Biology subjects

Petri, G.

Publications and source records attributed to Petri, G..

At least 19 recordsLinked to original sources

bioRxiv2026

Higher-order topology uncovers the multidimensional dynamics of naturalistic emotion

Emotions are thought to emerge from co-activation among distributed brain systems, yet traditional fMRI analyses typically examine localized responses and pairwise connections, potentially overlooking interactions among groups of regions. Here, we use time-resolved higher-order topology to characterize these group interactions during naturalistic viewing of 14 films totaling over 2.5 h, continuously annotated across 50 affective features. The homological scaffold, representing evolving group-level topology, most closely tracks recurrent emotional states and best predicts fine-grained affective profiles. This sensitivity diminishes when emotion is compressed into dimensions of valence, arousal, and power, where pairwise connectivity captures the dominant arousal signal. Across three independent datasets, arousal predictions transfer most robustly through pairwise connectivity, while the relative geometry of broad affective states remains conserved across film narratives despite poor valence generalization. These findings reveal complementary neural representations of emotion: higher-order topology captures fine-grained affective structure, whereas pairwise connectivity provides a portable readout of broad arousal.

neuroscience↗

bioRxiv2026

Preserved brain hierarchy supports residual vision without awareness after visual cortex damage

Damage to the primary visual cortex causes loss of conscious vision, yet some patients retain the ability to respond to stimuli despite reporting no visual experience. Why similar lesions produce such different behavioral phenotypes remains unclear. While research to date has focused primarily on spared pathways that bypass V1, here we asked whether these divergent outcomes are also linked to the brain's intrinsic functional architecture. In the largest resting-state fMRI cohort of patients with unilateral V1 damage reported to date, we quantified information sharing between regions across cortical and subcortical parcels in blindsight-positive and blindsight-negative patients, as well as in age-matched healthy controls. Despite comparable lesions, the two patient groups displayed distinct hierarchical patterns on the cortex: B+ patients preserved a sensory-to-association organization as in healthy controls, whereas B- patients exhibited a marked flattening of this hierarchy. The effect was driven by abnormally low shared-information coupling within unimodal cortices and scaled continuously with single-subject behavioral blind-field detection performance. A thalamic region consistent with the pulvinar, linking the contralesional visual cortex and the frontal eye field, discriminated B+ from B- patients. These findings highlight the system-level consequences of V1 damage supporting blindsight, suggesting that the unimodal-transmodal axis might track not only global states of consciousness, but also whether sensory information can guide behavior without awareness.

neuroscience↗

bioRxiv2026

Sperm whale foraging combines persistent search with flexible prey-capture tactics

Predators searching patchy environments must coordinate movements across scales, yet this behavioural hierarchy is not yet technically possible to observe in the deep ocean. Here we show that sperm-whale foraging is organized across two nested levels: directionally persistent search paths and flexible prey-capture tactics. We combine acoustic recordings with reconstructed three-dimensional trajectories from 34 sensor-tag deployments on 20 individual whales in both the eastern Caribbean and the mid-Atlantic. Complete foraging paths exhibit heavy-tailed step lengths and superdiffusive displacement, consistent with Levy-like search. Within these paths, echolocation buzzes resolve into two recurrent acoustic-kinematic tactics. Type A involves brief ramp-ups, slower and more oscillatory movement, and longer post-buzz pauses. Type B involves prolonged ramp-ups, faster and vertically persistent movement, greater roll, slightly straighter approaches, and an energy-model estimate approximately seven times higher. Every sampled whale used both tactics and switched between them within dives, showing that the contrast is not driven by specialization among individuals. An Azorean deployment with prey-field echograms links the tactics to local prey conditions, while independent Dominica deployments recover their core kinematic contrast. Together, these results reveal a foraging hierarchy in which flexible capture actions unfold within persistent, deliberate large-scale search.

animal behavior and cognition↗

bioRxiv2026

Global topology of brain-wide co-fluctuations links task states, personality, and behavioral symptom dimensions

