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Sporns, O.

Publications and source records attributed to Sporns, O..

At least 19 recordsLinked to original sources

bioRxiv2026

Structural signatures of synergy and redundancy in human brain function

A fundamental goal in neuroscience is to understand how the brains physical architecture supports complex functional dynamics. While the relationship between structural connectivity and pairwise functional connectivity has been extensively studied, the anatomical basis of higher-order interactions remains poorly understood. In this study, we use multivariate information theory -specifically the O-information- to investigate how the human connectome constrains subsets of brain regions characterized by predominantly redundant or synergistic information sharing. By analyzing the topology and community embedding of these subsets, we reveal two different structural profiles. Redundant subsets are characterized by high internal connection density and strong weights. Their nodes have high clustering and occupy globally less central positions. In contrast, synergistic subsets consist of globally central nodes with high betweenness centrality. We further demonstrate that leveraging these structural features, in particular node centrality, significantly improves the identification of synergistic subsets compared to random sampling. Together, these results demonstrate that the human connectome imposes specific constraints on higher-order information sharing, extending structure-function relationships beyond pairwise interactions and providing new insight into the structural origins of multivariate functional organization.

neuroscience↗

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

Trait-Relevant Tasks Improve Personality Prediction from Structural-Functional Brain Network Coupling

Personality traits capture stable patterns of behavior and thought, and neurobiological correlates were identified in structural and functional brain networks. Here, we investigate whether the coupling between structural and functional brain networks (SC-FC coupling), during resting state and seven tasks of varying trait-relevance, is associated with individual differences in the Big Five personality traits. We used diffusion-weighted and functional magnetic resonance imaging from 764 participants of the Human Connectome Project and modelled individual differences in SC-FC coupling with similarity and communication measures. These measures approximate functional interactions unfolding on top of the structural connectome and were set in relation to individual variations in personality traits. Significant associations were only observed during trait-relevant tasks: for agreeableness during social cognition, and conscientiousness could be predicted from task-general coupling patterns. We conclude that optimizing trait-relevance of tasks during neuroscientific measurements presents a promising means to increase effect sizes in studies on brain-behavior associations.

neuroscience↗

bioRxiv2025

Decoding the Human Brain during Intelligence Testing

Understanding the brain mechanisms underlying complex human cognition is a major objective in neuroscience. Previous studies have identified neural correlates of intelligence at different temporal and spatial scales using functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) separately. This study treats intelligence as a multilayer phenomenon across temporal and spatial scales and examines how the connectedness of brain regions as well as the complexity of multilayer brain dynamics relates to performance in an established intelligence test. Graph-theoretical analyses of fMRI-derived functional connectivity (N=67) revealed that the connectedness of frontal and parietal regions was associated with individual performance. Further, multiscale entropy analyses of EEG signals (N=131) disclosed that higher test scores were linked to more complex long-range processes and, at a trend level, to less complex short-range processes. These findings support the Multilayer Processing Theory, proposing the interplay between flexible long-range and modular short-range processes as the neural bases of intelligence.

neuroscience↗

bioRxiv2025

No Disconnection Syndrome after Near-Complete Callosotomy

Sensorimotor processing in the human brain is largely lateralized, with the corpus callosum integrating these processes into a unified experience. Following complete callosotomy, this integration breaks down, resulting in disconnection syndromes. We asked how much of the corpus callosum is sufficient to support functional unity--the absence of disconnection syndrome--by comparing three complete callosotomy patients with one retaining only the splenium. Using lateralized tasks across visual, tactile, visuospatial, and language domains, we predicted domain-specific deficits in the splenium-only patient based on established anatomical models of callosal topography. Strikingly, while complete callosotomy patients exhibited disconnection syndromes, the splenium patient demonstrated functional unity across all domains--as if his entire corpus callosum were intact. Our findings highlight the brains remarkable capacity to maintain behavioral integration through minimal preserved pathways, highlighting how the structure-dependent reorganizational capacity of the human brain allows to preserve functional unity.

