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Individual differences in cognition during childhood are associated with important social, physical, and mental health outcomes in adolescence and adulthood. Given that cortical surface arealization during development reflects the brain’s functional prioritization, quantifying variation in the topography of functional brain networks across the developing cortex may provide insight regarding individual differences in cognition. We test this idea by defining personalized functional networks (PFNs) that account for interindividual heterogeneity in functional brain network topography in 9-10 year olds from the Adolescent Brain Cognitive DevelopmentSMStudy. Across matched discovery (n=3,525) and replication (n=3,447) samples, the total cortical representation of fronto-parietal PFNs positively correlated with general cognition. Cross-validated ridge regressions trained on PFN topography predicted cognition across domains, with prediction accuracy increasing along the cortex’s sensorimotor-association organizational axis. These results establish that functional network topography heterogeneity is associated with individual differences in cognition before the critical transition into adolescence.

Animal studies of neurodevelopmental plasticity have shown that intrinsic brain activity evolves from high amplitude and globally synchronized to suppressed and sparse as plasticity declines and the cortex matures. Leveraging resting-state functional MRI data from 1033 individuals (8-23 years), we reveal that this stereotyped refinement of intrinsic activity occurs during human development and provides evidence for a cortical gradient of neurodevelopmental plasticity during childhood and adolescence. Specifically, we demonstrate that declines in the amplitude of intrinsic activity are initiated heterochronously across regions, coupled to the maturation of a plasticity-restricting structural feature, and temporally staggered along a hierarchical sensorimotor-association axis from ages 8 to 18. Youth from disadvantaged environments exhibit reduced intrinsic activity in regions further up the sensorimotor-association axis, suggestive of a reduced level of plasticity in late-maturing cortices. Our results uncover a hierarchical axis of neurodevelopment and offer insight into the temporal sequence of protracted neurodevelopmental plasticity in humans.

SummaryMany scientific questions can be formulated as hypotheses about conditional correlations. For instance, in tests of cognitive and physical performance, the trade-off between speed and accuracy motivates study of the two variables together. A natural question is whether speed-accuracy coupling depends on other variables, such as sustained attention. Classical regression techniques, which posit models in terms of covariates and outcomes, are insufficient to investigate the effect of a third variable on the symmetric relationship between speed and accuracy. In response, we propose CoCoA (Conditional Correlation Model with Association Size), a likelihood-based statistical framework to estimate the conditional correlation between speed and accuracy as a function of additional variables. We propose novel measures of the association size, which are analogous to effect sizes on the correlation scale, while adjusting for confound variables. In simulation studies, we compare likelihood-based estimators of conditional correlation to semi-parametric estimators adapted from genome association studies, and find that the former achieves lower bias and variance under both ideal settings and model assumption misspecification. Using neurocognitive data from the Philadelphia Neurodevelopmental Cohort, we demonstrate that greater sustained attention is associated with stronger speed-accuracy coupling in a complex reasoning task while controlling for age. By highlighting conditional correlations as the outcome of interest, our model provides complementary insights to traditional regression modelling and partitioned correlation analyses.

Prior work has shown that there is substantial interindividual variation in the spatial distribution of functional networks across the cerebral cortex, or functional topography. However, it remains unknown whether there are sex differences in the topography of individualized networks in youth. Here, we leveraged an advanced machine learning method (sparsity-regularized non-negative matrix factorization) to define individualized functional networks in 693 youth (ages 8 to 23 y) who underwent functional MRI as part of the Philadelphia Neurodevelopmental Cohort. Multivariate pattern analysis using support vector machines classified participant sex based on functional topography with 82.9% accuracy ( P < 0.0001). Brain regions most effective in classifying participant sex belonged to association networks, including the ventral attention, default mode, and frontoparietal networks. Mass univariate analyses using generalized additive models with penalized splines provided convergent results. Furthermore, transcriptomic data from the Allen Human Brain Atlas revealed that sex differences in multivariate patterns of functional topography were spatially correlated with the expression of genes on the X chromosome. These results highlight the role of sex as a biological variable in shaping functional topography.