Functional connectivity in network neuroscience is traditionally characterized using time-averaged correlations between brain regions. While these summaries capture stable large-scale organization, they do not fully reflect the temporal structure of moment-to-moment interactions. Here, we investigate how the order of interaction used to represent brain dynamics shapes the organization recovered from neural data. We compare three interaction representations of fMRI dynamics: regional activation (node time series), pairwise co-fluctuations (edge time series), and higher-order triplet interactions (triangle time series); within a common topological framework using Mapper from topological data analysis (TDA). Across task and resting-state data, Mapper representations derived from pairwise co-fluctuations more distinctly segregate task conditions than activation-based or higher-order representations. This organization reflects structured coordination patterns beyond activation polarity and is driven by high-amplitude interaction events. Beyond task states, modularity quality computed across all Mapper representations is highest for edge time series and selectively associated with stable individual differences: higher modularity relates to higher conscientiousness and lower internalizing and externalizing symptom dimensions. Together, these findings suggest that behaviorally relevant information is reflected in the topology of moment-to-moment brain interactions. Topological analysis of interaction-level dynamics therefore provides a complementary and interpretable framework for linking large-scale neural coordination to cognition, personality, and mental health.

neuroscience↗

bioRxiv2025

Beyond Pairwise Interactions: Charting Higher-Order Models of Brain Function

Traditional models of brain connectivity have primarily focused on pairwise interactions, over-looking the rich dynamics that emerge from simultaneous interactions among multiple brain regions. Although a plethora of higher-order interaction (HOI) metrics have been proposed, a systematic evaluation of their comparative properties and utility is missing. Here, we present the first large-scale analysis of information-theoretic and topological HOI metrics, applied to both resting-state and task fMRI data from 100 unrelated subjects of the Human Connectome Project. We identify a clear taxonomy of HOI metrics -- redundant, synergistic, and topological--, with the latter acting as bridges along the redundancy-synergy continuum. Despite methodological differences, all HOI metrics align with the brains overarching unimodal-to-transmodal functional hierarchy. However, certain metrics show specific associations with the neurotransmitter receptor architecture. HOI metrics outperform traditional pairwise models in brain fingerprinting and perform comparably in task decoding, underscoring their value for characterizing individual functional profiles. Finally, multivariate analysis reveals that -- among all HOI metrics -- topological descriptors are key to linking brain function with behavioral variability, positioning them as valuable tools for linking neural architecture and cognitive function. Overall, our findings establish HOIs as a powerful framework for capturing the brains multidimensional dynamics, providing a conceptual map to guide their application across cognitive and clinical neuroscience.

neuroscience↗

bioRxiv2025

Beyond Reorganization: Intrinsic cortical hierarchies constrain experience-dependent plasticity in sensory-deprived humans

Innate cortical organisation and postnatal sensory experience interact dynamically to shape the functional architecture of the human brain. Using naturalistic stimulation, functional gradient analyses, and comparative approaches in congenitally blind, congenitally deaf, and typically developed individuals, we investigated how intrinsic hierarchical structures and sensory experiences influence cortical organisation. Our findings demonstrate that the principal functional gradients spanning from unimodal sensory to transmodal association cortices are consistently preserved across all groups, suggesting a robust genetically determined cortical scaffold. Nonetheless, congenital sensory deprivation selectively reshapes the geometry of modality-specific gradients, characterised by reduced functional differentiation within sensory-deprived cortical regions. These geometric contractions promote experience-driven plastic reorganization, enabling deprived sensory areas to establish enhanced functional connectivity with transmodal and non-deprived sensory cortices. Critically, this reorganisation aligns systematically with pre-existing cortical gradients, highlighting intrinsic hierarchical constraints that guide experience-dependent plasticity. Moreover, sensory-deprived regions exhibiting heightened connectivity actively engage in processing structured perceptual information from intact modalities, reflecting specific feature-driven cross-modal adaptations. Collectively, these results underscore a fundamental duality in cortical organisation: innate hierarchical principles impose constraints on cortical architecture, while sensory experience drives adaptive refinement, demonstrating the brains intrinsic capacity for flexible functional reconfiguration in response to sensory deprivation.

