a visuo-motor behavioral task, to determine whether sonication alone can affect higher order synaptically connected circuits involved in motor initiation and execution.

Figure 1 with 1 supplement see all
Experimental setup.

(A) Diagram depicting the primary motor cortex hand knob in coronal section, with the ultrasound transducer coupled to the scalp via compressible hydrogel and held to the underside of a transcranial magnetic stimulation (TMS) coil with a 3D printed plastic holder (not to scale). The transducer and holder measure 10 mm thick, allowing for adequate magnetic stimulation of cortical neuron populations. (B) Photograph of the custom TUS-TMS delivery apparatus components, showing: (i) the active face of the TUS transducer, (ii) plastic 3D-printed holder, and (iii) 70 mm figure-eight TMS coil. The yellow dashed line indicates the recessed cutout for the hydrogel coupling pad.

Results

Safety and precision of acoustic and magnetic stimulation

At basic parameters, the estimated intracranial intensity of TUS at basic parameters was calculated to correspond to ISPTA = 0.69 W/cm2, ISPPA = 2.32 W/cm2, and a mechanical index of 0.19. The FDA cephalic acoustic exposure guidelines are defined as ISPTA ≤94 mW/cm2, and either MI ≤ 1.9 or derated ISPPA ≤ 190 W/cm2 (Duck, 2007; United States Food and Drug Administration, 2017). Intensity values for other parameters examined in this study can be found in Table 1. Cumulative verum sonication time per participant ranged between 157.7 and 192.5 s. No subjects reported any adverse effects, but two (13%) participants reported a transient warm sensation at the sonication site after multiple experimental blocks. Neurological examinations after each experimental visit were normal, supporting the safety profile of the current pulsing schemes. The probabilistic averaged map of the induced electromagnetic current across all participants (n = 16) confirmed that the M1 hand knob was effectively targeted with TMS (Figure 2A). Similarly, the individual simulations of ultrasound propagation for each participant confirmed acoustic targeting of a portion of M1, as well as underlying corticospinal white matter. (Figure 2—figure supplement 1). The focal length within the −6 dB of the focus was 23 mm, and the focal width at half-maximum of our transducer was determined to be 6 mm (Figure 3).

Figure 2 with 1 supplement see all
Electromagnetic and acoustic characterization.

(A) Characterization of TMS-induced electromagnetic current distribution around the left primary motor cortex, anterior to the central sulcus (dashed line), averaged across 16 participants rendered using SIMNIBS. (B) Simulation of transcranial ultrasound pressure field in a characteristic participant (k-Wave MATLAB toolbox), showing high pressure at the skull, and a cigar-shaped volume of tissue activation over the motor cortex and underlying white matter, with a focus centered 30 mm away from the face. (C) Scalp position of the US transducer on the same participant as determined by neuronavigation software during the experiment on axial and (D) sagittal views, showing the underlying hand knob of the precentral gyrus, anterior to the central sulcus (dashed line).

Transducer characterization.

(A) Characterization of the longitudinal (Z-axis) of the two-element 500 kHz transducer with focus 30 mm away from the face, within a simulated free-water field. Within −6 dB and −3 dB of the focal point, the focal lengths are 23 mm (large rectangle), and 17 mm (small rectangle), respectively. (B) Radial (X and Y axes) hydrophone quantification of the transducer at 30 mm away from the active face (top), with focal width at half maximum outlined in black square. (C) Radial pressure profile through the origin demonstrating a focal width at half maximum of 6 mm.

Table 1
Calculated extracranial and estimated intracranial acoustic intensity values by parameter.
10% DC, PRF = 1000 Hz, SD 0.1–0.5 s30% DC, PRF = 1000 Hz, SD 0.1–0.5 s50% DC, PRF = 1000 Hz, SD 0.1–0.5 s
Extracranial (quantified)Intracranial (estimated)Extracranial (quantified)Intracranial (estimated)Extracranial (quantified)Intracranial (estimated)
ISPTA (W/cm2)0.930.232.780.694.631.16
ISPPA(W/cm2)9.262.329.262.329.262.32
MI (unitless)0.740.190.740.190.740.19

Effects of TUS parameters on single-pulse MEPs

The mean TMS stimulator intensity required to generate a 0.5 mV MEP was 58% (range 50–80%). Basic parameters suppressed MEP amplitudes (0.33 ± 0.06 mV; n = 12) compared to active sham (0.63 ± 0.09 mV; n = 12; two-tailed paired t-test, t = 6.36, df = 11, p<0.001), and basic parameters also suppressed mean MEP voltage (0.21 ± 0.04 mV; n = 4) compared to inactive sham (0.49 ± 0.09 mV; n = 4; two-tailed paired t-test, t = 5.41, df = 3, p=0.012) (Figure 4).

Effect of baseline ultrasound versus active and inactive sham on TMS-induced resting motor-evoked potential (MEP) amplitudes as measured by FDI EMG.

(A) Baseline parameters (PRF = 1000 Hz, DC = 30%, SD = 0.5 s) suppressed mean MEP voltage compared to active sham, or powered transducer pointing upward (p<0.001, paired t-test) N = 12. (B) Individual MEP values by participant by condition (C) Baseline parameters suppressed mean MEP voltage compared to inactive sham, or unpowered transducer pointing toward the scalp (p=0.012, paired t-test) N = 4. (d) Individual MEP values by participant by condition. Error bars represent standard error.

Varying the duty cycle showed a significant effect on MEP amplitude (RM one-way ANOVA, F = 5.98, df = 15, p=0.015). Compared to sham (0.63 ± 0.14 mV; n = 16), post-hoc two-tailed paired t-tests with correction for multiple comparisons showed that TUS at a duty cycle of 10% suppressed MEPs significantly (0.36 ± 0.06 mV; n = 16; t = 2.93, df = 15, adjusted p=0.027). Sonication at 30% DC (0.48 ± 0.10 mV; n = 16; t = 2.08, df = 15, adjusted p=0.10) and 50% DC (0.44 ± 0.08 mV; n = 16; t = 2.12, df = 15, adjusted p=0.10) had no significant effect (Figure 5).

Figure 5 with 2 supplements see all
Effects of ultrasound parameters on TMS-induced resting peak-to-peak motor-evoked potential (MEP) amplitudes as measured by FDI EMG (N = 16).

Means of MEPs were plotted across the different sub-experiments which varied different parameters (A) Duty cycle (p=0.015; RM one-way ANOVA), 10% DC suppressed MEPs compared to sham (p=0.027, paired t-test). (B) Sonication duration had an effect proportional to the length of sonication (p<0.001; RM one-way ANOVA), with significant suppression compared to sham with 0.4 s (p=0.003), and 0.5 s (p=0.004). (C) Varying the pulse repetition frequency with fixed DC (p=0.08; RM one-way ANOVA) or (D) adjusted DC to keep constant burst duration (p=0.31; RM one-way ANOVA) did not have a significant effect. Error bars represent standard error. Asterisks are indicative of a significant post-hoc two-tailed paired t-test, and p-values are adjusted with the Holm-Bonferroni method (α = 0.05).

Varying the sonication duration also had a significant effect on MEP amplitude (RM one-way ANOVA, F = 15.12, df = 15, p<0.001). Post-hoc two-tailed paired t-tests with correction for multiple comparisons showed significant suppression compared to sham (0.89 ± 0.13 mV; n = 16) when sonicating for 0.4 s (0.47 ± 0.08 mV; n = 16; t = 4.29, df = 15, adjusted p=0.003), and 0.5 s (0.47 ± 0.11 mV; n = 16; t = 4.12, df = 15, adjusted p=0.004). Sonication for 0.1 s (0.87 ± 0.15 mV; n = 16; t = 0.28, df = 15, adjusted p=1.00), 0.2 s (0.84 ± 0.14 mV; n = 16; t = 0.73, df = 15, adjusted p=0.92), or 0.3 s (0.66 ± 0.11 mV; n = 16; t = 2.33, df = 15, adjusted p=0.12) was not significantly different from sham stimulation.

Varying the PRF with fixed DC did not have a significant effect on evoked potentials (RM one-way ANOVA, F = 2.90, p=0.08). Compared to the sham condition (0.63 ± 0.12 mV; n = 16), post-hoc two-tailed t-tests with correction for multiple comparisons did not reveal a difference for 200 Hz (0.41 ± 0.07 mV; n = 16; t = 1.96, df = 15, adjusted p=0.162), 500 Hz (0.41 ± 0.07 mV; n = 16; t = 1.94, df = 15, adjusted p=0.170), or 1000 Hz (0.42 ± 0.08 mV; n = 16, t = 1.86, df = 15, adjusted p=0.193).

Similarly, varying the PRF with adjusted DC to ensure a fixed pulse duration did not have a significant effect on MEPs (RM one-way ANOVA, F = 1.22, p=0.31). Compared to the sham condition (0.56 ± 0.09 mV; n = 16), post-hoc two-tailed t-tests with correction for multiple comparisons did not reveal a difference for 200 Hz (0.50 ± 0.08 mV; n = 16; t = 0.92, df = 15, adjusted p=0.689), 500 Hz (0.44 ± 0.09 mV; n = 16; t = 1.67, df = 15, adjusted p=0.261), or 1000 Hz (0.55 ± 0.11 mV; n = 16, t = 0.14, df = 15, adjusted p=1.00).

Effects of blocked versus interleaved parameter variation

Varying the duty cycle with interleaved trials (Figure 5—figure supplement 1) showed a significant effect on MEP amplitudes (n = 16, F = 3.94, df = 15, p=0.02; RM one-way ANOVA). Compared to sham (0.56 ± 0.06 mV; n = 16), post-hoc two-tailed paired t-tests with correction for multiple comparisons showed that a DC of 10% (0.41 ± 0.05 mV; n = 16; t = 3.12, df = 15, adjusted p=0.020) significantly suppressed MEPs, while a DC of 30% (0.48 ± 0.05 mV; n = 16; t = 1.47, df = 15, adjusted p=0.35) and 50% (0.45 ± 0.07 mV; n = 16; t = 2.06, df = 15, adjusted p=0.14) did not.

Blocked variation of the DC also showed a significant effect on MEP amplitudes (n = 16, F = 11.2, df = 15, p<0.0001; RM one-way ANOVA). Compared to sham (0.59 ± 0.07 mV; n = 16), post-hoc two-tailed paired t-tests with correction for multiple comparisons showed that a DC of 10% (0.34 ± 0.04 mV; n = 16; t = 3.51, df = 15, adjusted p=0.010) and 30% (0.28 ± 0.05 mV; n = 16; t = 4.54, df = 15, adjusted p=0.002) significantly suppressed MEPs, while a DC of 50% (0.49 ± 0.06 mV; n = 16; t = 1.59, df = 15, adjusted p=0.297) had no significant effect. Post-hoc analysis of individual trials (Figure 5—figure supplement 2) showed no significant differences between early and late trials in the sham, 10%, 30%, or 50% conditions (n = 16, adjusted p=0.31, 0.31, 0.81, 0.81; paired t-tests).

Randomly interleaving the pulse repetition frequency with constant DC parameters did not result in significant differences across conditions (n = 16, F = 2.24, df = 15, p=0.1226; RM one-way ANOVA). However, grouping parameters in a blocked paradigm resulted in significant effects on MEP amplitudes (n = 16, df = 15, F = 14.0, p<0.001; RM one-way ANOVA). Compared to sham (0.75 ± 0.10 mV; n = 16), post-hoc two-tailed paired t-tests with correction for multiple comparisons showed that a PRF of 200 Hz (0.56 ± 0.10 mV; n = 16; t = 4.43, df = 15, adjusted p=0.001), 500 Hz (0.50 ± 0.09 mV; n = 16; t = 5.29, df = 15, adjusted p<0.001), and 1000 Hz (0.37 ± 0.07 mV; n = 16; t = 5.53, df = 15, adjusted p<0.001) all significantly suppressed MEPs. Post-hoc analysis of individual trials showed no significant differences between early and late trials in the sham, 100, 500, or 1000 Hz conditions when DC was held constant (n = 16, adjusted p=0.09, 1.0, 1.0, 1.0; paired t-tests).

Interleaving the pulse repetition frequency with adjusted DC parameters did not result in significant differences across conditions (n = 16, F = 0.50, df = 15, p=0.648; RM one-way ANOVA). However, grouping parameters in a blocked paradigm resulted in significant effects on MEP amplitudes (n = 16, df = 15, F = 9.39, p=0.003; RM one-way ANOVA). Compared to sham (0.72 ± 0.11 mV; n = 16), post-hoc two-tailed paired t-tests with correction for multiple comparisons showed that a PRF of 200 Hz (0.50 ± 0.09 mV; n = 16; t = 3.49, df = 15, adjusted p=0.011), 500 Hz (0.45 ± 0.09 mV; n = 16; t = 5.33, df = 15, adjusted p<0.001), and 1000 Hz (0.42 ± 0.09 mV; n = 16; t = 3.16, df = 15, adjusted p=0.021) all significantly suppressed MEPs. Post-hoc analysis of individual trials showed no significant differences between early and late trials in the sham, 100, 500, or 1000 Hz conditions (n = 16, adjusted p=0.92, 1.0, 1.0, 1.0; paired t-tests).

Effects of TUS on active MEP

During muscle contraction, active MEP amplitudes did not significantly differ between sham (7.65 ± 0.75 mV; n = 12) compared to TUS (7.79 ± 0.88 mV; n = 12; two-tailed paired t-test, t = 0.44, df = 11, p=0.67). The silent period (SP) duration after sham (131 ± 10 ms; n = 12) did not significantly differ from TUS (138 ± 9 ms; n = 12; two-tailed paired t-test, t = 0.52, df = 11, p=0.61). (Figure 6).

Results of single-pulse experiments investigating active MEP, including (A) amplitude, and (B) silent period (SP) duration of sham and TUS condition.

N = 12. Error bars represent standard error.

Paired pulse TMS-TUS

Among all subjects, the mean TMS stimulator output required to produce a 1 mV MEP with sonication at basic parameters (78.8, range 55–97% MSO; n = 12) was significantly higher (mean of differences = +1.3%; n = 12; two-tailed paired t-test, t = 2.86, df = 11, p=0.02) than the output required to produce a 1 mV MEP under sham (77.4 range 57–99% MSO; n = 12) (Figure 7).

Results of the paired-pulse experiments.

A mean increase of +1.3% in stimulator intensity is required to elicit a 1 mV MEP under the TUS condition, compared to sham (p=0.016, paired t-test). After adjusting stimulator output to compensate, suppression, the TS and S1 conditions are not significantly different between sham and TUS. Results shown include short-interval intracortical inhibition (SICI), intracortical facilitation (ICF), long-interval intracortical inhibition (LICI), and short-interval cortical facilitation (SICF). N = 12. The data is plotted as a ratio to the test stimulus (TS) or stimulus alone (S1) amplitude. Ratios higher than 1.0 indicate facilitation and ratios below 1.0 indicate inhibition. TUS applied before the test stimulus significantly reduced SICI compared to sham (p=0.038, paired t-test). Error bars represent standard error.

