Network Neuroscience最新文献

In graph theory, "multilayer networks" represent systems involving several interconnected topological levels. One example in neuroscience is the stratification of connections between different cortical depths or "laminae," which is becoming noninvasively accessible in humans using ultrahigh-resolution functional MRI (fMRI). Here, we applied multilayer graph theory to examine functional connectivity across different cortical depths in humans, using 7-T fMRI (1-mm³ voxels; 30 participants). Blood oxygenation level dependent (BOLD) signals were derived from five depths between the white matter and pial surface. We compared networks where the interregional connections were limited to a single cortical depth only ("layer-by-layer matrices") with those considering all possible connections between areas and cortical depths ("multilayer matrix"). We utilized global and local graph theory features that quantitatively characterize network attributes including network composition, nodal centrality, path-based measures, and hub segregation. Detecting functional differences between cortical depths was improved using multilayer connectomics compared with the layer-by-layer versions. Superficial depths of the cortex dominated information transfer, and deeper depths drove clustering. These differences were largest in frontotemporal and limbic regions. fMRI functional connectivity across different cortical depths may contain neurophysiologically relevant information; thus, multilayer connectomics could provide a methodological framework for studies on how information flows across this stratification.

A network neuroscience perspective can significantly advance the understanding of neurodegenerative disorders, particularly frontotemporal dementia (FTD). This study employed an innovative multiplex connectomics approach, integrating cortical thickness (CTH) and fluorodeoxyglucose-positron emission tomography (FDG-PET) in a dual-layer model to investigate network alterations in FTD subtypes across two geographically distinct sites. The cohort included groups of behavioral variant FTD (bvFTD), primary progressive aphasia (PPA), mild cognitive impairment (MCI), and cognitively normal (CN) individuals who were analyzed from two separate sites. Site 1 included 28 bvFTD, 20 PPA, and 27 MCI participants, whereas Site 2 included 26 bvFTD, 43 PPA, and 43 CN individuals, respectively. Utilizing CTH and FDG-PET data after standard preprocessing, a multiplex network pipeline in BRAPH2 toolbox was used to derive multiplex participation coefficient (MPC) between the groups. The analysis revealed an increase in global MPC as an indicator of disease in PPA at both sites. Additionally, nodal MPC alterations in the anterior cingulate, frontal, and temporal lobes in PPA were compared with bvFTD. Comparisons with the CN showed that nodal MPC alterations were more extensive in PPA when compared with bvFTD. These findings underscore the potential utility of multiplex connectomes for identifying network disruptions in neurodegenerative disorders, offering promising implications for future research and clinical applications.

Functional connectomes (FCs) are estimations of brain region interaction derived from brain activity, often obtained from functional magnetic resonance imaging recordings. Quantifying the distance between FCs is important for understanding the relation between behavior, disorders, disease, and changes in connectivity. Recently, tangent space projections, which account for the curvature of the mathematical space of FCs, have been proposed for calculating FC distances. We compare the efficacy of this approach relative to the traditional method in the context of subject identification using the Midnight Scan Club dataset in order to study resting-state and task-based subject discriminability. The tangent space method is found to universally outperform the traditional method. We also focus on the subject identification efficacy of subnetworks. Certain subnetworks are found to outperform others, a dichotomy that largely follows the "control" and "processing" categorization of resting-state networks, and relates subnetwork flexibility with subject discriminability. Identification efficacy is also modulated by tasks, though certain subnetworks appear task independent. The uniquely long recordings of the dataset also allow for explorations of resource requirements for effective subject identification. The tangent space method is found to universally require less data, making it well suited when only short recordings are available.

Functional connectivity (FC) reflects brain-wide communication essential for cognition, yet the role of underlying biophysical factors in shaping FC remains unclear. We quantify the influence of physical factors-structural connectivity (SC) and Euclidean distance (DC), which capture anatomical wiring and regional distance-and molecular factors-gene expression similarity (GC), and neuroreceptor congruence (RC), representing neurobiological similarity-on resting-state FC. We assess how these factors impact graph-theoretic and gradient features, capturing pairwise and higher-order interactions. By generating remnant functional networks after selectively removing connections tied to specific factors, we show that molecular factors, particularly RC, dominate graph-theoretic features, while gradient features are shaped by a mix of molecular and physical factors, especially GC and DC. SC has a surprisingly minor role. We also link FC alterations to biophysical factors in schizophrenia, bipolar disorder, and attention deficit/hyperactivity disorder (ADHD), with physical factors differentiating these groups. These insights are key for understanding FC across various applications, including task performance, development, and clinical conditions.

Recent neuroimaging studies demonstrated that task-evoked functional connectivity (FC) may better predict individual traits than resting-state FC. However, the prediction properties of task-evoked effective connectivity (EC) remain unexplored. We investigated this by predicting individual reaction time (RT) performance in the stimulus-response compatibility task and age, using intrinsic EC (I-EC; calculated at baseline) and task-modulated EC (M-EC; induced by experimental conditions) with dynamic causal modeling (DCM) across various data processing conditions, including different general linear model (GLM) designs, Bayesian model reduction, and different cross-validation schemes and prediction models. We report evident differences in predicting RT and age between I-EC and M-EC, as well as between event-related and block-based GLM and DCM designs. M-EC outperformed both I-EC and task-evoked FC in RT prediction, while all types of connectivity performed similarly for age. Event-related GLM and DCM designs performed better than block-based designs. Our findings suggest that task-evoked I-EC and M-EC may capture different phenotypic attributes, with performance influenced by data processing and modeling choices, particularly the GLM-DCM design. This evaluation of methods for behavior prediction from brain EC may contribute to a meta-scientific understanding of how data processing and modeling frameworks influence neuroimaging-based predictions, offering insights for improving their robustness and efficacy.