neuroscience↗

bioRxiv2025

Full inter-hemispheric integration sustained by a fraction of posterior callosal fibers

The dynamic integration of the lateralized and specialized capacities of the cerebral hemispheres constitutes a hallmark feature of human brain function. This inter-hemispheric exchange of information critically depends upon the corpus callosum. Classical descriptions of callosal organization outline a topographic gradient, such that specific fibers integrate distinct aspects of brain function. Here we present a challenge to this conventional model. Using neuroimaging data obtained from a new cohort of adult corpus callosotomy patients, we leverage modern network neuroscience techniques to show--for the first time--that full inter-hemispheric integration can be achieved via a small proportion ([~]1 cm) of intact posterior callosal fibers. Only complete callosotomy patients demonstrated the expected dissolution of typical inter-hemispheric network architectures, aligning with disconnection syndromes long-thought to reflect diminished information propagation and communication across the brain. These findings motivate a novel mechanistic understanding of synchronized inter-hemispheric neural activity for large-scale human brain function and behavior.

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

Development and early life stress sensitivity of the rat cortical microstructural similarity network

Understanding how early life experiences shape brain network development is a key challenge in the neuroscience of mental health disorders. To address this, we used magnetic resonance imaging (MRI) similarity network analysis to study the effects of stress in the rat, an important animal model in neuropsychiatry. We measured magnetization transfer ratio (MTR) at each of 53 distinct cortical areas and estimated a cortical similarity network for each individual scan, in two independent experimental datasets. We first characterized normative network development in rats scanned repeatedly between postnatal days 20 (weanling) and 290 (mid-adulthood) (N=47), and then contrasted these findings with a cohort exposed to early life stress in the form of repeated maternal separation (RMS, N=40). The normative rat cortical similarity network exhibited biologically meaningful organization, aligning with prior cytoarchitectonic and tract-tracing data, and displayed complex topological features, including rich club organization. During postnatal and adolescent development, brain regions became more similar, including an early phase of fronto-hippocampal convergence. Early increases in inter-areal similarity were reversed in a later phase of fronto-hippocampal divergence in mid-adulthood. RMS exposure altered inter-areal similarity, especially between frontal and parahippocampal regions, that were also most active developmentally and in aging. Our results reveal how normative cortical network changes in the developing brain are influenced by early life stress. These findings suggest a new translational framework for elucidating how environmental risk factors lead to atypical development of cortical networks.

neuroscience↗

bioRxiv2024

Cell Type Differentiation Using Network Clustering Algorithms

Single cell RNA-seq (scRNA-seq) technologies provide unprecedented resolution representing transcriptomics at the level of single cell. One of the biggest challenges in scRNA-seq data analysis is the cell type annotation, which is usually inferred by cell separation approaches. In-silico algorithms that accurately identify individual cell types in ongoing single-cell sequencing studies are crucial for unlocking cellular heterogeneity and understanding the biological basis of diseases. In this study, we focus on robustly identifying cell types in single-cell RNA sequencing data; we conduct a comparative analysis using methods established in biology, like Seurat, Leiden, and WGCNA, as well as Infomap, statistical inference via Stochastic Block Models (SBM), and single-cell Graph Neural Networks (scGNN). We also analyze preprocessing pipelines to identify and optimize key components in the process. Leveraging two independent datasets, PBMC and ROSMAP, we employ clustering algorithms on cell-cell networks derived from gene expression data. Our findings reveal that while clusters detected by WGCNA exhibit limited correspondence with cell types, those identified by multiresolution Infomap and Leiden, and SBM show a closer alignment, with Infomap standing out as a particularly effective approach. Infomap notably offers valuable insights for the precise characterization of cellular landscapes related to neurodegenration and immunology in scRNA-seq.

