Biology subjects

Zucca, S.

Publications and source records attributed to Zucca, S..

10 recordsLinked to original sources

bioRxiv2026

PhenoXtract: combining Large Language Model and Knowledge Graph embedding to extract phenotypes from clinical descriptions

MotivationStandardized phenotypic descriptions are essential for accurate diagnosis, yet clinicians and researchers face challenges in manually extracting and mapping phenotypes from scientific literature or patient clinical records to the Human Phenotype Ontology. Recent advances in deep learning offer new opportunities for automation. We developed PhenoXtract, a novel phenotype extraction approach that combines Large Language Models and Knowledge Graph embedding. PhenoXtract is a multistep pipeline that takes clinical descriptions as input, extracts candidate phenotype entities using large language models, and maps them to terms from an enriched version of the Human Phenotype Ontology, processed as a knowledge graph. ResultsEvaluation against expert-curated ground-truth datasets show a recall of 0.70 and precision of 0.85 for PhenoXtract, demonstrating concordance with manually extracted phenotypes, with a computation time of 10-20 seconds for each text analyzed. Moreover, PhenoXtract surpasses rule-based and deep learning-based state-of-the-art tools in two out of the three ground-truth datasets evaluated. These results suggest that hybrid approaches combining Large Language Models and Knowledge Graph embeddings represent a promising direction for automated clinical phenotyping at scale. Contactsberardelli@engenome.com

genomics↗

bioRxiv2024

Loom response in mouse superior colliculus depends on sensorimotor context

Visual motion is produced both by an organisms movement through the world, and by objects moving in the world such as potential predators. Choosing appropriate behaviour therefore requires organisms to distinguish these sources of visual motion. Here we asked how mice integrate self-movement with looming visual motion by combining virtual reality and neural recordings from superior colliculus (SC), a brain area important in visually-guided approach and avoidance behaviours. We first measured locomotion behaviour and neural activity while animals approached an object in virtual reality, and while the same object loomed at them. In both cases, vision dominated activity in superficial layers (SCs), while locomotion had more influence on activity in intermediate layers (SCim). In addition, animals instinctively slowed their locomotion when nearing the object, or when the object neared them. To directly test animals ability to distinguish self-from object motion we replayed the visual images generated during object approach. Locomotion behaviour often changed during replay, showing animals are able to establish if visual motion is matched to their self-movement. Further, decoders trained on locomotion behaviour, or on population activity in SC, particularly in SCim, were able to reliably discriminate epochs of replay and object approach. We conclude that both mouse behaviour and SC activity encode whether looming visual motion arises from self-or object movement, with implications for understanding sensorimotor coordination in dynamic environments. HighlightsO_LIWe recorded from superficial (SCs) and intermediate (SCim) superior colliculus in VR C_LIO_LIVision dominated SCs, while SCim was modulated by both vision and locomotion C_LIO_LIMice altered behaviour when visual experience did not match that expected from their locomotion C_LIO_LIPopulation activity differed between matched and unmatched visual experiences, particularly in SCim C_LI

neuroscience↗

bioRxiv2024

Pupillometry and Whole-Brain c-Fos Mapping Uncover Multimodal Mirror Mechanisms in Emotional Contagion Networks of Mice

Emotional contagion (ECo) represents a fundamental form of empathy. In this study, we used pupillometry to quantify ECo by assessing pupil responses of a mouse watching another mouse receive a tail shock. Pupil dilation effectively measured both direct and vicarious emotional response thresholds at the individual level through psychometric curve analysis. The pupillary ECo response diminished when the observer could not see the demonstrator, suggesting a multisensory process involving vision. Viewing videos of tail-shocked mice was sufficient to elicit a pupil response in the observer. Whole-brain c-Fos mapping revealed a broad network of 88 brain regions activated during ECo, with all areas activated in the demonstrator also engaged in the observer. Additionally, in certain brain regions, correlated activation was detected between each observer-demonstrator pair, indicating that ECo promotes a shared neural state. These findings advance our understanding of the neural basis of empathy, with implications for analyzing neuropsychiatric disorder models.