neuroscience↗

bioRxiv2025

The Topological Architecture of Brain Identity

Accurately identifying individuals from brain activity--functional fingerprinting--is a powerful tool for understanding individual variability and detecting brain disorders. Most current approaches rely on functional connectivity (FC), which measures pairwise correlations between brain regions. However, FC is limited in capturing the higher-order, multiscale structure of brain organization. Here, we propose a novel fingerprinting method based on homological scaffolds, a topological repre-sentation derived from persistent homology of resting-state fMRI data. Using data from the Human Connectome Project (n = 100), we show that scaffold-based fingerprints achieve near-perfect identification accuracy ([~] 100%), outperforming FC-based methods (90%), and remain robust across preprocessing pipelines, atlas choices, and even with drastically shortened scan durations. Unlike FC, in which fingerprinting features localize within networks, scaffolds derive their discriminative power from inter-network connections, revealing the existence of individual mesoscale organizational signatures. Finally, we show that scaffolds bridge redundancy and synergy by balancing redundant information along high-FC border edges with synergistic interactions across the topological voids they enclose. These findings establish topological scaffolds as a powerful tool for capturing individual variability, revealing that unique signatures of brain organization are encoded in the interplay between mesoscale network integration and information dynamics.

neuroscience↗

bioRxiv2025

Understanding the high-order network plasticity mechanisms of ultrasound neuromodulation

Transcranial ultrasound stimulation (TUS) is an emerging non-invasive neuromodulation technique, offering a potential alternative to pharmacological treatments for psychiatric and neurological disorders. While functional analysis has been instrumental in characterizing TUS effects, understanding the underlying mechanisms remains a challenge. Here, we developed a whole-brain model to represent functional changes as measured by fMRI, enabling us to investigate how TUS-induced effects propagate throughout the brain with increasing stimulus intensity. We implemented two mechanisms: one based on anatomical distance and another on broadcasting dynamics, to explore plasticity-driven changes in specific brain regions. Finally, we highlighted the role of higher-order functional interactions in localizing spatial effects of off-line TUS at two target areas--the right thalamus and inferior frontal cortex--revealing distinct patterns of functional reorganization. This work lays the foundation for mechanistic insights and predictive models of TUS, advancing its potential clinical applications. Significance StatementTranscranial ultrasound stimulation (TUS) offers a non-invasive approach to modulating brain activity, holding promise for treating psychiatric and neurological disorders. Despite its potential, the mechanisms underlying its effects remain poorly understood. By integrating human fMRI data with whole-brain computational models, we identified how high-order functional interactions localize and propagate TUS-induced effects from local to global brain scales. This work introduces two mechanisms--distance-based propagation and diffusion-like broadcasting--that predict functional plasticity changes, providing a foundation for understanding and optimizing the biological and cognitive outcomes of TUS. Our findings offer critical insights into the dynamics of neuromodulation, bridging experimental results and clinical applications.

neuroscience↗

bioRxiv2024

Higher-order and distributed synergistic functional interactions encode information gain in goal-directed learning

Goal-directed learning arises from distributed neural circuits including the prefrontal, posterior parietal and temporal cortices. However, the role of cortico-cortical functional interactions remains unclear. To address this question, we integrated information dynamics analysis with magnetoencephalography to investigate the encoding of learning signals through neural interactions. Our findings revealed that information gain (the reduction in uncertainty about the causal relationship between actions and outcomes) is represented over the visual, parietal, lateral prefrontal and ventromedial/orbital prefrontal cortices. Cortico-cortical interactions encoded information gain synergistically at the level of pairwise and higher-order relations, such as triplets and quadruplets. Higher-order synergistic interactions were characterized by long-range relationships centered in the ventromedial and orbitofrontal cortices, which served as key receivers in the broadcast of information gain across cortical circuits. Overall, this study provides evidence that information gain is encoded through synergistic and higher-order functional interactions and is broadcast to prefrontal reward circuits.

