Biology subjects

Gozzi, A.

Publications and source records attributed to Gozzi, A..

At least 19 recordsLinked to original sources

bioRxiv2026

Endogenous oxytocin reconfigures brainwide network dynamics toward salience-related states

Oxytocin (OXT) can exert diverse effects across social, affective and motivational domains, yet its impact on brain function at the systems level remains unclear. Here, we combine chemogenetics with fMRI and electrophysiology in mice to investigate how endogenous OXT reshapes intrinsic large-scale brain network dynamics. Using mice constitutively expressing hM3Dq DREADD receptors in OXT-releasing hypothalamic neurons, we show that chemogenetic stimulation of OXT neurons elicits widespread increases in cerebral blood volume, with prominent effects in fronto-striatal regions and dorsal hippocampal areas. This broad functional response is accompanied by a network-specific reconfiguration of brain-wide functional connectivity, characterized by increased coupling between key components of the rodent social brain, and negative functional coupling between hippocampal and fronto-cortical regions. In vivo electrophysiology under matched conditions shows concordant changes in slow (<1 Hz) and delta-band coherence across frontalthalamic and fronto-hippocampal circuits, revealing a plausible neural correlate of the observed connectivity effects. Notably, these changes are accompanied by a marked reconfiguration of the temporal architecture of fMRI network activity, with increased occupancy of co-activation states engaging saliencerelated regions and a concomitant suppression of sensory-dominated states. Together, these findings identify dynamic network-state reconfiguration as a systems-level mechanism through which endogenous OXT can tune functional communication across social-affective circuits.

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bioRxiv2026

Cortical excitability inversely modulates fMRI connectivity via low-frequency neuronal coupling

The neural mechanisms supporting fMRI connectivity are poorly understood. Leveraging an aggregated analysis of novel and existing chemogenetic manipulations and electrophysiological recordings in the mouse medial prefrontal cortex (PFC), we show that local cortical excitability inversely modulates large-scale fMRI connectivity. Specifically, we find that bidirectional chemogenetic manipulations of cortical excitability produce opposite effects on fMRI connectivity, resulting in fMRI hypoconnectivity when excitability is enhanced, and fMRI hyperconnectivity when excitability is suppressed, despite corresponding increases or decreases in local neuronal firing. Notably, while each chemogenetic manipulation produces a distinct profile of interareal electrophysiological coherence, only low-frequency (< 4Hz) coherence predicts the direction and magnitude of the ensuing fMRI connectivity changes. Biophysical network modelling shows that the observed electrophysiological coherence profiles can arise from the interaction between direct inter-area communication changes mediated by local firing-rate variations, and shared larger-scale low-frequency covariation of ongoing neuronal activity. Together, our results reveal an inverse relationship between regional cortical excitability and large-scale fMRI connectivity, and indicate that fMRI connectivity is primarily supported by low frequency (<4 Hz) electrophysiological coupling. These findings open new avenues for modeling and interpreting fMRI connectivity in health, and in response to pathological or exogenous perturbations linked to cortical excitability.

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bioRxiv2026

Structural and dynamic embedding of the mouse functional connectome revealed by functional ultrasound imaging (fUSI)

Functional ultrasound imaging (fUSI) is an emerging hemodynamic neuroimaging modality whose potential for connectome-scale network mapping remains largely untested. Here, we establish a non-invasive transcranial fUSI protocol and an fMRI-inspired preprocessing framework that enable robust resting-state functional connectomics in the mouse. We show that fUSI resolves canonical brain-wide cortical and subcortical networks with high spatial concordance to fMRI, including a default-mode (DMN) and a laterocortical network. Notably, fUSI networks are robustly embedded within the structural connectome, with structure-function coupling being parsimoniously described by four dominant axes that differentially relate functional systems to known anatomical substrates. We also show that, beyond static organization, fUSI reproduces hallmark resting state fMRI dynamics, including dominant anti-correlated coactivation patterns (CAPs) and a structured transition architecture that converges onto three stable attractor modes. Together, these results establish transcranial fUSI as a portable and scalable complement to fMRI for connectome-scale mapping of mouse brain networks.