Short-interval intracortical iinhibition at 2 ms was significantly increased with TUS application at basic parameters (0.40 ± 0.07 ratio to TS; n = 12) compared to sham (0.60 ± 0.10 ratio to TS; n = 12; two-tailed paired t-test, t = 2.32, df = 11, p=0.038). Long-interval intracortical inhibition (LICI) at 100 ms, short-interval intracortical facilitation (SICF) at 1.5 ms, SICF at 2.9, and intracortical facilitation (ICF) at 10 ms revealed no significant differences between sham and active TUS application time-locked to the first stimulus (n = 12; two-tailed t-tests, df = 11, p-values 0.91, 0.66, 0.75, 0.90, respectively).

Behavioral task

The pooled reaction time to the presentation of visual stimulus was significantly shorter with TUS applied at basic intensity and parameters (363 ± 68 ms; n = 12) compared to sham stimulation (521.57 ± 20 ms; n = 12; two-tailed paired t-test, t = 2.27, df = 11, p=0.04) (Figure 8).

Figure 8 with 1 supplement see all
Results of the visuo-motor behavioral task experiment, showing reduction in a pooled reaction time to the presentation of visual stimulus with TUS at 2.32 W/cm2, p=0.043, paired t-test (N = 12).

Reaction times for TUS at 2.32 W/cm2 compared to 0.54 W/cm2 were not significantly different (N = 4). Bottom row: Comparison of Sham and TUS trials sorted by distance to target (near, medium, far). N = 12. Asterisk denotes significance p<0.05 on paired t-test. Error bars represent standard error.

In four additional subjects, the reaction times were not significantly different between TUS applied at 0.54 W/cm2 subthreshold intensity (305 ± 45 ms; n = 4) compared to basic 2.32 W/cm2 intensity (301 ± 44 ms; n = 4; two-tailed paired t-test, t = 2.68, df = 3, p=0.08). There was no significant difference between application of TUS or sham in the mean total distance travelled, deviation scores, or time taken per trial (one-way, RM one-way ANOVA, p>0.05). This remained the case even when sorting by target location.

Discussion

To our knowledge, our experiment represents the first systematic investigation of low-intensity ultrasound parameters on human cortical activity. We found a dose-response effect of prolonging the sonication duration on the suppression of TMS-induced MEPs, up to a maximum duration of 0.5 s, and show that a lower duty cycle (10%) has the greatest efficacy in suppressing motor cortex potentials. Similar studies with large animals appear to be in line with our data. A recent systematic examination of TUS parameters in sheep showed that low duty cycles (<10%) and long sonication durations (~1 min) yielded suppressive effects on sensory-evoked potentials, while high duty cycles (>30%) and short sonication durations (<500 ms) were excitatory on the motor cortex (Yoon et al., 2019) However, unlike our study, suppressive parameters were only applied to the sensory cortex, and the excitatory parameters to the motor cortex, and the animals were de anesthetized during the experiment.

Interestingly, our initial results show that although a particular combination of parameters can robustly suppress TMS-elicited cortical activity when applied in a sham-controlled block design (Figure 4), some parameters such as PRF when randomized and varied individually do not have the same robust effect, whereas others such as sonication duration do (Figure 5C–D). To address this discrepancy, we conducted follow-up experiments (Figure 5—figure supplement 1) in which we delivered three distinct parameter sets in blocked, and interleaved fashion. We found that sonication at 10% DC consistently results in suppression regardless of experimental design, whereas 30% DC only results in suppression when applied in blocked fashion. A duty cycle of 50% did not show any difference compared to sham regardless of experimental design. In contrast, we observed that sonication at three different pulse repetition frequencies (200, 500, and 1000 Hz) applied in a blocked design resulted in reduced cortical excitability compared to sham, whereas interleaving these parameters yielded no significant difference. These results held whether duty cycle was fixed, or whether the DC was adjusted to maintain an equal burst duration.

In summary, our findings suggest that when delivered in blocked fashion, longer sonication durations, lower duty cycles, and all three PRFs tested yield effective suppression of TMS-elicited MEPs. The trend toward greater suppression with increasing blocked PRF agree with recent literature, where higher PRF (1500 Hz) in combination with low duty cycles were found to be more effective than lower PRF (300 Hz) in neuromodulation of mouse motor cortex in vivo (King et al., 2013) and in vitro (Manuel et al., 2020). Furthermore, recent large animal studies reveal a bidirectional neuromodulation effects of varying TUS parameters (Yoon et al., 2019). As such, interleaving different parameters in short succession may lead to spillover effects, due to the random order of parameter delivery. From animal studies where randomized delivery of TUS parameters was studied, non-linear effects were found, possibly related to non-linear piezoelectric accumulation across the neural membrane capacity under the Neuronal Bilayer Sonophore model (Kim et al., 2014; Plaksin et al., 2014). In addition, excitatory or inhibitory changes in short-term plasticity may occur with repeated TUS stimulation, similar to those observed with repeated magnetic (Watanabe et al., 2014) and electrical stimulation (Udupa et al., 2016). These are currently under study in our laboratory and in emerging reports on LITUS short-term plasticity in animal models (Yu et al., 2019).

To test whether a progressive accumulation of inhibition might be responsible for the observed difference in results between the blocked and interleaved study designs, we performed a post-hoc analysis of our blocked experiments, stratifying by trial number across all participants (Figure 5—figure supplement 2). Within each parameter condition, we did not detect a significant difference between the magnitude of early and late trials and conclude that there is no temporal accumulation of MEP-suppressive effects over blocks of 15 trials, corresponding to a block length of approximately 90 s. Instead, the suppression appears to be instantaneous, with effective parameters increasing the likelihood of generating a lower TMS-elicited MEP, but not potentiating the effect of a subsequent stimulation. Notably, our blocked design features a 20 s pause between each set of 15 replicate stimulations, whereas interleaved delivery of random parameters involves an uninterrupted session of 60 stimulations. We speculate that this 20-s rest period may lead to a resetting of cortical excitability to a more TUS-sensitive state. The lack of resetting could play a role in our observation of less robust effects when certain parameters are randomized in succession.

Possibly because of these longer term effects and the heterogeneity of experimental methods and sonication parameters used between human studies, conflicting reports have emerged. For instance, Legon et al., 2018a applied simultaneous 500 kHz TUS and TMS to the M1 of healthy human subjects to examine effects on the MEP, intracortical excitability, and reaction time (Legon et al., 2018b). TUS was found to attenuate TMS-evoked MEP amplitude, reduce ICF, but with no effect on intracortical inhibition, and improve reaction time. However, another study by Gibson found that application of continuous sonication for 2 min to M1 increased subsequent TMS-induced corticospinal excitability compared to baseline (Gibson et al., 2018). Notably, both studies were single-blinded, used a single set of sonication parameters and used a blocked study design to examine sham and active TUS conditions. The study by Gibson also used an unfocused imaging transducer operating at high fundamental frequencies (2.32 MHz), decreasing the likelihood that the acoustic energy reached the brain due to the lower transmission coefficient of the skull at this frequency range (Mueller et al., 2017). Since Gibson’s study measured motor cortical excitability only after ultrasound was applied for several minutes, the potential neuromodulatory mechanisms may have opposing effects on excitability compared to the on-line TUS-TMS paradigm studied herein, and in Legon’s experiments (Gibson et al., 2018; Legon et al., 2018b).

Since the piezoelectric element within our ultrasound transducer emits a slightly audible buzzing tone when activated, also reported in other human TUS-TMS studies (Legon et al., 2018b), we implemented a novel method of delivering a masking audio stimulus to reduce potential auditory confounding effects. This is particularly salient because our sham conditions were randomly interleaved with active TUS delivery in order to reduce the effects of time-related fluctuations of cortical excitability and to blind the experimenter (Huber et al., 2013). Recent literature demonstrating potential auditory confounding in rodent TUS cortical neuromodulation (Guo et al., 2018; Niu et al., 2018; Sato et al., 2018), highlight the importance of strategies aimed at prevent conscious or unconscious auditory contamination in ultrasound neuromodulation experiments. Human TMS studies have shown that pairing TMS with an audible stimulus, especially human speech, can lead to increases in cortical excitability. For instance, TMS to the M1 hand area, concomitant with different auditory stimuli Flöel et al., 2003 have shown that pure tonal sounds do not alter the magnitude of MEP, while language perception significantly increases motor excitability. In agreement, studies examining TMS motor response of the leg (Liuzzi et al., 2008) and tongue (Fadiga et al., 2002) show a motor facilitation with semantic speech stimuli, but not with audible tonal sounds or noise. In our study, we did not find facilitation, but rather decreased excitability with application of TUS to the hand area of M1, while controlling for audible tones in all delivered conditions including sham. Furthermore, our audible masking tone was played for the same 0.5 s duration for every experimental condition, including the sham, relative to the TMS pulse, and we therefore expect that any TMS-acoustic pairing would be controlled for.

A recent report examining masked and unmasked delivery of TUS conditions highlights the electrophysiological basis of potential auditory confounds in human single-transducer TUS experiments (Braun et al., 2020). In particular, this report shows that an audio-masked TUS sonication and audio-masked sham evoke identical auditory ERP's, whereas TUS alone without a mask evokes a significantly different ERP, and moreover can also be reliably differentiated from sham by participants (Braun et al., 2020). This report appears to corroborate our strategy and highlights the importance of controlling for auditory confounds in future human ultrasound neuromodulation studies.

The results from our paired-pulse experiments suggest that TUS potentiates short-interval intracortical inhibition (SICI). Previous pharmacological and physiological investigations have shown that SICI is a marker of GABAA-receptor-mediated inhibition and may reflect activity of inhibitory interneurons (Stagg et al., 2011; Udupa et al., 2010). Our finding may suggest that cortical interneurons in layers II/III which are well-encompassed within the acoustic focus (Figure 2B) may be preferentially sensitive to ultrasound applied for long durations and a short duty cycle and confer an overall suppressive effect by increasing GABAA activity. Indeed, emerging electrophysiology work in animal models is suggesting that sensitivity to low-intensity TUS is mediated not only by parameter selection but also that excitatory and inhibitory neurons have different sensitivities to sonication (Yu et al., 2019). One possible mechanobiological explanation for the cell-type selective effects of ultrasound is the neuronal intramembrane cavitation excitation (NICE) model. Predictive modeling studies within the NICE framework, corroborated with animal electrophysiological studies are suggesting that inhibitory cortical neurons are hypersensitive to discontinuous pulsed bursts of TUS (Plaksin et al., 2014; Plaksin et al., 2016). The T-type voltage-gated calcium channels within these neurons predispose to accumulation of electrical charge between short bursts of ultrasound, predisposing to inhibitory neural activation during low duty-cycle sonication (Plaksin et al., 2016; Plaksin et al., 2014).

In Legon’s study, TUS was found to attenuate ICF but did not affect intracortical inhibition (Legon et al., 2018b). Several methodological differences from our study should be noted, such as time-locking the ultrasound to begin 100 ms prior to the conditioning stimulus, whereas our sonication was time-locked to 490 ms prior to the first TMS pulse. Since sonication duration is a key parameter of TMS-mediated EMG suppression according to our findings, the earlier onset of TUS in our experiment relative to the CS and TS pulses might have potentiated greater GABAA-mediated inhibition, despite the total sonication time being the same. In addition, we randomized the TS-alone condition with the paired pulse conditions in our experiment, whereas (Legon et al., 2018b) compared to a baseline MEP conducted in a temporally discrete block, potentially introducing variability inherent in MEP fluctuations over time (Ellaway et al., 1998).

The cortical silent period, thought to be a marker of GABAB activity (Tremblay et al., 2013; Ziemann et al., 2015), did not significantly change with application of TUS in our experiments. Likewise, we did not observe an effect of TUS on LICI, another TMS marker of GABAB-ergic inhibition (Premoli et al., 2014). Reports using voltage-clamp and in-vivo experiments are beginning to show that TUS has preferential effects on certain ion channel currents (Kubanek et al., 2016; Yu et al., 2019), and our results may suggest that the ligand-gated ionotropic GABAA channel may be such a mechanosensitive substrate.

Similar to prior reports of M1 TUS in humans (Gibson et al., 2018; Legon et al., 2018b), we were unable to evoke motor potentials or EMG activities by application of TUS alone to the motor cortex. Interestingly, studies in small rodents consistently show EMG activity and frank motor contractions of hindlimbs (Gulick et al., 2017), whiskers (Mehić et al., 2014), and tail (Yoo et al., 2013) contralateral to the sonicated motor cortex, as well as inducing eye movements and pupillary dilatation (Kamimura et al., 2016). Although the majority of these studies were done under complete or partial anesthesia, recent studies with awake, freely moving rats have also found that TUS alone can also trigger truncal and limb movements (Lee et al., 2018). Applying a similar paradigm to larger animals such as sheep failed to trigger visible motor contractions with TUS alone, although time-locked and parameter-dependent EMG activity in the corresponding hindlimbs was seen (Lee et al., 2016c; Yoon et al., 2019). In the even larger human brain, we speculate that the narrow acoustic focus of a single transducer targets a small proportion of the entire motor cortex and either recruits insufficient cortical neurons to trigger a contraction, or preferentially recruits an inhibitory ensemble of interneurons thereby having the opposite effect. Moreover, recent studies with deafened rodents (Guo et al., 2018; Sato et al., 2018) have challenged the notion that low-intensity TUS can directly mediate small animal motor cortical activation, instead proposing that an indirect auditory mechanism, or a peripherally mediated startle response due may be involved. The narrow ellipsoid-shaped acoustic focus may also explain why we observed an increase in the stimulator output required to generate a 1 mV MEP (Figure 7A) and reduction in resting MEP amplitude, but found no change in the active motor threshold or active MEP amplitude (Figure 6), given the active condition is likely associated with larger area of activation in the motor cortex.

Safety

The healthy participants in this study reported no adverse effects after participating in study visits. Detailed neurological examinations after each visit were normal. The 2017 Federal Drug Administration (FDA) guidelines for adult cephalic ultrasound suggest a maximum ISPTA of 94 mW/cm2, a maximum ISPPA of 190 W/cm2, and a MI of <1.9 to avoid cavitation and heating (United States Food and Drug Administration, 2017). Prior experimental estimates of the attenuation of human skull bone show a −12 db (Deffieux and Konofagou, 2010) or a 3.7- to 4.1-fold drop in intensity (Legon et al., 2014) at the fronto-parietal bone. Based on these attenuation values and our quantification of the transducer in free water, we estimate that our intracranial intensity at the acoustic focus under baseline parameters is ISPTA = 0.64 W/cm2, a ISPPA = 2.32 W/cm2 and a MI of 0.19 (Table 1). Despite the ISPTA falling above the FDA acoustic exposure guidelines, in Europe, the International Electrotechnical Commission 60601-2-5 standard for low-intensity therapeutic ultrasound equipment suggests a higher limit of 3 W/cm2 on effective acoustic intensity (Duck, 2007). Indeed, computational models have suggested that typical low-intensity TUS parameters delivered for 0.5 s durations lead to negligible increases in brain temperature (4.27 × 10–3 °C). (Mueller et al., 2016). Although no human studies have reported any permanent adverse effects from application of low-intensity TUS (Legon et al., 2020), a single sheep study showed small microhemorrhages at the cortical site of sonication, albeit after prolonged repetitive (>500) trials at intensities (ISPPA = 10.5 W/cm2) higher than used in our study (Gaur et al., 2020; Lee et al., 2016c). The absence of edema, necrosis, or local inflammatory responses, as well as a lack of control group may implicate postmortem brain extraction as the cause of these findings, rather than sonication itself. Indeed, a follow-up sheep study by the same group using fewer consecutive sonications (<80 trials) and an inter-stimulus interval of 5 s, as in our study, did not yield any abnormalities on histological examination of sonicated brain tissue (Yoon et al., 2019).