neuroscience↗

bioRxiv2023

Homological landscape of human brain functional sub-circuits

Human whole-brain functional connectivity networks have been shown to exhibit both local/quasilocal (e.g., set of functional sub-circuits induced by node or edge attributes) and non-local (e.g., higher-order functional coordination patterns) properties. Nonetheless, the non-local properties of topological strata induced by local/quasilocal functional sub-circuits have yet to be addressed. To that end, we proposed a homological formalism that enables the quantification of higher-order characteristics of human brain functional sub-circuits. Our results indicated that each homological order uniquely unravels diverse, complementary properties of human brain functional sub-circuits. Noticeably, the H1 homological distance between rest and motor task were observed at both whole-brain and sub-circuit consolidated level which suggested the self-similarity property of human brain functional connectivity unraveled by homological kernel. Furthermore, at the whole-brain level, the rest-task differentiation was found to be most prominent between rest and different tasks at different homological orders: i) Emotion task (H0), ii) Motor task (H1), and iii) Working memory task (H2). At the functional sub-circuit level, the rest-task functional dichotomy of default mode network is found to be mostly prominent at the first and second homological scaffolds. Also at such scale, we found that the limbic network plays a significant role in homological reconfiguration across both task- and subject-domain which sheds light to subsequent investigations on the complex neuro-physiological role of such network. From a wider perspective, our formalism can be applied, beyond brain connectomics, to study non-localized coordination patterns of localized structures stretching across complex network fibers.

neuroscience↗

bioRxiv2023

Higher-order connectomics of human brain function reveals local topological signatures of task decoding, individual identification, and behavior

Traditional models of human brain activity often represent it as a network of pairwise interactions between brain regions. Going beyond this limitation, recent approaches have been proposed to infer higher-order interactions from temporal brain signals involving three or more regions. However, to this day it remains unclear whether methods based on inferred higher-order interactions outperform traditional pairwise ones for the analysis of fMRI data. To address this question, we conducted a comprehensive analysis using fMRI time series of 100 unrelated subjects from the Human Connectome Project. We show that higher-order approaches greatly enhance our ability to decode dynamically between various tasks, to improve the individual identification of unimodal and transmodal functional subsystems, and to strengthen significantly the associations between brain activity and behavior. Overall, our approach sheds new light on the higher-order organization of fMRI time series, improving the characterization of dynamic group dependencies in rest and tasks, and revealing a vast space of unexplored structures within human functional brain data, which may remain hidden when using traditional pairwise approaches.

neuroscience↗

bioRxiv2023

An Information-Theoretic Approach to Reward Rate Optimization in the Tradeoff Between Controlled and Automatic Processing in Neural Network Architectures

This article introduces a quantitative approach to modeling the cost of control in a neural network architecture when it is required to execute one or more simultaneous tasks, and its relationship to automaticity. We begin by formalizing two forms of cost associated with a given level of performance: an intensity cost that quantifies how much information must be added to the input to achieve the desired response for a given task, that we treat as the contribution of control ; and an interaction cost that quantifies the degree to which performance is degraded as a result of interference between processes responsible for performing two or more tasks, that we treat as inversely related to automaticity. We develop a formal expression of the relationship between these two costs, and use this to derive the optimal control policy for a desired level of performance. We use that, in turn, to quantify the tradeoff between control and automaticity, and suggest how this can be used as a normative framework for understanding how people adjudicate between the benefits of control and automaticity.

neuroscience↗

bioRxiv2023

Hi-Fi fMRI: High-resolution, fast-sampled and sub-second whole-brain functional MRI at 3T in humans

Functional magnetic resonance imaging (fMRI) is a methodological cornerstone of neuroscience. Most studies measure blood-oxygen-level-dependent (BOLD) signal using echo-planar imaging (EPI), Cartesian sampling, and image reconstruction with a one-to-one correspondence between the number of acquired volumes and reconstructed images. However, EPI schemes are subject to trade-offs between spatial and temporal resolutions. We make strides in overcoming these limitations by measuring BOLD with a gradient recalled echo (GRE) with a 3D radial-spiral phyllotaxis trajectory at a high sampling rate (28.49ms) on standard 3T field strength. The framework enables the reconstruction of 3D signal time courses with whole-brain coverage at simultaneously higher nominal spatial (1mm3) and temporal (up to 250ms) resolutions, as compared to optimized EPI schemes. Additionally, we apply motion correction directly to the k-space raw data, enabling flexible motion-corrected reconstructions; the desired temporal resolution to observe hemodynamic responses can be chosen after scanning. By showing activation in the calcarine sulcus of 20 participants completing an ON-OFF visual paradigm, we demonstrate the reliability of our method for applications in cognitive neuroscience research.

