Biology subjects

Luppi, A. I.

Publications and source records attributed to Luppi, A. I..

At least 19 recordsLinked to original sources

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.

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bioRxiv2026

Generalisable signatures of anaesthesia in the large-scale functional organisation of the marmoset brain

How the activity and connectivity of the brain support consciousness remains a central question in neuroscience. Recent progress driven by the use of functional MRI has seen growing recognition that large-scale distributed functional organisation of the human and non-human primate brain are systematically and consistently reshaped by anaesthetic-induced unconsciousness, across anaesthetics and across human and macaque. Here, we generalise these results to a different primate species that is gaining traction as model organism in neuroscience, the marmoset (Callithrix jacchus). We also generalise results to an additional anaesthetic, isoflurane, which we compare with propofol and sevoflurane. We report that under anaesthesia with propofol, sevoflurane, or isoflurane, distributed brain activity from functional MRI is increasingly constrained by the underlying structural connectivity across scales. Anaesthesia also induces a collapse of the principal gradient and intrinsic functional geometry of the marmoset brain, coinciding with a breakdown of hierarchical integration. Altogether, the present results indicate generalisable signatures of anaesthesia in the large-scale organisation of the primate brain.

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bioRxiv2026

The blueprint of human functional architecture shifts from cognition to anatomy during perturbations of consciousness

Consciousness and cognition arise from the ongoing interactions between brain regions. Synchronous fluctuations of fMRI signals may indicate that two brain regions perform similar cognitive functions, but neural interactions are also constrained by anatomical connectivity and regions molecular, cytoarchitectonic, and metabolic profiles. Here we disentangle the respective contributions of ongoing cognition and multimodal neurobiological constraints in shaping functional connectivity. We jointly contextualise haemodynamic FC against eight distinct multimodal representations of the human connectome: (i) structural connectivity from diffusion tractography; (ii) spatial embedding; (iii) similarity of transcriptional profiles from gene expression; (iv) similarity of receptor profiles from Positron Emission Tomography; (v) laminar profile similarity from histology; (vi) correlated electrophysiological activity from magnetoencephalography; (vii) correlated metabolic activity from PET glucose uptake; (viii) coordinated activation across 123 cognitive operations from the NeuroSynth meta-analytic engine. We demonstrate that cognitive co-activation is the dominant predictor of inter-regional fMRI synchrony in the awake human brain, even when quantified using intracranial electrical stimulation. Crucially, this predominance of cognitive co-activation for shaping functional connectivity is systematically obliterated across five datasets of pharmacological and pathological perturbations of consciousness (chronic disorders of consciousness; anaesthesia with sevoflurane, propofol, or ketamine) when cognition is disconnected from the environment or altogether abolished. Altogether, we show that multimodal predictors of functional architecture shift away from cognitive co-activation and toward anatomicalmolecular constraints during pharmacological and pathological perturbations of consciousness.

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bioRxiv2026

Homophily-informed generative models of brain maps

The structural and functional organization of the brain can be studied across multiple scales, yielding richly detailed topographic brain maps of biological features. What are the underlying forces that shape their spatial patterning? Here we introduce a simple generative model of multiscale brain maps based on the concept of inter-regional homophily: the tendency for regions that are proximal in a given physical, molecular or functional space to display similar biological features. We evaluate the model with respect to six definitions of inter-regional homophily, including physical proximity, structural and functional connectivity, and laminar, receptor and transcriptional similarity, and across 43 empirical brain maps estimated using multiple imaging, electrophysiological and histological technologies. We show that homophilic principles are sufficient to accurately reconstruct many maps, with biological similarity and functional connectivity often contributing more than the brains geometry. We also identify consistent patterns of unexplained variation in maps with low homophily, revealing axes of cortical organization not captured by canonical inter-regional relationships. Finally, we show that homophily-informed generative models can be used to disentangle complex relationships between brain features and make new inferences on how they fit together. Collectively, this work highlights the fundamental contribution of homophily to the topographic layout of numerous biological features of the brain.