Behavior

We report a reduction in mean motor reaction time with M1 TUS alone applied at basic parameters and intensity compared to sham. This result is consistent with a previous study, where subjects pressed a button when a visual stimulus appeared on the screen (Legon et al., 2018b). Contralateral M1 sonication 100 ms prior to the stimulus resulted in a significantly shortened response time compared to sham, although the magnitude of the difference was only about 10 ms. In our study, sonication was applied 250 ms prior, and the task was more complex; nevertheless, the sonication parameters and cortical location were similar, and we observed a larger effect size , although with higher variability from subject-to-subject. A potential mechanisms for this effect is TUS-mediated enhancement of surround inhibition (Beck and Hallett, 2011). In animal studies, GABA-antagonist drugs applied to M1 cause merging of motor hotspots (Schneider et al., 2002), and in primates, corticospinal pyramidal neurons are known to activate several nearby muscles (Andersen et al., 1975). If M1 TUS has a direct effect on reaction time, a possible mechanism might be potentiation of GABAA inhibitory interneuron activity surrounding the FDI hotspot, with a resulting sharpening of corticospinal motor output to the target muscle effectuating the movement. Although the slightly audible sound of the piezoelectric transducer element was masked with an audible tone, we cannot rule out intersensory facilitation (Diederich and Colonius, 1987; Forster et al., 2002) as contributing to the difference in reaction times between conditions. Given that acoustic waves are mechanical in nature, it is possible that a subtle tactile sensation on the scalp at the site of transducer placement might act as an extra sensory cue which is difficult to replicate with a sham protocol that delivers no acoustic energy. In a follow-up control experiment with four participants, the reaction times were not significantly different when a lower near-somatosensory threshold intensity (0.54 W/cm2) was used when compared to the 2.32 W/cm2 intensity used in all other experiments. Since individual somatosensory thresholds differ, and the threshold of transcranial acoustic energy necessary to affect motor tasks is unknown, further experiments are necessary to disentangle the role of somatosensory confounding in this behavioral task.

We were unable to detect any significant difference in mean trajectory distance, deviation score, or in time taken for each trial. This is consistent with our hypothesis that low-intensity TUS has a mainly in short-term and focal effect on neural circuits. Since sonication was only maintained for a total of 250 ms from the onset of the visual cue, we did not expect to see differences between conditions in the coordination of the relatively long motor task that underlies the full trajectory.

Limitations

From a technical standpoint, precise targeting of intracranial structures remains challenging with single-element US transducers. Although we did not use on-line neuronavigation, we estimate that the epicenter of the transducer fell directly over the primary motor cortex based on post-hoc subject-specific MR image registration and acoustic simulations (Figure 2—figure supplement 1), inter-participant variability in skull thickness and cortical geometry may distort the geometry and result in a diminished actual intensity at the acoustic focus (McDannold et al., 2004; Mueller et al., 2017). Because of the complex propagation of ultrasound through intervening tissues of different acoustic impedances (i.e.: gel pad, hair, trapped air bubbles, scalp, skull, dura), the precise proportion of M1 targeted by the acoustic field, nor the precise intensity at the focus cannot be precisely ascertained. In addition, simultaneous sonication of all cortical sublayers with different neural population, as well as underlying white matter may have contributed to the MEP variability we saw in some subjects. Although our acoustic simulations were based on individualized brain imaging and transducer positions were captured by neuronavigation, the simulations are limited to two-dimensions, and the tissues are treated as homogenous layers due to absence of CT-derived density data which limits the fidelity of the estimated focus. In the behavioural task, we did not conduct formal somatosensory threshold testing for individual participants, so intersensory facilitation cannot be ruled out as responsible for the shortened reaction times. Lastly, we did not explicitly characterize the effect of the TMS-induced electromagnetic field on the operation of the ultrasound transducer, nor the effects of the transducer housing on the coil’s induced electrical field. However, such a characterization was rigorously performed in a prior study (Legon et al., 2018b) using the same TMS coil and a similarly sized custom non-ferromagnetic ultrasound transducer, and found no significant effects in either direction.

Conclusions

Low-intensity TUS stimulation holds appeal for its unique combination of non-invasiveness, safety, high precision, and broad range of neuromodulatory effects. We found that lower duty cycle and longer sonication duration resulted in inhibition of cortical excitability. PRF showed suppression at all three frequencies tested (200, 500, 1000 Hz), but only when delivered in blocked design. In addition, TUS increased SICI, suggesting a possible GABAergic mechanism. Although we have shown that our human M1 TUS sonication scheme is safe and produces suppressive effects, a wider range of parameters and brain regions remain to be systematically tested, and further prospective studies are needed to elucidate region- and neuron-specific sensitivity to focused ultrasound. In addition, many cortical and subcortical cerebral targets have yet to be explored in terms of potential electrophysiological and behavioral responses to ultrasound. Dedicated study designs will be needed to answer the question of both short-term and long-term effects of TUS, as well as its on-line and off-line interaction with TMS. Finally, basic in vitro electrophysiologic experiments will help illuminate potential cellular, synaptic, and ionic mechanisms of ultrasound neuromodulation. Clinical applications are already being foreshadowed with human trials underway using low-intensity sonication for the treatment of epilepsy (Bystritsky et al., 2015; Stern, 2014), Alzheimer’s disease (Meng et al., 2017), Parkinson’s disease (Wagner and Fregni, 2012), disorders of consciousness (Monti et al., 2016), and stroke (Tsivgoulis and Alexandrov, 2007). The current landscape of ultrasound neuromodulation holds great promise as a functional brain-mapping tool and a potential intervention against disabling brain disorders.

Materials and methods

Subject recruitment and study visits

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Eighteen healthy participants aged 29–59 years (8 males, 10 females; 1 left-handed, 17 right-handed) were recruited from advertisements posted at the University Health Network (UHN). No participants had any known medical conditions or taking any prescription medications, and all had normal neurological examination before the experiment. All patients gave written informed consent, and the protocol was approved by the UHN Research Ethics Board in accordance with the Declaration of Helsinki on the use of human subjects in experiments.

Subjects were studied on four separate visits. The first visit involved a three-dimensional T1-weighted 3T MRI scan of the brain for all subjects, in order to later confirm the stimulated cortical target with individualized neuronavigation (Brainsight, Rogue Research, Montreal, Quebec). MRI acquisition parameters consisted of: 3-D fast spoiled gradient recalled sequence, TR = 7.8 ms, TE = Min, FA = 12, Inversion time = 450 ms, matrix = 256 × 256, FOV = 240 mm, slice thickness = 1.1 mm, no gap, number of slices = 172, acquisition time = 10 min. On the same day, a TMS threshold assessment with a 70 mm figure-eight coil held against the scalp at the first dorsal interosseous (FDI) hotspot was performed. Since a scalp-coil distance of 10 mm is used in all subsequent experiments, two participants requiring very high stimulation intensities (>95% of maximum stimulator output to generate a 1 mV peak-to-peak MEP in the contralateral FDI) were excluded, leaving 16 participants on which to perform data collection. The second visit involved delivery of single-pulse TMS-TUS in blocked design at basic parameters, followed by different sonication parameters in interleaved design, with recording of resting and active motor potentials. The third visit involved paired-pulse TMS-TUS experiments as well as a behavioral task involving application of TUS alone. The fourth visit involved varying selected sonication parameters in blocked, followed by interleaved fashion. For each acoustic parameter of interest (i.e. DC), a block was defined as 15 consecutive sonications of the same parameter value (i.e.: DC of 50%) with an ISI of 5 s. Four blocks were administered in random order (i.e.: DC 50%, Sham, 30%, 10%), with a 20 s rest period between blocks (Lee et al., 2016c). In the interleaved design, parameter values are interleaved by the MATLAB delivery script for a total of 60 randomized sonications balanced across the four parameter values, with an ISI of 5 s and no rest periods. Subjects received a detailed sensorimotor neurological assessment by a neurologist before and after each study visit.

Ultrasound transducer

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A custom two-element annular array ultrasound transducer (Sonic Concepts Inc, Bothell, Washington) operating at a fundamental frequency of 500 kHz and housed in a MRI-safe non-ferromagnetic brass cylinder measuring 38 mm in diameter and 10 mm thick was used (Figure 1—figure supplement 1). The transducer was coupled to a programmable two-channel 0–80 Watt radiofrequency amplifier (Sonic Concepts Inc, Bothell, Washington) via a 50 Ω impedance matching module. The amplifier contained an Arduino module controlling the phasing of a two-element annular array which allowed for adjustable sonication depths. The phasing was set to a fixed 30 mm sonication depth for all experiments, based on literature estimates of the scalp-cortex distance to the hand motor area (Fox et al., 2004; Stokes et al., 2005).

Transducer characterization

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To characterize the intensity of the active sonication paradigms (Figure 3B), the transducer was submerged in a degassed distilled water tank with the active face 30 mm from a calibrated fiber optic hydrophone (Precision Acoustics, PAFOH03, Dorchester, UK). The transducer was continuously excited at the fundamental frequency (500 kHz) in power steps of 1W, up to a maximum of 20W. The oscilloscope measurements of maximum peak voltages per cycle was multiplied by the hydrophone sensitivity (94.6923 mV/MPa) to derive a corresponding instantaneous pressure (Pi). The instantaneous intensity (Ii) was then calculated by squaring the Pi and multiplying by the inverse of the density (997 kg/m3) and speed of sound (1498 m/s) in the propagating medium. The pulse intensity integral (PII) is then derived by integrating the instantaneous intensity over the duration of the entire pulse. From the PII, two measures of acoustic exposure can be derived: the spatial-peak temporal average (ISPTA), and the spatial-peak pulse average (ISPPA). To characterize the axial intensity profile at the acoustic focus, a three-axis robotic stage moved the hydrophone at 0.2 mm increments parallel to the transducer face, recording the intensity profile within a 64 mm2 plane centered at the acoustic focus. Calculated intensity values at different parameters used are found in Table 1:

Electromagnetic and acoustic field brain mapping

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The scalp position of each subject’s TMS coil during sonication, including the 10 mm scalp-coil offset, was captured with neuronavigation based on the subject’s individual structural T1-weighted MRI. After applying the Montreal Neurological Institute (MNI) atlas registration to individualized scans, the MNI stereotaxic coordinates corresponding to each stimulator position were recorded. SIMNIBS, a TMS simulation software (Thielscher et al., 2015) was used to create a probabilistic averaged map of the induced electromagnetic current across the cortex all participants, mapped to an MNI head model (Figure 2A).

To model acoustic propagation of applied TUS, each participant’s coronal MRI slice corresponding to the neuronavigation-captured transducer position was manually segmented into scalp, skull, and brain tissue planes (Hynynen and Sun, 1999; Rosnitskiy et al., 2019; Appendix 1). The segmented tissue layers were treated as homogenous tissue masks with material properties such as attenuation coefficient, sound velocity, and density derived from the literature (Mueller et al., 2017; Robertson et al., 2018; Robertson et al., 2017). An acoustic simulation toolbox, k-Wave (Treeby and Cox, 2010), was used to generate simulations of the acoustic focus for each participant based on individualized transducer positions acquired via neuronavigation (Figure 2B–C) and the resulting pressure field was mapped back onto the original MRI images (Figure 2—figure supplement 1).

TMS-TUS stimulation delivery

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A custom 3D-printed holder was developed to hold the transducer to the underside of the TMS coil (Figure 1); the source file for 3D printing has been made freely available online (Fomenko, 2019a; copy archived at swh:1:rev:9e494e2f296518d4a596e5e221e3b5481cc07cda). The ultrasound transducer was rigidly fixed to the underside of the Figure 8 coil, and held in the center of the coil, between the two windings. Previous validation studies (Opitz et al., 2014) showed that the measured electromagnetic maxima of a figure-of-8 TMS coil is located between the two coil windings, justifying our central placement of the transducer holder.

Single-pulse TMS delivery was via a 70-mm figure-eight coil powered by a magnetic stimulator (Magstim Company, Dyfed, UK). The handle of the coil pointed backwards and laterally at 45° from midline. At the beginning of each experimental session, we determined the FDI motor hotspot on the scalp, defined as the location over which TMS evoked MEPs of highest peak-to-peak amplitude in the target muscle at a given suprathreshold stimulator intensity (Bashir et al., 2013). A marker was used to precisely trace outline of the transducer holder on the scalp, to ensure that the angle and position of the wand was captured. TMS output intensity was adjusted to produce MEPs with a mean peak-to-peak amplitude of approximately 0.5 mV over 10 trials in the relaxed contralateral FDI muscle (Hamada et al., 2008). The direction of the induced current was from posterior to anterior and activated the corticospinal neurons transynaptically. We captured the position of the coil in stereotactic space by registering the subject’s individual T1 anatomical MRI with the position of the TMS coil, using a TMS tracker and infrared camera via Brainsight.

Aqueous compressible gel pads (Aquaflex, Parker Laboratories, NJ, USA) were cut into 1.5 mm thick pads 40 mm in diameter and placed between the surface of the transducer and the subject’s scalp. Any visible air bubbles at the transducer-pad interface were manually extruded, and a small amount of ultrasound gel (Aquasonic 100, Parker Laboratories, NJ, USA) was applied at the scalp-pad interface (Appendix 2). The pads were replaced between each sonication block, or approximately every 6 min.

Sham condition

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For our initial experiments aiming to reproduce the suppressive effects found with a single set of basic parameters (Legon et al., 2018a), we used two types of sham: active and inactive (Figure 9). The active sham consisted of manually flipping the transducer so that the active face was pointing away from the scalp, as described previously (Legon et al., 2018a). The novel inactive sham consisted of maintaining the active face of the transducer facing toward the scalp, to later allow for efficient randomization blocks of active and sham conditions without the need to manipulate the transducer between trials.

Depiction of the two types of TUS sham and masking used in experiments.

(A) The active sham condition, as per Legon et al., 2018b involves flipping the active face (green) of the transducer to point away from the scalp and delivering acoustic energy away from the subject (left). During the active TUS condition, the transducer is flipped over to deliver acoustic energy transcranially (right). (B) Our inactive sham condition used for quick successive delivery of experimental conditions involves keeping the active face of the transducer always oriented toward the scalp (left), and only activating the transducer and delivering acoustic energy transcranially during an active TUS condition (right). This allows for interleaving experimental conditions with sham without the need to manually reposition the transducer. An audible tone lasting 0.5 s is played near the ipsilateral ear during both conditions to mask TUS delivery.

To mask the slightly audible buzz of the active piezoelectric element during sonication, a signal generator (Agilent 33220A, Keysight Technologies) delivered a tonal waveform 0.5 s in duration to a set of speakers positioned two meters lateral to the subject’s left ear (Braun et al., 2020). The sound was tuned to match the audible frequency (10–15 kHz) of the active transducer until the subject could not distinguish the speaker noise from a 0.5 s activation of the transducer. This sound was triggered identically every time a TUS or sham condition was delivered to the transducer.

