Spatiotemporal Clustering of Functional Ultrasound Signals at the Single-Voxel Level.

Abstract

Functional ultrasound (fUS) imaging is a well-established neuroimaging technology that offers high spatiotemporal resolution and a large field of view. Typical strategies for analysing fUS data comprise either region-based averaging, typically based on reference atlases, or correlation with experimental events. Nevertheless, these methodologies possess several inherent limitations, including a restricted utilisation of the spatial dimension and a pronounced bias influenced by preconceived notions about the recorded activity. In this study, we put forth single-voxel clustering as a third method to address these issues. A comparison was conducted between the three strategies on a typical dataset comprising visually evoked activity in the superior colliculus in awake mice. The application of single-voxel clustering yielded the generation of detailed activity maps, which revealed a consistent layout of activity and a clear separation between haemodynamic responses. This method is best considered as a complement to region-based averaging and correlation. It has direct applicability to challenging contexts, such as paradigm-free analysis on behaving subjects and brain decoding.Significance Statement The application of spatiotemporal clustering at single-voxel resolution for functional ultrasound (fUS) signal analysis significantly enhances sensitivity in comparison to conventional methods, such as region-based averaging or event correlation. Conventional approaches frequently rely on predefined atlases or specific experimental conditions, which inherently restrict spatiotemporal resolution. In contrast, single-voxel clustering optimises the potential of fUS, facilitating the detection of intricate activity patterns throughout the brain without the necessity for prior assumptions. This approach enables more precise differentiation of hemodynamic responses and more reliable activity mapping. It is particularly advantageous in complex or paradigm-free studies, offering a high-resolution alternative to standard techniques.