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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

Restoring signatures of consciousness by thalamic stimulation in a whole-brain model of an anesthetized nonhuman primate

Treatment options for Disorders of Consciousness (DoC) are limited due to insufficient understanding of the underlying neurobiological mechanisms. Two primary strategies for characterizing DoC and assessing treatment efficacy are in vivo experiments with animal models, and in silico computational models. We combined both approaches by creating a whole-brain model tailored to the experimental functional magnetic resonance imaging (fMRI) data of a single anesthetized macaque. It was previously reported in an in vivo experiment that anesthesia-induced loss of consciousness was partially reversed by specific electrical stimulation of the thalamic central nuclei. The in silico model reproduced the brain dynamics underlying the restoration of consciousness, providing a potential explanation for the transition between these brain states as continuous trajectories unfolding in a low-dimensional space. Our results demonstrate that whole-brain computational models reproduce the spatiotemporal properties of fMRI recordings during loss of consciousness and during its recovery induced by electrical stimulation, enabling computational exploration of perturbation-based interventions to potentially personalize treatment and aid recovery of consciousness in DoC patients.

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bioRxiv2025

netneurotools: a trainee-oriented approach to network neuroscience

Brain imaging is an increasingly inter-disciplinary field, encompassing multiple data types and multiple analytic traditions. Projects typically involve many moving parts, such as building customized preprocessing pipelines, transforming between data formats, preparing datasets for analysis, and ultimately displaying results. The field is conventionally built on highly specialized software packages that solve these individual challenges well, but are not necessarily designed to be interoperable. Trainees new to the field are therefore often left to come up with isolated heuristics and workarounds to complete a project. Here we present a way to navigate the increasingly complex informatics ecosystem of brain imaging. netneurotools is our labs internal Python toolkit that has been continuously developed and maintained by the labs trainees. The philosophy of the toolkit is that it should be the Swiss army knife of the lab: functions and routines that we often use but that are not part of any established pipeline or package. Since its inception, the toolkit has been open and welcomes contribution from neuroscientists across the globe. netneurotools presents a necessary counterweight to out-of-the-box software packages and highlights the importance of smaller, ad hoc functions for implementing projects. By opening a window into the inner workings of a lab, netneurotools also presents an opportunity to begin a new type of discourse among groups and establish tangible links within the community.

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bioRxiv2025

Accurate and Interpretable Prediction of Antidepressant Treatment Response from Receptor-informed Neuroimaging

Conventional antidepressants show moderate efficacy in treating major depressive disorder. Psychedelic-assisted therapy holds promise, yet individual responses vary, underscoring the need for predictive tools to guide treatment selection. Here, we present graphTRIP (graph-based Treatment Response Interpretability and Prediction) - a geometric deep learning architecture that enables three advances: 1) accurate prediction of post-treatment depression severity using only pretreatment clinical and neuroimaging data; 2) identification of robust biomarkers; and 3) causal analysis of treatment effects and underlying mechanisms. Trained on data from a clinical trial comparing psilocybin and escitalopram (NCT03429075), graphTRIP achieves strong predictive accuracy (r = 0.72, p = 6.8 x10-8), and shows clear generalization to both an independent dataset and across brain atlases. The model identifies stronger functional connectivity within sensory networks as a robust predictor of poorer response across both treatments. In contrast, causal analysis implicates frontoparietal and default mode networks as key moderators of differential response, with stronger 5-HT1A- and 5-HT2A-related signalling in the frontoparietal network predicting escitalopram response but psilocybin resistance. Overall, this work advances precision medicine and biomarker discovery in depression.

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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.

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bioRxiv2025

Neuromorphic hierarchical modular reservoirs

Modularity is a fundamental principle of brain organization, reflected in the presence of segregated sub-networks that enable specialized information processing. These small, densely connected modules are often nested within larger, higher-order modules, giving rise to a hierarchical modular architecture. This structure is posited to balance information segregation in specialized neuronal communities and global integration via intermodular communication. Yet, how hierarchical modularity shapes network function remains unclear. Here we introduce a simple blockmodeling framework for generating and comparing multi-level hierarchical modular networks and implement them as recurrent neural net-work reservoirs to evaluate their computational capacity. We show that hierarchical modular networks enhance memory capacity, support multitasking, and give rise to a broader range of temporal dynamics compared to strictly modular and random networks. These functional advantages can be traced to topological features enriched in hierarchical modular networks, which include reciprocal and cyclic network motifs. To test whether the computational advantages of hierarchical modularity subsist in empirical human brain structural connectivity patterns, we develop a novel hierarchical modularity-preserving network null model, allowing us to isolate the positive effect of empirical hierarchical modularity patterns on memory capacity. To evaluate the biomimetic validity of connectome-informed reservoir dynamics, we compare reservoir timescales to empirical brain timescales derived from MEG data and find that hierarchical modularity contributes to shaping brain-like neural timescales. Altogether, across multiple benchmarks, these results show that hierarchical modularity endows networks with computationally advantageous properties, providing insight into the relationship between neural network structure and function with potential applications for the design of neuromorphic computing architectures.

