Canonical neurodevelopmental trajectories of structural and functional manifolds

Опубликована: Янв. 13, 2025

Organisational gradients refer to a continuous low-dimensional embedding of brain regions and can quantify core organisational principles complex systems like the human brain. Mapping how these are altered or refined across development phenotypes is essential understanding relationship between behaviour. Taking developmental approach leveraging longitudinal cross-sectional data from two multi-modal neuroimaging datasets, spanning full neurotypical-neurodivergent continuum, we charted variability structural (N = 887) functional 728) gradients, childhood adolescence (6-19 years old). Across despite differing phenotypes, observe highly similar gradients. These principles, stable development, with exact same ordering early into mid-adolescence. However, there substantial change in strength within those gradients: by modelling trajectories as non-linear splines, show that exhibit sensitive periods development. Specifically, gradually contract space networks become more integrated, whilst manifold expands, indexing specialisation. The coupling follows unimodal-association axis varies individuals, effects concentrated plastic higher-order networks. Importantly, on coupling, networks, attenuated neurodivergent sample. Finally, mapped structure-function onto dimensions psychopathology cognition demonstrate robust predictor cognition, such working memory, but not psychopathology. In summary, clinical community samples, consistent organisation, progressive integration segregation. established life, through their memory.

Язык: Английский

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Canonical neurodevelopmental trajectories of structural and functional manifolds

Опубликована: Янв. 13, 2025

Organisational gradients refer to a continuous low-dimensional embedding of brain regions and can quantify core organisational principles complex systems like the human brain. Mapping how these are altered or refined across development phenotypes is essential understanding relationship between behaviour. Taking developmental approach leveraging longitudinal cross-sectional data from two multi-modal neuroimaging datasets, spanning full neurotypical-neurodivergent continuum, we charted variability structural (N = 887) functional 728) gradients, childhood adolescence (6-19 years old). Across despite differing phenotypes, observe highly similar gradients. These principles, stable development, with exact same ordering early into mid-adolescence. However, there substantial change in strength within those gradients: by modelling trajectories as non-linear splines, show that exhibit sensitive periods development. Specifically, gradually contract space networks become more integrated, whilst manifold expands, indexing specialisation. The coupling follows unimodal-association axis varies individuals, effects concentrated plastic higher-order networks. Importantly, on coupling, networks, attenuated neurodivergent sample. Finally, mapped structure-function onto dimensions psychopathology cognition demonstrate robust predictor cognition, such working memory, but not psychopathology. In summary, clinical community samples, consistent organisation, progressive integration segregation. established life, through their memory.

Язык: Английский

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Exploring the transmission of cognitive task information through optimal brain pathways

PLoS Computational Biology, Год журнала: 2025, Номер 21(3), С. e1012870 - e1012870

Опубликована: Март 7, 2025

Understanding the large-scale information processing that underlies complex human cognition is central goal of cognitive neuroscience. While emerging activity flow models demonstrate task transferred by interregional functional or structural connectivity, graph-theory-based typically assume neural communication occurs via shortest path brain networks. However, whether optimal route for empirical transmission remains unclear. Based on a mapping framework, we found performance prediction with was significantly lower than direct path. The routing superior to other network strategies, including search information, ensembles, and navigation. Intriguingly, outperformed in when physical distance constraint asymmetric contribution were simultaneously considered. This study not only challenges assumption through but also suggests constrained spatial embedding network.

Язык: Английский

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Connectional axis of individual functional variability: Patterns, structural correlates, and relevance for development and cognition

Proceedings of the National Academy of Sciences, Год журнала: 2025, Номер 122(12)

Опубликована: Март 18, 2025

The human cerebral cortex exhibits intricate interareal functional synchronization at the macroscale, with substantial individual variability in these connections. However, spatial organization of connectivity (FC) across connectome edges and its significance cognitive development remain unclear. Here, we identified a connectional axis edge-level FC variability. declined continuously along this from within-network to between-network connections linking association networks those sensorimotor networks. This is associated pattern structural Moreover, evolves youth an flatter slope. We also observed that slope was positively related performance higher-order cognition. Together, our results reveal linked variability, refines during development, relevant

Язык: Английский

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A weighted generative model of the human connectome

