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•Network neuroscience combines elements from many disciplines.•We discuss three key areas of inquiry: descriptive, predictive, and perturbative.•Descriptive approaches employ advanced tools from graph theory.•Predictive approaches employ machine learning to predict behavior from features.•Perturbative approaches employ network control theory to explain system energetics. The field of network neuroscience has emerged as a natural framework for the study of the brain and has been increasingly applied across divergent problems in neuroscience. From a disciplinary perspective, network neuroscience originally emerged as a formal integration of graph theory (from mathematics) and neuroscience (from biology). This early integration afforded marked utility in describing the interconnected nature of neural units, both structurally and functionally, and underscored the relevance of that interconnection for cognition and behavior. But since its inception, the field has not remained static in its methodological composition. Instead, it has grown to use increasingly advanced graph-theoretic tools and to bring in several other disciplinary perspectives—including machine learning and systems engineering—that have proven complementary. In doing so, the problem space amenable to the discipline has expanded markedly. In this review, we discuss three distinct flavors of investigation in state-of-the-art network neuroscience: (i) descriptive network neuroscience, (ii) predictive network neuroscience, and (iii) a perturbative network neuroscience that draws on recent advances in network control theory. In considering each area, we provide a brief summary of the approaches, discuss the nature of the insights obtained, and highlight future directions.

Abstract Over the past three decades, research from the field of social neuroscience has identified a constellation of brain regions that relate to social cognition. Although these studies have provided important insights into the specific neural regions underlying social behavior, they may overlook the broader neural context in which those regions and the interactions between them are embedded. Network neuroscience is an emerging discipline that focuses on modeling and analyzing brain networks—collections of interacting neural elements. Because human cognition requires integrating information across multiple brain regions and systems, we argue that a novel social cognitive network neuroscience approach—which leverages methods from the field of network neuroscience and graph theory—can advance our understanding of how brain systems give rise to social behavior. This review provides an overview of the field of network neuroscience, discusses studies that have leveraged this approach to advance social neuroscience research, highlights the potential contributions of social cognitive network neuroscience to understanding social behavior and provides suggested tools and resources for conducting network neuroscience research.

Schizophrenia has been conceived as a disorder of brain connectivity, but it is unclear how this network phenotype is related to the underlying genetics. We used morphometric similarity analysis of MRI data as a marker of interareal cortical connectivity in three prior case–control studies of psychosis: in total, n = 185 cases and n = 227 controls. Psychosis was associated with globally reduced morphometric similarity in all three studies. There was also a replicable pattern of case–control differences in regional morphometric similarity, which was significantly reduced in patients in frontal and temporal cortical areas but increased in parietal cortex. Using prior brain-wide gene expression data, we found that the cortical map of case–control differences in morphometric similarity was spatially correlated with cortical expression of a weighted combination of genes enriched for neurobiologically relevant ontology terms and pathways. In addition, genes that were normally overexpressed in cortical areas with reduced morphometric similarity were significantly up-regulated in three prior post mortem studies of schizophrenia. We propose that this combined analysis of neuroimaging and transcriptional data provides insight into how previously implicated genes and proteins as well as a number of unreported genes in their topological vicinity on the protein interaction network may drive structural brain network changes mediating the genetic risk of schizophrenia.

This study presents a cognitive neuroscience framework for understanding what we term “supercomplex experiences,” a concept describing experiences that simultaneously engage multiple neural networks and cognitive faculties in ways that resist decomposition into simpler processes. Drawing on recent advances in network neuroscience, we argue that these experiences emerge from the coordinated activity of distributed brain systems, including the salience network, default mode network, and central executive network. These experiences are distinguished by five essential characteristics: (1) simultaneous engagement of multiple neural networks, (2) specialized neural architectures developed through training, (3) specialized conceptual frameworks and vocabularies, (4) emergent properties from dynamic interactions, and (5) coherent gestalt properties. Through examination of expert performance in domains such as wine tasting, musical performance, visual art, perfumery, and several others we reveal how these experiences are characterized by sophisticated integration of sensory, emotional, and cognitive processes, implemented through dynamic network interactions and expertise-dependent neural plasticity. Our framework emphasizes three key mechanisms underlying supercomplex experiences: predictive processing architectures that generate and update multi-level predictions, expertise-dependent network reorganization that enables enhanced sensory discrimination and conceptual integration, and dynamic network flexibility that supports adaptive processing of complex stimuli. While acknowledging debates between different theoretical approaches, we show how interoceptive predictions and embodied simulations, implemented through the anterior insula and related networks, provide a foundation for integrating bodily signals with external sensory input. The development of expertise in domains characterized by supercomplex experiences involves significant modifications of neural architecture, from local circuit refinement to large-scale network reorganization. This work extends beyond existing frameworks in cognitive neuroscience by providing a mechanistic account of how the brain processes and generates richly textured, multifaceted experiences that have previously been studied primarily through separate disciplinary lenses. The framework has implications for understanding expertise development, individual differences in complex skill acquisition, and the neural bases of sophisticated cognitive-perceptual capabilities.

Activity-dependent self-organization plays an important role in the formation of specific and stereotyped connectivity patterns in neural circuits. By combining neuronal cultures, and tools with approaches from network neuroscience and information theory, we can study how complex network topology emerges from local neuronal interactions. We constructed effective connectivity networks using a transfer entropy analysis of spike trains recorded from rat embryo dissociated hippocampal neuron cultures between 6 and 35 days in vitro to investigate how the topology evolves during maturation. The methodology for constructing the networks considered the synapse delay and addressed the influence of firing rate and population bursts as well as spurious effects on the inference of connections. We found that the number of links in the networks grew over the course of development, shifting from a segregated to a more integrated architecture. As part of this progression, three significant aspects of complex network topology emerged. In agreement with previous in silico and in vitro studies, a small-world architecture was detected, largely due to strong clustering among neurons. Additionally, the networks developed in a modular topology, with most modules comprising nearby neurons. Finally, highly active neurons acquired topological characteristics that made them important nodes to the network and integrators of modules. These findings leverage new insights into how neuronal effective network topology relates to neuronal assembly self-organization mechanisms.