Electromyography recording

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Surface electromyography (EMG) was recorded from the right FDI muscle contralateral to the stimulated motor cortex using disposable disc electrodes with a belly-tendon montage. EMG was 1K amplified (Intronix Technologies Corporation Model 2024F, Bolton, Ontario, Canada), filtered (band pass 2 Hz–2.5 kHz), digitally sampled at 5 kHz (Micro 1401, Cambridge Electronics Design, Cambridge, UK) and stored in a personal computer for off-line analysis. Subjects were seated upright in a chair and asked to keep eyes open throughout the experimental session.

Effects of basic TUS parameters on resting motor cortex excitability

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Two blocks of 10 TMS-TUS stimuli to M1 were administered to 12 subjects, with the active transducer face coupled to the scalp for the verum TUS block, and pointing away from the scalp (sham) for the sham block. For four additional subjects, two blocks of 20 TMS-TUS stimuli were delivered, with one block consisting of verum TUS and the other inactive sham. Overall, each participant received a cumulative sonication time between 5 and 10 s. Block order was random for each subject. The interstimulus interval was 5 s, and the resultant MEPs were collected for analysis. Basic sonication parameters were similar to those in prior human reports (Ai et al., 2016; Legon et al., 2018b): fundamental frequency = 500 kHz, pulse repetition frequency (PRF) = 1000 Hz, duty cycle (DC) = 30%, sonication duration = 0.5 s, and intensity = 2.32 W/cm2 ISPPA. The TMS pulse was time-locked to 10 ms before the end of sonication. (Figure 10).

Acoustic parameters and timing relative to TMS.

(A) Acoustic parameters of low-intensity ultrasound which were varied in the experiments include sonication duration, pulse repetition frequency, and duty cycle. The fundamental frequency was held constant at 500 kHz. (B) TMS and TUS stimulation delivery while varying the sonication duration parameter. Different durations (0.1, 0.2, 0.3, 0.4, 0.5 s) were randomized with the sham condition, where TUS was not delivered. TMS was always time-locked to 10 ms before the end of sonication. An inter-stimulus interval of 5 s was used between each stimulation epoch.

Effects of different TUS parameters on resting motor cortex excitability

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Four separate experiments each featuring systematic examination of a distinct sonication parameter were conducted in the following order: DC (sham, 10, 30, 50%), Sonication duration (sham, 0.1, 0.2, 0.3, 0.4, 0.5 s), PRF (sham, 200, 500, 1000 Hz), and adjusted PRF (sham, 200, 500, 1000 Hz). The fundamental frequency, intensity, and other parameters not being varied in each experiment were kept the same as the basic parameters. In the adjusted PRF experiment, the burst duration was held constant, requiring an adjusted DC (10, 25, 50%) for the PRF conditions 200, 500, 1000 Hz, respectively. For all experiments, the TMS pulse was time-locked to 10 ms before the end of sonication. For each parameter of interest, the order of condition delivery was randomized using a custom MATLAB 2019b script controlling both the TMS and TUS delivery (Fomenko, 2019b; copy archived at swh:1:rev:fe4df0a5372942406324efa09ec93fe2192487ea). For 12 participants, 10 TMS-TUS stimuli per condition were delivered in randomized order with an interstimulus interval of 5 s, and the resultant MEPs were collected for analysis. For four additional subjects, 15 stimuli were collected per condition. Each participant received a cumulative sonication time between 60 and 90 s. For all conditions, the active face of the transducer was coupled to the scalp, and the masking sound was played for 0.5 s on the speaker. For the sham condition, the transducer was not excited, but the masking sound was played identically to verum parameters.

Effects of TUS on active MEP and silent period

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The maximum voluntary contraction (MVC) was acquired by noting the oscilloscope display output, which was connected to the EMG amplifier through a leaky integrator while the subject squeezed a plastic cube between the right thumb and index finger with maximal effort. For the active conditions, we recorded EMG during 20% of MVC of the right FDI while stimulating the left motor cortex at 120% of resting motor threshold (RMT). The RMT was defined as the minimum stimulation intensity that could elicit MEP of no less than 50 μV in 5 out of 10 trials when the participant was fully relaxed. The silent period duration was measured from the onset of MEP to the reoccurrence of any background EMG activity according to previously described methods (Farzan et al., 2013). TMS was delivered 10 ms before the end of sham or active TUS, and the sonication parameters were the same as the basic parameters described above. Each participant received 15 trials each of sham and TUS, with a cumulative sonication time of 7.5 s.

Effects of TUS on intracortical inhibition and facilitation tested with paired-pulse TMS

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The outputs of two Magstim 200 stimulators were directed to a Bistim2 module (Magstim Company, Dyfed, UK), which was connected to the 70 mm TMS coil. The timing of the pulses was controlled by the output features of the A/D converter (Micro 1401, Cambridge Electronics Design, Cambridge, UK). The sonication parameters were the same as the basic parameters described above. The first TMS stimulus was delivered 10 ms before the end of TUS. To ensure that any differences in paired-pulse inhibition or facilitation between TUS and sham are not due to a TUS-mediated reduction of test stimulus (TS) MEP amplitude, but rather to differences in susceptibilities of pathways tested by the paired TMS paradigms to TUS, we further measured the RMT when delivering with sham and TUS, and adjusted the stimulator intensity to account for the effects of TUS. For each distinct paired-pulse paradigm (i.e.: SICI), the number of trials per participant were as follows: TS alone: 20 trials (10 TUS, 10 sham); conditioning stimulus (CS)-TS: 20 trials (10 TUS, 10 sham). Each condition block consisted of 20 trials delivered in random order, and each participant received eight paired-pulse blocks, with a cumulative sonication time of 40 s.

For the SICI and ICF experiments, we delivered a subthreshold CS at 80% RMT and the TS intensity was at the intensity that elicited MEP of ~1 mV. The interval between CS and TS was 2 ms for SICI and 10 ms for ICF. For the LICI, both the CS and TS consisted of an identical suprathreshold intensity able to evoke mean MEPs of ~1 mV. For SICF, the first stimulus intensity was set to generate MEP of ~1 mV. The second stimulus was at RMT. The interval between stimuli was 1.5 ms for the first peak and 2.9 ms for the second peak of SICF.

Effects of TUS on a visuo-motor task

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To measure the effects of TUS on voluntary motor behavior, we used a visuo-motor task similar to one previously used to assess motor performance and reaction time after electrical deep brain stimulation (de Almeida Marcelino et al., 2019). Each participant was seated in a comfortable chair facing a computer screen, with an eye-to-screen distance of 60 cm. A three-axis accelerometer was attached to the dorsal aspect of the distal right index finger and calibrated so that abduction of the FDI triggered a voltage rise in the horizontal plane of the accelerometer. The ultrasound transducer was positioned at the previously-marked FDI hotspot over the left M1, coupled with a gel pad, and secured to the head using a 3D-printed holder with Velcro straps (Fomenko, 2019a).

Participants were instructed to steer a cursor using their index finger on a digital tablet (Intuos Draw, Wacom) as quickly and accurately as possible to target circles appearing in one of three on-screen positions (Figure 8—figure supplement 1). Participants were asked to only abduct or adduct the index finger, while keeping the hand, arm, and remaining digits still. Trials started with a red circle at the starting location (diameter = 50 pixels) that turned green after 2 s to indicate to the participants that they should move to the target location. The target location was indicated with a blue circle (diameter = 200 pixels) with a white cross at the center, which appeared simultaneously as the start location turned green. The target location appeared at one of three randomized locations with 60 trials for each: (1) close target, 600 pixels away, (2) medium target, 1200 pixels, or (3) far target, 1800 pixels away. On each trial, TUS or sham was applied 250 ms before the blue circular target appeared on the screen for a total sonication duration of 500 ms. TUS was applied either at full intensity (2.32 W/cm2), as with the single- and paired-pulse experiments, or at a close to somatosensory threshold acoustic intensity (0.54 W/cm2). The near-threshold intensity was chosen to correspond to a lack of subjective scalp tactile sensation in all participants after five test sonications were applied at basic parameters. For both TUS and sham conditions, the masking sound was played over a speaker in a similar fashion as the TMS experiments. Subjects first practiced the task until performance speed plateaued, after which the transducer was positioned on the scalp and 180 trials pseudorandomized across TUS/sham conditions (90 trials each) were performed, with a cumulative sonication time of 45 s. MATLAB 2019b was used to record all finger traces via a modified Psychophysics Toolbox script and Spike2 (Cambridge Electronics Design, Cambridge, UK) was used to record the accelerometer trace (Brainard, 1997; Fomenko, 2019b).

Statistical analysis

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For the MEP data, a custom script in SIGNAL (Cambridge Electronics Design, Cambridge, UK) was used to extract peak-to-peak MEP amplitudes, and to extract the median amplitude by condition. PRISM 8 (GraphPad Software Inc, San Diego, California) was used for statistical analysis. For the single-pulse basic parameters and active MEP experiments, two-tailed paired t-tests were used to compare means of participant MEP amplitudes between verum TUS and sham conditions. For the interleaved and blocked systematic parameter variation experiments, MEP mean amplitudes were first analyzed using repeated-measures (RM) one-way ANOVA. Post-hoc two-tailed paired t-tests were then used to compare each parameter to its respective sham group, and p-values were adjusted for multiple comparisons using Holm’s sequential Bonferroni procedure (Holm, 1979), with omnibus significance α = 0.05. Analysis of blocked experiments by trial number was performed with multiple two-tailed paired t-tests, between the means of the first three trials (first 20%) and the last three trials (last 20%) of all participants, for each block of 15 total trials. Paired-pulse experiments were expressed as the ratio of the conditioned (with preceding CS) to the unconditioned (TS alone) MEP amplitudes, for both sham and TUS, and two-tailed paired t-tests were used. For the behavioral task, reaction time was defined as the time from target stimulus presentation to the horizontally-directed accelerometer voltage trace exceeding two standard deviations from baseline. Total path travelled was defined as the number of pixels contained within the path from start to finish, while deviation score was the deviation in pixels from an ideal straight line from start to finish target. Mean values of TUS and sham were analyzed with two-tailed paired t-tests.

Appendix 1

Acoustic simulation methods

Appendix 1—figure 1
Neuronavigation software (Brainsight) showing the individualized location of the US transducer on the scalp captured during US/TMS stimulation of one participant.

Panel A: Coronal T1-weighted MRI of the brain at the location of the US transducer (red dot). Panel B: Three-dimensional reconstruction of the participant’s cortical surface, with the central sulcus highlighted with a yellow dashed line. The red dot represents the scalp location of the transducer and the green path represents the predicted path of sonication perpendicular to the transducer face.

Appendix 1—figure 2
MRI-based manual tissue segmentation pipeline shown for a single participant.

Tissue properties were set as follows, with a medium alpha power (y) = 1.43. Panel (A) Skin and scalp (green). Alpha coefficient: 2.05 dB/(MHz y cm), sound speed: 1732 m/s, density: 1100 kg/m3. Panel (B) Bone (red). Alpha coefficient: 8.83 dB/(MHz y cm), sound speed: 2850 m/s, bulk density: 1732 kg/m3. Panel (C) Brain (blue). Alpha coefficient: 1.00 dB/(MHz y cm), sound speed: 1552 m/s, density: 1040 kg/m3. Panel (D) The three masks were used to set the properties of the acoustic propagation medium in the subsequent numerical simulation. The background medium was set as water (black) with the following properties: Alpha coefficient: 0.05 dB/(MHz y cm), sound speed: 1482 m/s, density: 1000 kg/m3.

Appendix 1—figure 3
Acoustic simulation and visualization for the same participant using the k-WAVE Matlab toolbox.

The parameters of the simulation were as follows: Fundamental frequency: 500 kHz, Grid points: 472 × 472, Points-per-wavelength: 10, Points per period: 67, Courant-Friedrichs-Lewy number: 0.15, Size of perfectly matched layer: 20 grid points. Panel A: Acoustic pressure profile of simulated 500 kHz transducer sonicating against the participant’s scalp at the scalp location determined by neuronavigation. Cigar-shaped focus within the brain, and artifact from non-active transducer face can be seen. Panel B: Pressure profile deep to the transducer overlaid onto participant’s T1 MRI (left hemisphere only shown), with the primary motor cortex cortical surface shown with a dotted line. Areas outside the acoustic focus are cropped.

Appendix 2

Validation of air-free interfaces

Appendix 2—figure 1
Visual and imaging validation of air-free coupling interfaces.

Panel (A): Visible air bubble at the TUS transducer-gel interface, indicated by the red arrow (left), and corresponding refraction and shadowing artifact seen when an imaging transducer (Hitachi Aloka ProSound Alpha 7) is placed over the air bubble (right). Panel (B) Manual smoothing of the gel pad is performed until any small bubbles are extruded, leaving a homogenous black interface (left), and confirmatory imaging with ultrasound probe at the gel pad surface shows no artifact at the TUS transducer-gel interface (right). Panel (C) Application of gel pad over the frontal bone with the imaging transducer applied over the gel, visualizing the scalp-pad interface (left). Imaging of the scalp-pad interface, showing underlying tissue layers with no visible artifacts (right).

References

  1. 1
    Transcranial focused ultrasound for BOLD fMRI signal modulation in humans
    1. L Ai
    2. JK Mueller
    3. A Grant
    4. Y Eryaman
    5. W Legon
    (2016)
    Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS.
    https://doi.org/10.1109/EMBC.2016.7591057
  2. 2
  3. 3
  4. 4
  5. 5
  6. 6
  7. 7
  8. 8
  9. 9
  10. 10
  11. 11
  12. 12
  13. 13
  14. 14
  15. 15
  16. 16
  17. 17
  18. 18
  19. 19
  20. 20
  21. 21
  22. 22
  23. 23
  24. 24
  25. 25
  26. 26
  27. 27
  28. 28
  29. 29
  30. 30
  31. 31
  32. 32
  33. 33
  34. 34
  35. 35
  36. 36
  37. 37
  38. 38
  39. 39
  40. 40
  41. 41
  42. 42
  43. 43
  44. 44
  45. 45
  46. 46
  47. 47
  48. 48
  49. 49
  50. 50
  51. 51
  52. 52
  53. 53
  54. 54
  55. 55
  56. 56
  57. 57
  58. 58
  59. 59
  60. 60
  61. 61
  62. 62
  63. 63
  64. 64
    Human Brain Stimulation with Transcranial Ultrasound
    1. JL Sanguinetti
    2. E Smith
    3. JJB Allen
    4. S Hameroff
    (2014)
    In: P. J Rosch, editors. Bioelectromagnetic and Subtle Energy Medicine. Tylor and Francis. pp. 355–363.
  65. 65
  66. 66
  67. 67
  68. 68
  69. 69
    Low-intensity focused ultrasound pulsation (LIFUP) for treatment of temporal lobe epilepsy
    1. J Stern
    (2014)
    Clinicaltrials.gov ID: NCT02151175. Accessed November 20, 2019.
  70. 70
  71. 71
  72. 72
  73. 73
  74. 74
  75. 75
  76. 76
  77. 77
  78. 78
    Marketing Clearance of Diagnostic Ultrasound Systems and Transducers
    1. United States Food and Drug Administration
    (2017)
    Draft Guidance for Industry and Food and Drug Administration Staff.
  79. 79
    Non-invasive neurostimulation in Parkinson’s Disease
    1. T Wagner
    2. F Fregni
    (2012)
    Clinicaltrials.gov ID: NCT01615718. Accessed November 20, 2019.
  80. 80
  81. 81
  82. 82
  83. 83
  84. 84

Decision letter

  1. Richard B Ivry
    Senior Editor; University of California, Berkeley, United States
  2. Laura Dugué
    Reviewing Editor; Université de Paris, France
  3. Wynn Legon
    Reviewer

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Acceptance summary:

In their study, Fomenko and colleagues measure the effects of transcranial ultrasound stimulation (TUS) on corticospinal excitability changes as assessed by transcranial magnetic stimulation (TMS) and motor evoked potentials (MEP) in primary motor cortex. This study is very timely as only a few TUS studies have been published in humans so far. Importantly, they systematically investigated the impact of various TUS parameters and TMS pulses on cortical excitability.

