Network Neuroscience网络神经科学
Network Neuroscience(英文缩写 NETW NEUROSCI),ISSN 2472-1751,eISSN 2472-1751,中文译名:网络神经科学 是一本学术期刊。本页汇总该期刊的最新影响因子、分区信息以及最新收录于 PubMed 的文献,帮助您快速了解期刊全貌。
发文量统计区间：2025-10-01 至 2026-09-30，按本站收录文献的发表日期统计。
期刊介绍
历年影响因子趋势
| JCR 数据年份 | 影响因子 | JCR 分区 |
|---|---|---|
| 2021 | 4.980 | Q2 |
| 2022 | 4.700 | Q2 |
| 2023 | 3.600 | Q2 |
| 2024 | 3.100 | Q2 |
| 2025 | 3.400 | Q2 |
Network Neuroscience 最新收录文献
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1. Brain network reconfiguration during reward prediction error processing.
PMID:日期:2026-08-28Learning from experience is theorized to be driven by reward prediction error (RPE) signals that reflect updates to our expectations of reward. Despite numerous studies on the neural correlates of RPEs, the question of how large-scale networks (LSN) in the brain reconfigure in response to an RPE learning signal remains open. Here, we examine how functional networks change in response to RPEs depending on the context. In our study, participants performed a probabilistic reversal learning task while we acquired fMRI data in two experimental settings: reward-seeking and punishment-avoiding. Participants' behavior was best explained by models with different learning rates for positive and negative RPEs. Furthermore, no evidence was found for context-dependent learning rates. Using behaviorally fitted RPE models, we performed a whole-brain network analysis. This analysis revealed classical reward structures, where striatal reward networks emerge as modules when the community structure is examined at a finer resolution, and a ventromedial prefrontal network emerges at a coarser resolution. Using the same behavioral model, we found that, compared with negative RPEs, positive RPEs increased within-network integration and decreased between-community integration. This indicates that there are distinctly different neural processes for positive and negative RPEs.
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2. Effects of light on brain-state dynamics and energy landscape.
PMID:日期:2026-08-25Light influences human cognition and behavior, and neuroimaging studies show that brain activity is modulated by light intensity. However, how light affects temporal brain-state transitions and the control energy required for these transitions remains unclear. To investigate this, we applied a network control theory approach to fMRI data collected from 20 healthy participants who performed an auditory discrimination task under four light intensities. Despite similar task performance, higher light intensity increased the number of transitions between brain states. Increasing light intensity enhanced the occurrence of a visual network dominated brain state, while decreasing the occurrence of brain states characterized by suppressed default mode activity and elevated frontoparietal activity. Furthermore, light intensity affected transition probabilities among different brain states contributed by redistributions of energy demands with the dorso-posterior thalamus appearing to play a key role in mediating light-related effects. Regionally, higher light intensity was associated with a trend toward reduced control energy in the visual network; frontal, cingulate, and insular cortices; and caudate and task-related regions, while showing a trend toward increased control energy in the somatomotor network and temporal pole. These findings suggest that high-intensity light may enhance neural efficiency and flexibility by redistributing control energy demands across brain regions.
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3. Parallel multilink group joint ICA (pmg-jICA): Fusion of 3D structural and 4D functional data across multiple resting fMRI networks.
PMID:日期:2026-08-25Integrating neuroimaging data enhances our understanding of the brain. Structural magnetic resonance imaging (sMRI) offers high-resolution anatomical detail, while functional MRI (fMRI) captures dynamic neural activity. Combining these modalities can reveal significant structure-function relationships in the brain. However, existing approaches typically link sMRI to only a single fMRI network, overlooking the spatial complexity of multiple networks and thereby missing distributed structure-function relationships. To address this limitation, we present parallel multilink group joint ICA (pmg-jICA), a data-driven framework that fuses gray matter images from sMRI with multiple intrinsic fMRI networks within a single model. pmg-jICA captures cross-network structure-function coupling, preserves subject-specific variability, and enables robust group-level statistical analyses. To demonstrate the approach, we applied pmg-jICA to an Alzheimer's disease (AD) dataset, recovering linked structural and functional components for 53 brain networks. Notably, patients with AD exhibited alterations in subcortical, cognitive control and visual regions. Importantly, the subject loadings enabled the computation of functional network connectivity, revealing additional alterations in subcortical, visual, and cognitive control systems. Overall, our results demonstrate that pmg-jICA overcomes key limitations of existing multimodal fusion techniques, yielding deeper insights into structure-function disruptions in AD and potentially offering a flexible framework for studying other neurological and psychiatric disorders.