neuroscience↗

bioRxiv2022

Mesoscopic patterns of functional connectivity alterations in autism by contrast subgraphs

Despite the breakthrough achievements in understanding structural and functional connectivity alterations that underlie autism spectrum disorder (ASD), the exact nature and type of such alterations are not yet clear due to conflicting reports of hyper-connectivity, hypo-connectivity, and -in some cases- combinations of both. In this work, we approach the debate about hyper- vs hypoconnectivity in ASD using a novel network comparison technique designed to capture mesoscopic-scale differential structures. In particular, we build on recent algorithmic advances in the sparsification of functional connectivity matrices, in the extraction of contrast subgraphs, and in the computation of statistically significant maximal frequent itemsets, and develop a method to identify mesoscale structural subgraphs that are maximally dense and different in terms of connectivity levels between the different sets of networks. We apply our method to analyse brain networks of typically developed individuals and ASD patients across different developmental phases and find a set of altered cortical-subcortical circuits between healthy subjects and patients affected by ASD. Specifically, our analysis highlights in ASD patients a significantly larger number of functional connections among regions of the occipital cortex and between the left precuneus and the superior parietal gyrus. At the same time, reduced connectivity characterised the superior frontal gyrus and the temporal lobe regions. More importantly, we can simultaneously detect regions of the brain that show hyper and hypo-connectivity in ASD in children and adolescents, recapitulating within a single framework multiple previous separate observations.

neuroscience↗

bioRxiv2021

Social and asocial learning in zebrafish are encoded by a shared brain network that is differentially modulated by local activation

Group living animals can use social and asocial cues to predict the presence of a reward or a punishment in the environment through associative learning. The degree to which social and asocial learning share the same mechanisms is still a matter of debate, and, so far, studies investigating the neuronal basis of these two types of learning are scarce and have been restricted to primates, including humans, and rodents. Here we have used a Pavlovian fear conditioning paradigm in which a social (fish image) or an asocial (circle image) conditioned stimulus (CS) have been paired with an unconditioned stimulus (US=food), and we have used the expression of the immediate early gene c-fos to map the neural circuits associated with social and asocial learning. Our results show that the learning performance is similar with social (fish image) and asocial (circle image) CSs. However, the brain regions involved in each learning type are distinct. Social learning is associated with an increased expression of c-fos in olfactory bulbs, ventral zone of ventral telencephalic area, ventral habenula and ventromedial thalamus, whereas asocial learning is associated with a decreased expression of c-fos in dorsal habenula and anterior tubercular nucleus. Using egonetworks, we further show that each learning type has an associated pattern of functional connectivity across brain regions. Moreover, a community analysis of the network data reveals four segregated functional submodules, which seem to be associated with different cognitive functions involved in the learning tasks: a generalized attention module, a visual response module, a social stimulus integration module and a learning module. Together, these results suggest that, although there are localized differences in brain activity between social and asocial learning, the two learning types share a common learning module and social learning also recruits a specific social stimulus integration module. Therefore, our results support the occurrence of a common general-purpose learning module, that is differentially modulated by localized activation in social and asocial learning.

animal behavior and cognition↗

bioRxiv2021

Oxytocin regulation of social transmission of fear in zebrafish reveals its evolutionary conserved role in emotional contagion