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bioRxiv2025

An engine for systematic discovery of cause-effect relationships between brain structure and function

Characterising how perturbations of brain architecture influence brain function is essential to understand the origins of brain dysfunction, and devise potential avenues of treatment. Here we introduce a computational engine for systematic causal discovery of the functional consequences of altering network architecture and local biophysics in the brain. We integrate multimodal anatomical and functional neuroimaging to implement over 2, 000 in-silico brains, and provide mechanistic insight into the functional consequences of local lesions, global wiring, and empirically-derived maps of regional cytoarchitecture and chemoarchitecture. We comprehensively assess how each manipulation of brain macrostructure reshapes spatial and temporal signal coordination, information dynamics, and functional hierarchy--as well as spontaneous co-activation of meta-analytic cognitive circuits, and > 6, 000 dimensions of local neural dynamics. Our computational model systematically identifies which features of brain architecture have overlapping or antagonistic causal influence over each dimension of brain function, and how functional properties are traded off against each other across disorders and neuromodulation. We find that regions functional vulnerability to lesions in silico recapitulates their vulnerability to neurodevelopmental and psychiatric in vivo, along a core-periphery organisation. We provide convergent evidence that the brains wiring diagram is finely tuned to favour the hierarchical integration of information. Notably, our model successfully recapitulates known empirical results that have not been modelled before, including desynchronisation and flattening of the brains functional hierarchy induced by psychedelic 5HT2A agonists. To catalyse future discoveries, we make this resource freely available to the neuroscience community through an interactive website (https://systematic-causal-mapping.up.railway.app/), where users can interrogate our systematic database of simulated cause-effect relationships. Altogether, we provide a powerful computational engine to predict the functional consequences of experimental or clinical interventions, and drive neuroscientific hypothesis-generation.

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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

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

Benchmarking macaque gene expression for horizontal and vertical translation

The spatial patterning of gene expression shapes cortical organization and emergent function. Advances in spatial transcriptomics make it possible to comprehensively map cortical gene expression in both humans and model organisms. The macaque is a particularly valuable model organism, due to its evolutionary similarity with the human. The translational potential of macaque gene expression rests on the assumption that it is a good proxy for spatial patterns of corresponding proteins (vertical translation) and for spatial patterns of ortholog human genes (horizontal translation). Here we systematically benchmark the spatial distribution of gene expression in the macaque cortex against (a) cortical receptor density in the macaque and (b) cortical gene expression in the human. We find that there is moderate cortex-wide correspondence between gene expression and protein density in the macaque, which is improved by considering layer-specific gene expression. We find greater correspondence between orthologous gene expression in the macaque and human. Inter-species correspondence of gene expression exhibits systematic regional heterogeneity, with greater correspondence in unimodal than transmodal cortex, mapping onto patterns of evolutionary cortical expansion. We extend these results to additional micro-architectural features using macaque immunohistochemistry and T1w:T2w ratio, and replicate them using macaque RNA-seq and human RNA-seq gene expression. Collectively, the present results showcase both the potential and limitations of macaque spatial transcriptomics as an engine of translational discovery within and across species.

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bioRxiv2024

Ketamine-Induced Unresponsiveness Shows a Harmonic Shift from Global to Localised Functional Organisation.

