Network Neuroscience最新文献

Generative network models (GNMs) have been proposed to identify the mechanisms/constraints that shape the organization of the connectome. These models parameterize the formation of interregional connections using a trade-off between connection cost and topological complexity or biophysical similarity. Despite their simplicity, GNMs can generate synthetic networks that capture many topological properties of empirical brain networks. However, current models often fail to capture the topography (i.e., spatial embedding) of many such properties, such as the anatomical location of network hubs. In this study, we investigate a diverse array of GNM formulations and find that none can accurately capture empirical patterns of long-range connectivity. We demonstrate that the spatial embedding of longer-range connections is critical in defining hub locations and that it is precisely these connections that are poorly captured by extant models. We further show how standard measures used for model optimization and evaluation mask these and other differences between synthetic and empirical brain networks, highlighting the need for care when interpreting GNMs and metrics. Overall, our findings demonstrate common failure modes of GNMs, identify why these models do not fully capture brain network organization, and suggest ways the field can move forward to address these challenges.

Reading difficulty (RD; dyslexia) is a developmental condition with neurological origins and persistent academic consequences. Children with RD often show deficits in audiovisual integration (AVI) and executive functions. Visual rhythmic reading training (RRT) has been associated with improvements in these domains, but it remains unclear whether such effects generalize to the resting-state brain activity. English-speaking children aged 8-12 years, including typical readers (TRs) and children with RD, were randomly assigned to an 8-week visual RRT or control math training group. Reading assessments and resting-state functional MRI data were collected before and after the intervention. Functional connectivity (FC) analyses examined AVI and its interaction with frontoparietal-cingulo-opercular (FP-CO) cognitive control networks during rest. Following RRT, children with RD showed significant improvements in reading fluency. The RRT group also demonstrated greater changes in AVI, which were associated with increased FC between FP-CO networks and sensory regions during the resting state. RRT improves reading performance and promotes enhanced integration between sensory and executive networks in children with RD, even in the absence of task demands. These findings support the role of RRT in fostering domain-general neuroplasticity beyond reading-specific contexts.

Alzheimer's disease (AD) causes cognitive decline with aging, hypothetically due to the accumulation of beta-amyloid (Aβ) plaques. The 3xTg-AD mouse model is increasingly used due to its initial absence of significant physical or behavioral impairments in youth and progressive Aβ plaque development with age. This mouse model thus provides an opportunity for comparison with human AD through two stages of study. Using wild-type (WT) and 3xTg-AD mice, aged 22 and 40 weeks (before and after the large increase in Aβ plaques), we measured functional connectivity (FC) and structural connectivity (SC) between brain regions. At 22 weeks, 3xTg-AD mice unexpectedly had higher SC and FC, and there was positive correlation between behavioral performance and FC density. By 40 weeks, SC and FC was lower in AD mice (similar to human AD patients), but the behavior-functional correlation was negative. Thus, our methods identified a shift in 3xTg-AD mice between two abnormal states relative to WT, moving from a hyperconnected to a hypoconnected state. Such a shift matches the hyperexcitability phenotype of AD observed in human patients, and thus suggests that 3xTg-AD mice can model the multistage etiology of AD of that phenotype.

Understanding white matter (WM) functional connectivity is crucial for unraveling brain function and dysfunction. In this study, we present a novel WM intrinsic connectivity network (ICN) template derived from over 100,000 fMRI scans, identifying 97 robust WM ICNs using spatially constrained independent component analysis (scICA). This WM template, combined with a previously identified gray matter (GM) ICN template from the same dataset, was applied to analyze a resting-state fMRI (rs-fMRI) dataset from the Bipolar-Schizophrenia Network on Intermediate Phenotypes 2 (BSNIP2; 590 subjects) and a task-based fMRI dataset from the MIND Clinical Imaging Consortium (MCIC; 75 subjects). Our analysis highlights distinct spatial maps for WM and GM ICNs, with WM ICNs showing higher frequency profiles. Visually modular structure within WM ICNs and interactions between WM and GM modules were identified. Task-based fMRI revealed event-related BOLD signals in WM ICNs, particularly within the corticospinal tract, lateralized to finger movement. Notable differences in static functional network connectivity (sFNC) matrices were observed between controls (HC) and schizophrenia (SZ) subjects in both WM and GM networks. This open-source WM NeuroMark template and automated pipeline offer a powerful tool for advancing WM connectivity research across diverse datasets.

Understanding the structural organization of the brain is essential for deciphering how complex functions emerge from neural circuits. The Allen Mouse Brain Connectivity Atlas (AMBCA) has revolutionized our ability to quantify anatomical connectivity at a mesoscale resolution, bridging the gap between microscopic cellular interactions and macroscopic network organization. To leverage AMBCA for automated network construction and analysis, here, we introduce NeuroCarta, an open-source MATLAB toolbox designed to extract, process, and analyze brain-wide connectivity networks. NeuroCarta generates directed and weighted connectivity graphs, computes key network metrics, and visualizes topological features of brain circuits. As an application example, using NeuroCarta on viral tracer data from the AMBCA, we demonstrate that the mouse brain exhibits a densely connected architecture, with a degree of separation of approximately four synapses, suggesting an optimized balance between local specialization and global integration. We identify attractor nodes that may serve as key convergence points in brain-wide neural computations and show that NeuroCarta facilitates comparative network analyses, revealing regional variations in projection patterns. While the toolbox is currently constrained by the resolution and coverage of the AMBCA dataset, it provides a scalable and customizable framework for investigating brain network topology, interregional communication, and anatomical constraints on mesoscale circuit organization.