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4. Functional connectivity is linked to symbolic BOLD patterns: Replication, extension, and clinical application of the human "Complexome".
PMID:日期:2026-08-25Functional connectivity (FC) quantifies the temporal coherence of blood-oxygen-level-dependent (BOLD) signals across brain regions. Recently, the information-theoretic "complexome" framework has linked FC to coinciding "complexity drops": transient moments in which regional BOLD signals simultaneously become regular. Here, we replicate this relationship in an independent dataset and extend the framework by (a) integrating it with signal cofluctuation analysis; (b) extending the previous binary concept of simultaneous complexity drops to a continuous, threshold-free calculation based on a temporal unwrapping procedure; (c) providing evidence of clinical relevance in the model disease of anti-N-methyl-D-aspartate-receptor encephalitis; and (d) deriving a new measure of pairwise dissimilarity in local BOLD patterns. This "index of pattern incongruency" (IPI) explains clinically relevant FC reductions and maps onto distinct associations with cognition beyond FC. These findings show that global FC is closely related to local patterns within underlying BOLD signals, strengthening the link between complexity dynamics and the brain's functional organization as a large-scale network.
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5. States of dynamic connectivity flow in temporal multiplex networks: A case study in human epilepsy and postictal aphasia.
PMID:日期:2026-08-25We present a methodological framework for analyzing multifrequency dynamic functional connectivity (dFC) in electrophysiological recordings. The approach characterizes not only the magnitude of network reconfiguration over time but also whether these changes are spatially random or, instead, spatially organized in ways that drive a slower reconfiguration of modular structure. We define a generative null model of multiscale connectivity fluctuations that differ in their degree of spatiotemporal organization, and we describe dFC flows through the joint assessment of (a) instantaneous reconfiguration speed and (b) the extent and quality of ongoing modular reorganization. Different combinations of these features delineate distinct "flow styles," ranging from more liquid to more frozen dynamics. As a case study, we apply this framework to stereo-electroencephalography recordings from epileptic patients. We identify transitions between dynamic "allegiance states," whose flow styles closely mirror those of the null model. Seizure onset is associated with a pronounced slowing of dFC speed, while a specific postictal regime combines low speed with highly frozen allegiance and aligns most strongly with clinician-annotated aphasia. These pilot results suggest that temporal multiplex network analyses can reveal transient, frequency-specific network regimes linked to symptom expression and offers a generalizable tool for dissecting fast network dynamics in intracranial recordings.
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6. Regional activity and interregional functional connectivity uniquely contribute to social cognitive judgments during movie-watching.
PMID:日期:2026-07-27Understanding how the brain gives rise to social cognition has been a key goal of neuroimaging research. Both changes in regional activation as well as functional connectivity have been implicated as potential mechanisms underlying social cognition, but the two have rarely been examined concurrently. Moreover, because the neural processes underlying social cognition are dynamic, developing approaches to capture dynamic changes in regional activity and functional connectivity are critical. Here, we describe a novel analysis approach that captures both regional activity and dynamic functional connectivity simultaneously during a naturalistic, socially focused movie-watching task. We found that both regional activation and functional connectivity were uniquely related to awkwardness, a judgment associated with social faux pas detection and theory of mind. Regional activation within sensorimotor networks was positively associated with awkwardness, whereas activation in the default network was negatively associated. Models including functional connectivity accounted for unique variance beyond models with activity alone. Specifically, dynamic functional connectivity between networks, primarily the frontoparietal control network, was positively associated with awkwardness. Together, these findings suggest that both dynamic regional brain activity and functional connectivity each uniquely contribute to complex and dynamic social judgments.
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7. Neural dynamics of mindfulness training: A longitudinal EEG network analysis of focused attention and open monitoring meditation.
PMID:日期:2026-07-27Neural oscillatory activity within the alpha and theta bands have long been considered putative markers of state mindfulness, yet understanding of their functional role has been limited by the challenges of linking objective brain indices with subjective experience. To address this gap, the current study applied longitudinal network analysis to a unique dataset from 16 novices who completed up to 24 laboratory training sessions of both focused attention (FA) and open monitoring (OM) meditation, during which both EEG and self-report measures of state mindfulness quality were collected. This approach enabled the parsimonious characterization of both cross-lagged temporal (across-session) and contemporaneous (within-session) influences of regional spectral power on state mindfulness. The analysis revealed distinguishable neurophenomenological network structures for each practice, providing data-driven support for their theoretical differentiation. These distinctions emerged alongside shared commonalities to both practices, including strong autoregressive effects for state mindfulness, consistent with training-related skill acquisition, and opposing regional influences of frontal versus posterior alpha power. Taken together, these findings challenge monolithic interpretations of meditation-related EEG activity, advancing a more nuanced neurophenomenological approach wherein the functional significance of neural activity is dynamically situated within the specific type and time course of training.
