Alan Urban

Rapid defensive actions require the transformation of sensory cues into appropriate motor responses. The superior colliculus is central to this transformation, containing defined cell-types that form parallel output pathways linking visual inputs to distinct behaviors. However, whether these pathways act independently or cooperate remains unclear. Here we combine cell-type-specific inhibition, large-scale electrophysiology, and functional ultrasound imaging to determine how these pathways contribute to visually evoked defensive behaviors. Inhibiting either narrow-field or wide-field neurons impaired escape to overhead visual threats, demonstrating that both pathways contribute to escape. Recordings from downstream targets revealed a compact representation in the deeper layers of the superior colliculus that selectively distinguished visual stimuli associated with escape, whereas the lateral posterior thalamus contained a broader representation of visual stimuli. The representation of visual threat in the deeper layers of the superior colliculus predicted escape initiation and vigor in freely moving animals. Across the brain, functional ultrasound imaging showed that visual stimuli recruited distinct patterns of activity that could not be explained by response magnitude alone or by the contribution of single cell-types. Together, these results reveal a cooperative circuit architecture in which parallel pathways carry distinct but complementary signals that jointly support visually evoked escape.

Linking circuit level activity to large scale functional organization requires imaging methods combining high spatial resolution, broad coverage, and single trial sensitivity. We present volumetric functional ultrasound imaging (3D-fUS) in behaving macaques, enabling imaging of ~1 cm³ cortical volumes at high spatiotemporal resolution (100 × 150 × 150 μm³ voxels, 1.67 Hz). Visually evoked responses were reliably detected at the level of single trials and single voxels, substantially reducing experimental time. To enable model-based analyses analogous to functional magnetic resonance imaging (fMRI), we estimated a fUS hemodynamic response function (fUS-HRF) that was consistent across subjects, cortical areas, and visual stimuli and was well approximated by a gamma function. Compared with fMRI HRFs, the fUS-HRF exhibited faster dynamics, enabling shorter and more closely spaced stimulus presentations. Together, these results establish 3D-fUS as a fast, volumetric, and circuit relevant imaging modality for efficient investigation of distributed cortical dynamics in primates.

Linking circuit level activity to large scale functional organization requires imaging methods combining high spatial resolution, broad coverage, and single trial sensitivity. We present volumetric functional ultrasound imaging (3D-fUS) in behaving macaques, enabling imaging of ~1 cm ³ cortical volumes at high spatiotemporal resolution (100 × 150 × 150 μm ³ voxels, 1.67 Hz). Visually evoked responses were reliably detected at the level of single trials and single voxels, substantially reducing experimental time. To enable model-based analyses analogous to functional magnetic resonance imaging (fMRI), we estimated a canonical fUS hemodynamic response function (fUS-HRF) that was consistent across subjects, cortical areas, and visual stimuli and was well approximated by a gamma function. Compared with canonical fMRI HRFs, the fUS-HRF exhibited faster dynamics, enabling shorter and more closely spaced stimulus presentations. Together, these results establish 3D-fUS as a fast, volumetric, and circuit relevant imaging modality for efficient investigation of distributed cortical dynamics in primates.

La conversion des résidus U en pseudouridine (?) est la modification la plus fréquente dans les ARN. Cette réaction est catalysée par des ARN:?-synthases qui fonctionnent soit seules, soit au sein de particules snoRNP H/ACA composées de 4 protéines et d’un snoRNA. Nous avons démontré que l’ARN:?-synthase Pus7p de S. cerevisiae est capable d’agir sur les ARNt cytoplasmiques (position 13 dans 10 ARNt et position 35 dans le pré-ARNtTyr contenant un intron) (Behm-Ansmant et al., 2003). En parallèle, j’ai recherché les requis en séquence et en structure pour qu’un ARN soit substrat de Pus7p. J’ai également réalisé une étude préliminaire de l’importance des domaines additionnels de Pus7p par rapport à l’enzyme TruD d’E. coli. Comme les systèmes enzymatiques responsables de la formation de trois résidus ? du snRNA U2 de S. cerevisiae avaient été identifiés, nous avons pu débuter une étude de l’importance fonctionnelle de ces résidus pour la réaction d’épissage en utilisant l’approche globale par puces à ADN que l’équipe de J. Beggs (Edinburgh University) avait mise au point pour S. cerevisiae. Nous avons ainsi observé une baisse de l’efficacité d’épissage de certains ARN pré-messagers lorsque plusieurs gènes codant des U2 snRNA:?-synthases sont inactivés simultanément. L’ARN:?-synthase Pus7p est présente dans les 3 domaines du vivant et en particulier chez les archaea où l’activité de cette enzyme n’avait jamais été étudiée. Nous avons découvert l’existence de 2 systèmes enzymatiques différents pour la modification d’une même position d’un ARNt chez 2 espèces d’archaea thermophiles : une enzyme seule chez Pyrococcus abyssi contre un système de guide sRNP H/ACA chez Sulfolobus solfataricus dans lequel la protéine Pus7 est mutée au niveau d’acides aminés requis pour l’activité. Finalement, nous avons produit chacune de ces enzymes en grandes quantités et nous avons réalisé des tests de cristallisation. Des cristaux de Pus7 de P. abyssi ont été obtenus et une mesure complète de données de diffraction de rayons X a été réalisée au laboratoire à 2,5 Å de résolution.

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.

Continuous real-time assessment of cerebral blood flow (CBF) and cerebrovascular autoregulation (CA) remains a major unmet clinical need in acute brain injury. Methods such as laser Doppler flowmetry (LDF), transcranial Doppler, or indirect indices lack accuracy and robustness. Functional ultrasound (fUS) is an emerging modality combining high spatiotemporal resolution, large field-of-view, and sensitivity to blood velocity and volume, making it a promising neuromonitoring tool. Piglets were equipped with arterial blood pressure (ABP), intracranial pressure (ICP), and LDF probes, plus cranial windows for fUS and red blood cell (RBC) flux imaging. CA was challenged by non-pharmacological ABP manipulation via intraaortic or intracaval balloon inflation. fUS hemodynamic parameters were compared with other modaliters across a CPP range of 10–150 mmHg. fUS provided continuous, stable intensity- and velocity-derived parameters across vessels types. CBF estimates correlated strongly with RBC flux and showed reproducibility comparable to LDF, with lower inter-animal variability. Autoregulation breakpoints were reliably identified by fUS, particularly the lower limit, while the upper limit was more variable. Parcellation confirmed robustness of fUS across brain regions. fUS images CBF and CA with higher stability and reproducibility than standard approaches, supporting its applicability for bedside neuromonitoring and clinical translation.

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.

Functional ultrasound (fUS) imaging is a well-established neuroimaging technology that offers high spatiotemporal resolution and a large field of view. Typical strategies for analyzing 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 utilization 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 hemodynamic 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.

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.