bioinformatics↗

bioRxiv2024

Structural-Functional Brain Network Coupling During Task Performance Reveals Intelligence-Relevant Communication Strategies

Intelligence is a broad mental capability influencing human performance across tasks. Individual differences in intelligence have been linked to characteristics of structural and functional brain networks. Here, we consider their alignment, the structural-functional brain network coupling (SC-FC coupling) during resting state and during active cognition, to predict general intelligence. Using DWI and fMRI data from 764 participants of the Human Connectome Project (Replication: N1=126, N2=180), we modelled SC-FC coupling with similarity and communication measures that capture functional interactions unfolding on top of structural brain networks. By accounting for variations in brain region-specific neural signaling strategies, we show that individual differences in SC-FC coupling patterns predict individual intelligence scores. Most accurate predictions resulted from cognitively demanding tasks and task combinations. Our study suggests the existence of an intrinsic SC-FC coupling organization enabling fine-drawn intelligence-relevant adaptations that support efficient information processing by facilitating brain region-specific adjustment to external task demands.

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

Time-varying synergy/redundancy dominance in the human cerebral cortex

Recent work has emphasized the ubiquity of higher-order interactions in brain function. These interactions can be characterized as being either redundancy or synergy-dominated by the heuristic O-information [1]. Though the O-information can be decomposed into local values to measure the synergy-redundancy dominance at each point in a time series [2] no such analysis of fMRI dynamics has been carried out. Here we analyze the moment-to-moment synergy and redundancy dominance of the fMRI BOLD signal during rest for 95 unrelated subjects. We present results from several interaction sizes. The whole brain is rarely synergy-dominated, with some subjects never experiencing a whole-brain synergistic moment. Randomly sampled subsets of many sizes reveal that subsets that are the most redundancy dominated on average exhibit both the most synergistic and most redundant time points. Exhaustive calculation of the optimally synergistic and optimally redundant triads further emphasizes this finding, with recurrent nodes frequently belonging to a single coherent functional system. We find that when a triad is momentarily synergistic, it is often split between two instantaneously co-fluctuating communities, but is collectively co-fluctuating when it is momentarily redundant. After optimizing for synergy and redundancy in subsets of size five to seventy-five, we show that this effect is consistent across interaction sizes. Additionally, we find notable temporal structure in all optimized redundant and synergistic subsets: higher order redundant and synergistic interactions change smoothly in time and recur more than expected by chance.

neuroscience↗

bioRxiv2024

Revealing non-trivial information structures in aneural biological tissues via functional connectivity

A central challenge in the progression of a variety of open questions in biology, such as morphogenesis, wound healing, and development, is learning from empirical data how information is integrated to support tissue-level function and behavior. Information-theoretic approaches provide a quantitative framework for extracting patterns from data, but so far have been predominantly applied to neuronal systems at the tissue-level. Here, we demonstrate how time series of Ca2+ dynamics can be used to identify the structure and information dynamics of other biological tissues. To this end, we expressed the calcium reporter GCaMP6s in an organoid system of explanted amphibian epidermis derived from the African clawed frog Xenopus laevis, and imaged calcium activity pre- and post- a puncture injury, for six replicate organoids. We constructed functional connectivity networks by computing mutual information between cells from time series derived using medical imaging techniques to track intracellular Ca2+. We analyzed network properties including degree distribution, spatial embedding, and modular structure. We find organoid networks exhibit more connectivity than null models, with high degree hubs and mesoscale community structure with spatial clustering. Utilizing functional connectivity networks, we show the tissue retains non-random features after injury, displays long range correlations and structure, and non-trivial clustering that is not necessarily spatially dependent. Our results suggest increased integration after injury, possible cellular coordination in response to injury, and some type of generative structure of the anatomy. While we study Ca2+ in Xenopus epidermal cells, our computational approach and analyses highlight how methods developed to analyze functional connectivity in neuronal tissues can be generalized to any tissue and fluorescent signal type. Our framework therefore provides a bridge between neuroscience and more basal modes of information processing. Author summaryA central challenge in understanding several diverse processes in biology, including morphogenesis, wound healing, and development, is learning from empirical data how information is integrated to support tissue-level function and behavior. Significant progress in understanding information integration has occurred in neuroscience via the use of observable live calcium reporters throughout neural tissues. However, these same techniques have seen limited use in the non-neural tissues of multicellular organisms despite similarities in tissue communication. Here we utilize methods designed for neural tissues and modify them to work on any tissue type, demonstrating how non-neural tissues also contain non-random and potentially meaningful structures to be gleaned from information theoretic approaches. In the case of epidermal tissue derived from developing amphibians, we find non-trivial informational structure over greater spatial and temporal scales than those found in neural tissue. This hints at how more exploration into information structures within these tissue types could provide a deeper understanding into information processing within living systems beyond the nervous system.