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bioRxiv2025

Cognitive cartography of mammalian brains using meta-analysis of AI experts

The complexity of the brain is increasingly mirrored by the complexity of the neuroscientific literature, yet no individual mind can fully grasp the diversity of scales, methodologies and model organisms. Where human experts flag, the latest AI models excel: large language models can seamlessly integrate knowledge across scientific domains. Here we show how large language models can systematically and quantitatively synthesise literature-wide neuroscientific knowledge about the cognitive operations and dysfunctions associated with each brain region. Meta-analysis of AI experts reveals structure-function mappings to which existing meta-analytic frameworks are blind, demonstrated by lesions and direct intracranial stimulation. It also unlocks the possibility of extending quantitative literature meta-analysis and decoding of brain maps to other model organisms beyond human. As proof of concept, we integrate LLM meta-analysis with species-specific transcriptomics in human, macaque, and mouse, to discover an evolutionarily conserved molecular circuit for cognition. Altogether, meta-analysis of AI experts can fundamentally catalyze neuroscientific discovery by overcoming the barrier of data aggregation from heterogeneous studies, finally bringing together a scattered literature to identify emergent patterns and latent insights across disparate subfields, modalities, and species.

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bioRxiv2025

Developmental excitation-inhibition imbalance permanently reprograms autism-relevant social brain circuits

An influential theory proposes that an imbalance between excitation and inhibition (E:I) plays a central role in the etiology of autism and related developmental disorders. However, controversy exists as to whether this imbalance is a direct causal mechanism for autism, or a compensatory response to other primary etiological factors. Using chemogenetic manipulations in neonatal mice, we show that a transient E:I imbalance during development is sufficient to permanently reprogram autism-relevant social brain circuits. Chemogenetically manipulated mice exhibit lifelong impairments in sociability, persistent dysregulation of multiple autism-risk synaptic genes, and sustained cortical hyperexcitability in adulthood. Importantly, these social impairments are robustly rescued by pharmacological inhibition of neuronal excitability. Developmental E:I imbalance also disrupts functional connectivity in social brain regions enriched for transcriptionally dysregulated genes, suggesting a convergence of transcriptional and circuit-level pathology. Finally, multivariate modelling shows that behavioral dysfunction in chemogenetically manipulated animals closely associates with disrupted connectivity between prefrontal and mesolimbic dopaminergic regions. Collectively, our findings reconcile conflicting theories in the field and point to activity-dependent transcriptional remodeling as a foundational mechanism by which transient E:I imbalance during development can cause lasting, autism-relevant circuit dysfunction.

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bioRxiv2025

The cost of cognition: Measuring the energy consumption of non-equilibrium computation

In biological systems, survival is predicated on an animal being able to perform computations quickly on a minimal energy budget. What is the energy consumption of non-equilibrium brain computation, i.e., what is the cost of cognition? Previous literature has estimated the metabolic cost using neuroimaging measures of glucose consumption but complementary to these findings, here we directly estimate the computational costs by combining the new field of stochastic thermodynamics with whole-brain modelling. We developed the COCO (COst of COgnition) framework using an analytical expression quantifying the links between energy cost, non-equilibrium and information processing for any given brain state measured with neuroimaging. Importantly, this key relationship also holds at the level of individual brain regions. We used this to quantify the benefits of information processing on the highly anatomically, interconnected hierarchical systems of the brain. Crucially, in empirical neuroimaging data we demonstrate that the human brain uses significantly less energy overall than other mammals (including non-human primates and mice), suggestive of an evolutionary optimisation of the effectiveness of computation. Focusing on the cost of cognition, using large-scale human neuroimaging data of 970 healthy human participants, we show that the resting state uses significantly less energy that seven different cognitive tasks. Furthermore, different kinds of tasks require different amounts of non-equilibrium, information processing and energy consumption. We found that tasks requiring more distributed computation also use more energy. Overall, these results directly quantify the cost of cognition, i.e., the non-equilibrium and energetic demands of information processing, allowing a deeper understanding of how the brain compute in a way that is far more energy efficient than current generations of digital computers and artificial intelligence.

