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

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.


Sample-size estimation
• You should state whether an appropriate sample size was computed when the study was being designed • You should state the statistical method of sample size computation and any required assumptions • If no explicit power analysis was used, you should describe how you decided what sample (replicate) size (number) to use Please outline where this information can be found within the submission (e.g., sections or figure legends), or explain why this information doesn't apply to your submission:

Replicates
• You should report how often each experiment was performed • You should include a definition of biological versus technical replication • The data obtained should be provided and sufficient information should be provided to indicate the number of independent biological and/or technical replicates • If you encountered any outliers, you should describe how these were handled • Criteria for exclusion/inclusion of data should be clearly stated • High-throughput sequence data should be uploaded before submission, with a private link for reviewers provided (these are available from both GEO and ArrayExpress) Please outline where this information can be found within the submission (e.g., sections or figure legends), or explain why this information doesn't apply to your submission: Given the novelty of our stimulation paradigm, and the unknown effect size of stimulating the cortex with ultrasound, an explicit sample size was not calculated. 2 Statistical reporting • Statistical analysis methods should be described and justified • Raw data should be presented in figures whenever informative to do so (typically when N per group is less than 10) • For each experiment, you should identify the statistical tests used, exact values of N, definitions of center, methods of multiple test correction, and dispersion and precision measures (e.g., mean, median, SD, SEM, confidence intervals; and, for the major substantive results, a measure of effect size (e.g., Pearson's r, Cohen's d) • Report exact p-values wherever possible alongside the summary statistics and 95% confidence intervals. These should be reported for all key questions and not only when the p-value is less than 0.05.
Please outline where this information can be found within the submission (e.g., sections or figure legends), or explain why this information doesn't apply to your submission: (For large datasets, or papers with a very large number of statistical tests, you may upload a single table file with tests, Ns, etc., with reference to sections in the manuscript.)

Group allocation
• Indicate how samples were allocated into experimental groups (in the case of clinical studies, please specify allocation to treatment method); if randomization was used, please also state if restricted randomization was applied • Indicate if masking was used during group allocation, data collection and/or data analysis Please outline where this information can be found within the submission (e.g., sections or figure legends), or explain why this information doesn't apply to your submission: Additional data files ("source data") • We encourage you to upload relevant additional data files, such as numerical data that are represented as a graph in a figure, or as a summary table • Where provided, these should be in the most useful format, and they can be uploaded as "Source data" files linked to a main figure or table • Include model definition files including the full list of parameters used • Include code used for data analysis (e.g., R, MatLab) Please indicate the figures or tables for which source data files have been provided: Statistical and analytic methods are fully described in the Methods section (Statistical analysis, p.31), and results with associated errors and p-values for both significant and non-significant outcomes are presented and depicted in the Results, p.4-9 and Figures 2-8.
The experimental design was within-subjects, and for most experiments, active conditions were randomized with sham on a participant using a custom MATLAB script controlling the FUS and TMS stimulator (Materials & Methods, Supporting Zip document). Masking of conditions was achieved by randomization as well as a masking sound played through the speakers for both active and sham conditions (Materials & Methods, Figure 9)