neuroscience↗

bioRxiv2024

Developmental encoding of ultrasound vocalizations in the mouse auditory cortex

Mice communicate through high-frequency ultrasound vocalizations (USVs), which are crucial for social interactions such as courtship and aggression. Although USV representation has been found in adult brain areas along the auditory pathway, including the auditory cortex (ACx), no evidence is available on the neuronal representation of USVs early in life. Using in vivo two-photon calcium imaging, we analyzed ACx layer 2/3 neuronal responses to USVs, pure tones (4-90 kHz), and high-frequency modulated sweeps from postnatal day 12 (P12) to P21. We found that ACx neurons are tuned to respond to USV syllables as early as P12-P13, with an increasing number of responsive cells as the mouse age. By P14, while pure tone responses showed a frequency preference, no syllable preference was observed. Additionally, at P14, USVs, pure tones, and modulated sweeps activate clusters of largely non-overlapping responsive neurons. Finally, we show that while cell correlation decreases with increasing processing of peripheral auditory stimuli, neurons responding to the same stimulus maintain highly correlated spontaneous activity after circuits have attained mature organization, forming neuronal sub-networks sharing similar functional properties.

neuroscience↗

bioRxiv2023

Digenic variant interpretation with hypothesis-driven explainable AI

MotivationThe digenic inheritance hypothesis holds the potential to enhance diagnostic yield in rare diseases. Computational approaches capable of accurately interpreting and prioritizing digenic combinations based on the probands phenotypic profiles and familial information can provide valuable assistance to clinicians during the diagnostic process. ResultsWe have developed diVas, a hypothesis-driven machine learning approach that can effectively interpret genomic variants across different gene pairs. DiVas demonstrates strong performance both in classifying and prioritizing causative pairs, consistently placing them within the top positions across 11 real cases (achieving 73% sensitivity and a median ranking of 3). Additionally, diVas exploits Explainable Artificial Intelligence (XAI) to dissect the digenic disease mechanism for predicted positive pairs. Availability and ImplementationPrediction results of the diVas method on a high-confidence, comprehensive, manually curated dataset of known digenic combinations are available at oliver.engenome.com.

bioinformatics↗

bioRxiv2022

NIT: an open-source tool for information theoretic analysis of neural population data

Information theory provides a popular and principled framework for the analysis of neural data. It allows to uncover in an assumption-free way how neurons encode and transmit information, capturing both linear and non-linear coding mechanisms and including the information carried by interactions of any order. To facilitate its application, here we present Neuroscience Information Toolbox (NIT), a new toolbox for the accurate information theoretical analysis of neural data. NIT contains widely used tools such as limited sampling bias corrections and discretization of neural probabilities for the calculation of stimulus coding in low-dimensional representation of neural activity (e.g. Local Field Potentials or the activity of small neural population).Importantly, it adds a range of recent tools for quantifying information encoding by large populations of neurons or brain areas, for the directed transmission of information between neurons or areas, and for the calculation of Partial Information Decompositions to quantify the behavioral relevance of neural information and the synergy and redundancy among neurons and brain areas. Further, because information theoretic algorithms have been previously validated mainly with electrophysiological recordings, here we used realistic simulations and analysis of real data to study how to optimally apply information theory to the analysis of two-photon calcium imaging data, which are particularly challenging due to their lower signal-to-noise and temporal resolution. We also included algorithms (based on parametric and non-parametric copulas) to compute robustly information specifically with analog signals such as calcium traces. We provide indications on how to best process calcium imaging traces and to apply NIT depending on the type of calcium indicator, imaging frame rate and firing rate levels. In sum, NIT provides a toolbox for the comprehensive and effective information theoretic analysis of all kinds of neural data, including calcium imaging.

neuroscience↗

bioRxiv2022

Network States Classification based on Local Field Potential Recordings in the Awake Mouse Neocortex

Recent studies using intracellular recordings in awake behaving mice revealed that cortical network states, defined based on membrane potential features, modulate sensory responses and perceptual outcomes. Single cell intracellular recordings are difficult to achieve and have low yield compared to extracellular recordings of population signals, such as local field potentials (LFPs). However, it is currently unclear how to identify these behaviorally-relevant network states from the LFP. We used simultaneous LFP and intracellular recordings in the somatosensory cortex of awake mice to design and calibrate a model-based analysis method, the Network State Index (NSI), that enables network state classification from the LFP. We used the NSI to analyze the relationship between single-cell (intracellular) and population (LFP) signals over different network states of wakefulness. We found that graded levels of population signal faithfully predicted the levels of single cell depolarization in non-rhythmic regimes whereas, in delta ([2-4 Hz]) oscillatory regimes, the graded levels of rhythmicity in the LFP mapped into a stereotypical oscillatory pattern of membrane potential. Finally, we showed that the variability of network states, beyond the occurrence of slow oscillatory activity, critically shaped the average correlations between single cell and population signals. NSI-based characterization provides a ready-to-use tool to understand from LFP recordings how the modulation of local network dynamics shapes the flexibility of sensory processing during behavior. Significance statementSensation during behaviour is strongly modulated by the animals internal state. Such context-dependent modulation of sensory processing is believed to largely stem from top-down control of network states in sensory cortices, with different network states being associated with distinct computational properties of the circuit. So far, a detailed characterization of network states in the awake cortex has mostly been achieved through single-cell intracellular recordings, which however cannot be easily recorded. Here, we developed a new method to classify network states from the easily accessible extracellular LFP recordings of population activity. Given the widespread use of LFPs, our work provides a critical methodology to greatly expand our understanding of the mechanisms underlying state-dependent computations in neocortex.