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bioRxiv2025

Comprehensive profiling of anaesthetised brain dynamics across phylogeny

Intrinsic dynamics of neuronal circuits shape information processing. Combining neuroimaging with causal perturbation offers the opportunity to understand how local dynamics mediate the link between neurobiology and functional repertoire. We compile a unique dataset of multi-scale neural activity during wakefulness and anaesthetic-induced suppression of information processing encompassing human, macaque, marmoset, mouse, zebrafish and nematode. Applying massive feature extraction, we comprehensively characterise local neural dynamics across >6,000 time-series features. Using dynamics as a common space for cross-species comparison reveals a conserved dynamical profile of anaesthesia across species, characterised by shorter intrinsic timescales of neural activity and dampened interregional synchrony. This dynamical regime is experimentally reversed in vivo by deep-brain stimulation of the macaque centromedian thalamus, restoring behavioural responsiveness. Spatially, this conserved dynamical phenotype covaries with conserved transcriptional profiles of excitatory and inhibitory neurotransmission across human, macaque, marmoset and mouse cortex. Biophysical modelling provides a mechanistic link between the macroscale dynamical phenotype of anaesthesia, and microscale effects of key molecular targets on the timescales of synaptic excitation and inhibition. Altogether, comprehensive dynamical phenotyping reveals a shared neural endpoint of anaesthesia: across species and scales, anaesthetics induce spatio-temporal isolation of local neural activity.

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bioRxiv2025

Assigning Targetable Molecular Pathways to Transdiagnostic Subgroups Across Autism and Related Neurodevelopmental Disorders

The heterogeneity of autism and related neurodevelopmental conditions has impeded accurate prognoses and treatment discovery. Using translational neuroimaging across 135 mouse models (3,515 mice) and two human MRI datasets (n = 1,234 and n = 1,015), we derived participant subgroups from shared neuroanatomical features. These subgroups did not distinguish autism, ADHD, or OCD diagnoses and only modestly differentiated cognitive and behavioural phenotypes. Instead, they mapped onto four molecular pathways: (1) synaptic function; (2) MAPK and Wnt signalling; (3) chromatin modification and cellular stress responses; and (4) broader chromatin, immune, and second-messenger signalling pathways. This framework bridges preclinical models and idiopathic human neurodevelopmental conditions, linking patients to biologically relevant molecular mechanisms.

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bioRxiv2025

Biological subtyping of autism via cross-species fMRI

It is frequently assumed that the phenotypic heterogeneity in autism spectrum disorder reflects underlying pathobiological variation. However, direct evidence in support of this hypothesis is lacking. Here, we leverage cross-species functional neuroimaging to examine whether variability in brain functional connectivity reflects distinct biological mechanisms. We find that fMRI connectivity alterations in 20 distinct mouse models of autism (n=549 individual mice) can be clustered into two prominent hypo- and hyperconnectivity subtypes. We show that these connectivity profiles are linked to distinct signaling pathways, with hypoconnectivity being associated with synaptic dysfunction, and hyperconnectivity reflecting transcriptional and immune-related alterations. Extending these findings to humans, we identify analogous hypo- and hyperconnectivity subtypes in a large, multicenter resting state fMRI dataset of n=940 autistic and n=1036 neurotypical individuals. Remarkably, hypo- and hyperconnectivity autism subtypes are replicable across independent cohorts (accounting for 25.1% of all autism data), exhibit distinct functional network architecture, are behaviorally dissociable, and recapitulate synaptic and immune mechanisms identified in corresponding mouse subtypes. Our cross-species investigation, thus, decodes the heterogeneity of fMRI connectivity in autism into distinct pathway-specific etiologies, offering a new empirical framework for targeted subtyping of autism.