SUMMARYThe brain is organized into networks at multiple resolutions, or scales, yet studies of functional network development typically focus on a single scale. Here, we derived personalized functional networks across 29 scales in a large sample of youths (n=693, ages 8-23 years) to identify multi-scale patterns of network re-organization related to neurocognitive development. We found that developmental shifts in inter-network coupling systematically adhered to and strengthened a functional hierarchy of cortical organization. Furthermore, we observed that scale-dependent effects were present in lower-order, unimodal networks, but not higher-order, transmodal networks. Finally, we found that network maturation had clear behavioral relevance: the development of coupling in unimodal and transmodal networks dissociably mediated the emergence of executive function. These results delineate maturation of multi-scale brain networks, which varies according to a functional hierarchy and impacts cognitive development.

Complex human cognition arises from the integrated processing of multiple brain systems. However, little is known about how brain systems and their interactions might relate to, or perhaps even explain, human cognitive capacities. Here, we address this gap in knowledge by proposing a mechanistic framework linking frontoparietal system activity, default mode system activity, and the interactions between them, with individual differences in working memory capacity. We show that working memory performance depends on the strength of functional interactions between the frontoparietal and default mode systems. We find that this strength is modulated by the activation of two newly described brain regions, and demonstrate that the functional role of these systems is underpinned by structural white matter. Broadly, our study presents a holistic account of how regional activity, functional connections, and structural linkages together support integrative processing across brain systems in order for the brain to execute a complex cognitive process., Working memory is a critical component of executive function that allows people to complete complex tasks in the moment. Here, the authors show that this ability is underpinned by two newly defined brain networks.

Adolescence is hypothesized to be a critical period for the development of association cortex. A reduction of the excitation:inhibition (E:I) ratio is a hallmark of critical period development; however it has been unclear how to assess the development of the E:I ratio using non-invasive neuroimaging techniques. Here, we used pharmacological fMRI with a GABAergic benzodiazepine challenge to empirically generate a model of E:I ratio based on multivariate patterns of functional connectivity. In an independent sample of 879 youth (ages 8-22 years), this model predicted reductions in the E:I ratio during adolescence, which were specific to association cortex and related to psychopathology. These findings support hypothesized shifts in E:I balance of association cortices during a neurodevelopmental critical period in adolescence.TeaserInhibitory maturation of the association cortex reflects an adolescent critical period.

Signal propagation along the structural connectome of the brain induces changes in the patterns of activity. These activity patterns define global brain states and contain information in accordance with their expected probability of occurrence. The structural connectome, in conjunction with the dynamics, determines the set of possible brain states and constrains the transition between accessible states. Yet, precisely how these structural constraints on state-transitions relate to their information content remains unexplored. To address this gap in knowledge, we defined the information content as a function of the activation distribution, where statistically rare values of activation correspond to high information content. With this numerical definition in hand, we studied the spatiotemporal distribution of information content in fMRI data from the Human Connectome Project during different tasks, and report four key findings. First, information content strongly depends on the cognitive task. Second, while information content shows similarities to other measures of brain activity, it is distinct from both Neurosynth maps and task contrast maps generated by a general linear model applied to the fMRI data. Third, the brain's structural wiring constrains the cost to control its state, where the cost to transition into high information content states is larger than that to transition into low information content states. Finally, all state transitions - especially those to high information content states - are less costly than expected from random network null models, thereby indicating the brain's marked efficiency. Taken together, our findings establish an explanatory link between the information contained in a brain state and the energetic cost of attaining that state, thereby laying important groundwork for our understanding of large-scale cognitive computations., 16 pages, 4 figures + supplement (5 pages, 5 figures)

