Neuroscience Needs Network Science

Journal of Neuroscience, Journal Year: 2023, Volume and Issue: 43(34), P. 5989 - 5995

Published: Aug. 23, 2023

The brain is a complex system comprising myriad of interacting elements, posing significant challenges in understanding its structure, function, and dynamics. Network science has emerged as powerful tool for studying such intricate systems, offering framework integrating multiscale data complexity. Here, we discuss the application network study brain, addressing topics models metrics, connectome, role dynamics neural networks. We explore opportunities multiple streams transitions from development to healthy function disease, potential collaboration between neuroscience communities. underscore importance fostering interdisciplinary through funding initiatives, workshops, conferences, well supporting students postdoctoral fellows with interests both disciplines. By uniting communities, can develop novel network-based methods tailored circuits, paving way towards deeper functions.

Language: Английский

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Virtual resection predicts surgical outcome for drug-resistant epilepsy

Brain, Journal Year: 2019, Volume and Issue: 142(12), P. 3892 - 3905

Published: Sept. 9, 2019

Patients with drug-resistant epilepsy often require surgery to become seizure-free. While laser ablation and implantable stimulation devices have lowered the morbidity of these procedures, seizure-free rates not dramatically improved, particularly for patients without focal lesions. This is in part because it unclear where intervene cases. To address this clinical need, several research groups published methods map epileptic networks but applying them improve patient care remains a challenge. In study we advance translation by: (i) presenting sharing robust pipeline rigorously quantify boundaries resection zone determining which intracranial EEG electrodes lie within it; (ii) validating brain network model on retrospective cohort 28 implanted prior surgical resection; (iii) all neuroimaging, annotated electrophysiology, metadata facilitate future collaboration. Our accurately forecast whether are likely benefit from intervention based synchronizability (area under receiver operating characteristic curve 0.89) provide novel information that traditional electrographic features do not. We further report removing synchronizing regions associated improved outcome, postulate sparing desynchronizing may be beneficial. findings suggest data-driven network-based can identify resective or ablative therapy, perhaps prevent invasive interventions those unlikely so.

Language: Английский

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A practical guide to methodological considerations in the controllability of structural brain networks

Journal of Neural Engineering, Journal Year: 2020, Volume and Issue: 17(2), P. 026031 - 026031

Published: Jan. 22, 2020

Objective. Predicting how the brain can be driven to specific states by means of internal or external control requires a fundamental understanding relationship between neural connectivity and activity. Network theory is powerful tool from physical engineering sciences that provide insights regarding relationship; it formalizes study dynamics complex system arise its underlying structure interconnected units. Approach. Given recent use network in neuroscience, now timely offer practical guide methodological considerations controllability structural networks. Here we systematic overview framework, examine impact modeling choices on frequently studied metrics, suggest potentially useful theoretical extensions. We ground our discussions, numerical demonstrations, advances dataset high-resolution diffusion imaging with 730 directions acquired over approximately 1 h scanning ten healthy young adults. Main results. Following didactic introduction theory, probe selection affects four common statistics: average controllability, modal minimum energy, optimal energy. Next, extend current state-of-the-art two ways: first, developing an alternative measure accounts for radial propagation activity through abutting tissue, second, defining complementary metric quantifying complexity energy landscape system. close recommendations discussion constraints. Significance. Our hope this accessible account will inspire neuroimaging community more fully exploit potential tackling pressing questions cognitive, developmental, clinical neuroscience.

Language: Английский

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Towards precise resting-state fMRI biomarkers in psychiatry: synthesizing developments in transdiagnostic research, dimensional models of psychopathology, and normative neurodevelopment

Current Opinion in Neurobiology, Journal Year: 2020, Volume and Issue: 65, P. 120 - 128

Published: Nov. 23, 2020

Language: Английский

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FitzHugh–Nagumo oscillators on complex networks mimic epileptic-seizure-related synchronization phenomena

Chaos An Interdisciplinary Journal of Nonlinear Science, Journal Year: 2020, Volume and Issue: 30(12)

Published: Dec. 1, 2020

We study patterns of partial synchronization in a network FitzHugh-Nagumo oscillators with empirical structural connectivity measured human subjects. report the spontaneous occurrence phenomena that closely resemble ones seen during epileptic seizures humans. In order to obtain deeper insights into interplay between dynamics and topology, we perform long-term simulations oscillatory on different paradigmatic structures: random networks, regular nonlocally coupled ring networks fractal connectivities, small-world various rewiring probability. Among these intermediate probability best mimics findings achieved using connectivity. For other topologies, either no spontaneously occurring epileptic-seizure-related can be observed simulated dynamics, or overall degree remains high throughout simulation. This indicates topology some balance regularity randomness favors self-initiation self-termination episodes seizure-like strong synchronization.