Brain connectivity analysis is pivotal to understanding mechanisms underpinning neurological diseases. However, current methodologies for quantitatively mapping the connectivity in vivo face challenges when focal lesions are present and can introduce strong biases in the estimates. We present a novel approach to address these challenges by introducing a multi-compartment description of the connectome, which explicitly incorporates lesion information during the estimation process. We extended the Convex Optimization Modeling for Microstructure Informed Tractography (COMMIT) framework to integrate an additional tissue compartment in voxels affected by pathology, allowing us to infer accurately the contributions of streamlines passing through lesions and to provide unbiased connectivity estimates. We evaluated the effectiveness of our approach on data from healthy subjects of the Human Connectome Project, in which we artificially introduced focal lesions to simulate pathology with varying levels of axonal damage. We also tested the performances obtained when comparing healthy subjects with patients affected by multiple sclerosis. Results demonstrate that our approach significantly enhances sensitivity to pathological changes even at low degeneracy levels compared with state-of-the-art techniques, thus representing a significant step forward to advance our understanding of neurodegenerative diseases.

Anesthetisia is an important surgical and explorative tool in the study of consciousness. Much work has been done to connect the deeply anesthetized condition with decreased complexity. However, anesthesia-induced unconsciousness is also a dynamic condition in which functional activity and complexity may fluctuate, being perturbed by internal or external (e.g., noxious) stimuli. We use fMRI data from a cohort undergoing deep propofol anesthesia to investigate resting state dynamics using dynamic brain state models and spatiotemporal network analysis. We focus our analysis on group-level dynamics of brain state temporal complexity, functional activity, connectivity, and spatiotemporal modularization in deep anesthesia and wakefulness. We find that in contrast to dynamics in the wakeful condition, anesthesia dynamics are dominated by a handful of sink states that act as low-complexity attractors to which subjects repeatedly return. On a subject level, our analysis provides tentative evidence that these low-complexity attractor states appear to depend on subject-specific age and anesthesia susceptibility factors. Finally, our spatiotemporal analysis, including a novel spatiotemporal clustering of graphs representing hidden Markov models, suggests that dynamic functional organization in anesthesia can be characterized by mostly unchanging, isolated regional subnetworks that share some similarities with the brain's underlying structural connectivity, as determined from normative tractography data.

Resting-state functional connectivity (RSFC) methods are the most widely applied tools in the network neurosciences, but their reliability remains an active area of study. We use back-to-back 10-min resting-state scans in a healthy aging (n = 41) and traumatic brain injury (TBI) sample (n = 45) composed of older adults to assess the replicability of RSFC using a "mini" multiverse approach. The goal was to evaluate the reproducibility of commonly used graph metrics and determine if aging and moderate-severe TBI influences RSFC reliability using intraclass correlation coefficients (ICCs). There is clear evidence for reliable results in aging and TBI. Global network metrics such as within-network connectivity and segregation were most reliable whereas other whole-brain connectivity estimates (e.g., clustering coefficient, eigenvector centrality) were least reliable. Analysis of canonical networks revealed the default mode and salience networks as most reliable. There was a notable influence of motion scrubbing on ICCs, with diminished reliability proportional to the number of volumes removed. Choice of brain atlas had a modest effect on findings. Overall, RSFC reproducibility is preserved in older adults and after significant neurological compromise. We also identify a subset of graph metrics and canonical networks with promising reliability.

Functional connectivity derived from functional magnetic resonance imaging (fMRI) data has been increasingly used to study brain activity. In this study, we model brain dynamic functional connectivity during narrative tasks as a temporal brain network and employ a machine learning model to classify in a supervised setting the modality (audio, movie), the content (airport, restaurant situations) of narratives, and both combined. Leveraging Shapley values, we analyze subnetwork contributions within Yeo parcellations (7- and 17-subnetworks) to explore their involvement in narrative modality and comprehension. This work represents the first application of this approach to functional aspects of the brain, validated by existing literature, and provides novel insights at the whole-brain level. Our findings suggest that schematic representations in narratives may not depend solely on preexisting knowledge of the top-down process to guide perception and understanding, but may also emerge from a bottom-up process driven by the temporal parietal subnetwork.

Intrinsic brain dynamics play a fundamental role in cognitive function, but their development is incompletely understood. We investigated pubertal changes in temporal fluctuations of intrinsic network topologies (focusing on the strongest connections and coordination patterns) and signals, in an early longitudinal sample from the Adolescent Brain Cognitive Development (ABCD) study, with resting-state fMRI (n = 4,099 at baseline; n = 3,376 at follow-up [median age = 10.0 (1.1) and 12.0 (1.1) years; n = 2,116 with both assessments]). Reproducible, inverse associations between low-frequency signal and topological fluctuations were estimated (p < 0.05, β = -0.20 to -0.02, 95% confidence interval (CI) = [-0.23, -0.001]). Signal (but not topological) fluctuations increased in somatomotor and prefrontal areas with pubertal stage (p < 0.03, β = 0.06-0.07, 95% CI = [0.03, 0.11]), but decreased in orbitofrontal, insular, and cingulate cortices, as well as cerebellum, hippocampus, amygdala, and thalamus (p < 0.05, β = -0.09 to -0.03, 95% CI = [-0.15, -0.001]). Higher temporal signal and topological variability in spatially distributed regions were estimated in girls. In racial/ethnic minorities, several associations between signal and topological fluctuations were in the opposite direction of those in the entire sample, suggesting potential racial differences. Our findings indicate that during puberty, intrinsic signal dynamics change significantly in developed and developing brain regions, but their strongest coordination patterns may already be sufficiently developed and remain temporally consistent.