systems biology↗

bioRxiv2024

Emergence of a synergistic scaffold in the brains of human infants

The human brain is a complex organ comprising billions of interconnected neurons which enables interaction with both physical and social environments. Neural dynamics of the whole brain go far beyond just the sum of its individual elements; a property known as "synergy". Previously it has been shown that synergy is crucial for many complex brain functions and cognition, however, it remains unknown how and when the large number of discrete neurons evolve into the unified system able to support synergistic interactions. Here we analysed high-density electroencephalography data from late fetal to early postnatal period. We found that the human brain transitions from redundancy-dominated to synergy-dominated system around birth. Frontal regions lead the emergence of a synergistic scaffold comprised of overlapping subsystems, while the integration of sensory areas developed gradually, from occipital to central regions. Strikingly, early developmental trajectories of brain synergy were modulated by environmental enrichment associated with enhanced mother-infant interactions, and the level of synergy near term equivalent age was associated with later neurocognitive development.

neuroscience↗

bioRxiv2023

Can machine learning-based predictive modelling improve our understanding of human cognition?

A growing body of research predicts individual cognitive ability levels from brain characteristics including functional brain connectivity. The majority of this research achieves good prediction performance but provides limited insight into neurobiological processes underlying the predicted concepts. The insufficient identification of predictive characteristics may present an important factor critically contributing to this constraint. Here, we encourage to design predictive modelling studies with an emphasis on interpretability to enhance our conceptual understanding of human cognition. As an example, we investigated in a preregistered study which functional brain connections successfully predict general, crystallized, and fluid intelligence in a sample of 806 healthy adults (replication: N = 322). The choice of the predicted intelligence component as well as the task during which connectivity was measured proved crucial for better understanding intelligence at the neural level. Further, intelligence could be predicted not solely from one specific set of brain connections, but from various combinations of connections with system-wide locations. Such partially redundant, system-wide functional characteristics complement intelligence-relevant connectivity of brain regions proposed by established intelligence theories. In sum, our study showcases how future predictive studies on human cognition can enhance explanatory value by prioritizing a systematic evaluation of predictive characteristics over maximizing prediction performance. Significance StatementIntelligence represents a hallmark of human behavior, and a surge number of studies predicted individual scores from functional brain connectivity. However, actual understanding about its neural basis remains limited. We demonstrate how predictive modelling can be applied strategically to improve tracing predictive functional brain connections to enhance our understanding of intelligence. Our study unveils crucial findings about intelligence: differences in the neural code of distinct intelligence facets not detectable on a behavioral level and a brain-wide distribution of functional brain characteristics relevant to intelligence that extends those proposed by major intelligence theories. In a broader context, it offers a framework for future prediction studies that prioritize meaningful insights into the neural basis of complex human traits over predictive performance.

neuroscience↗

bioRxiv2023

Temporal Variability of Brain-Behavior Relationships in Fine-Scale Dynamics of Edge Time Series

