Théo Lambert

Functional ultrasound (fUS) imaging is rapidly gaining interest for its unprecedented ability to study large-scale brain dynamics, yet its adoption and broader dissemination have been hindered by the lack of standardized tools and methodologies to analyze and interpret its rich datasets. We present OpenfUS Analyzer (OfUSA), a companion software suite designed to help researchers quickly engage with fUS data and perform the full range of analyses needed to generate publication-ready results and figures without relying on additional software. OfUSA offers a versatile and modular architecture including preprocessing, recording quality assessment, signal dynamics exploration, statistical analysis and visualization. These functions are separated yet easily combined into analytic pipelines through a programming-free graphical interface. The framework can be applied across species and experimental contexts, either by registering data to anatomical atlases, as shown here for the mouse brain, or by analyzing data without atlas constraints, as illustrated in a primate dataset. This flexibility, together with its comprehensive functionality, makes OfUSA a practical solution for standardized and reproducible analysis of fUS data in both preclinical and translational research. Using OfUSA, we demonstrate the capacity to detect stimulus-evoked responses with high sensitivity, identify their spatial localization within brain networks, and quantify both their extent and temporal dynamics. These results highlight the software’s ability to capture robust activation patterns and provide detailed insights into brain function, thereby accelerating the use of fUS as a powerful tool for systems neuroscience. Highlights: We present OpenfUS Analyzer (OfUSA), a novel software platform for the complete analysis of functional ultrasound (fUS) datasets. OfUSA combines a user-friendly graphical interface with a standardized, flexible workflow and powerful visualization tools, making it an ideal solution for fUS researchers at all experience levels. The software’s utility is first demonstrated through the analysis of rodent fUS data using a standardized atlas, while its versatility is further emphasized by the successful analysis of a primate fUS dataset without a template, thereby illustrating its adaptability to non-standard experimental conditions.

Over the last decade, functional ultrasound (fUS) has risen as a critical tool in functional neuroimaging, leveraging hemodynamic changes to infer neural activity indirectly. Recent studies have established a strong correlation between neural spike rates (SR) and functional ultrasound signals. However, understanding their spatial distribution and variability across different brain areas is required to thoroughly interpret fUS signals. In this regard, we conducted simultaneous fUS imaging and Neuropixels recordings during stimulus-evoked activity in awake mice within three regions of the visual pathway. Our findings indicate that the temporal dynamics of fUS and SR signals are linearly correlated, providing an approximate calibration between the two signals. Conversely, the spatial correlation between the two signals remains consistent across all regions with a spread of approximately 300 micrometers. Finally, we introduce a model that integrates the spatial and temporal components of the fUS signal, allowing for a more accurate interpretation of fUS images.

Over the last decade, functional ultrasound (fUS) has risen as a critical tool in functional neuroimaging, leveraging hemodynamic changes to infer neural activity indirectly. Recent studies have established a strong correlation between neural spike rates (SR) and functional ultrasound signals. However, understanding their spatial distribution and variability across different brain areas is required to thoroughly interpret fUS signals. In this regard, we conducted simultaneous fUS imaging and Neuropixels recordings during stimulus-evoked activity in awake mice within three regions the visual pathway. Our findings indicate that the temporal dynamics of fUS and SR signals are linearly correlated, though the correlation coefficients vary among visual regions. Conversely, the spatial correlation between the two signals remains consistent across all regions with a spread of approximately 300 micrometers. Finally, we introduce a model that integrates the spatial and temporal components of the fUS signal, allowing for a more accurate interpretation of fUS images.

The brain is composed of a dense and ramified vascular network comprising various sizes of arteries, veins, and capillaries. One way to assess the risk of cerebrovascular pathologies is to use computational models to predict the physiological effects of a reduction of blood supply and correlate these responses with observations of brain damage. Therefore, it is crucial to establish a detailed 3D organization of the brain vasculature, which could be used to develop more accurate in silico models. For this purpose, we have adapted our open-access functional ultrasound imaging platform previously designed for recording brain-wide activity that is now capable of fast and reproducible acquisition, segmentation, and reconstruction of the cortical vasculature. For the first time, it allows us to digitize the cortical vasculature in awake rodents with a ∼100 µm ³ spatial resolution. Contrary to most available strategies, our approach can be performed in vivo within minutes. Moreover, it is easy to implement since it neither requires exogenous contrast agents nor long post-processing time. Hence, we performed a cortex-wide reconstruction of the vasculature and its quantitative analysis, including i) classification of descending arteries versus ascending veins in more than 1500 vessels/animal, ii) quick estimation of their length. Importantly, we confirmed the relevance of our approach in a model of cortical stroke, which enables quick visualization of the ischemic lesion. This development contributes to extending the capabilities of ultrasound neuroimaging to understand better cerebrovascular pathologies such as stroke, vascular cognitive impairment, and brain tumors and is highly scalable for the clinic.

Positioning and navigation are essential components of neuroimaging as they improve the quality and reliability of data acquisition, leading to advances in diagnosis, treatment outcomes, and fundamental understanding of the brain. Functional ultrasound (fUS) imaging is an emerging technology providing high-resolution images of the brain vasculature, allowing for the monitoring of brain activity. However, as the technology is relatively new, there is no standardized tool for inferring the position in the brain from the vascular images. This study presents a deep learning-based framework designed to address this challenge. Our approach uses an image classification task coupled with a regression on the resulting probabilities to determine the position of a single image. We conducted experiments using a dataset of 51 rat brain scans to evaluate its performance. The training positions were extracted at intervals of 375 µm, resulting in a positioning error of 176 µm. Further GradCAM analysis revealed that the predictions were primarily driven by subcortical vascular structures. Finally, we assessed the robustness of our method in a cortical stroke where the brain vasculature is severely impaired. Remarkably, no specific increase in the number of misclassifications was observed, confirming the method’s reliability in challenging conditions. Overall, our framework provides accurate and flexible positioning, not relying on a pre-registered reference but on conserved vascular patterns.

SUMMARY: Imaging of large-scale circuit dynamics is crucial to gain a better understanding of brain function, but most techniques have a limited depth of field. Here we describe vfUSI, a platform for brain-wide volumetric functional ultrasound imaging of hemodynamic activity in awake head-fixed mice. We combined high-frequency 1024-channel 2D-array transducer with advanced multiplexing and high-performance computing for real-time 3D Power Doppler imaging at high spatiotemporal resolution (220×280×175-μm ³ voxel size, up to 6 Hz). In addition, we developed a standardized software pipeline for registration and segmentation based on the Allen Mouse Common Coordinate Framework, allowing for temporal analysis in 268 individual brain regions. We demonstrate the high sensitivity of vfUSI in multiple experimental situations where stimulus-evoked activity can be recorded using a minimal number of trials. We also mapped neural circuits in vivo across the whole brain during optogenetic activation of specific cell-types. Moreover, we revealed the sequential activation of sensory-motor regions during a grasping water droplet task. vfUSI will become a key neuroimaging technology because it combines ease of use, reliability, and affordability.