Adaptive rewiring: a general principle for neural network development DOI Creative Commons
Jia Li, Roman Bauer, Ilias Rentzeperis

и другие.

Frontiers in Network Physiology, Год журнала: 2024, Номер 4

Опубликована: Окт. 29, 2024

The nervous system, especially the human brain, is characterized by its highly complex network topology. neurodevelopment of some features has been described in terms dynamic optimization rules. We discuss principle adaptive rewiring, i.e., reorganization a according to intensity internal signal communication as measured synchronization or diffusion, and recent generalization for applications directed networks. These have extended rewiring from oversimplified networks more neurally plausible ones. Adaptive captures all key brain topology: it transforms initially random regular into with modular small-world structure rich-club core. This effect specific sense that can be tailored computational needs, robust does not depend on critical regime, flexible parametric variation generates range variant configurations. Extreme associated at macroscopic level disorders such schizophrenia, autism, dyslexia, suggest relationship between dyslexia creativity. cooperates growth interacts constructively spatial organization principles formation topographically distinct modules structures ganglia chains. At mesoscopic level, enables development functional architectures, convergent-divergent units, sheds light early divergence convergence in, example, visual system. Finally, we future prospects rewiring.

Язык: Английский

Brain network communication: concepts, models and applications DOI
Caio Seguin, Olaf Sporns, Andrew Zalesky

и другие.

Nature reviews. Neuroscience, Год журнала: 2023, Номер 24(9), С. 557 - 574

Опубликована: Июль 12, 2023

Язык: Английский

Процитировано

129
Connectome-based modelling of neurodegenerative diseases: towards precision medicine and mechanistic insight DOI
Jacob W. Vogel, Nick Corriveau‐Lecavalier, Nicolai Franzmeier

и другие.

Nature reviews. Neuroscience, Год журнала: 2023, Номер 24(10), С. 620 - 639

Опубликована: Авг. 24, 2023

Язык: Английский

Процитировано

77
Information decomposition and the informational architecture of the brain DOI Creative Commons
Andrea I. Luppi, Fernando Rosas, Pedro A. M. Mediano

и другие.

Trends in Cognitive Sciences, Год журнала: 2024, Номер 28(4), С. 352 - 368

Опубликована: Янв. 9, 2024

To explain how the brain orchestrates information-processing for cognition, we must understand information itself. Importantly, is not a monolithic entity. Information decomposition techniques provide way to split into its constituent elements: unique, redundant, and synergistic information. We review disentangling redundant interactions redefining our understanding of integrative function neural organisation. navigates trade-offs between redundancy synergy, converging evidence integrating structural, molecular, functional underpinnings synergy redundancy; their roles in cognition computation; they might arise over evolution development. Overall, provides guiding principle informational architecture cognition.

Язык: Английский

Процитировано

62
Structural-functional brain network coupling predicts human cognitive ability DOI Creative Commons
Johanna L. Popp, Jonas A. Thiele, Joshua Faskowitz

и другие.

NeuroImage, Год журнала: 2024, Номер 290, С. 120563 - 120563

Опубликована: Март 16, 2024

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

Язык: Английский

Процитировано

18
Towards a biologically annotated brain connectome DOI
Vincent Bazinet, Justine Y. Hansen, Bratislav Mišić

и другие.

Nature reviews. Neuroscience, Год журнала: 2023, Номер 24(12), С. 747 - 760

Опубликована: Окт. 17, 2023

Язык: Английский

Процитировано

38
Evidence for increased parallel information transmission in human brain networks compared to macaques and male mice DOI Creative Commons
Alessandra Griffa, Mathieu Mach,

Julien Dedelley

и другие.

Nature Communications, Год журнала: 2023, Номер 14(1)

Опубликована: Дек. 11, 2023

Abstract Brain communication, defined as information transmission through white-matter connections, is at the foundation of brain’s computational capacities that subtend almost all aspects behavior: from sensory perception shared across mammalian species, to complex cognitive functions in humans. How did communication strategies macroscale brain networks adapt evolution accomplish increasingly functions? By applying a graph- and information-theory approach assess information-related pathways male mouse, macaque human brains, we show gap between selective non-human mammals, where regions share single polysynaptic pathways, parallel humans, multiple pathways. In acts major connector unimodal transmodal systems. The layout unique individuals different pointing individual-level specificity routing architecture. Our work provides evidence patterns are tied networks.

