Network Neuroscience最新文献

Overcoming an initial psychotic episode does not always lead to recovery; relapses and subsequent psychotic episodes may happen afterward. Even if the characterization of psychotic disorders can be related to alterations in brain connectivity, clear identification of the brain areas for relapse is missing. Here, we leverage on whole-brain modeling linking anatomical structural information with functional activity as measured by MRI in 196 participants. Patients were classified into Stage II (first episode), IIIa (incomplete remission), IIIb (remission followed by one relapse), and IIIc (remission followed by several relapses), depending on the course of psychosis up to the time of the brain scan. From these data, a low-dimensional manifold reduction of the brain dynamics was obtained using deep learning variational autoencoders in which the different stages are represented, and a classification model can be trained to distinguish them. Then, a dimensionality analysis was performed to find the optimal dimension that allows the distinction between first episode and relapsing cases with high accuracy. Finally, perturbations were introduced in the model to reveal the brain regions associated with the absence of relapse, which could help predict which brain regions to target during therapy and assist the treatment of patients suffering from psychotic disorders.

Brain networks exhibit non-trivial modular organization, with groups of densely connected areas participating in specialized functions. Traditional community detection algorithms assign each node to one module, but this representation cannot capture integrative, multifunctional nodes that span multiple communities. Despite the increasing availability of overlapping community detection algorithms (OCDAs) to capture such integrative nodes, there is no objective procedure for selecting the most appropriate method and its parameters for a given problem. Here, we overcome this limitation by introducing a data-driven method for selecting an OCDA and its parameters from performance on a tailored ensemble of generated benchmark networks, assessing 22 unique algorithms and parameter settings. Applied to the human right-hemisphere structural connectome, we find that the "order statistics local optimization method" (OSLOM) best identifies ground-truth overlapping structure in the benchmark ensemble, yielding a seven-network decomposition of the right-hemisphere cortex. These modules are bridged by 15 overlapping regions that generally sit at the apex of the putative cortical hierarchy-suggesting integrative, higher order function-with network participation increasing along the cortical hierarchy, a finding not supported using a non-overlapping modular decomposition. This data-driven approach to selecting OCDAs is applicable across domains, opening new avenues to detecting and quantifying informative structures in complex real-world networks.

Estimation of brain connectivity from neuroelectric data is a fundamental problem in modern neuroscience, and it is used to assess the network properties of brain function. In the present work, we critically assess the virtues and limitations of temporal Granger causality (using both conditional and unconditional formulations) for the estimation of functional brain connectivity, using a neural mass model as the ground truth. The model simulates transmission among different brain rhythms (in the θ, α, β, and γ bands) via excitatory and inhibitory synapses. The results show that Granger causality is able to detect relative changes in connectivity, but the estimated values are influenced by the operative conditions (sampling frequency, signal length, delay). Moreover, the absolute value of Granger causality depends on the particular rhythm transmitted and is affected by nonlinear phenomena, especially the activity level in the connected regions. In the case of complex connectivity networks, conditional Granger causality overwhelms the unconditional one, since the latter often discovers spurious connections. Finally, inhibitory connections can be revealed more easily by Granger causality than similar excitatory connections, a result generally neglected in brain network studies. The present results can drive the correct interpretation of Granger-causality-based connectivity networks derived from neuroelectric signals.

Decades of neuroimaging have revealed that the functional organization of the brain is roughly consistent across individuals, and at rest, it resembles group-level task-evoked networks. A fundamental assumption in the field is that the functional specialization of a brain region arises from its connections to the rest of the brain, but limitations in the amount of data that can be feasibly collected in a single individual leave open the following question: Is the association between task activation and connectivity consistent across the brain and many cognitive tasks? To answer this question, we fit ridge regression models to activation maps from 33 cognitive domains (generated with NeuroQuery) using resting-state functional connectivity data from the Human Connectome Project as the predictor. We examine how well functional connectivity fits activation and find that all regions and all cognitive domains have a very robust relationship between brain activity and connectivity. The tightest relationship exists for higher order, domain-general cognitive functions. These results support the claim that connectivity is a general organizational principle of brain function by comprehensively testing this relationship in a large sample of individuals for a broad range of cognitive domains and provide a reference for future studies engaging in individualized predictive models.

[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.