Ketamine is classified as a dissociative anaesthetic that, in sub-anaesthetic doses, can produce an altered state of consciousness characterised by dissociative symptoms, visual and auditory hallucinations, and perceptual distortions. Given the anaesthetic-like and psychedelic-like nature of this compound, it is expected to have different effects on brain dynamics in anaesthetic doses than in low, sub-anaesthetic doses. We investigated this question using connectome harmonic decomposition (CHD), a recently developed method to decompose brain activity in terms of the network organisation of the underlying human structural connectome. Previous research using this method has revealed connectome harmonic signatures of consciousness and responsiveness, with increased influence of global network structure in disorders of consciousness and propofol-induced sedation, and increased influence of localised patterns under the influence of classic psychedelics and sub-anaesthetic doses of ketamine, as compared to normal wakefulness. When we applied the CHD analytical framework to resting-state fMRI data of volunteers during ketamine-induced unresponsiveness, we found increased prevalence of localised harmonics, reminiscent of altered states of consciousness. This is different from traditional GABAergic sedation, where instead the prevalence of global rather than localised harmonics seems to increase with higher doses. In addition, we found that ketamines harmonic signature shows higher alignment with those seen in LSD- or psilocybin-induced psychedelic states than those seen in unconscious individuals, whether due to propofol sedation or brain injury. Together, the results indicate that ketamine-induced unresponsiveness, which does not necessarily suppress conscious experience, seems to influence the prevalence of connectome harmonics in the opposite way compared to GABAergic hypnotics. We conclude that the CHD framework offers the possibility to track alterations in conscious awareness (e.g., dreams, sensations) rather than behavioural responsiveness - a discovery made possible by ketamines unique property of decoupling these two facets.

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bioRxiv2024

A thalamic perspective of (un)consciousness in pharmacological and pathological states in humans

Currently, there is substantial ongoing discussion around the functional role of the thalamus in consciousness. What is missing in the literature, however, is a systematic investigation of the relevance of specific thalamic nuclei in pharmacologically and pathologically altered states of consciousness in humans. Using functional neuroimaging in both healthy anaesthetised volunteers and patients with disorders of consciousness (DOC), we sought to identify which specific thalamic subregions in both cohorts may be differentially significant for loss of consciousness. Our findings revealed that the pulvinar (Pu) and ventral-latero-ventral (VLV) nuclei, in anaesthesia, and the VLV, in DOC, had distinct functional connectivity patterns related to the default mode and somatomotor networks. Remarkably, among all nuclei, the Pu was found to have the strongest functional connectivity change with anaesthetic-induced loss of consciousness, while in DOC patients, we found the VLV revealed the strongest connectivity change in comparison with healthy controls. Furthermore, we provide evidence that this neural connectivity biomarker in patients also mirrors the changes observed at the behavioural level, which could have clinical implications for targeted deep brain stimulation in therapy for DOC.

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bioRxiv2024

Benchmarking methods for mapping functional connectivity in the brain

The networked architecture of the brain promotes synchrony among neuronal populations and the emergence of coherent dynamics. These communication patterns can be comprehensively mapped using noninvasive functional imaging, resulting in functional connectivity (FC) networks. Despite its popularity, FC is a statistical construct and its operational definition is arbitrary. While most studies use zero-lag Pearsons correlations by default, there exist hundreds of pairwise interaction statistics in the broader scientific literature that can be used to estimate FC. How the organization of the FC matrix varies with the choice of pairwise statistic is a fundamental methodological question that affects all studies in this rapidly growing field. Here we comprehensively benchmark the topological and geometric organization, neurobiological associations, and cognitive-behavioral relevance of FC matrices computed using a large library of 239 pairwise interaction statistics. We comprehensively investigate how canonical features of FC networks vary with the choice of pairwise statistic, including (1) hub mapping, (2) weight-distance trade-offs, (3) structure-function coupling, (4) correspondence with other neurophysiological networks, (5) individual fingerprinting, and (6) brain-behavior prediction. We find substantial quantitative and qualitative variation across FC methods. Throughout, we observe that measures such as covariance (full correlation), precision (partial correlation) and distance display multiple desirable properties, including close correspondence with structural connectivity, the capacity to differentiate individuals and to predict individual differences in behavior. Using information flow decomposition, we find that differences among FC methods may arise from differential sensitivity to the underlying mechanisms of inter-regional communication, with some more sensitive to redundant and some to synergistic information flow. In summary, our report highlights the importance of tailoring a pairwise statistic to a specific neurophysiological mechanism and research question, providing a blueprint for future studies to optimize their choice of FC method.

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