Random noise stimulation (tRNS) applied to the dorsolateral prefrontal cortex (DLPFC) enhances fluency and originality in verbal divergent thinking tasks. However, the underlying neural mechanisms of this behavioral change remain unclear. Given that the DLPFC is a key node of the executive control network (ECN) and that creativity is a two-stage process in which the ECN is primarily involved in the final idea selection stage, application of tRNS to this region shall not only result in an increase of originality and flexibility but also in a modulation of EEG activity. To test these assumptions, we collected 256-channel EEG of 40 participants before and after tRNS/sham applied to the DLPFC, during which participants performed two verbal creativity tasks. To assess stimulation-induced connectivity changes and to capture large-scale cortical communication, a source space alpha (8-12 Hz) imaginary coherence was calculated. We found that the tRNS-induced improvements in originality and flexibility were associated with bilateral DLPFC alpha coherence changes. From a large-scale networks perspective, these results suggest that tRNS-induced ECN activity is associated with increased originality and flexibility, potentially by enhancing selectivity in the idea evaluation phase. This study, for the first time, indicates a link between neurophysiological activity and tRNS-induced changes in verbal creativity.

Recent advancements in neuroimaging data analysis facilitate the characterization of adaptive changes in brain network integration. This study introduces a distinctive approach that merges knowledge-informed and data-driven methodologies, offering a nuanced way to more effectively understand these changes. Utilizing graph network analysis, along with existing neurobiological knowledge of domain-specific brain network systems, we uncover a deeper understanding of brain network interaction and integration. As a proof of concept, we applied our approach to the language domain, a well-known large-scale network system as a representative model system, using functional imaging datasets with specific language tasks for validation of our proposed approach. Our results revealed a double dissociation between motor and sensory language modules during word generation and comprehension tasks. Furthermore, by introducing a hierarchical nature of brain networks and introducing local and global metrics, we demonstrated that hierarchical levels of networks exhibit distinct ways of integration of language brain networks. This innovative approach facilitates a differentiated and thorough interpretation of brain network function in local and global manners, marking a significant advancement in our ability to investigate adaptive changes in brain network integration in health and disease.

In recurrent networks of leaky integrate-and-fire neurons, the mean-field theory has been instrumental in capturing the statistical properties of neuronal activity, like firing rate distributions. This theory has been applied to networks with either homogeneous synaptic weights and heterogeneous connections per neuron or vice versa. Our work expands mean-field models to include networks with both types of structural heterogeneity simultaneously, particularly focusing on those with synapses that undergo plastic changes. The model introduces a spike trace for each neuron, a variable that rises with neuron spikes and decays without activity, influenced by a degradation rate r _p and the neuron's firing rate ν. When the ratio α = ν/r _p is significantly high, this trace effectively estimates the neuron's firing rate, allowing synaptic weights at equilibrium to be determined by the firing rates of connected neurons. This relationship is incorporated into our mean-field formalism, providing exact solutions for firing rate and synaptic weight distributions at equilibrium in the high α regime. However, the model remains accurate within a practical range of degradation rates, as demonstrated through simulations with networks of excitatory and inhibitory neurons. This approach sheds light on how plasticity modulates both activity and structure within neuronal networks, offering insights into their complex behavior.

Translational network neuroscience aims to integrate advanced neuroimaging and data analysis techniques into clinical practice to better understand and treat neurological disorders. Despite the promise of technologies such as functional MRI and diffusion MRI combined with network analysis tools, the field faces several challenges that hinder its swift clinical translation. We have identified nine key roadblocks that impede this process: (a) theoretical and basic science foundations; (b) network construction, data interpretation, and validation; (c) MRI access, data variability, and protocol standardization; (d) data sharing; (e) computational resources and expertise; (f) interdisciplinary collaboration; (g) industry collaboration and commercialization; (h) operational efficiency, integration, and training; and (i) ethical and legal considerations. To address these challenges, we propose several possible solution strategies. By aligning scientific goals with clinical realities and establishing a sound ethical framework, translational network neuroscience can achieve meaningful advances in personalized medicine and ultimately improve patient care. We advocate for an interdisciplinary commitment to overcoming translational hurdles in network neuroscience and integrating advanced technologies into routine clinical practice.

Reliability of imaging parameters is of pivotal importance for further correlation analyses. Here, we investigated the test-retest reliability of two dynamic functional connectivity (dFC) brain states and related parameters for different scan length, atlases with 116 versus 442 regions, and data centering in 23 participants and reproduced the findings in 501 subjects of the Human Connectome Project. Results showed an integrated and a segregated brain state with high intraclass correlation coefficient (ICC) values of the states between sessions (0.67 ≥ ICC ≥ 0.99). The most reliable dFC parameter was state prevalence with an ICC ≈ 0.5 for ∼15 min of uncentered data, while other parameters, such as mean dwell time, were much less reliable. While shorter scans and within-subject data centering further reduce reliability, the atlas choice had no effects. Spearman's correlations among dFC parameters strongly depend on data centering. The effect of global signal regression and a higher number of states is discussed. In conclusion, we recommend formulating hypotheses on cross-sectional differences and correlations between dFC measures of brain integration and other subject-specific measures in terms of state prevalence, especially in small-scale studies.