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bioRxiv2025

Synaptome architecture shapes regional dynamics in the mouse brain

Synapses are the connections that transform neurons from simple electrically charged cells into complex circuits that support perception, cognition and action. Recent advances in single-punctum synapse mapping in mice have made it possible to study the diversity of synapses and how these synapse types are differentially expressed across the brain. A salient question is how synapse diversity shapes the spatial patterning of whole-brain dynamics. Here we derive > 6 000 time-series features from fMRI recordings in awake mice to construct a comprehensive macroscale dynamical phenotype of each synapse type. We find that spatial variation in synapse types colocalizes with spatial variation in regional dynamics. Time-series in regions enriched for SAP102-expressing synapses display high-amplitude events while time-series in regions enriched for PSD95-expressing synapses display low stationarity. These regional variations in synapse types and dynamics are associated with patterns of structural and functional connectivity and the placement of hubs. Finally, using two additional fMRI datasets in anaesthetized mice, we show that synapses expressing short- and long-lifetime proteins are differentially engaged across behavioural states. Collectively, this work demonstrates that the spatial organization of microscale synapse types fundamentally shapes whole-brain dynamics.

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bioRxiv2024

Competitive interactions shape brain dynamics and computation across species

Adaptive cognition relies on cooperation across anatomically distributed brain circuits. However, specialised neural systems are also in constant competition for limited processing resources. How does the brains network architecture enable it to balance these cooperative and competitive tendencies? Here we use computational whole-brain modelling to examine the dynamical and computational relevance of cooperative and competitive interactions in the mammalian connectome. Across human, macaque, and mouse we show that the architecture of the models that most faithfully reproduce brain activity, consistently combines modular cooperative interactions with diffuse, long-range competitive interactions. The model with competitive interactions consistently outperforms the cooperative-only model, with excellent fit to both spatial and dynamical properties of the living brain, which were not explicitly optimised but rather emerge spontaneously. Competitive interactions in the effective connectivity produce greater levels of synergistic information and local-global hierarchy, and lead to superior computational capacity when used for neuromorphic computing. Altogether, this work provides a mechanistic link between network architecture, dynamical properties, and computation in the mammalian brain.

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bioRxiv2024

Synaptic-dependent developmental dysconnectivity in 22q11.2 deletion syndrome

Chromosome 22q11.2 deletion is among the strongest known genetic risk factors for neuropsychiatric disorders, including autism and schizophrenia. Brain imaging studies have reported disrupted large-scale functional connectivity in people with 22q11 deletion syndrome (22q11DS). However, the significance and biological determinants of these functional alterations remain unclear. Here, we use a cross-species design to investigate the developmental trajectory and neural underpinnings of brain dysconnectivity in 22q11DS. We find that LgDel mice, an established mouse model of 22q11DS, exhibit age-specific patterns of functional MRI (fMRI) dysconnectivity, with widespread fMRI hyper-connectivity in juvenile mice reverting to focal hippocampal hypoconnectivity over puberty. These fMRI connectivity alterations are mirrored by co-occurring developmental alterations in dendritic spine density, and are both transiently normalized by developmental GSK3{beta} inhibition, suggesting a synaptic origin for this phenomenon. Notably, analogous hyper-to hypoconnectivity reconfiguration occurs also in human 22q11DS, where it affects hippocampal and cortical regions spatially enriched for synaptic genes that interact with GSK3{beta}, and autism-relevant transcripts. Functional dysconnectivity in somatomotor components of this network is predictive of age-dependent social alterations in 22q11.2 deletion carriers. Taken together, these findings suggest that synaptic-related mechanisms underlie developmentally mediated functional dysconnectivity in 22q11DS.