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8. Higher-order statistics for constructing centered edge functional connectivity.
PMID:日期:2026-07-27Functional connectivity is often constructed to understand functional organizations in neural systems, where the brain regions and their pairwise interactions are viewed as nodes and edges, respectively. In practice, functional connectivity is commonly estimated via the correlation of pairs of brain regions. One limitation is that the correlation coefficient captures only the pairwise linear dependence relationship between pairs of nodes and may fail to capture complex higher-order relationships. Recently, a novel concept known as edge-centric functional connectivity (eFC) has been introduced to measure interactions between pairs of edges based on the cofluctuation of two nodal time series, offering a new perspective for understanding brain networks. Nevertheless, eFC considers the absolute levels of edge time series, that is, their mean values, in estimation. If the parameter of interest is the covariation between a pair of edges, incorporating mean values of edge time series can introduce bias or deviation, resulting in a skewed estimation. In this manuscript, we propose an alternative approach to estimate the unbiased covariation between pairs of edges, termed centered edge functional connectivity (ceFC), with theoretical foundations. We demonstrate that the proposed estimator is consistent with a sufficient sample size or number of time frames. Additionally, we develop a multiple hypothesis testing framework with a controlled false discovery rate to evaluate the strength of the unbiased covariation among edges. Furthermore, we employ thresholding to obtain a thresholded estimator that has been shown to converge to the true ceFC matrix in high-dimensional settings in which the number of nodes is much larger than the number of samples or time frames. We validate the finite sample performance of the proposed methods via numerical studies and a data application using the Midnight Scan Club dataset.
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9. MIND versus MSN: A systematic evaluation of test-retest reliability and age sensitivity for T1-weighted structural similarity networks.
PMID:日期:2026-07-20Constructing structural similarity networks from T1-weighted MRI offers a powerful means to characterize brain organization. Two prominent methods for constructing such networks, Morphometric Similarity Network (MSN) and Morphometric INverse Divergence (MIND), have been proposed. However, a systematic evaluation of the test-retest reliability and age sensitivity of both MIND and MSN is still lacking. The present study comprehensively assessed these properties to inform the reliability and validity of both approaches. Test-retest reliability was evaluated by the intraclass correlation coefficient (ICC) using two public datasets containing repeated MRI scans. Age sensitivity was examined by conducting edge-wise comparisons between younger and older age groups, as well as by training machine learning models to predict individual age, using two public lifespan datasets. Additionally, several practical variants of MIND and MSN were explored by constructing networks with different morphological feature sets. Results demonstrated that MSN exhibited higher test-retest reliability, whereas MIND showed greater age sensitivity when both methods employed the same five features. Both methods revealed distinct spatial patterns that differentiate older from younger adults. Notably, the choice of feature sets substantially influenced reliability and age sensitivity. These findings offer empirical guidance for methodological selection and highlight the importance of feature optimization in future studies.
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10. The turbulent brain: Modeling vortex interactions for understanding human cognition.
PMID:日期:2026-07-20The human brain needs distributed, time-critical computation to efficiently solve complex problems. Turbulence provides such highly efficient spacetime information processing and transmission across widespread brain networks, yet we have been missing a mechanistic understanding of the interactions of turbulent vortices underlying human cognition. Here, we build the first whole-brain model of turbulent vortices as defined by the levels of local synchronization in brain signals quantifying turbulent interactions in vortex space. Specifically, using large-scale human neuroimaging data, we found that the interactions of turbulent vortices is an excellent framework for understanding cognition and brain computation. In particular, we show that when combined with connectome-based predictive modeling, this significantly predict the -factor and the scores on the underlying tasks. In addition, turbulent vortices also distinguish the detailed spacetime dynamics of rest and cognition-and can even distinguish between subtle subcomponents of cognitive tasks, where manipulation of vortices can be shown to change cognition. Overall, this whole-brain framework creates a natural vortex space for the brain computation underlying cognition, as well as potentially providing novel ways of controlling turbulent interactions in disease.