Genuinely new discovery transcends existing knowledge. Despite this, many analyses in systems neuroscience neglect to test new speculative hypotheses against benchmark empirical facts. Some of these analyses inadvertently use circular reasoning to present existing knowledge as new discovery. Here, I discuss that this problem can confound key results and estimate that it has affected more than three thousand studies in network neuroscience over the last decade. I suggest that future studies can reduce this problem by limiting the use of speculative evidence, integrating existing knowledge into benchmark models, and rigorously testing proposed discoveries against these models. I conclude with a summary of practical challenges and recommendations.

Recollection of one’s personal past, or autobiographical memory (AM), varies across individuals and across the life span. This manifests in the amount of episodic content recalled during AM, which may reflect differences in associated functional brain networks. We take an individual differences approach to examine resting-state functional connectivity of temporal lobe regions known to coordinate AM content retrieval with the default network (anterior and posterior hippocampus, temporal pole) and test for associations with AM. Multiecho resting-state functional magnetic resonance imaging (fMRI) and autobiographical interviews were collected for 158 younger and 105 older healthy adults. Interviews were scored for internal (episodic) and external (semantic) details. Age group differences in connectivity profiles revealed that older adults had lower connectivity within anterior hippocampus, posterior hippocampus, and temporal pole but greater connectivity with regions across the default network compared with younger adults. This pattern was positively related to posterior hippocampal volumes in older adults, which were smaller than younger adult volumes. Connectivity associations with AM showed two significant patterns. The first dissociated connectivity related to internal vs. external AM across participants. Internal AM was related to anterior hippocampus and temporal pole connectivity with orbitofrontal cortex and connectivity within posterior hippocampus. External AM was related to temporal pole connectivity with regions across the lateral temporal cortex. In the second pattern, younger adults displayed temporal pole connectivity with regions throughout the default network associated with more detailed AMs overall. Our findings provide evidence for discrete ensembles of brain regions that scale with systematic variation in recollective styles across the healthy adult life span.

Network models of anatomical connections allow for the extraction of quantitative features describing brain organization, and their comparison across brains from different species. Such comparisons can inform our understanding of between-species differences in brain architecture and can be compared to existing taxonomies and phylogenies. Here we performed a quantitative comparative analysis using the MaMI database (Tel Aviv University), a collection of brain networks reconstructed from ex vivo diffusion MRI spanning 125 species and 12 taxonomic orders or superorders. We used a broad range of metrics to measure between-mammal distances and compare these estimates to the separation of species as derived from taxonomy and phylogeny. We found that within-taxonomy order network distances are significantly closer than between-taxonomy network distances, and this relation holds for several measures of network distance. Furthermore, to estimate the evolutionary divergence between species, we obtained phylogenetic distances across 10,000 plausible phylogenetic trees. The anatomical network distances were rank-correlated with phylogenetic distances 10,000 times, creating a distribution of coefficients that demonstrate significantly positive correlations between network and phylogenetic distances. Collectively, these analyses demonstrate species-level organization across scales and informational sources: we relate brain networks distances, derived from MRI, with evolutionary distances, derived from genotyping data.

Whole-brain resting-state functional MRI (rs-fMRI) during 2 wk of upper-limb casting revealed that disused motor regions became more strongly connected to the cingulo-opercular network (CON), an executive control network that includes regions of the dorsal anterior cingulate cortex (dACC) and insula. Disuse-driven increases in functional connectivity (FC) were specific to the CON and somatomotor networks and did not involve any other networks, such as the salience, frontoparietal, or default mode networks. Censoring and modeling analyses showed that FC increases during casting were mediated by large, spontaneous activity pulses that appeared in the disused motor regions and CON control regions. During limb constraint, disused motor circuits appear to enter a standby mode characterized by spontaneous activity pulses and strengthened connectivity to CON executive control regions.

The symptoms of acute ischemic stroke can be attributed to disruption of the brain network architecture. Systemic thrombolysis is an effective treatment that preserves structural connectivity in the first days after the event. Its effect on the evolution of global network organisation is, however, not well understood. We present a secondary analysis of 269 patients from the randomized WAKE-UP trial, comparing 127 imaging-selected patients treated with alteplase with 142 controls who received placebo. We used indirect network mapping to quantify the impact of ischemic lesions on structural brain network organisation in terms of both global parameters of segregation and integration, and local disruption of individual connections. Network damage was estimated before randomization and again 22 to 36 h after administration of either alteplase or placebo. Evolution of structural network organisation was characterised by a loss in integration and gain in segregation, and this trajectory was attenuated by the administration of alteplase. Preserved brain network organization was associated with excellent functional outcome. Furthermore, the protective effect of alteplase was spatio-topologically nonuniform, concentrating on a subnetwork of high centrality supported in the salvageable white matter surrounding the ischemic cores. This interplay between the location of the lesion, the pathophysiology of the ischemic penumbra, and the spatial embedding of the brain network explains the observed potential of thrombolysis to attenuate topological network damage early after stroke. Our findings might, in the future, lead to new brain network-informed imaging biomarkers and improved prognostication in ischemic stroke.