Decision letter after peer review:

Thank you for submitting your article "Systematic examination of low-intensity ultrasound parameters on human motor cortex excitability and behaviour" for consideration by eLife. Your article has been reviewed by Richard Ivry as the Senior Editor, Laura Dugué as the Reviewing Editor, and three reviewers. The following individual involved in review of your submission has agreed to reveal their identity: Wynn Legon (Reviewer #3).

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

Summary:

Fomenko et al., combined transcranial ultrasound stimulation (TUS) and transcranial magnetic stimulation (TMS) of the primary motor cortex (M1) in humans to measure the effects of TUS on corticospinal excitability changes as assessed by TMS and measures of motor evoked potentials (MEP). They systematically investigated the impact of important TUS parameters, such as pulse repetition frequency, duty cycle, and sonication duration on several single- and paired-pulse TMS related indices for GABA-A- (SICI) and -B-receptor mediated inhibition (LICI, cortical silent period) as well as facilitation (SICF, ICF). They found corticospinal excitability to be generally decreased by TUS, and stronger so for longer sonication and shorter duty cycles, as well as a stronger SICI during TUS. They also observed shorter response times during TUS in a visuomotor task. No impact of TUS was found on any of the other parameters.

The reviewers and the editors appreciates the need for such methods paper as TUS is a very promising neurostimulation technique that is at the stage of translating from animal to human research. Despite a few (<10) studies being already published in humans, the impact of the most basic TUS parameters on cortical excitability is still unclear, and systematic studies like this one are very much needed.

That said, there are significant methodological and statistical concerns that need to be addressed before the paper can be published. The following comments highlight the "essential revisions.

Essential revisions:

1) In general, the data lacks apparent robustness. They authors should collect data from more participants (preferably with more trials) to allow for correction for multiple comparisons across all the tested indices and solve the apparent lack of robustness (failed within-study replications) of the data. The following points describe the different results in which robustness issues have been noticed.

1a) Given the relatively low number of subjects and a total of ~16 different measurements being investigated, there is a certain risk of false positive results. Do the results survive correction for multiple comparisons?

1b) Several of the experiments that either vary TUS parameters or investigate paired-pulse effects also contain a single-pulse MEPS with TUS at "basic parameters" for which a clear suppressive effect was found (p = 0.0018) in the beginning. However, in those four experiments, this effect does not seem to replicate: Figure 5C: DC of 30%; Figure 5E: Pulse repetition frequency of 1000 Hz; Figure 7: TS; Figure 7: S1.

1c) Statistical analyses for more than two conditions seem based on one-way rm-ANOVAs (such as 5 different sonication durations normalized to sham), "and conditional on a significant f-value, Dunnett's multiple comparisons was performed to explore groups with significant differences from sham" (subsection “Statistical analysis”). The Dunnett's tests presumably test something very different (namely the differences between each TUS condition against Sham) than the ANOVA, which testes for differences of the sham-normalized conditions with respect to each other, and not of the conditions relative to sham. (a) The Dunnett's test should thus not be conditional on the f-test, as they simply answer very different questions. This would change if the ANOVA would based on raw MEP values and include the sham condition as level. (b) This also means that there is no post-hoc evaluation of the differences between conditions, which would require e.g., post-hoc paried t-tests. (c) Potentially, some legitimate basic comparisons against sham have not been performed, because the f-test was non-significant, even though it tested something very different.

2) The MEP analysis and results need to be reported with more details. The following points should be clarified in the revised version of the manuscript.

2a). It is unclear how many MEPs (for paired-pulse, how many CS+TS and TS alone trials, respectively) were acquired per condition of each of the experiments. It reads like only 10 MEPs were acquired per condition (20 for paired-pulse blocks, so again 10 CS+TS and 10 TS alone). Given the known high variability of the MEP (cf. also subsection “Limitations” "the MEP variability we saw in some subjects") and the low number of participants (N = 12; which is understandable for TUS in humans but still low) these low MEP numbers are problematic. A larger number (20 or more MEPs) per condition would provide a much more stable estimate and allow the detection of small effects. Although there are quite some clinical neurophysiology papers out there with 10 or 15 MEPs, the kind of results presented here may shape the translation of TUS for human application and are thus too important for the community to suffer from low statistical power. Some of the findings (also of the negative ones) are in contrast to a previous study (Legon et al., 2018), and it is unclear whether this has to be attributed to differences in experimental design or simply noise. Many separate measures were obtained for the same subjects, and the effort of the authors is acknowledged, but maybe less measures and more trials would have been the better choice? Please report to point 1.

2b) The authors used the procedure of outlier exclusion. Outlier removal is a controversial method for MEP analyses. MEP amplitudes are not normally distributed but rather follow a power law, and removal of extremes is therefore corrupting the data. Was the pre-activation controlled and excluded together with the outliers? How many MEPs were left for the statistical analysis after exclusion? How do the results change when all trials are kept but the median is used per condition (instead of the mean) or the mean of log-transformed MEP values? Given that robustness is key for these kind of results, transformations should be avoided (or outlier removal at least).

3) Several concerns have been raised regarding M1 targeting and the use of a normalized brain and MNI coordinates. Specifically, in the newly collected data, M1 (or specific parts of it) should be properly targeted by using neuronavigation and individual head models with T1/T2 (instead of a normalized brain and MNI coordinates). Moreover, the following points should be addressed in the revised version of the manuscript.

3a) Why was a normalized brain used? You have a neuronavigation system and each participants' MRI. Why MNI coordinates? Should you not distinguish based on each participants' anatomy and TMS response to determine location?

3b) Why was a mark placed on the head when you have a neuronavigation system? Since BrainSight neuronavigation was used to identify the TUS transducer position on the scalp, why was it not used to ensure and maintain correct transducer placement throughout the many measurements and sessions? Given the small diameter of the sonication beam, tiny changes in tilt or position can have a massive effect on the actually stimulated part of cortex.

3c) Figure 2B: Please also provide sagittal and axial views to allow a better judgement of the targeting of M1. Is it actually targeting M1 or maybe premotor cortex? Which part of the precentral gyrus is actually sonicated?

3d) According to Fox, 2006 and Geyer, 1996 the motor cortex of human is allocated in the sulcus and at best to a small extent at the crown. In Figure 2B the white matter is targeted as well as in Figure 3A where the pyramidal neurons are allocated in the white matter. Thus, the first part of the following sentence might not be accurate: "Similarly, the individual simulations of ultrasound propagation for each participant confirmed acoustic targeting of a portion of M1, as well as underlying white matter tracts. (Figure 2B)." see also "Our finding may suggest that cortical interneurons in layers II/III which are well-encompassed within the acoustic focus (Figure 4)"

3e) Without a CT scan and only T1-weighetd images no really reliable simulations can be obtained for the acoustic waves. Figure 2B only shows one "representative" subject. Have simulations been performed for all subjects or was the transducer only placed on top of the TMS M1 hotspot for each subject without modelling the sonication beam individually? This assumes that the relevant motor neurons of M1 are actually directly beneath the coil center, which is not necessarily the case.

3f) Figure 3A: The location and orientation of corticospinal output neurons in M1 is incorrect and misleading. They are actually located in the anterior bank of the central sulcus and oriented tangentially to the scalp. This should be corrected.

4) The phrasing "… a portion of M1…" is disconcerting. Because the transducer was concentric with the intersection of the TMS coil, wherever you put the coil is where the US was. Is that accurate? How did you confirm this however in your models if you did not use individual MRI but rather normalized MRI. MNI coordinates are mentioned previously but not given anywhere in the manuscript. TUS is highly localized and using generalized MNI coordinates is not appropriate. Please provide an acoustic wave modeling of the sonication for each participant.

5) There is no data on how/if the transducer affected the TMS pulse or vice versa. This needs to be either collected or cited from Legon et al., 2018 and differences in transducer materials/design should be factored in if there are any.

6) Gel pads are notorious for trapping air bubbles between interfaces. This can be easily detected using imaging mode of your transducer. Was this checked for? Was any other coupling media used?

7) TMS was applied in order to measure the cortical excitability changes with MEP. The TMS pulses were locked to the end of FUS or sham stimulation and they had an interstimulus interval of 5 seconds. If there was no jitter for TMS pulses it means that rTMS at 0.2 Hz was applied simultaneously with FUS. Repetitive TMS applied at a very low frequency of 0.2 Hz has been shown to be effective in several studies (Urushihara, 2006; Hosono, 2008). For example, rTMS over PMC led to an increase in somatosensory evoked potentials. Could the possible effect of low frequency rTMS on cortical excitability when applied simultaneously with FUS be discussed?

8a) A further concern is "This sound was triggered every time a FUS or sham condition was delivered to the transducer." This could mean that the effects reflect acoustic TMS pairing, see e.g. doi: 10.3389/fnhum.2014.00398, other papers are around as well. Can the results in Figure 5D be due to longer acoustic stimulation? I expect the sham condition to be performed with the shortest duration, however not sure? Can this explain the lack of an effect in Figure 5E and F?

8b) Also: "and the task was more complex; nevertheless, the sonication parameters and cortical location were similar, and we observed an effect size of about 100 ms, though with higher variability." It may simply be that the start of the sonification sound leads via a kind of pre-triggering to shortened responses. This is discussed by the authors in subsection “Behaviour”. The effect in Figure 8A appears to be implausibly high. Control experiments seem to be reasonable with very light somatosensoric or close to threshold acoustic stimulation. The whole field of TMS-EEG suffers from acoustic and somatosensory contamination.

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

Thank you for resubmitting your article "Systematic examination of low-intensity ultrasound parameters on human motor cortex excitability and behaviour" for consideration by eLife. Your revised article has been reviewed by Richard Ivry as the Senior Editor, a Reviewing Editor, and two reviewers. The following individuals involved in review of your submission have agreed to reveal their identity: Til Ole Bergmann (Reviewer #2); Wynn Legon (Reviewer #3).

The reviewers have discussed the reviews with one another, and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

As the editors have judged that your manuscript is of interest, but as described below that additional experiments are required before it is published, we would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). First, because many researchers have temporarily lost access to the labs, we will give authors as much time as they need to submit revised manuscripts. We are also offering, if you choose, to post the manuscript to bioRxiv (if it is not already there) along with this decision letter and a formal designation that the manuscript is "in revision at eLife". Please let us know if you would like to pursue this option. (If your work is more suitable for medRxiv, you will need to post the preprint yourself, as the mechanisms for us to do so are still in development.)

Summary:

Fomenko et al., have thoroughly revised their manuscript. They added four new participants (now N = 16) with more trials (15-20 instead of 10 per condition), as well as more TUS modelling information. While this is not a large increase in sample size, the authors' efforts are appreciated. However, both the reviewers and the editors were concerned by the fact that the effects are robust for the 'block' experiment but not for the 'parameter' (or interleaved) experiment. Consequently, additional data should be collected in a sufficiently large sample (eg. keeping the N = 16 for interleaved and adding N = 16 for a blocked variation of stimulation parameters), targeting specifically the block vs. interleaved question for the duty cycle and pulse repetition frequency parameters.

Essential revisions:

1) Regarding the block vs. interleaved question:

If it makes indeed a difference for single trial MEP modulation whether TUS is applied in a block design with fixed parameters or with trial-by-trial variation of parameters, this would have important implications for TUS application and thus needs additional data collection, as well as detailed discussion. Specifically, for some parameters (sonication duration) the default parameter (0.5s) was effective in the interleaved design. However, for others (duty cycle) it was slightly different (10% instead of 30%), and for yet others (pulse repetition frequency) it was not different from sham anymore at all (but was previously with 1000 Hz). The blocked vs. interleaved interpretation is post-hoc and potentially valid only for duty cycle and pulse repetition frequency.

2) The authors added 4 subjects to the existing data set (N = 12) but the reviewers fail to see the significance of this. First, data from the original 12 is presented in Figure 4 and the data from the new N = 4 is presented on its own. Could the authors explain the added value of this? It looks from the manuscript that N = 16 was inclusive for the parameter testing but not the 'basic parameter' testing. The authors could either remove Figure 4C or include that data in Figure 4A.

3) It is still unclear how many MEPs were left for each participant for the statistical analysis after exclusion. It looks like the new data uses 15 trials but the old 10 and the block experiment 20. Furthermore, it is stated in the Materials and methods that MEPs {plus minus} 2SD of mean were excluded. Please include M+/-SD in the Materials and methods.

4) The following questions still need to be addressed:

- Was the pre-activation controlled and excluded together with the outliers?

- How do the results change when all trials are kept but the median is used per condition (instead of the mean) or the mean of log-transformed MEP values? Also, do the results hold when not removing the outliers?

- Why was the neuronavigation system not used to online maintain coil/transducer position? Coil angle cannot be reliably inferred from a felt pen drawing on the scalp.

5) Could the authors show the sonication beam model for all three slices (coronal, sagittal, axial)? The axial slices do not seem to be the most helpful ones when determining whether M1 was hit and which portion of it.

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

Thank you for resubmitting your article "Systematic examination of low-intensity ultrasound parameters on human motor cortex excitability and behaviour" for consideration by eLife. Your revised article has been reviewed by Richard Ivry as the Senior Editor, a Reviewing Editor, and one reviewers. The following individual involved in review of your submission has agreed to reveal their identity: Til Ole Bergmann.

The Reviewing Editor has drafted this decision to help you prepare a revised submission.

We would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). Specifically, we are asking editors to accept without delay manuscripts, like yours, that they judge can stand as eLife papers without additional data, even if they feel that they would make the manuscript stronger. Thus the revisions requested below only address clarity and presentation.

We thank the authors for addressing the comments raised in the review process.

The new results demonstrate that the effectiveness of TUS parameters does indeed depend on whether TUS parameters are varied across trials or kept constant within a block. These additional findings are important and will be very useful for others using this approach. These results though also point to one important question that we believe should be in the published study: Are the results in the block design condition due to cumulative effects? That is, is there a systematic change over time, indicative of a cumulative effect? (To quote the reviewer who noted this concern, "Specifically, is there a build-up of suppression across trials within a block? This could be tested e.g. by regressing the MEP amplitude based on within-block trial number or by comparing early and late MEPs within a block. It would be a very important outcome to know whether the observed suppression is instantaneous or accumulating across trials.").

https://doi.org/10.7554/eLife.54497.sa1

Author response

Essential revisions:

1) In general, the data lacks apparent robustness. They authors should collect data from more participants (preferably with more trials) to allow for correction for multiple comparisons across all the tested indices and solve the apparent lack of robustness (failed within-study replications) of the data. The following points describe the different results in which robustness issues have been noticed.

