Neuromodulation with Ultrasound: Hypotheses on the Directionality of Effects and Community Resource

Journal: medRxiv
Published Date:

Abstract

Low-intensity Transcranial Ultrasound Stimulation is a promising non-invasive technique for brain stimulation and focal neuromodulation. Research with humans and animal models has raised the possibility that TUS can be biased towards enhancing or suppressing neural function. Here, we first collate a set of hypotheses on the directionality of TUS effects and conduct an initial meta-analysis on the available healthy human participant TUS studies reporting stimulation parameters and outcomes (n = 47 studies, 52 experiments). In these initial exploratory analyses, we find that parameters such as the intensity and continuity of stimulation (duty cycle) with univariate tests show only statistical trends towards likely enhancement or suppressed of function with TUS. Multivariate machine learning analyses are currently limited by the small sample size. Given that human TUS sample sizes will continue to increase, predictability on the directionality of TUS effects could improve if this database can continue to grow as TUS studies more systematically explore the TUS stimulation parameter space and report outcomes. Therefore, we establish an inTUS database and resource for the systematic reporting of TUS parameters and outcomes to assist in greater precision in TUS use for brain stimulation and neuromodulation. The paper concludes with a selective review of human clinical TUS studies illustrating how hypotheses on the directionality of TUS effects could be developed for empirical testing in the intended clinical application, not limited to the examples provided. Collated set of hypotheses on using TUS to bias towards enhancement or suppression Meta-analysis results identify parameters that may bias directionality of TUS effects inTUS resource established for systematic reporting of TUS parameters and outcomes Selective review of patient TUS studies for enhancing or suppressing neural function

Authors

  • Hugo Caffaratti; Ben Slater; Nour Shaheen; Ariane Rhone; Ryan Calmus; Michael Kritikos; Sukhbinder Kumar; Brian Dlouhy; Hiroyuki Oya; Tim Griffiths; Aaron D. Boes; Nicholas Trapp; Marcus Kaiser; Jérôme Sallet; Matthew I. Banks; Matthew A. Howard; Mario Zanaty; Christopher I. Petkov

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