Functional connectivity (FC) networks are typically inferred from resting-state fMRI data using the Pearson correlation between BOLD time series from pairs of brain regions. However, alternative methods of estimating functional connectivity have not been systematically tested for their sensitivity or robustness to head motion artifact. Here, we evaluate the sensitivity of eight different functional connectivity measures to motion artifact using resting-state data from the Human Connectome Project. We report that FC estimated using full correlation has a relatively high residual distance-dependent relationship with motion compared to partial correlation, coherence, and information theory-based measures, even after implementing rigorous methods for motion artifact mitigation. This disadvantage of full correlation, however, may be offset by higher test-retest reliability, fingerprinting accuracy, and system identifiability. FC estimated by partial correlation offers the best of both worlds, with low sensitivity to motion artifact and intermediate system identifiability, with the caveat of low test-retest reliability and fingerprinting accuracy. We highlight spatial differences in the sub-networks affected by motion with different FC metrics. Further, we report that intra-network edges in the default mode and retrosplenial temporal sub-networks are highly correlated with motion in all FC methods. Our findings indicate that the method of estimating functional connectivity is an important consideration in resting-state fMRI studies and must be chosen carefully based on the parameters of the study.

The Adolescent Brain Cognitive Development Study (ABCD), a 10 year longitudinal neuroimaging study of the largest population based and demographically distributed cohort of 9-10 year olds (N=11,877), was designed to overcome reproducibility limitations of prior child mental health studies. Besides the fantastic wealth of research opportunities, the extremely large size of the ABCD data set also creates enormous data storage, processing, and analysis challenges for researchers. To ensure data privacy and safety, researchers are not currently able to share neuroimaging data derivatives through the central repository at the National Data Archive (NDA). However, sharing derived data amongst researchers laterally can powerfully accelerate scientific progress, to ensure the maximum public benefit is derived from the ABCD study. To simultaneously promote collaboration and data safety, we developed the ABCD-BIDS Community Collection (ABCC), which includes both curated processed data and software utilities for further analyses. The ABCC also enables researchers to upload their own custom-processed versions of ABCD data and derivatives for sharing with the research community. This NeuroResource is meant to serve as the companion guide for the ABCC. In section we describe the ABCC. Section II highlights ABCC utilities that help researchers access, share, and analyze ABCD data, while section III provides two exemplar reproducibility analyses using ABCC utilities. We hope that adoption of the ABCC’s data-safe, open-science framework will boost access and reproducibility, thus facilitating progress in child and adolescent mental health research.

Discrimination against racial and ethnic minority groups exists in the academy, and the associated biases impact hiring and promotion, publication rates, grant funding, and awards. Precisely how racial and ethnic bias impacts the manner in which the scientific community engages with the ideas of academics in minority groups has yet to be fully elucidated. Citations are a marker of such community engagement, as well as a currency used to attain career milestones. Here we assess the extent and drivers of racial and ethnic imbalance in the reference lists of papers published in five top neuroscience journals over the last 25 years. We find that reference lists tend to include more papers with a White person as first and last author than would be expected if race and ethnicity were unrelated to referencing. We show that this imbalance is driven largely by the citation practices of White authors, and is increasing over time even as the field diversifies. To further explain our findings, we examine co-authorship networks and find that while the network has become markedly more integrated in general, the current degree of segregation by race/ethnicity is greater now than it has been in the past. Citing further from oneself on the network is associated with greater balance, but White authors’ preferential citation of White authors remains even at high levels of network exploration. We also quantify the effects of intersecting identities, determining the relative costs of gender and race/ethnicity, and their combination in women of color. Our findings represent a call to scientists and journal editors of all disciplines to consider the ethics of citation practices, and actions to be taken in support of an equitable future.