Language: Английский

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Biologically Realistic Mean-Field Models of Conductance-Based Networks of Spiking Neurons with Adaptation

Neural Computation, Journal Year: 2019, Volume and Issue: 31(4), P. 653 - 680

Published: Feb. 15, 2019

Accurate population models are needed to build very large-scale neural models, but their derivation is difficult for realistic networks of neurons, in particular when nonlinear properties involved, such as conductance-based interactions and spike-frequency adaptation. Here, we consider based on adaptive exponential integrate-and-fire excitatory inhibitory neurons. Using a master equation formalism, derive mean-field model compare it the full network dynamics. The capable correctly predicting average spontaneous activity levels asynchronous irregular regimes similar vivo activity. It also captures transient temporal response complex external inputs. Finally, able quantitatively describe where high- low-activity states alternate (up-down state dynamics), leading slow oscillations. We conclude that biologically sense they can capture both evoked activity, naturally appear candidates involving multiple brain areas.

Language: Английский

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The neurophysiological basis of compassion: An fMRI meta-analysis of compassion and its related neural processes

Neuroscience & Biobehavioral Reviews, Journal Year: 2019, Volume and Issue: 108, P. 112 - 123

Published: Nov. 4, 2019

Language: Английский

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Leveraging multi-shell diffusion for studies of brain development in youth and young adulthood

Developmental Cognitive Neuroscience, Journal Year: 2020, Volume and Issue: 43, P. 100788 - 100788

Published: April 22, 2020

Diffusion weighted imaging (DWI) has advanced our understanding of brain microstructure evolution over development. Recently, the use multi-shell diffusion sequences coincided with advances in modeling signal, such as Neurite Orientation Dispersion and Density Imaging (NODDI) Laplacian-regularized Mean Apparent Propagator MRI (MAPL). However, relative utility recently-developed models for maturation remains sparsely investigated. Additionally, despite evidence that motion artifact is a major confound studies development, vulnerability metrics derived from contemporary to in-scanner not been described. Accordingly, sample 120 youth young adults (ages 12–30) we evaluated tensor (DTI), NODDI, MAPL associations age head at multiple scales. Specifically, examined mean white matter values, tracts, voxels, connections structural networks. Our results revealed data can be leveraged robustly characterize neurodevelopment, demonstrate stronger effects than equivalent single-shell data. MAPL-derived were less sensitive confounding motion. findings suggest techniques confer important advantages neurodevelopment.

Language: Английский

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Edges in brain networks: Contributions to models of structure and function

Network Neuroscience, Journal Year: 2021, Volume and Issue: unknown, P. 1 - 28

Published: Aug. 13, 2021

Abstract Network models describe the brain as sets of nodes and edges that represent its distributed organization. So far, most discoveries in network neuroscience have prioritized insights highlight distinct groupings specialized functional contributions nodes. Importantly, these are determined expressed by web their interrelationships, formed edges. Here, we underscore important made for understanding Different types different relationships, including connectivity similarity among Adopting a specific definition can fundamentally alter how analyze interpret network. Furthermore, associate into collectives higher order arrangements, time series, form edge communities provide topology complementary to traditional node-centric perspective. Focusing on edges, or dynamic information they provide, discloses previously underappreciated aspects structural

Language: Английский

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Desynchronization Transitions in Adaptive Networks

Physical Review Letters, Journal Year: 2021, Volume and Issue: 126(2)

Published: Jan. 15, 2021

Adaptive networks change their connectivity with time, depending on dynamical state. While synchronization in structurally static has been studied extensively, this problem is much more challenging for adaptive networks. In Letter, we develop the master stability approach a large class of This allows reducing to low-dimensional system, by decoupling topological and properties. We show how interplay between adaptivity network structure gives rise formation islands. Moreover, report desynchronization transition emergence complex partial patterns induced an increasing overall coupling strength. illustrate our findings using coupled phase oscillators FitzHugh-Nagumo neurons synaptic plasticity.

Language: Английский

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