Network Neuroscience最新文献

[This corrects the article DOI: 10.1162/netn_a_00461.].

Network control theory (NCT) models human connectomes as high-dimensional input-state-output stable systems where the efficiency of neural connections can be addressed by energy cost (of state transitions) and controllability (from/to reachable states). Different options are available to extract NCT features: initial/final states, control time horizon, structural (vs. functional), and static (vs. dynamic) connectivity measure. Leveraging the minimum control paradigm, assuming the Schur stability for discrete systems, we investigate intra- and inter-individual variability of NCT features, across different settings and datasets, and assess their potential as useful connectome metrics in clinical studies. NCT was applied to structural and functional MRI (fMRI), in a cohort of 82 cognitively unimpaired elderly subjects with normal or (age-related) sensorineural condition (hearing loss), and in young adults from the Human Connectome Project database. Results demonstrated low intra-individual and moderate within-group inter-individual variability of NCT features. The energy cost was related to the time horizon of the system but did not discriminate groups. Controllability analyses revealed significant group effects and acceptable discrimination between normal and disease-affected connectomes, particularly for the default-mode network. We provide a systematic evaluation of different settings for fMRI-derived NCT features that may help guiding clinical applications toward capturing neurologically meaningful changes in the human connectome.

Despite considerable efforts to uncover the neural basis of psychiatric disorders using neuroimaging, few methods utilize intrinsic brain-derived knowledge, leading to limited specificity and discriminability in biomarker identification. To leverage the inherent characteristics within the brain, we propose a prior-knowledge-guided feature selection method to flexibly unveil discriminative and target-oriented biomarkers of psychiatric disorders. Specifically, we construct a constrained sparse regularization allowing for the flexible integration of diverse prior knowledge to identify sparse neuroimaging features linked to specific psychopathology. Additionally, we simultaneously integrate graph-based regularization and redundancy-removal regularization to further ensure the discriminability and independence among the selected features. Different priors hold varying significance in identifying specific biomarkers. Four functional magnetic resonance imaging (fMRI) datasets from 708 healthy controls and 537 schizophrenia patients are used to evaluate our method integrated with various prior knowledge, revealing specific schizophrenia-related brain abnormalities. Compared with nine advanced feature selection methods, our method improves mean classification accuracy by 3.89% to 11.24%, particularly revealing reduced interactions within the visual domain and between subcortical and visual domains in schizophrenia patients. The proposed method offers flexible and precise biomarker identification tailored to specific targets, advancing the understanding and diagnosis of psychiatric conditions.

The brain's ability to undergo complex rewiring during development is a fascinating aspect of neuroscience. This study conducts a detailed comparison of Drosophila melanogaster's brain networks during larval and adult stages, revealing significant changes in neuronal wiring throughout development. The larval brain network exhibits a degree distribution that fits firmly to a Weibull model. In contrast, the sparser adult brain network follows a power-law distribution, with the out-degree exponent lying in the scale-free regime and the in-degree exponent close to it. This shift toward a scale-free pattern likely reflects an adaptation to enhance robustness against failures while minimizing costs associated with reduced density during development. We also observed alterations in the structural core in relation to cell composition and topological influence. The structural core of the larva comprises neurons in the mushroom body, while neurons in the antennal lobe form the core of the adult fly brain. Furthermore, the larval network solely shows a rich club organization of which the structural core is also a part. Analysis of connectivity, rich club, and network measures reveals that the shift in the core results from a reduction in the centrality of mushroom body neurons following metamorphosis. This work stands as a step forward in understanding the rewiring of brain networks across the life stages of D. melanogaster.

Suspense is an affective state that is ubiquitous in human life, from art to quotidian events. However, little is known about the behavior of large-scale brain networks during suspenseful experiences. To address this question, we examined the continuous brain responses of participants watching a suspenseful movie, along with reported levels of suspense from an independent set of viewers. We employ sliding window analysis and Pearson correlation to measure functional connectivity states over time. Then, we use Mapper, a topological data analysis tool, to obtain a graphical representation that captures the dynamical transitions of the brain across states; this representation enables the anchoring of the topological characteristics of the combinatorial object with the measured suspense. Our analysis revealed changes in functional connectivity within and between the salience, fronto-parietal, and default networks associated with suspense. In particular, the functional connectivity between the salience and fronto-parietal networks increased with the level of suspense. In contrast, the connections of both networks with the default network decreased. Together, our findings reveal specific dynamical changes in functional connectivity at the network level associated with variation in suspense, and suggest topological data analysis as a potentially powerful tool for studying dynamic brain networks.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by deficits in social cognition, interaction, communication, restricted behaviors, and sensory abnormalities. The heterogeneity in ASD's clinical presentation complicates its diagnosis and treatment. Recent technological advancements in graph neural networks (GNNs) have been extensively used to diagnose brain disorders such as ASD, but existing machine learning models often suffer from low accuracy and explainability. In this study, we proposed a novel, explainable, and generalized node-edge connectivity-based graph attention neural network (Ex-NEGAT) model, leveraging edge-centric high-order spatiotemporal organization of dynamic functional connectivity streams between large-scale functional brain networks implicated in autism. Using the Autism Brain Imaging Data Exchange I and II datasets (total samples = 1,500), the model achieved 88% accuracy and an F1-score of 0.89. Additionally, we used meta-connectivity subtypes to identify subgroups within ASD samples using the rough fuzzy c-means algorithm. We also used connectome-based prediction modeling, which revealed critical brain networks contributing to predictions that accurately correlate with Autism Diagnostic Observation Schedule (ADOS) and full intelligent quotient (FIQ) scores. The proposed framework offers a robust approach based on previously unexplored higher order spatiotemporal correlation features of dynamic functional connectivity, which may provide critical insight into ASD heterogeneity and improve diagnostic precision.

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.