We thank you for considering our manuscript and recognizing the need for systematic studies examining basic sonication parameters on human neural circuits. We want to thank the reviewers for their insightful suggestions and comments, which have led to a more clear and comprehensive revised manuscript. Our detailed point-by-point replies to the reviewer comments follow below.

A significant concern the reviewers raise is the limited number of subjects (n=12) in our study, and the small number of trials per condition. We thank the reviewers for giving us sufficient time to recruit 4 new healthy participants, bringing the total sample size to 16, in line with other within-subject human experiments studying TMS-elicited motor excitability and behaviour (doi: 10.1212/WNL.62.1.91, 10.1016/j.clinph.2007.09.062)

We have conducted additional modified experiments, as per the suggestion in point 2a to “reduce the number of measures and increase the number of trials”. With the 4 new subjects, we have focussed their experimental visit on testing only the five key sonication parameters, with an increase on the number of trials per parameter. Specifically, after obtaining an MRI for the new participants, we conducted the following experiments.

A) Basic parameters: Inactive Sham/TUS. 20 trials per condition, block design. 40 trials total. (Previously was 10 trials/condition).

B) Duty Cycle: Sham/10%/30%/50%. 15 trials per condition, randomized. 60 trials total. (previously was 10 trials/condition)

C) PRF: Sham/300Hz/500Hz/1000Hz. 15 trials per condition, randomized. 60 trials total (previously was 10 trials/condition)

D) Adjusted PRF: Sham/300hz/500Hz/1000Hz. 15 trials per condition, randomized. 60 trials total. (previously was 10 trials/condition)

E) Sonication duration: Sham/0.1s/0.2s/0.3s/0.4s/0.5s. 15 trials per condition, randomized. 90 trials total (previously was 10 trials/condition)

F) Near-threshold behavioural task control (See Essential revision 8b for details)

As the reviewers pointed out, auditory confounds are increasingly relevant in the TUS-neuromodulation literature, and sham conditions need to be carefully balanced and rationalized.

Accordingly, the new participants were subjected to a modified experiment A), where we have compared basic US parameters with an inactive audio-masked sham condition. The purpose of this was to validate that the type of sham we use for the rest of the study results in similar effects on the MEP as the active sham used in the original experiment A), which was also used in Legon et al., 2018).

We have created a new figure (Figure 9) explaining in detail the two types of sham, and the results are summarized in the newly-created Figure 5. For the remainder of the experiments (B-E), the inactive audio-masked sham was used, and the results were pooled with the original 12 participants, since the protocol remained the same (but with more trials).

1a) Given the relatively low number of subjects and a total of ~16 different measurements being investigated, there is a certain risk of false positive results. Do the results survive correction for multiple comparisons?

In the case of multiple conditions being compared to an independent sham condition (ie: 5 sonication durations versus sham etc.), we now use the Holm-Bonferroni method of multiple-comparison correction to perform post-hoc paired two-tailed t-tests following the ANOVA. Our original results survive correction for multiple comparisons, with the exception of the sonication duration condition “0.3 s”, which becomes non-significant when adjusted.

Materials and methods

“MEP means were first analyzed using repeated-measures (RM) one-way ANOVA. Post-hoc two-tailed paired t-tests were then used to compare each parameter to its respective sham group, and p-values were adjusted for multiple comparisons using Holm’s sequential Bonferroni procedure (Holm, 1979), with omnibus significance α = 0.05.”

1b) Several of the experiments that either vary TUS parameters or investigate paired-pulse effects also contain a single-pulse MEPS with TUS at "basic parameters" for which a clear suppressive effect was found (p = 0.0018) in the beginning. However, in those four experiments, this effect does not seem to replicate: Figure 5C: DC of 30%; Figure 5E: Pulse repetition frequency of 1000 Hz; Figure 7: TS; Figure 7: S1.

After pooling the single-pulse parameter trials from the 4 additional participants, we show results consistent with the original set of participants (See revised Results and Figure 5, Figure 6). We did not have specific data to draw a conclusion about why although the “basic” combination of US parameters suppressed cortical activity, and while some parameters replicated this result (ie: Sonication Duration), other parameters did not individually replicate when varied individually (notably PRF). We discuss potential reasons in the revised manuscript:

Discussion

“Interestingly, our results show that although a particular combination of parameters can robustly suppress TMS-elicited cortical activity when applied in a sham-controlled block design (Figure 5), parameters such as PRF when randomized and varied individually do not have the same robust effect (Figure 6C-D). From animal studies on TUS parameter dependence, PRF may be related to non-linear piezoelectric characters of the neural membrane capacity, while higher duty cycle or longer burst duration do not necessarily elicit neural activation more efficiently than the same parameter with lower magnitude (H. Kim et al., Brain Stimulation 2014). Furthermore, recent large animal studies reveal a bidirectional neuromodulation effects of varying TUS parameters (Yoon et al., 2019). Based on our results, we speculate that some PRFs may provide opposing, and potentially longer-lasting neuromodulation effects, and randomizing these stimulation blocks in succession might lead to spill-over effects from one trial to another. In contrast, the range of sonication durations tested in our experiment may have been those conducive to suppression and resulted in a robust and dose-dependent effect seen in our experiment (Figure 6B). However, this hypothesis needs further investigation.”

Regarding the TS and S1 results (Figure 8), the apparent lack of suppression seen in the TUS condition compared to sham was because of a stimulator adjustment in the calibration stage of the experiment to compensate for the robust suppressive effects of US. Before adjustment, basic parameters did in fact replicate the suppression previously seen (Figure 8, first panel), and we now add a paired t-test (p=0.016) showing that a significant mean increase of about +1.3% in stimulator intensity is required to elicit the same 1 mV average MEP values. We also now clarify in the caption that the TS/S1 MEP amplitudes were performed after this adjustment.

In paired-pulse TMS studies (e.g. SICI, SICF), It is known that the MEP amplitude induced by TS affects their inhibitory or facilitatory results (Sanger et al., 2001; Peurala et al., 2008; Ni et al., 2013). We found from our single-pulse experiments, and those of Legon, (2018) that US generally suppresses MEPs.

Therefore, we needed to ensure that any differences in paired-pulse inhibition/facilitation between FUS/sham are not due to a FUS-mediated reduction of TS/S1 MEP amplitude, but rather to differences in susceptibilities of pathways tested by the paired TMS paradigms to FUS. We clarify this rationale in the revised manuscript in the Materials and methods.

1c) Statistical analyses for more than two conditions seem based on one-way rm-ANOVAs (such as 5 different sonication durations normalized to sham), "and conditional on a significant f-value, Dunnett's multiple comparisons was performed to explore groups with significant differences from sham" (subsection “Statistical analysis”). The Dunnett's tests presumably test something very different (namely the differences between each TUS condition against Sham) than the ANOVA, which testes for differences of the sham-normalized conditions with respect to each other, and not of the conditions relative to sham. (a) The Dunnett's test should thus not be conditional on the f-test, as they simply answer very different questions. This would change if the ANOVA would based on raw MEP values and include the sham condition as level.

We thank the reviewer for this suggestion. We have now removed the stipulation that a significant F-test is required to perform post-hoc analysis in the revised manuscript. Based on this and other reviewer comments, we now show raw mean MEP amplitudes instead of sham-normalized ratios, allowing for direct comparison with the sham group.

(b) This also means that there is no post-hoc evaluation of the differences between conditions, which would require e.g., post-hoc paried t-tests. (c) Potentially, some legitimate basic comparisons against sham have not been performed, because the f-test was non-significant, even though it tested something very different.

Based on this suggestion, we now perform post-hoc paired t-tests for every parameter compared to sham, with an adjusted p-value to account for multiple comparisons using the Holm-Bonferroni method. We perform this test for all parameters, regardless of the outcome of the ANOVA, which can be found in the Results section.

2) The MEP analysis and results need to be reported with more details. The following points should be clarified in the revised version of the manuscript.

2a) It is unclear how many MEPs (for paired-pulse, how many CS+TS and TS alone trials, respectively) were acquired per condition of each of the experiments. It reads like only 10 MEPs were acquired per condition (20 for paired-pulse blocks, so again 10 CS+TS and 10 TS alone). Given the known high variability of the MEP (cf. also subsection “Limitations” "the MEP variability we saw in some subjects") and the low number of participants (N = 12; which is understandable for TUS in humans but still low) these low MEP numbers are problematic. A larger number (20 or more MEPs) per condition would provide a much more stable estimate and allow the detection of small effects. Although there are quite some clinical neurophysiology papers out there with 10 or 15 MEPs, the kind of results presented here may shape the translation of TUS for human application and are thus too important for the community to suffer from low statistical power. Some of the findings (also of the negative ones) are in contrast to a previous study (Legon et al., 2018), and it is unclear whether this has to be attributed to differences in experimental design or simply noise. Many separate measures were obtained for the same subjects, and the effort of the authors is acknowledged, but maybe less measures and more trials would have been the better choice? Please report to point 1.

We thank the reviewers for these comments. As detailed in our response to point 1, we recruited an additional 4 new subjects, increasing the sample size to 16 for the whole study. We prioritized their experimental visits on acquiring an anatomical MRI (Visit 1) and testing the five key sonication parameters on single-pulse TMS experiments (visit 2). As suggested by the reviewers, we increased the number of trials per parameter (15-20 trials), as well as performing a supplementary behavioural control experiment (See point 8b). Unfortunately, given the current global halting of research activity for the foreseeable future, we were unable to ask participants to return for a third visit to perform paired-pulse experiments. We also note that the International Federation of Clinical Neurophysiology (IFCN) recommendation is to use 8 to 10 trials per condition for paired pulse TMS studies (Rossini et al., (2015)).

We have clarified the paired-pulse methods to clarify the number of trials per condition, total number of trials, and total sonication time per session

Materials and methods

“For each distinct paired-pulse paradigm (ie: SICI), the number of trials per participant were as follows: TS alone: 20 trials (10 TUS, 10 sham), CS-TS: 20 trials (10 TUS, 10 Sham). Each condition block consisted of 20 trials delivered in random order, and each participant received 8 paired-pulse blocks, with a cumulative sonication time of 40 seconds.”

Regarding the differences between our paired-pulse findings and other reports, we detail possible reasons in our Discussion section, which we attribute to two key differences in experimental methodology:

Discussion:

“Several methodological differences from our study should be noted, such as a time-locking the ultrasound to begin 100 ms prior to the conditioning stimulus, whereas we applied ultrasound for 490 ms prior to the first TMS pulse. Since sonication duration is a key parameter of TMS-mediated EMG suppression according to our findings, the longer sonication time in our experiment might have produced greater GABAA-mediated inhibition. In addition, we randomized the TS-alone condition with the paired pulse conditions in our experiment, whereas (Legon et al., 2018b) compared to a baseline MEP conducted in a temporally discrete block, potentially introducing variability inherent in MEP fluctuations over time (Ellaway et al., 1998).”

2b) The authors used the procedure of outlier exclusion. Outlier removal is a controversial method for MEP analyses. MEP amplitudes are not normally distributed but rather follow a power law, and removal of extremes is therefore corrupting the data. Was the pre-activation controlled and excluded together with the outliers? How many MEPs were left for the statistical analysis after exclusion? How do the results change when all trials are kept but the median is used per condition (instead of the mean) or the mean of log-transformed MEP values? Given that robustness is key for these kind of results, transformations should be avoided (or outlier removal at least).

The number of trial outliers excluded was small and only encompassed severe outliers (±2 standard deviations); moreover, this was applied equally for all sham and US conditions. For pre-activation, the outlier exclusion was similarly applied. We agree with the reviewer that there is no consensus as to whether outliers should be excluded or not in TMS studies; our methods are consistent with other TMS papers in the literature. Please see: doi: 10.1038/s41598-018-30480-z, 10.1016/j.brs.2019.05.015, 10.1152/jn.00762.2012, among others.

In our original 12 subjects, in single-pulse parameter experiments, participants received 10 trials per condition; at most 1 trial per condition were excluded, leaving 9 trials for analysis. Out of a total of 180 trials/participant, at most 2 outliers were identified, representing 1% of total trials.

For paired-pulse experiments, subjects received 10 trials per condition, at most 2 trials per condition were infrequently excluded, leaving 8 for analysis. Out of 160 total trials per participant, at most 3 outliers were identified, representing 2% of the total.

3) Several concerns have been raised regarding M1 targeting and the use of a normalized brain and MNI coordinates. Specifically, in the newly collected data, M1 (or specific parts of it) should be properly targeted by using neuronavigation and individual head models with T1/T2 (instead of a normalized brain and MNI coordinates). Moreover, the following points should be addressed in the revised version of the manuscript.

3a) Why was a normalized brain used? You have a neuronavigation system and each participants' MRI. Why MNI coordinates? Should you not distinguish based on each participants' anatomy and TMS response to determine location?

Thank you for allowing us to clarify our use of neuronavigation, and to correct the erroneous phrase “normalized brain MRI”. The phrase is now corrected to:

Materials and methods:

“We captured the position of the coil in stereotactic space by registering the subject’s individual T1 anatomical MRI with the position of the TMS coil, using a TMS tracker and infrared camera via Brainsight. “

The method of selecting scalp position should also have been better elaborated, which we have done in the revised manuscript. Normalization to MNI coordinates was only done post-hoc via Brainsight software, solely for the purpose of generating Figure 2A (probabilistic average of electric field over a single standardized brain).

As the reviewer suggests, we did indeed use each participant’s TMS response to localize the position of the transducer on the scalp, and consequently the position of the TMS coil. As we now describe in the revised Materials and methods:

Materials and methods:

“At the beginning of each experimental session, we determined the FDI motor hotspot on the scalp, defined as the location over which TMS evoked MEPs of highest peak-to-peak amplitude in the target muscle at a given suprathreshold stimulator intensity (Bashir et al., 2013). A marker was used to precisely trace outline of the transducer holder on the scalp, to ensure that the angle and position of the wand was captured.”

In addition, we add:

“The ultrasound transducer was rigidly fixed to the underside of the figure-8 coil, and held in the centre of the coil, between the two windings. Previous validation studies (Opitz et al., 2015) showed that the measured electromagnetic maxima of a figure-of-8 TMS coil is located between the two coil windings, justifying our central placement of the transducer holder.”

Please see the individual acoustic simulation for validation (Figure 2—figure supplement 1) – we acknowledge in the limitations that the predicted sonication beam did not always encompass the anatomical location of the M1 cell bodies, which may account for individual variability in response to FUS. Nevertheless, the acoustic simulations show that our beam path targeted the primary motor cortex, and underlying white matter, in the majority of participants.

3b) Why was a mark placed on the head when you have a neuronavigation system? Since BrainSight neuronavigation was used to identify the TUS transducer position on the scalp, why was it not used to ensure and maintain correct transducer placement throughout the many measurements and sessions? Given the small diameter of the sonication beam, tiny changes in tilt or position can have a massive effect on the actually stimulated part of cortex.

