Lombardo, D. | bioRxiv Science

bioRxiv2025

Pathology of Influenza A (H5N1) infection in pinnipeds reveals novel tissue tropism and vertical transmission.

In 2023, an unprecedented outbreak of highly pathogenic avian influenza (HPAI) H5N1 resulted in the death of thousands of pinnipeds along the Argentinean coast, raising concerns about its ecological and epidemiological impact. Here, we present clinical, pathological, and molecular findings associated with HPAI H5N1 infection in pinnipeds from Chubut, Argentina. Necropsies were conducted on three South American Sea Lions (SASLs) (Otaria flavescens) and one Southern Elephant Seal (SES) (Mirounga leonina), followed by histopathological, immunohistochemical and RT-sqPCR analyses. Neurological clinical signs were observed in two SASLs, with one also exhibiting respiratory distress. Neuropathological findings included lymphoneutrophilic meningoencephalomyelitis and choroiditis, neuronal necrosis, gliosis, hemorrhages, and perivascular cuffing. Viral antigen was localized in neurons, glial cells, choroid plexus epithelial cells, ependymal cells, and the neuropil. Systemic manifestations included HPAI-related necrotizing myocarditis in the elephant seal and placental necrosis in a sea lion, with fetal tissues testing positive for HPAIV. Pulmonary lesions were minimal, limited to bronchial glands in one individual. RT-sqPCR confirmed HPAI H5 in all tested animals. Our findings highlight the neurotropism of HPAI H5N1 in pinnipeds, and expand the known systemic effects of the virus, revealing new tissue tropism and vertical transmission.

pathology↗

bioRxiv2023

Fast and tight control of motor vigor by accumulated decision evidence

Acting in the natural world requires not only deciding among multiple options but also converting decisions into motor commands. How the dynamics of decision formation influence the fine kinematics of response movement remains, however, poorly understood. Here we investigate how the accumulation of decision evidence shapes the response orienting trajectories in a task where freely-moving rats combine prior expectations and auditory information to select between two possible options. Response trajectories and their motor vigor are initially determined by the prior. Rats movements then incorporate sensory information as early as 60 ms after stimulus onset by accelerating or slowing depending on how much the stimulus supports their initial choice. When the stimulus evidence is in strong contradiction, rats change their mind and reverse their initial trajectory. Human subjects performing an equivalent task display a remarkably similar behavior. We encapsulate these results in a computational model that, by mapping the decision variable onto the movement kinematics at discrete time points, captures subjects choices, trajectories and changes of mind. Our results show that motor responses are not ballistic. Instead, they are systematically and rapidly updated, as they smoothly unfold over time, by the parallel dynamics of the underlying decision process.

neuroscience↗

bioRxiv2020

Modular slowing of resting-state dynamic Functional Connectivity as a marker of cognitive dysfunction induced by sleep deprivation

Dynamic Functional Connectivity (dFC) in the resting state (rs) is considered as a correlate of cognitive processing. Describing dFC as a flow across morphing connectivity configurations, our notion of dFC speed quantifies the rate at which FC networks evolve in time. Here we probe the hypothesis that variations of rs dFC speed and cognitive performance are selectively interrelated within specific functional subnetworks. In particular, we focus on Sleep Deprivation (SD) as a reversible model of cognitive dysfunction. We found that whole-brain level (global) dFC speed significantly slows down after 24h of SD. However, the reduction in global dFC speed does not correlate with variations of cognitive performance in individual tasks, which are subtle and highly heterogeneous. On the contrary, we found strong correlations between performance variations in individual tasks -including Rapid Visual Processing (RVP, assessing sustained visual attention)- and dFC speed quantified at the level of functional subnetworks of interest. Providing a compromise between classic static FC (no time) and global dFC (no space), modular dFC speed analyses allow quantifying a different speed of dFC reconfiguration independently for sub-networks overseeing different tasks. Importantly, we found that RVP performance robustly correlates with the modular dFC speed of a characteristic frontoparietal module. HighlightsO_LISleep Deprivation (SD) slows down the random walk in FC space implemented by Dynamic Functional Connectivity (dFC) at rest. C_LIO_LIWhole-brain level slowing of dFC speed does not selectively correlate with fine and taskspecific changes in performance C_LIO_LIWe quantify dFC speed separately for different link-based modules coordinated by distinct regional "meta-hubs" C_LIO_LIModular dFC speed variations capture subtle and task-specific variations of cognitive performance induced by SD. C_LI Author summaryWe interpreted dynamic Functional Connectivity (dFC) as a random walk in the space of possible FC networks performed with a quantifiable "speed". Here, we analyze a fMRI dataset in which subjects are scanned and cognitively tested both before and after Sleep Deprivation (SD), used as a reversible model of cognitive dysfunction. While global dFC speed slows down after a sleepless night, it is not a sufficiently sensitive metric to correlate with fine and specific cognitive performance changes. To boost the capacity of dFC speed analyses to account for fine and specific cognitive decline, we introduce the notion of modular dFC speed. Capitalizing on an edge-centric measure of functional connectivity, which we call Meta-Connectivity, we isolate subgraphs of FC describing relatively independent random walks (dFC modules) and controlled by distinct "puppet masters" (meta-hubs). We then find that variations of the random walk speed of distinct dFC modules now selectively correlate with SD-induced variations of performance in the different tasks. This is in agreement with the fact that different subsystems - distributed but functionally distinct- oversee different tasks. The high sensitivity of modular dFC analyses bear promise of future applications to the early detection and longitudinal characterization of pathologies such as Alzheimers disease.

neuroscience↗