Biology subjects

Puxeddu, M. G.

Publications and source records attributed to Puxeddu, M. G..

7 recordsLinked to original sources

bioRxiv2025

Hierarchical Community Structure of the Adult Drosophila Connectome Reveals Conserved Circuit Archetypes

The community structure of connectomes supports functional specialization, adaptability, and cost-efficient wiring. However, little is known about communities in connectomes mapped at the level of individual neurons and synapses. Here, we analyze a whole-brain Drosophila adult connectome using a nested stochastic blockmodel to uncover its hierarchical community structure. Most of the roughly 1500 fine-scale communities-the smallest, and best resolved level of the hierarchy- are spatially compact, mostly assortative, and aligned with biological features. Nonetheless, we find evidence of nonassortative communities, spatially co-localized within the optic lobe and vision-processing pathways. Seeking "functional primitives"-small circuits with functionally narrow feature profiles-we use data-driven clustering to group communities into 45 archetypical meta-clusters based on their spatial, functional, and molecular properties, revealing modular building blocks from which larger, functionally diverse communities are composed. This work advances our understanding of how structure and function are organized in the fruit fly brain and highlights the value of statistical network models in interpreting nanoscale connectomes.

neuroscience↗

bioRxiv2025

Connectome architecture favours within-module diffusion and between-module routing

Connectomes are the structural scaffold for signalling within nervous systems. While many network models have been proposed to describe connectome communication, current approaches assume that every pair of neural elements communicates according to the same principle. Connectomes, however, are heterogeneous networks, comprising elements with varied topological and neurobiological makeups. In this paper, we investigate how connectome architecture may facilitate different signalling regimes depending on the topological embedding of communicating neural elements. Specifically, we test the hypothesis that the modular structure of brain networks fosters a dual mode of communication balancing diffusion--passive signal broadcasting--and routing--selective transmission via efficient paths. To this end, we introduce the relative diffusion score (RDS), a measure to quantify the proportional capacity for network communication via diffusion versus routing. We examined the interplay between RDS and connectome architecture in 6 organisms spanning a wide range of spatial resolutions and connectivity mapping techniques--from the complete nervous system of the larval fly to the inter-areal human connectome. Our analyses establish multiple lines of evidence suggesting that connectomes may be universally organised to support within-module diffusion and between-module routing. Using a series of rewiring null models, we untangle the contributions of connectome topology and geometry to the relationship between routing, diffusion and modular architecture. In conclusion, our work puts forth a hybrid conceptualisation of neural communication, in which diffusion contributes to functional segregation by concentrating information within localised clusters, while specialised signal routes enable fast, long-range and cross-system functional integration.

neuroscience↗

bioRxiv2024

Parallel and converging multisensory cascades in the Drosophila connectome

Connectomes--maps of synaptic connectivity--constrain how signals flow through the nervous system, shaping the transmission of sensory information to downstream targets involved in perception, decision-making, and action. Here, we use a simple, network-based spreading model to simulate sensory signal propagation across the adult Drosophila connectome. This approach allows us to trace modality-specific cascades and quantify their zones of overlap--neurons activated by multiple sensory pathways. Extending the classical spreading model, we introduce cooperative and competitive dynamics to simulate multisensory integration scenarios. Finally, we classify neurons based on their dynamical response profiles across all simulations, yielding a data-driven taxonomy grounded in both structure and dynamics. Our results highlight how abstract models can reveal organizing principles of neural computation and generate hypotheses for future experimental validation.

neuroscience↗

bioRxiv2024

Leveraging multivariate information for community detection in functional brain networks

Embedded in neuroscience is the concept that brain functioning is underpinned by specialized systems whose integration enables cognition and behavior. Modeling the brain as a network of interconnected brain regions, allowed us to capitalize on network science tools and identify these segregated systems (modules, or communities) by optimizing the weights of pairwise connections within them. However, just knowing how strongly two brain areas are connected does not paint the whole picture. Brain dynamics is also engendered by interactions involving more areas at the same time, namely, higher-order interactions. In this paper, we propose a community detection algorithm that accounts for higher-order interactions and finds modules of brain regions whose brain activity is maximally redundant. Compared to modules identified with methods based on bivariate interactions, our redundancy-dominated modules are more symmetrical between the hemispheres, they overlap with canonical systems at the level of the sensory cortex, but describe a new organization of the transmodal cortex. By detecting redundant modules across spatial scales, we identified a sweet spot of maximum balance between segregation and integration of information as that scale where redundancy within modules and synergy between modules peaked. Moreover, we defined a local index that distinguishes brain regions in segregators and integrators based on how much they participate in the redundancy of their modules versus the redundancy of the whole system. Finally, we applied the algorithm to a lifespan dataset and tracked how redundant subsystems change across time. The results of this paper serve as educated guesses on how the brain organizes itself into modules accounting for higher-order interactions of its fundamental units, and pave the way for further investigation that could link them to cognition, behavior, and disease.