How an individual’s unique brain connectivity determines that individual’s cognition, behavior, and risk for pathology is a fundamental question in basic and clinical neuroscience. In seeking answers, many have turned to machine learning, with some noting the particular promise of deep neural networks in modelling complex non-linear functions. However, it is not clear that complex functions actually exist between brain connectivity and behavior, and thus if deep neural networks necessarily outperform simpler linear models, or if their results would be interpretable. Here we show that, across 52 subject measures of cognition and behavior, deep neural networks fit to each brain region’s connectivity outperform linear regression, particularly for the brain’s connector hubs—regions with diverse brain connectivity—whereas the two approaches perform similarly when fit to brain systems. Critically, averaging deep neural network predictions across brain regions results in the most accurate predictions, demonstrating the ability of deep neural networks to easily model the various functions that exists between regional brain connectivity and behavior, carving the brain at its joints. Finally, we shine light into the black box of deep neural networks using multislice network models. We determined that the relationship between connector hubs and behavior is best captured by modular deep neural networks. Our results demonstrate that both simple and complex relationships exist between brain connectivity and behavior, and that deep neural networks can fit both. Moreover, deep neural networks are particularly powerful when they are first fit to the various functions of a system independently and then combined. Finally, deep neural networks are interpretable when their architectures are structurally characterized using multislice network models.

Living systems break detailed balance at small scales, consuming energy and producing entropy in the environment in order to perform molecular and cellular functions. However, it remains unclear how broken detailed balance manifests at macroscopic scales, and how such dynamics support higher-order biological functions. Here we present a framework to quantify broken detailed balance by measuring entropy production in macroscopic systems. We apply our method to the human brain, an organ whose immense metabolic consumption drives a diverse range of cognitive functions. Using whole-brain imaging data, we demonstrate that the brain nearly obeys detailed balance when at rest, but strongly breaks detailed balance when performing physically and cognitively demanding tasks. Using a dynamic Ising model, we show that these large-scale violations of detailed balance can emerge from fine-scale asymmetries in the interactions between elements, a known feature of neural systems. Together, these results suggest that violations of detailed balance are vital for cognition, and provide a general tool for quantifying entropy production in macroscopic systems., Comment: 19 pages, 15 figures

A central challenge in the computational modeling of neural dynamics is the trade-off between accuracy and simplicity. At the level of individual neurons, nonlinear dynamics are both experimentally established and essential for neuronal functioning. One may therefore expect the collective dynamics of massive networks of such neurons to only increase in their complexity, thereby supporting an expanded repertoire of nonlinear behaviors. An implicit assumption has thus formed that an “accurate” computational model of whole-brain dynamics must inevitably be nonlinear whereas linear models may provide a first-order approximation. To what extent this assumption holds, however, has remained an open question. Here, we provide a rigorous and data-driven answer at the level of whole-brain blood-oxygen-level-dependent (BOLD) and macroscopic field potential dynamics by leveraging the theory of system identification. Using functional magnetic resonance imaging (fMRI) and intracranial electroencephalography (iEEG), we model the spontaneous, resting state activity of 700 subjects in the Human Connectome Project (HCP) and 122 subjects from the Restoring Active Memory (RAM) project using state-of-the-art linear and nonlinear model families. We assess relative model fit using predictive power, computational complexity, and the extent of residual dynamics unexplained by the model. Contrary to our expectations, linear auto-regressive models achieve the best measures across all three metrics, eliminating the trade-off between accuracy and simplicity. To understand and explain this linearity, we highlight four properties of macroscopic neurodynamics which can counteract or mask microscopic nonlinear dynamics: averaging over space, averaging over time, observation noise, and limited data samples. Whereas the latter two are technological limitations and can improve in the future, the former two are inherent to aggregated macroscopic brain activity. Our results demonstrate the discounted potential of linear models in accurately capturing macroscopic brain dynamics. This, together with the unparalleled interpretability of linear models, can greatly facilitate our understanding of macroscopic neural dynamics, which in turn may facilitate the principled design of model-based interventions for the treatment of neuropsychiatric disorders.