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bioRxiv2024

Connectomic and behavioral alterations in creatine transporter deficiency are partially normalized by gene therapy

Creatine Transporter Deficiency (CTD) is an X-linked disorder due to the loss of SLC6A8 gene and presenting with low brain creatine, intellectual disability, autistic-like behavior and seizures. No treatments are available yet for CTD, and little is known about the brain circuit alterations underlying its pathological endophenotypes. Here, we tracked brain network and behavioral dysfunction in a murine model of CTD at two stages of disease progression. fMRI mapping revealed widespread disruption of brain connectivity in Slc6a8-KO mice, with robust somatomotor hypoconnectivity in juvenile animals, and weaker and more focal alterations of cortical and subcortical connectivity in adulthood. Notably, perinatal AAV-mediated expression of human SLC6A8 in Slc6a8- KO mice robustly prevented juvenile fMRI hypoconnectivity, an effect accompanied by the regression of multiple translationally relevant phenotypes, including reduced stereotyped movements, improved declarative memory and increased body weight, all of which persisted into adulthood. However, early cognitive deficits, impairments in working memory and residual fMRI hypoconnectivity in adult mice were not ameliorated by gene therapy. Furthermore, significant cognitive impairments were observed in WT mice receiving gene therapy, highlighting a potential detrimental effect of ectopic expression of SLC6A8 in healthy brain circuits. Finally, multivariate modeling in adult mice revealed a basal forebrain network whose activity was associated with behavioral performance and modulated by brain creatine levels. This brain-behavior relationship was disrupted in Slc6a8-KO mice. Our results document robust network disruption in CTD and demonstrate that CTD pathology can be partially alleviated by perinatal genetic expression of SLC6A8, providing a foundation for the future development of experimental therapies for this genetic disorder.

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bioRxiv2023

Probing the Mechanisms of Global Brain Reconfiguration after Local Manipulations

Understanding how localized brain interventions translate into whole-brain dynamics is crucial for deciphering neural function and tailoring therapeutic strategies. Combining mouse experimental datasets of focal interventions (thalamic lesion and chemogenetic silencing of cortical hubs), we demonstrate both local and global effects. Using whole-brain simulations of experimental data, we not only confirm the distributed nature of local manipulations but also offer mechanistic insights into these processes. Our simulations predict specific alterations in firing rates and spectral characteristics across specific brain networks, leading to structured changes in functional connectivity patterns. Some of these predictions have been empirically validated. Notably, the affected brain subnetworks--and their resultant signatures of change--are contingent on the original intervention site, suggesting a method to accurately localize the source of alteration. Our results provide a general framework for interpreting localized intervention effects, offering insights that could refine clinical interventions for focal brain disorders by enabling targeted circuit-level neuromodulation strategies.

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bioRxiv2023

Evolutionarily conserved fMRI network dynamics in the mouse, macaque, and human brain

Evolutionarily relevant networks have been previously described in several mammalian species using time-averaged analyses of fMRI time-series. However, fMRI network activity is highly dynamic and continually evolves over timescales of seconds. Whether the dynamic organization of resting-state fMRI network activity is conserved across mammalian species remains unclear. Using frame-wise clustering of fMRI time-series, we find that intrinsic fMRI network dynamics in awake macaques and humans is characterized by recurrent transitions between a set of 4 dominant, neuroanatomically homologous fMRI coactivation modes (C-modes), three of which are also plausibly represented in the rodent brain. Importantly, in all species C-modes exhibit species-invariant dynamic features, including preferred occurrence at specific phases of fMRI global signal fluctuations, and a state transition structure compatible with infraslow coupled oscillator dynamics. Moreover, dominant C-mode occurrence reconstitutes the static organization of the fMRI connectome in all species, and is predictive of ranking of corresponding fMRI connectivity gradients. These results reveal a set of species-invariant principles underlying the dynamic organization of fMRI networks in mammalian species, and offer novel opportunities to relate fMRI network findings across the phylogenetic tree.

