Robust functional ultrasound imaging in the awake and behaving brain: A systematic framework for motion artifact removal.

Overview

1. Physics for Medicine Paris, Inserm, ESPCI Paris, CNRS, PSL Research University, Paris, France
2. Iconeus, Paris, France
3. Université Paris Cité, Institute of Psychiatry and Neuroscience of Paris (IPNP), INSERM U1266, Laboratory of Dynamics of Neuronal Structure in Health and Disease, Paris, France
4. Institute of Pharmacology and Toxicology, Jena University Hospital, Friedrich Schiller University Jena, Jena, Germany
5. Instituto de Farmacologia e Neurociências, Faculdade de Medicina, Universidade de Lisboa, Lisboa, Portugal
6. Gulbenkian Institute for Molecular Medicine, Lisboa, Portugal
7. Centro Cardiovascular da Universidade de Lisboa, CCUL, Faculdade de Medicina, Universidade de Lisboa, Lisboa, Portugal

Abstract

Functional ultrasound imaging (fUSI) is a promising tool for studying brain activity in awake and behaving animals, offering insights into neural dynamics that are more naturalistic than those obtained under anesthesia. However, motion artifacts pose a significant challenge, introducing biases that can compromise the integrity of the data. This study provides a comprehensive evaluation and benchmarking of strategies for detecting and removing motion artifacts in transcranial fUSI acquisitions of awake mice. We evaluated 792 denoising strategies across four datasets, focusing on clutter filtering, scrubbing, frequency filtering, and confound regression methods. Our findings highlight the superior performance of adaptive clutter filtering and aCompCor confound regression in mitigating motion artifacts while preserving functional connectivity patterns. We also demonstrate that high-pass filtering is generally more effective than band-pass filtering in the presence of motion artifacts. Additionally, we show that with effective clutter filtering, scrubbing may become optional, which is particularly beneficial for experimental designs where motion correlates with conditions of interest. Based on these insights, we propose two optimized denoising paradigms tailored to different experimental constraints, providing practical recommendations for enhancing the reliability and reproducibility of fUSI data. Our findings challenge current practices in the field and have immediate practical implications for existing fUSI analysis workflows, paving the way for more sophisticated applications of fUSI in studying complex brain functions and dysfunctions in awake experimental paradigms.

Reproduced under the paper's license (CC BY), from the paper cited above.

Code

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Data

Datasets cited

• zenodo:15557854, at Zenodo; found in “Data and Code Availability”

Data and Code Availability

The analyses presented in this study were conducted in large parts using a proprietary software currently developed by Iconeus (Paris, France). As such, the source code is not publicly available. The raw datasets supporting the conclusions of this article include raw beamformed I/Q data (>15 TB) and resampled power Doppler data (>1 TB). Given the substantial size of these datasets, they cannot be hosted on standard data repositories. A subset of power Doppler scans denoised using the proposed paradigms is provided on Zenodo for illustration purpose: https://zenodo.org/records/15557854. Researchers interested in accessing the raw data are invited to contact the Office of Research Contracts at Inserm to initiate discussions regarding data transfer or use.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 9 authors, 5 keywords, 77 references.

Cite

This paper

Le Meur-Diebolt, S., Cybis Pereira, F., Mariani, J.-C., Kliewer, A., Farinha-Ferreira, M., Bertolo, A., Osmanski, B.-F., Lenkei, Z., & Deffieux, T. (2026). Robust functional ultrasound imaging in the awake and behaving brain: A systematic framework for motion artifact removal. Imaging neuroscience (Cambridge, Mass.), 4, IMAG.a.1191. https://doi.org/10.1162/imag.a.1191

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