Given the long stimulation blocks (e.g. 90 randomized frames with ISI 5s in the sonication duration experiment), our priority was to ensure that the position of the FUS-TMS stimulator results in consistent MEP amplitudes. As such, after locating the FDI motor hotspot, we used a permanent marker to precisely trace outline of the transducer holder on the scalp, to ensure that the angle and position of the transducer/TMS wand was captured. We agree with the reviewer that tiny changes in tilt or position in the transducer can have a massive effect on the actually sonicated part of cortex. Indeed, we found that small changes in tilt/position of the TMS coil can also significantly alter the consistency of generated MEPs, and this is precisely what we wanted to avoid. As such, our priority was to keep the entire TMS-FUS wand in a consistent position on the scalp for all stimulation experiments, since the outcome of interest was MEP amplitude. The Brainsight was used as a confirmatory tool, as well as enabling us to conduct acoustic and electromagnetic simulations post-hoc (ie: Figure 2A-B).

3c) Figure 2B: Please also provide sagittal and axial views to allow a better judgement of the targeting of M1. Is it actually targeting M1 or maybe premotor cortex? Which part of the precentral gyrus is actually sonicated?

We have revised Figure 2, adding both an improved coronal acoustic simulation (2B), as well as the reviewer’s requested axial and sagittal views derived from the neuronavigation targeting software (2C). As can be seen from these views, the transducer is overlying the hand knob of the precentral gyrus, at the location of the primary motor cortex. Please also see Figure 2—figure supplement 1 for individual coronal acoustic simulations which depict the estimated portions of the precentral gyrus sonicated for every individual.

3d) According to Fox, 2006 and Geyer, 1996 the motor cortex of human is allocated in the sulcus and at best to a small extent at the crown. In Figure 2 B the white matter is targeted as well as in Figure 3A where the pyramidal neurons are allocated in the white matter. Thus, the first part of the following sentence might not be accurate: "Similarly, the individual simulations of ultrasound propagation for each participant confirmed acoustic targeting of a portion of M1, as well as underlying white matter tracts. (Figure 2B)." see also "Our finding may suggest that cortical interneurons in layers II/III which are well-encompassed within the acoustic focus (Figure 4)"

We now provide individual acoustic simulations (Figure 2—figure supplement 1), as well as axial, sagittal, and coronal views (Figure 2B-C) of the targeted sulcus of a representative participant. Given the limited fidelity of acoustic simulation software and other limitations such as the lack of CT-derived bone densiometry, these are at best approximations. Furthermore, the wide focal length of the elongated acoustic focus (22.95 mm) predisposes to sonication of not only most cortical layers (including II/III), but also underlying white matter. Nevertheless, as we state in the Discussion, it is reasonable to speculate that based on our SICI results and others:

Discussion:

“Indeed, emerging electrophysiology work in animal models is suggesting that sensitivity to low-intensity TUS is mediated not only by parameter selection, but also that excitatory and inhibitory neurons have different sensitivities to sonication (Yu et al., 2019).”

Further studies are needed to refine the mechanism of action of TUS-mediated cortical effects.

3e) Without a CT scan and only T1-weighetd images no really reliable simulations can be obtained for the acoustic waves. Figure 2B only shows one "representative" subject. Have simulations been performed for all subjects or was the transducer only placed on top of the TMS M1 hotspot for each subject without modelling the sonication beam individually? This assumes that the relevant motor neurons of M1 are actually directly beneath the coil center, which is not necessarily the case.

Despite the superiority of CT imaging in calculating skull density and morphology metrics relevant to acoustic simulation, we did not wish to expose our healthy participant volunteers to radiation. We instead acquired anatomical T1 MRIs, which allow us the dual purpose of enabling subject-specific neuronavigation, and provided us with the ability to segment the skin, skull, and brain to estimate the acoustic pressure field. We now cite a validation study (doi: 10.1109/58.764862) showing agreement between hydrophone-acquired and simulated pressure fields using MRI data. We used a similar method to studies simulating trans-skull ultrasound propagation using segmented T1 MRI data (doi: 10.1121/1.5126685), and describe this in the revised methods and newly added Appendix 1. We agree with the reviewer that by simulating the skull as a homogenous tissue, the simulations show an imperfect estimation of the acoustic focus, and have added this to our subsection “Limitations”:

“Although our acoustic simulations were based on individualized brain imaging and transducer positions were captured by neuronavigation, the simulations are limited to two-dimensions, and the tissues are treated as homogenous layers due to absence of CT-derived density data which limits the fidelity of the estimated focus”.

3f) Figure 3A: The location and orientation of corticospinal output neurons in M1 is incorrect and misleading. They are actually located in the anterior bank of the central sulcus and oriented tangentially to the scalp. This should be corrected.

We have corrected the orientation and location of corticospinal neurons in our revised Figure 1A.

4) The phrasing "… a portion of M1…" is disconcerting. Because the transducer was concentric with the intersection of the TMS coil, wherever you put the coil is where the US was. Is that accurate? How did you confirm this however in your models if you did not use individual MRI but rather normalized MRI. MNI coordinates are mentioned previously but not given anywhere in the manuscript. TUS is highly localized and using generalized MNI coordinates is not appropriate. Please provide an acoustic wave modeling of the sonication for each participant.

We now provide an acoustic wave modelling for each participant (Figure 2—figure supplement 1) based on the scalp location of the transducer captured by neuronavigation. As mentioned in point 3a, the phrasing “normalized MRI” was misplaced in the manuscript. We only used normalized coordinates to generate the electromagnetic current map in Figure 2A. Individualized locations were used for acoustic simulation, and based on the simulation, a portion of the M1 gray matter, as well as underlying white matter was targeted for each participant with the sonication beam.

5) There is no data on how/if the transducer affected the TMS pulse or vice versa. This needs to be either collected or cited from Legon et al. 2018 and differences in transducer materials/design should be factored in if there are any.

We now include in our revised manuscript a more detailed description of the transducer material properties, dimensions, and mounting within the 70-mm TMS coil in the form of a supplementary Figure (Figure 1—figure supplement 1). We also cite Legon et al., 2018’s validation studies, which used the same 70-mm TMS coil as ours, and a custom transducer with similar dimensions and non-ferromagnetic properties. Of note, our transducer is constructed to be MRI-safe, and contains a non-ferromagnetic (brass) housing and is designed with a radiofrequency shield.

We now add, in our subsection “Limitations”:

“Lastly, we did not explicitly characterize the effect of the TMS-induced electromagnetic field on the operation of the ultrasound transducer, nor the effects of the transducer housing on the coil’s induced electrical field. However, such a characterization was rigorously performed in a prior study (Legon et al., 2018b) using the same TMS coil and a similarly-sized custom non-ferromagnetic ultrasound transducer, and found no significant effects in either direction.”

6) Gel pads are notorious for trapping air bubbles between interfaces. This can be easily detected using imaging mode of your transducer. Was this checked for? Was any other coupling media used?

Although our custom TUS transducer does not feature the software or hardware to allow image reconstruction, we have performed additional experiments with a dedicated imaging ultrasound system (Hitachi Aloka ProSound Α 7) to validate the lack of air bubbles between two relevant interfaces, using our methodology.

(Newly added Appendix 2)

Panel A: Visible air bubble at the TUS transducer-gel interface, indicated by the red arrow (left), and corresponding refraction and shadowing artifact seen when an imaging transducer is placed over the air bubble (right).

Panel B: Manual smoothing of the gel pad is performed until any small bubbles are extruded, leaving a homogenous black interface (left), and confirmatory imaging with ultrasound probe at the gel pad surface shows no artefact at the TUS transducer-gel interface (right).

Panel C: Application of gel pad over the frontal bone with the imaging transducer applied over the gel, visualizing the scalp-pad interface (left). Imaging of the scalp-pad interface, showing underlying tissue layers with no visible artefacts (right).

We now clarify this methodology in the revised Materials and methods:

“Aqueous compressible gel pads (Aquaflex, Parker Laboratories, NJ, USA) were cut into 1.5 mm thick pads 40 mm in diameter and placed between the surface of the transducer and the subject’s scalp. Any visible air bubbles at the transducer-pad interface were manually extruded, and a small amount of ultrasound gel (Aquasonic 100, Parker Laboratories, NJ, USA) was applied at the scalp-pad interface (Appendix 2)”.

7) TMS was applied in order to measure the cortical excitability changes with MEP. The TMS pulses were locked to the end of FUS or sham stimulation and they had an interstimulus interval of 5 seconds. If there was no jitter for TMS pulses it means that rTMS at 0.2 Hz was applied simultaneously with FUS. Repetitive TMS applied at a very low frequency of 0.2 Hz has been shown to be effective in several studies (Urushihara, 2006; Hosono, 2008). For example, rTMS over PMC led to an increase in somatosensory evoked potentials. Could the possible effect of low frequency rTMS on cortical excitability when applied simultaneously with FUS be discussed?

Thank you for raising this important question. We do not believe low-frequency rTMS will affect motor evoked potentials (MEP), as supported by three papers we cited and describe below:

1) Chen et al., 1997 conducted 0.1 Hz rTMS for 1 hour (360 pulses) and did not find motor cortical excitability changes.

2) Furukawa et al., 2010 carried out 100 sessions of M1 TMS at the same frequency as our inter-stimulus interval (0.2 Hz), and showed that MEP amplitude were neither enhanced nor reduced.

3) Cincotta et al.,2003 found that MEP size is not changed after 0.3 Hz rTMS intervention to M1 (total 540 pulses).

Overall, the rTMS frequency in these studies ranged from 0.1-0.3 Hz, with up to 540 pulses, without significantly altering MEP size. In our study, we tested sham and real FUS interventions with 15-20 frames per condition, with TMS delivered at 0.2Hz, and showed suppression of MEP size under certain condition. The short duration of TMS trains alone should not affect the MEP amplitude.

The two papers mentioned by the reviewer were done by the same group and show that 0.2 Hz rTMS can enhance the SEP N30 ratio. However, the stimulation target (pre-motor sensory cortex) and readout (SEP) were different from those in our study, which was the M1 and MEPs, respectively

8a) A further concern is "This sound was triggered every time a FUS or sham condition was delivered to the transducer." This could mean that the effects reflect acoustic TMS pairing, see e.g. doi: 10.3389/fnhum.2014.00398, other papers are around as well. Can the results in Figure 5D be due to longer acoustic stimulation? I expect the sham condition to be performed with the shortest duration, however not sure? Can this explain the lack of an effect in Figure 5E and F?

Thank you for the opportunity to clarify our choice of auditory control, and we detail our rationale in the Discussion. We recognize that several studies show that pairing TMS with an audible stimulus, especially human speech, can lead to increases in cortical excitability. Since the piezoelectric element within our ultrasound transducer emits a slightly audible buzzing tone when activated, also reported in other human FUS studies (eg: Legon et al., 2018), it was precisely to control for acoustic-TMS coupling that we decided to implement an audible masking sound in all conditions of the experiment.

To elaborate, we ensured that the audible masking sound was played for the same duration of time (0.5 seconds) for every experimental condition, including the sham, regardless of the parameters, relative to the TMS pulse. Furthermore, our sound did not consist of speech – rather it was a low-volume continuous high-pitched tone. Studies that have examined the TMS motor response of the hand area to different auditory stimuli (doi: 10.1046/j.1460-9568.2003.02774.x, Figure 3 (bottom) ) have shown that concomitant noise/tonal sounds do not alter the magnitude of MEP, while language perception significantly increases motor excitability. In agreement, studies examining TMS motor response of the leg (doi: 10.1016/j.neuropsychologia.2008.05.015, Figure 3) and tongue (doi: 10.1046/j.0953-816x.2001.01874.x) show a motor facilitation with semantic speech stimuli, but not with audible tonal sounds or noise. In our study, we did not find facilitation, but rather decreased excitability with application of transcranial ultrasound to the hand area of M1, while controlling for audible tones in all delivered conditions including sham.

The study referenced by the reviewer (doi: 10.3389/fnhum.2014.00398) found that acoustic-TMS pairing elicits significantly larger MEPs relative to a control condition without associated auditory stimulus. In all conditions within our experiments, the TMS pulse was preceded by an auditory stimulus of duration 0.5s, and we therefore expect that any TMS-acoustic pairing would be controlled for.

Interestingly, while writing these revisions, we came across a relevant preprint which examined audio masked vs. unmasked delivery of FUS/sham conditions (doi:10.1101/2020.03.07.982033). Of particular interest are Figure 1C and Figure 2A, showing that an audio-masked FUS sonication and audio-masked sham evoke identical auditory ERP's, whereas FUS alone without a mask evokes a significantly different ERP, and moreover can also be reliably differentiated from sham by participants. We believe this preliminary report corroborates our similar audio masking strategy, and highlights the importance of controlling for auditory confounds in future human ultrasound neuromodulation studies.

8b) Also: "and the task was more complex; nevertheless, the sonication parameters and cortical location were similar, and we observed an effect size of about 100 ms, though with higher variability." It may simply be that the start of the sonification sound leads via a kind of pre-triggering to shortened responses. This is discussed by the authors in subsection “Behaviour”. The effect in Figure 8A appears to be implausibly high. Control experiments seem to be reasonable with very light somatosensoric or close to threshold acoustic stimulation. The whole field of TMS-EEG suffers from acoustic and somatosensory contamination.

Although we control for auditory contamination with a masking sound, we agree with the reviewer that the start of sonication may contribute to a somatosensory pre-triggering, predisposing to shortened responses. We discuss this in Subsection “Behaviour”:

“Although the slightly audible sound of the piezoelectric transducer element was masked until participants could not distinguish between sham and active condition, we cannot rule out intersensory facilitation (Diederich and Colonius, 1987; Forster et al., 2002) as contributing to the difference in reaction times between conditions. Given that acoustic waves are mechanical in nature, it is conceivable that a subtle tactile sensation on the scalp at the site of transducer placement might act as an extra sensory cue which is difficult to replicate with a sham protocol that delivers no acoustic energy”

As suggested by the reviewer, we have conducted an additional control experiment with the additional 4 participants we recruited during the revision process. The results are seen in revised Figure 8, top panels. We analyze these results with paired t-test in the Results section, and discuss in subsection “Behaviour”:

In a follow-up control experiment with 4 participants, the reaction times were not significantly different when a lower near-somatosensory threshold intensity (0.54 W/cm2) was used when compared to the 2.32 W/cm2 intensity used in all other experiments. Since individual somatosensory thresholds differ, and the threshold of transcranial acoustic energy necessary to affect motor tasks is unknown, further experiments are necessary to disentangle the role of somatosensory confounding in this behavioral task.”

In light of this control experiment, we have also added to the Abstract: “…and decreased reaction time on a visuomotor task compared to sham but not compared to a near-threshold intensity.”

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

Summary:

Fomenko et al., have thoroughly revised their manuscript. They added four new participants (now N = 16) with more trials (15-20 instead of 10 per condition), as well as more TUS modelling information. While this is not a large increase in sample size, the authors' efforts are appreciated. However, both the reviewers and the editors were concerned by the fact that the effects are robust for the 'block' experiment but not for the 'parameter' (or interleaved) experiment. Consequently, additional data should be collected in a sufficiently large sample (eg. keeping the N = 16 for interleaved and adding N = 16 for a blocked variation of stimulation parameters), targeting specifically the block vs. interleaved question for the duty cycle and pulse repetition frequency parameters.

Thank you for the opportunity to revise our manuscript. The reviewer and editor comments were insightful and have resulted in a stronger publication.

As requested by the reviewers, we have called back 16 healthy participants, in order to address the concern in the original manuscript; namely, that varying some sonication parameters (ie: PRF and DC) in blocked versus interleaved design yielded different effects on TMS-elicited MEP, whereas other parameters (ie: Sonication Duration) yielded robust effects regardless of experimental design.