neuroscience↗

bioRxiv2020

Different RNA profiles in plasma derived small and large extracellular vesicles of Neurodegenerative diseases patients.

BackgroundIdentifying robust biomarkers is essential for early diagnosis of neurodegenerative diseases (NDs). Large (LEVs) and small extracellular vesicles (SEVs) are extracellular vesicles (EVs) of different sizes and biological functions transported in blood and they may be valid biomarkers for NDs. The aim of our study was to investigate common and different mRNA/miRNA signatures in plasma derived LEVs and SEVs of Alzheimers Disease (AD), Parkinsons disease (PD), Amyotrophic Lateral Sclerosis (ALS) and Fronto-Temporal Dementia (FTD) patients. MethodsLEVs and SEVs were isolated from plasma of patients and healthy volunteers (CTR) by filtration and ultracentrifugation and RNA was extracted. Whole transcriptome and miRNA libraries were carried out by Next Generation Sequencing (NGS). ResultsWe detected different deregulated RNAs in LEVs and SEVs from patients with the same disease. MiRNAs resulted to be the most interesting subpopulation of transcripts transported by plasma derived SEVs since they appeared to discriminate all NDs disease from CTRs and they can provide a signature for each NDs. Common enriched pathways for SEVs were mainly linked to ubiquitin mediated proteolysis and Toll-like receptor signaling pathways and for LEVs to neurotrophin signaling and Glycosphingolipid biosynthesis pathway. ConclusionLEVs and SEVs are involved in different pathways and this might give a specificity to their role in the spreading/protection of the disease. The study of common and different RNAs transported by LEVs and SEVs can be of great interest for biomarker discovery and for pathogenesis studies in neurodegeneration.

neuroscience↗

bioRxiv2020

BonVision - an open-source software to create and control visual environments

Real-time rendering of closed-loop visual environments is necessary for next-generation understanding of brain function and behaviour, but is prohibitively difficult for non-experts to implement and is limited to few laboratories worldwide. We developed BonVision as an easy-to-use open-source software for the display of virtual or augmented reality, as well as standard visual stimuli. As the architecture is based on the open-source Bonsai graphical programming language, BonVision benefits from native integration with experimental hardware. BonVision therefore enables easy implementation of closed-loop experiments, including real-time interaction with deep neural networks and communication with behavioural and physiological measurement and manipulation devices.

neuroscience↗

bioRxiv2018

The Spectrum of Asynchronous Dynamics in Spiking Networks: A Theory for the Diversity of Non-Rhythmic Waking states in Neocortex

The cerebral cortex of awake animals exhibits frequent transitions between diverse non-rhythmic network states. However, it is still unclear how these different activity states emerge within the same network and how each state impacts network function. Here, we demonstrate that model networks of spiking neurons with moderate recurrent interactions dynamically change their asynchronous dynamics depending upon the level of afferent excitation. We found that the model network displayed a spectrum of asynchronous states, ranging from afferent input-dominated (AD) regimes, characterized by unbalanced synaptic currents and sparse firing, to recurrent input-dominated (RD) regimes, characterized by balanced synaptic currents and dense firing. The model predicted regime-specific relationships between several different neural biophysical properties which were all experimentally confirmed by intracellular recordings in the somatosensory cortex of awake mice. Moreover, theoretical analysis showed that AD regimes more precisely encode spatiotemporal patterns of presynaptic activity, while RD regimes better encoded the strength of afferent inputs. These results provide a theoretical foundation for how recurrent neocortical circuits generate non-rhythmic waking states and how these different states modulate the processing of incoming information.

neuroscience↗