Язык: Английский

Процитировано

22
A multi-modal, asymmetric, weighted, and signed description of anatomical connectivity DOI Creative Commons
Jacob Tanner, Joshua Faskowitz, Andreia Sofia Teixeira

и другие.

Nature Communications, Год журнала: 2024, Номер 15(1)

Опубликована: Июль 12, 2024

Abstract The macroscale connectome is the network of physical, white-matter tracts between brain areas. connections are generally weighted and their values interpreted as measures communication efficacy. In most applications, weights either assigned based on imaging features–e.g. diffusion parameters–or inferred using statistical models. reality, ground-truth unknown, motivating exploration alternative edge weighting schemes. Here, we explore a multi-modal, regression-based model that endows reconstructed fiber with directed signed weights. We find fits observed data well, outperforming suite null estimated subject-specific highly reliable, even when fit relatively few training samples, networks maintain number desirable features. summary, offer simple framework for data, demonstrating both its ease implementation while benchmarking utility typical analyses, including graph theoretic modeling brain-behavior associations.

Язык: Английский

Процитировано

7
Relation of connectome topology to brain volume across 103 mammalian species DOI Creative Commons
Maria Grazia Puxeddu, Joshua Faskowitz, Caio Seguin

и другие.

PLoS Biology, Год журнала: 2024, Номер 22(2), С. e3002489 - e3002489

Опубликована: Фев. 5, 2024

The brain connectome is an embedded network of anatomically interconnected regions, and the study its topological organization in mammals has become paramount importance due to role scaffolding function behavior. Unlike many other observable networks, connections incur material energetic cost, their length density are volumetrically constrained by skull. Thus, open question how differences volume impact topology. We address this issue using MaMI database, a diverse set mammalian connectomes reconstructed from 201 animals, covering 103 species 12 taxonomy orders, whose size varies over more than 4 orders magnitude. Our analyses focus on relationships between modular organization. After having identified modules through multiresolution approach, we observed connectivity features relate structure these relations vary across volume. found that as increases, spatially compact dense, comprising costly connections. Furthermore, investigated spatial embedding shapes communication, finding nodes’ distance progressively impacts communication efficiency. modes variation policies, smaller bigger brains show higher efficiency routing- diffusion-based signaling, respectively. Finally, bridging modularity larger brains, imposes stronger constraints signaling. Altogether, our results systematically related topology tighter restrictions brains.

Язык: Английский

Процитировано

6
Commentary on Pang et al. (2023)Nature DOI Creative Commons
Joshua Faskowitz, Daniel Moyer, Daniel A. Handwerker

и другие.

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

Опубликована: Июль 22, 2023

Abstract Pang et al. (2023) present novel analyses demonstrating that brain dynamics can be understood as resulting from the excitation of geometric modes, derived shape brain. Notably, they demonstrate linear combinations modes reconstruct patterns fMRI data more accurately, and with fewer dimensions, than comparable connectivity-derived modes. Equipped these results, underpinned by neural field theory, authors contend geometry cortical surface provides a parsimonious explanation activity structural connectivity. This claim runs counter to prevailing theories information flow in brain, which emphasize role long-distance axonal projections fasciculated white matter relaying signals between regions (Honey 2009; Deco 2011; Seguin al., 2023). While we acknowledge plays an important shaping human function, feel presented work falls short establishing brain’s is “a fundamental constraint on complex interregional connectivity” (Pang Here, provide 1) brief critique paper’s framing 2) evidence showing their methodology lacks specificity orientation shape. Ultimately, recognize mode approach powerful representational framework for analysis, but also believe there are key caveats consider alongside claims made manuscript.

Язык: Английский

Процитировано

11
Bayesian approaches for revealing complex neural network dynamics in Parkinson’s disease DOI Creative Commons
Hina Shaheen, Roderick Melnik

Journal of Computational Science, Год журнала: 2025, Номер unknown, С. 102525 - 102525

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0