Most work on functional connectivity (FC) in neuroimaging data prefers longer scan sessions or greater subject count to improve reliability of brain-behavior relationships or predictive models. Here, we investigate whether systematically isolating moments in time can improve brain-behavior relationships and outperform full scan data. We perform optimizations using a temporal filtering strategy to identify time points that improve brain-behavior relationships across 58 different behaviors. We analyzed functional brain networks from resting state fMRI data of 352 healthy subjects from the Human Connectome Project. Templates were created to select time points with similar patterns of brain activity. Optimizations were performed to produce templates for each behavior that maximize brain-behavior relationships from reconstructed functional networks. With 10% of scan data, optimized templates of select behavioral measures achieved greater strength of brain-behavior correlations and greater transfer between groups of subjects than full FC across multiple cross validation splits of the dataset. Therefore, selectively filtering time points may allow for development of more targeted FC analyses and increased understanding of how specific moments in time contribute to behavioral prediction. Significance StatementIndividuals exhibit significant variations in brain functional connectivity, and these individual differences relate to variations in behavioral and cognitive measures. Here we show that the strength and similarity of brain-behavior associations across groups vary over time and that these relations can be improved by selecting time points that maximize brain-behavior correlations. By employing an optimization strategy for 58 distinct behavioral variables we find that different behaviors load onto different moments in time. Our work suggests new strategies for revealing brain signatures of behavior.

neuroscience↗

bioRxiv2023

Structural-Functional Brain Network Coupling Predicts Human Cognitive Ability

Individual differences in general cognitive ability (GCA) have a biological basis within the structure and function of the human brain. Network neuroscience investigations revealed neural correlates of GCA in structural as well as in functional brain networks. However, whether the relationship between structural and functional networks, the structural-functional brain network coupling (SC-FC coupling), is related to individual differences in GCA remains an open question. We used data from 1030 adults of the Human Connectome Project, derived structural connectivity from diffusion weighted imaging, functional connectivity from resting-state fMRI, and assessed GCA as a latent g-factor from 12 cognitive tasks. Two similarity measures and six communication measures were used to model possible functional interactions arising from structural brain networks. SC-FC coupling was estimated as the degree to which these measures align with the actual functional connectivity, providing insights into different neural communication strategies. At the whole-brain level, higher GCA was associated with higher SC-FC coupling, but only when considering path transitivity as neural communication strategy. Taking region-specific variations in the SC-FC coupling strategy into account and differentiating between positive and negative associations with GCA, allows for prediction of individual cognitive ability scores in a cross-validated prediction framework (correlation between predicted and observed scores: r = .25, p < .001). The same model also predicts GCA scores in a completely independent sample (N = 567, r = .19, p < .001). Our results propose structural-functional brain network coupling as a neurobiological correlate of GCA and suggest brain region-specific coupling strategies as neural basis of efficient information processing predictive of cognitive ability.

neuroscience↗

bioRxiv2023

Co-Evolving Dynamics and Topology in a Coupled Oscillator Model of Resting Brain Function

Dynamic models of ongoing BOLD fMRI brain dynamics and models of communication strategies have been two important approaches to understanding how brain network structure constrains function. However, dynamic models have yet to widely incorporate one of the most important insights from communication models: the brain may not use all of its connections in the same way or at the same time. Here we present a variation of a phase delayed Kuramoto coupled oscillator model that dynamically limits communication between nodes on each time step. An active subgraph of the empirically derived anatomical brain network is chosen in accordance with the local dynamic state on every time step, thus coupling dynamics and network structure in a novel way. We analyze this model with respect to its fit to empirical time-averaged functional connectivity, finding that it significantly outperforms standard Kuramoto models with phase delays. We also perform analyses on the novel structural edge time series it produces, demonstrating a slowly evolving topology moving through intermittent episodes of integration and segregation. We hope to demonstrate that the exploration of novel modeling mechanisms and the investigation of dynamics of networks in addition to dynamics on networks may advance our understanding of the relationship between brain structure and function.

neuroscience↗