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bioRxiv2023

Modular subgraphs in large-scale connectomes underpin spontaneous co-fluctuation "events" in mouse and human brains

Previous studies have adopted an edge-centric framework to study fine-scale dynamics in human fMRI. To date, however, no studies have applied this same framework to data collected from model organisms. Here, we analyze structural and functional imaging data from lightly anesthetized mice through an edge-centric lens. We find evidence of "bursty" dynamics and events - brief periods of high-amplitude network connectivity. Further, we show that on a per-frame basis events best explain static FC and can be divided into a series of hierarchically-related clusters. The co-fluctuation patterns associated with each centroid link distinct anatomical areas and largely adhere to the boundaries of algorithmically detected functional brain systems. We then investigate the anatomical connectivity undergirding high-amplitude co-fluctuation patterns. We find that events induce modular bipartitions of the anatomical network of inter-areal axonal projections. Finally, we replicate these same findings in a human imaging dataset. In summary, this report recapitulates in a model organism many of the same phenomena observed in previously edge-centric analyses of human imaging data. However, unlike human subjects, the murine nervous system is amenable to invasive experimental perturbations. Thus, this study sets the stage for future investigation into the causal origins of fine-scale brain dynamics and high-amplitude co-fluctuations. Moreover, the cross-species consistency of the reported findings enhances the likelihood of future translation.

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bioRxiv2023

Macroscale coupling between structural and effective connectivity in the mouse brain

How the emergent functional connectivity (FC) relates to the underlying anatomy (structural connectivity, SC) is one of the biggest questions of modern neuroscience. At the macro-scale level, no one-to-one correspondence between structural and functional links seems to exist. And we posit that to better understand their coupling, two key aspects should be taken into account: the directionality of the structural connectome and the limitations of describing network functions in terms of FC. Here, we employed an accurate directed SC of the mouse brain obtained by means of viral tracers, and related it with single-subject effective connectivity (EC) matrices computed by applying a recently developed DCM to whole-brain resting-state fMRI data. We analyzed how SC deviates from EC and quantified their couplings by conditioning both on the strongest SC links and EC links. We found that when conditioning on the strongest EC links, the obtained coupling follows the unimodal-transmodal functional hierarchy. Whereas the reverse is not true, as there are strong SC links within high-order cortical areas with no corresponding strong EC links. This mismatch is even more clear across networks. Only the connections within sensory motor networks align both in terms of effective and structural strength.

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bioRxiv2022

Redefining the connectome: A multi-modal, asymmetric, weighted, and signed description of anatomical connectivity

The macroscale connectome is the network of physical, white-matter tracts between brain areas. The connections are generally weighted and their values interpreted as measures of communication efficacy. In most applications, weights are either assigned based on imaging features-e.g. diffusion parameters-or inferred using statistical models. In reality, the ground-truth weights are unknown, motivating the exploration of alternative edge weighting schemes. Here, we explore a multi-modal (combining diffusion and functional MRI data) regression-based, explanatory model that endows reconstructed fiber tracts with directed and signed weights. Benchmarking this method on Human Connectome Project data, we find that the model fits observed data well, outperforming a suite of null models. The estimated weights are subject-specific and highly reliable, even when fit using relatively few training samples. Next, we analyze the resulting network using graph-theoretic tools from network neuroscience, revealing bilaterally symmetric communities that span cerebral hemispheres. These communities exhibit a clear mapping onto known functional systems. We also study the shortest paths structure of this network, discovering that almost every edge participates in at least one shortest path. We also find evidence of robust asymmetries in edge weights, that the network reconfigures in response to naturalistic stimuli, and that estimated edge weights differ with age. In summary, we offer a simple framework for weighting connectome data, demonstrating both its ease of implementation while benchmarking its utility for typical connectome analyses, including graph theoretic modeling and brain-behavior associations.

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