Emotional contagion is the most ancestral form of empathy that relies on simple perception-action mechanisms, on top of which more complex forms of empathic behaviors, such as consolation and helping, have evolved. Here we tested to what extent the proximate mechanisms of emotional contagion are evolutionary conserved by assessing the role of oxytocin, known to regulate empathic behaviors in mammals, in social fear contagion in zebrafish, which represents an evolutionary divergent line to that of tetrapods, within vertebrates. Using mutants for the ligand of the fish oxytocin nonapeptide and both of its receptors in zebrafish we showed that oxytocin is necessary for observer zebrafish to copy the distressed behavior of conspecific demonstrators. Exogeneous administration of oxytocin to the ligand mutant rescued the ability of observers to express social fear transmission, indicating that oxytocin is not only necessary but also sufficient for emotional contagion. The brain regions in the ventral telencephalon that are associated with emotional contagion in zebrafish are homologous to those known to be involved in the same process in rodents (e.g. striatum, lateral septum), and receive direct projections from oxytocinergic neurons located in the pre-optic area. Finally, we ruled out the hypothesis that social transmission of fear in zebrafish merely relies on behavior contagion by motor imitation, and we showed that it rather relies on emotion discrimination. Together our results support an evolutionary conserved role for oxytocin as a key regulator of basic empathic behaviors across vertebrates. One-Sentence SummaryOxytocin is necessary and sufficient for social fear contagion in zebrafish supporting an evolutionary conserved role for oxytocin in emotional contagion among vertebrates.

animal behavior and cognition↗

bioRxiv2020

Developmental effects of oxytocin neurons on social affiliation and processing of social information

Hormones regulate behavior either through activational effects that facilitate the acute expression of specific behaviors or through organizational effects that shape the development of the nervous system thereby altering adult behavior. Much research has implicated the neuropeptide oxytocin (OXT) in acute modulation of various aspects of social behaviors across vertebrate species, and OXT signaling is associated with the developmental social deficits observed in autism spectrum disorders, however, little is known about the role of OXT in the neurodevelopment of the social brain. We show that perturbation of OXT neurons during early zebrafish development led to a loss of dopaminergic neurons, associated with visual processing and reward, and blunted the neuronal response to social stimuli in the adult brain. Ultimately, adult fish whose OXT neurons were ablated in early life, displayed altered functional connectivity within social decision-making brain nuclei both in naive state and in response to social stimulus and became less social. We propose that OXT neurons have an organizational role, namely to shape forebrain neuroarchitecture during development and to acquire an affiliative response towards conspecifics. Significance StatementSocial behavior is developed over the lifetime of an organism and the neuropeptide oxytocin modulates social behaviors across vertebrate species, and is associated with neuro-developmental social deficits such as autism. However, whether oxytocin plays a role in the developmental maturation of neural systems that are necessary for social behavior remains poorly explored. We show that proper behavioral and neural response to social stimuli depends on a developmental process orchestrated by oxytocin neurons. Animals whose oxytocin system is ablated in early life show blunted neuronal and behavioral responses to social stimuli as well as wide ranging disruptions in the functional connectivity of the Social Brain. We provide a window into the mechanisms underlying oxytocin-dependent developmental processes that implement adult sociality.

neuroscience↗

bioRxiv2020

Topological Features of Electroencephalography are Reference-Invariant

Electroencephalography (EEG) is among the most widely diffused, inexpensive, and applied neuroimaging techniques. Nonetheless, EEG requires measurements against a reference site(s), which is typically chosen by the experimenter, and specific pre-processing steps precede analysis. It is therefore valuable to obtain quantities that are reference-independent and minimally affected by pre-processing choices. Here, we show that the topological structure of embedding spaces, constructed either from multi-channel EEG timeseries or from their temporal structure, are subject-specific and robust to re-referencing and pre-processing pipelines. By contrast, the shape of correlation spaces, that is, discrete spaces where each point represents an electrode and the distance between them that is in turn related to the correlation between the respective timeseries, were neither significantly subject-specific nor robust to changes of reference. Our results suggest that the shape of spaces describing the observed configurations of EEG signals holds information about the individual specificity of the underlying individuals brain dynamics, and that temporal correlations constrain to a large degree the set of possible dynamics. In turn, these encode the differences between subjects space of resting state EEG signals. Finally, our results and proposed methodology provide tools to explore the individual topographical landscapes and how they are explored dynamically. We propose therefore to augment conventional topographic analyses with an additional - topological - level of analysis, and to consider them jointly. More generally, these results provide a roadmap for the incorporation of topological analyses within EEG pipelines.

neuroscience↗