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2023, Номер unknown

Опубликована: Июнь 25, 2023

Abstract Probabilistic generative network models have offered an exciting window into the constraints governing human connectome’s organization. In particular, they highlighted economic context of formation and special roles that physical geometry self-similarity likely play in determining topology. However, a critical limitation these is do not consider strength anatomical connectivity between regions. This significantly limits their scope to answer neurobiological questions. The current work draws inspiration from principle redundancy reduction develop novel weighted model. model significant advance because it only incorporates theoretical advancements previous models, but also has ability capture dynamic strengthening or weakening connections over time. Using state-of-the-art Convex Optimization Modelling for Microstructure-Informed Tractography (COMMIT) approach, sample children adolescents ( n = 88, aged 8 18 years), we show this can accurately approximate simultaneously topology edge-weights connectome (specifically, MRI signal fraction attributed axonal projections). We achieve at both sparse dense densities. Generative fits are comparable to, many cases better than, published findings simulating absence weights. Our implications future research by providing new avenues exploring normative developmental trends, neural computation wider conceptual economics connectomics supporting functioning.

Язык: Английский

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

NeuroImage, Год журнала: 2022, Номер 264, С. 119673 - 119673

Опубликована: Окт. 17, 2022

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

Язык: Английский

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Using network control theory to study the dynamics of the structural connectome

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2023, Номер unknown

Опубликована: Авг. 24, 2023

Network control theory (NCT) is a simple and powerful tool for studying how network topology informs constrains dynamics. Compared to other structure-function coupling approaches, the strength of NCT lies in its capacity predict patterns external signals that may alter dynamics desired way. We have extensively developed validated application human structural connectome. Through these efforts, we studied (i) different aspects connectome affect neural dynamics, (ii) whether outputs cohere with empirical data on brain function stimulation, (iii) vary across development correlate behavior mental health symptoms. In this protocol, introduce framework applying connectomes following two main pathways. Our primary pathway focuses computing

Язык: Английский

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Comparing models of information transfer in the structural brain network and their relationship to functional connectivity: diffusion versus shortest path routing

Brain Structure and Function, Год журнала: 2023, Номер 228(2), С. 651 - 662

Опубликована: Фев. 1, 2023

The relationship between structural and functional connectivity in the human brain is a core question network neuroscience, topic of paramount importance to our ability meaningfully describe predict outcomes. Graph theory has been used produce measures based on that are related connectivity. These commonly either shortest path routing model or diffusion model, which carry distinct assumptions about how information transferred through network. Unlike routing, assumes most efficient always known, makes no such assumption, lets diffuse parallel number connections other regions. Past research also developed hybrid use concepts from both models, have better predicted than length alone. We examined extent each these models can account for structure-function interest using graph exclusively model. This analysis was performed multiple parcellations Human Connectome Project approaches, all converged same finding. found accounts much more variance suggesting suited describing at macroscale.

Язык: Английский

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Communication dynamics in the human connectome shape the cortex-wide propagation of direct electrical stimulation

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2022, Номер unknown

Опубликована: Июль 6, 2022

Communication between gray matter regions underpins all facets of brain function. To date, progress in understanding large-scale neural communication has been hampered by the inability current neuroimaging techniques to track signaling at whole-brain, high-spatiotemporal resolution. Here, we use 2.77 million intracranial EEG recordings, acquired following 29,055 single-pulse electrical stimulations a total 550 individuals, study inter-areal human brain. We found that network models—computed on structural connectivity inferred from diffusion MRI—can explain propagation direct, focal stimulation through white matter, measured millisecond time scales. Building this finding, show parsimonious statistical model comprising structural, functional and spatial factors can accurately robustly predict cortex-wide effects (out-of-sample R 2 =54%). Our work contributes towards biological validation concepts neuroscience provides insight into how shapes signaling. anticipate our findings will have implications for research macroscale information processing design paradigms.

Язык: Английский

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Spatially-embedded recurrent neural networks reveal widespread links between structural and functional neuroscience findings

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2022, Номер unknown

Опубликована: Ноя. 18, 2022

ABSTRACT Brain networks exist within the confines of resource limitations. As a result, brain network must overcome metabolic costs growing and sustaining its physical space, while simultaneously implementing required information processing. To observe effect these processes, we introduce spatially-embedded recurrent neural (seRNN). seRNNs learn basic task-related inferences existing 3D Euclidean where communication constituent neurons is constrained by sparse connectome. We find that seRNNs, similar to primate cerebral cortices, naturally converge on solving using modular small-world networks, in which functionally units spatially configure themselves utilize an energetically-efficient mixed-selective code. all features emerge unison, reveal how many common structural functional motifs are strongly intertwined can be attributed biological optimization processes. serve as model systems bridge between research communities move neuroscientific understanding forward.

Язык: Английский

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