neuroscience↗

bioRxiv2024

Controlling the human connectome with spatially diffuse input signals

The human brain is never at "rest"; its activity is constantly fluctuating over time, transitioning from one brain state-a whole-brain pattern of activity-to another. Network control theory offers a framework for understanding the effort - energy - associated with these transitions. One branch of control theory that is especially useful in this context is "optimal control", in which input signals are used to selectively drive the brain into a target state. Typically, these inputs are introduced independently to the nodes of the network (each input signal is associated with exactly one node). Though convenient, this input strategy ignores the continuity of cerebral cortex - geometrically, each region is connected to its spatial neighbors, allowing control signals, both exogenous and endogenous, to spread from their foci to nearby regions. Additionally, the spatial specificity of brain stimulation techniques is limited, such that the effects of a perturbation are measurable in tissue surrounding the stimulation site. Here, we adapt the network control model so that input signals have a spatial extent that decays exponentially from the input site. We show that this more realistic strategy takes advantage of spatial dependencies in structural connectivity and activity to reduce the energy (effort) associated with brain state transitions. We further leverage these dependencies to explore near-optimal control strategies such that, on a per-transition basis, the number of input signals required for a given control task is reduced, in some cases by two orders of magnitude. This approximation yields network-wide maps of input site density, which we compare to an existing database of functional, metabolic, genetic, and neurochemical maps, finding a close correspondence. Ultimately, not only do we propose a more efficient framework that is also more adherent to well-established brain organizational principles, but we also posit neurobiologically grounded bases for optimal control.

neuroscience↗

bioRxiv2023

Hierarchical communities in the larval Drosophila connectome: Links to cellular annotations and network topology

One of the longstanding aims of network neuroscience is to link a connectomes topological properties-i.e. features defined from connectivity alone-with an organisms neurobiology. One approach for doing so is to compare connectome properties with maps of metabolic, functional, and neurochemical annotations. This type of analysis is popular at the meso-/macro-scale, but is less common at the nano-scale, owing to a paucity of neuron-level connectome data. However, recent methodological advances have made possible the reconstruction of whole-brain connectomes at single-neuron resolution for a select set of organisms. These include the fruit fly, Drosophila melanogaster, and its developing larvae. In addition to fine-scale descriptions of neuron-to-neuron connectivity, these datasets are accompanied by rich annotations, documenting cell type and function. Here, we use a hierarchical and weighted variant of the stochastic blockmodel to detect multi-level communities in a recently published larval Drosophila connectome. We find that these communities partition neurons based on function and cell type. We find that communities mostly interact assortatively, reflecting the principle of functional segregation. However, a small number of communities interact non-assortatively. The neurons that make up these communities also form a "rich-club", composed mostly of interneurons that receive sensory/ascending inputs and deliver outputs along descending pathways. Next, we investigate the role of community structure in shaping neuron-to-neuron communication patterns. We find that polysynaptic signaling follows specific trajectories across modular hierarchies, with interneurons playing a key role in mediating communication routes between modules and hierarchical scales. Our work suggests a relationship between the system-level architecture of an organisms complete neuronal wiring network and the precise biological function and classification of its individual neurons. We envision our study as an important step towards bridging the gap between complex systems and neurobiological lines of investigation in brain sciences.

neuroscience↗

bioRxiv2022

Multi-modal and multi-subject modular organization of human brain networks

The human brain is a complex network of anatomically interconnected brain areas. Spontaneous neural activity is constrained by this architecture, giving rise to patterns of statistical dependencies between the activity of remote neural elements. The non-trivial relationship between structural and functional connectivity poses many unsolved challenges about cognition, disease, development, learning and aging. While numerous studies have focused on statistical relationships between edge weights in anatomical and functional networks, less is known about dependencies between their modules and communities. In this work, we investigate and characterize the relationship between anatomical and functional modular organization of the human brain, developing a novel multi-layer framework that expands the classical concept of multi-layer modularity optimization. By simultaneously mapping anatomical and functional networks estimated from different subjects into communities, this approach allows us to carry out a multi-subject and multi-modal analysis of the brains modular organization. Here, we investigate the relationship between anatomical and functional modules during resting state, finding unique and shared structures. The proposed framework constitutes a methodological advance in the context of multi-layer network analysis and paves the way to further investigate the relationship between structural and functional network organization in clinical cohorts, during cognitively demanding tasks, and in developmental or lifespan studies.

neuroscience↗