For each participant, six new experiments were performed. The parameters examined were Duty Cycle (Sham, 10%, 30%, 50%), PRF constant duty cycle (Sham, 200Hz, 500Hz, 1000Hz), and PRF adjusted duty cycle (Sham, 200Hz, 500Hz, 1000Hz). For each individual parameter including sham, 15 replicate MEPs were acquired either serially in blocked fashion, or randomly interleaved with other parameters. We describe the protocol in detail in the Materials and methods section, and have used a rest period of 20 seconds between blocks, consistent with doi:10.1016/j.ultrasmedbio.2015.10.001. All parameters including sham were identically masked with a speaker tone lasting 0.5 during sonication. The results are discussed below in response to point 1.

Essential revisions:

1) Regarding the block vs. interleaved question:

If it makes indeed a difference for single trial MEP modulation whether TUS is applied in a block design with fixed parameters or with trial-by-trial variation of parameters, this would have important implications for TUS application and thus needs additional data collection, as well as detailed discussion. Specifically, for some parameters (sonication duration) the default parameter (0.5s) was effective in the interleaved design. However, for others (duty cycle) it was slightly different (10% instead of 30%), and for yet others (pulse repetition frequency) it was not different from sham anymore at all (but was previously with 1000 Hz). The blocked vs. interleaved interpretation is post-hoc and potentially valid only for duty cycle and pulse repetition frequency.

Please see additional figure added to the manuscript (Figure 5—figure supplement 1), as well as new results and in the Discussion section, where we write:

“Interestingly, our initial results show that although a particular combination of parameters can robustly suppress TMS-elicited cortical activity when applied in a sham-controlled block design (Figure 4), some parameters such as PRF when randomized and varied individually do not have the same robust effect, whereas others such as sonication duration do (Figure 5C-D). To address this discrepancy, we conducted follow-up experiments (Figure 5—figure supplement 1) in which we delivered three distinct parameter sets in blocked, and interleaved fashion. We found that sonication at 10% DC consistently results in suppression regardless of experimental design, whereas 30% DC only results in suppression when applied in blocked fashion. A duty cycle of 50% did not show any difference compared to sham regardless of experimental design. In contrast, we observed that sonication at three different pulse repetition frequencies (200, 500, and 1000 Hz) applied in a blocked design resulted in reduced cortical excitability compared to sham, whereas interleaving these parameters yielded no significant difference. These results held whether duty cycle was fixed, or whether the DC was adjusted to maintain an equal burst duration.

In summary, our findings suggest that when delivered in blocked fashion, longer sonication durations, lower duty cycles, and all three PRFs tested yield effective suppression of TMS-elicited MEPs. The trend towards greater suppression with increasing blocked PRF agree with recent literature, where higher PRF (1500Hz) in combination with low duty cycles were found to be more effective than lower PRF (300Hz) in neuromodulation of mouse motor cortex in vivo (King et al., 2013) and in vitro (Manuel et al., 2020). Furthermore, recent large animal studies reveal a bidirectional neuromodulation effects of varying TUS parameters (Yoon et al., 2019). As such, interleaving different parameters in short succession may lead to spillover effects, due to the random order of parameter delivery. From animal studies where randomized delivery of TUS parameters was studied, nonlinear effects were also found, which may be related to non-linear piezoelectric accumulation across the neural membrane capacity under the Neuronal Bilayer Sonophore model (Kim et al., 2014; Plaksin et al., 2014). In addition, excitatory or inhibitory changes in short-term plasticity may occur with repeated TUS stimulation, similar to those observed with repeated magnetic (Watanabe et al., 2014) and electrical stimulation (Udupa et al., 2016). These are currently under study in our laboratory and in emerging reports on LITUS short-term plasticity in animal models (Yu et al., 2019).”

Further experiments are ongoing in our lab to investigate the potential longer-term plasticity effects of FUS on the motor cortex, as well as potential nonlinear effects of individual FUS parameters.

2) The authors added 4 subjects to the existing data set (N = 12) but the reviewers fail to see the significance of this. First, data from the original 12 is presented in Figure 4 and the data from the new N = 4 is presented on its own. Could the authors explain the added value of this? It looks from the manuscript that N = 16 was inclusive for the parameter testing but not the 'basic parameter' testing. The authors could either remove Figure 4C or include that data in Figure 4A.

We presented the additional n=4 subjects in a separate panel of Figure 4 because these subjects received a different form of sham (inactive sham, or unpowered transducer facing the scalp) from the original n=12 subjects (active sham, or transducer flipped away from the scalp and powered, as in Legon, 2018). Given the active debate over the role of auditory confounds in the FUS neuromodulation literature as well as the first set of reviewer questions regarding our sham approach, we demonstrated that both forms of sham were able to suppress TMS-elicited MEPs, and then proceeded to use the Inactive sham for the remainder of the experiments, since it was less cumbersome and did not require manually flipping the transducer. We clarify in the revised Figure 4 caption:

“Effect of baseline ultrasound versus active and inactive sham on TMS-induced resting MEP amplitudes as measured by FDI EMG. A) Baseline parameters suppressed mean MEP amplitude compared to active sham, or powered transducer pointing upwards (p=0.002, paired t-test) N=12. B) Individual MEP values by participant by condition C) Baseline parameters suppressed mean MEP amplitude compared to inactive sham, or unpowered transducer pointing towards the scalp (p=0.012, paired t-test) N=4."

The reviewers are also directed to Figure 9, where the two types of sham are illustrated for clarity.

3) It is still unclear how many MEPs were left for each participant for the statistical analysis after exclusion. It looks like the new data uses 15 trials but the old 10 and the block experiment 20. Furthermore, it is stated in the Materials and methods that MEPs {plus minus} 2SD of mean were excluded. Please include M+/-SD in the Materials and methods.

The reviewers are correct in that new data uses either 15 or 20 trials per parameter, compared to the old data using 10. This was done as per the first revision reviewer request to increase the number of replicate trials per condition, and decrease the number of comparison.

In light of the reviewer comments regarding outlier exclusion, we have also chosen to now include all data, and use the median as the measure of central tendency for replicate trials in the TMS experiments including the new blocked vs. interleaved trials. Please see the Results section for our reanalysis, which now includes all data.

4) The following questions still need to be addressed:

- Was the pre-activation controlled and excluded together with the outliers?

- How do the results change when all trials are kept but the median is used per condition (instead of the mean) or the mean of log-transformed MEP values? Also, do the results hold when not removing the outliers?

As mentioned in Point 3, we have now decided to include all data and use the median as measure of central tendency; therefore, outliers are now no longer excluded. Please see revised manuscript and figures, where we demonstrate that our results hold.

- Why was the neuronavigation system not used to online maintain coil/transducer position? Coil angle cannot be reliably inferred from a felt pen drawing on the scalp.

We chose not to use online neuronavigation, which we now explicitly mention and discuss in the subsection “Limitations”. Our rationale was that we prioritized positioning the coil/transducer assembly at the TMS motor “hotspot”, to best achieve consistent motor-evoked potentials. We show in Author response image 1 the relevant markings we use on the TMS-TUS stimulator to ensure a precise surface positioning (contour tracing) and angulation (directional marking). Regarding the lateral angulation over the scalp, we maintain a perpendicular position to achieve maximal transducer coupling with the scalp.

Author response image 1

5) Could the authors show the sonication beam model for all three slices (coronal, sagittal, axial)? The axial slices do not seem to be the most helpful ones when determining whether M1 was hit and which portion of it.

We now show the sonication beam model for the coronal and sagittal sections in the revised Figure 2B and 2D, showing a representative participant. Individual simulations for each participant coronal sections can be found in Figure 2—figure supplement 1.

Given that our study goal was not computational, and that the beam models are at best a two-dimensional approximation, which we mention in the limitations, we did not choose to pursue sophisticated 3D beam modelling, which we reserve for a future study, and therefore do not include an axial slice model. We also agree with the reviewers that the axial slices are not very helpful with respect to ascertaining which portion of the M1 was targeted. Instead, our 2D simulations serve as a post-hoc validation that our beam paths likely traversed the primary motor cortex and underlying corticospinal tracts.

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

The new results demonstrate that the effectiveness of TUS parameters does indeed depend on whether TUS parameters are varied across trials or kept constant within a block. These additional findings are important and will be very useful for others using this approach. These results though also point to one important question that we believe should be in the published study: Are the results in the block design condition due to cumulative effects? That is, is there a systematic change over time, indicative of a cumulative effect? (To quote the reviewer who noted this concern, "Specifically, is there a build-up of suppression across trials within a block? This could be tested e.g. by regressing the MEP amplitude based on within-block trial number or by comparing early and late MEPs within a block. It would be a very important outcome to know whether the observed suppression is instantaneous or accumulating across trials.").

Thank you for pointing us toward an interesting analysis which might shed light into the potential mechanisms involved in our findings. In our revised manuscript, we now perform a post-hoc analysis of our blocked experiments, stratifying by trial number across all participants (Please see new Figure 5—figure supplement 2).

Similar to Volpert-Esmond et al., (2017), and also suggested by the reviewer, we then compare early trials (defined as the first 3 trials, or the first 20%) to late trials (last 3 trials, or last 20% of trials) of each parameter. We add the analytic methods and discussion to the revised manuscript as tracked changes.

On paired T-tests corrected for multiple comparisons, we do not detect a significant difference between the early and late trials across the three parameter sets. Please see the revised Results section for corrected p-values, which are non-significant.

We now add to our Discussion:

“To test whether a progressive accumulation of inhibition might be responsible for the observed difference in results between the blocked and interleaved study designs, we performed a post-hoc analysis of our blocked experiments, stratifying by trial number across all participants (Figure 5 – figure supplement 2). Within each parameter condition, we did not detect a significant difference between the magnitude of early and late trials and conclude that there is no temporal accumulation of MEP suppressive effects over blocks of fifteen trials, corresponding to a block length of approximately 90 seconds. Instead, the suppression appears to be instantaneous, with effective parameters increasing the likelihood of generating a lower TMS-elicited MEP, but not potentiating the effect of a subsequent stimulation. Notably, our blocked design features a 20 second pause between each set of 15 replicate stimulations – whereas interleaved delivery of random parameters involves an uninterrupted session of 60 random stimulations. We speculate that this 20 second rest period may lead to a resetting of cortical excitability to a more TUS-sensitive state. The lack of resetting could play a role in our observation of less robust effects when certain parameters are randomized in succession.”

Dedicated study designs will be needed to answer the question of both short-term and long-term effects of TUS, as well as its on-line and off-line interaction with TMS. Further questions to be answered include the role of somatosensory confounds/masking, as well as the use of on-line TMS navigation for targeting verification from trial to trial.

https://doi.org/10.7554/eLife.54497.sa2

Article and author information

Author details

  1. Anton Fomenko

    Krembil Research Institute, University Health Network, Toronto, Canada
    Contribution
    Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing
    Contributed equally with
    Kai-Hsiang Stanley Chen
    For correspondence
    anton.fomenko@uhnresearch.ca
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4131-6784
  2. Kai-Hsiang Stanley Chen

    1. Krembil Research Institute, University Health Network, Toronto, Canada
    2. Department of Neurology, National Taiwan University Hospital Hsin-Chu Branch, Hsin-Chu, Taiwan
    Contribution
    Conceptualization, Resources, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Writing - review and editing
    Contributed equally with
    Anton Fomenko
    Competing interests
    No competing interests declared
  3. Jean-François Nankoo

    Krembil Research Institute, University Health Network, Toronto, Canada
    Contribution
    Conceptualization, Investigation, Methodology, Writing - review and editing
    Competing interests
    No competing interests declared
  4. James Saravanamuttu

    Krembil Research Institute, University Health Network, Toronto, Canada
    Contribution
    Data curation, Formal analysis, Investigation, Writing - review and editing
    Competing interests
    No competing interests declared
  5. Yanqiu Wang

    Krembil Research Institute, University Health Network, Toronto, Canada
    Contribution
    Investigation, Writing - review and editing
    Competing interests
    No competing interests declared
  6. Mazen El-Baba

    Krembil Research Institute, University Health Network, Toronto, Canada
    Contribution
    Software, Visualization
    Competing interests
    No competing interests declared
  7. Xue Xia

    Division of Neurology, Department of Medicine, University of Toronto, Toronto, Canada
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  8. Shakthi Sanjana Seerala

    Sunnybrook Research Institute, Toronto, Canada
    Contribution
    Resources, Investigation
    Competing interests
    No competing interests declared
  9. Kullervo Hynynen

    Sunnybrook Research Institute, Toronto, Canada
    Contribution
    Resources, Writing - review and editing
    Competing interests
    No competing interests declared
  10. Andres M Lozano

    1. Krembil Research Institute, University Health Network, Toronto, Canada
    2. Division of Neurosurgery, Department of Surgery, Toronto Western Hospital, University of Toronto, Toronto, Canada
    Contribution
    Conceptualization, Supervision, Writing - review and editing
    For correspondence
    lozano@uhnresearch.ca
    Competing interests
    No competing interests declared
  11. Robert Chen

    1. Krembil Research Institute, University Health Network, Toronto, Canada
    2. Division of Neurology, Department of Medicine, University of Toronto, Toronto, Canada
    Contribution
    Conceptualization, Supervision, Writing - review and editing
    For correspondence
    robert.chen@uhn.ca
    Competing interests
    No competing interests declared

Funding

Canadian Institutes of Health Research (Banting and Best Doctoral Award)

  • Anton Fomenko

NSERC (Discovery Grant RGPIN-2020-04176)

  • Robert Chen

Canadian Institutes of Health Research (Foundation Grant FDN 154292)

  • Robert Chen

University of Manitoba (Clinician Investigator Program)

  • Anton Fomenko

Canada Research Chairs (Neuroscience)

  • Andres M Lozano

University Health Network (R.R. Tasker Chair in Stereotactic and Functional Neurosurgery)

  • Andres M Lozano

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We thank Cricia Rinchon and Julianne Baarbé for their help in a piloting the optimal coil-scalp distance for this experiment. Dr. Neil M. Drummond is thanked for constructive discussion and assistance. We thank Dr. Suneil K Kalia and Dr. William Hutchison for their guidance during committee meetings. Dr. Ryan M Jones is thanked for his advice regarding acoustic simulations, and we are grateful for Frank Vidic’s electrical expertise relating to devices used in the project. This work was supported by the Canadian Institutes for Health Research (CIHR) Foundation Grant (FDN 154292, RC), Banting and Best Doctoral Award (AF), the Clinician Investigator Program – University of Manitoba (AF), and the Canada Research Chair in Neuroscience (AML)

Ethics

Human subjects: All patients gave written informed consent and the protocol was approved by the UHN Research Ethics Board (Protocol #18-5082) in accordance with the Declaration of Helsinki on the use of human subjects in experiments.

Senior Editor

  1. Richard B Ivry, University of California, Berkeley, United States

Reviewing Editor

  1. Laura Dugué, Université de Paris, France

Reviewer

  1. Wynn Legon

Publication history

  1. Received: December 16, 2019
  2. Accepted: November 24, 2020
  3. Accepted Manuscript published: November 25, 2020 (version 1)
  4. Version of Record published: December 10, 2020 (version 2)

Copyright

© 2020, Fomenko et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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