A complex system can be represented and analyzed as a network, where nodes represent the units of the network and edges represent connections between those units. For example, a brain network represents neurons as nodes and axons between neurons as edges. In many networks, some nodes have a disproportionately high number of edges as well as many edges between each other and are referred to as the “rich club”. In many different networks, the nodes of this club are assumed to support global network integration. Here we show that another set of nodes, which have edges diversely distributed across the network, form a “diverse club”. The diverse club exhibits, to a greater extent than the rich club, properties consistent with an integrative network function—these nodes are more highly interconnected and their edges are more critical for efficient global integration. Finally, these two clubs potentially evolved via distinct selection pressures., Complex networks represent systems such as neural networks and air traffic as interconnected nodes that organize themselves into subsets. Here Bertolero et al. propose a subset which they call the diverse club, which offers an alternative to the commonly used rich club.

Coordinated brain activity reflects underlying cognitive processes and can be modeled as a network of inter-regional functional connections. The most costly connections in the network are long-distance correlations that, in the absence of underlying structural connections, are maintained by sustained energetic inputs. Here, we present a spatial modeling approach that amplifies contributions made by long-distance functional connections to whole-brain network architecture, while simultaneously suppressing contributions made by short-range connections. We use this method to characterize the long-distance architecture of functional networks and to identify aspects of community and hub structure that are driven by long-distance correlations and that, we argue, are of greater functional significance. We find that based only on patterns of long-distance connectivity, primary sensory cortices occupy increasingly central positions and appear more “hub-like”. Additionally, we show that the community structure of long-distance connections spans multiple topological levels and differs from the community structure detected in networks that include both short-range and long-distance connections. In summary, these findings highlight the complex relationship between the brain’s physical layout and its functional architecture. The results presented here inform future analyses of community structure and network hubs in health, across development, and in the case of neuropsychiatric disorders.

The human brain network is modular-comprised of communities of tightly interconnected nodes1. This network contains local hubs, which have many connections within their own communities, and connector hubs, which have connections diversely distributed across communities2,3. A mechanistic understanding of these hubs and how they support cognition has not been demonstrated. Here, we leveraged individual differences in hub connectivity and cognition. We show that a model of hub connectivity accurately predicts the cognitive performance of 476 individuals in four distinct tasks. Moreover, there is a general optimal network structure for cognitive performance-individuals with diversely connected hubs and consequent modular brain networks exhibit increased cognitive performance, regardless of the task. Critically, we find evidence consistent with a mechanistic model in which connector hubs tune the connectivity of their neighbors to be more modular while allowing for task appropriate information integration across communities, which increases global modularity and cognitive performance.

Network neuroscience represents the brain as a collection of regions and inter‐regional connections. Given its ability to formalize systems‐level models, network neuroscience has generated unique explanations of neural function and behavior. The mechanistic status of these explanations and how they can contribute to and fit within the field of neuroscience as a whole has received careful treatment from philosophers. However, these philosophical contributions have not yet reached many neuroscientists. Here we complement formal philosophical efforts by providing an applied perspective from and for neuroscientists. We discuss the mechanistic status of the explanations offered by network neuroscience and how they contribute to, enhance, and interdigitate with other types of explanations in neuroscience. In doing so, we rely on philosophical work concerning the role of causality, scale, and mechanisms in scientific explanations. In particular, we make the distinction between an explanation and the evidence supporting that explanation, and we argue for a scale‐free nature of mechanistic explanations. In the course of these discussions, we hope to provide a useful applied framework in which network neuroscience explanations can be exercised across scales and combined with other fields of neuroscience to gain deeper insights into the brain and behavior., On the nature of explanations offered by network science: A perspective from and for practicing neuroscientists This paper focuses on the level of neural networks. It argues that tools and models in network science can successfully bridge explanations of neural and cognitive phenomena at multiple scales. On the basis of various examples, Bertolero and Bassett explain how network neuroscience can uncover aspects the neural mechanisms of cognitive phenomena.

The spatial layout of large-scale functional brain networks differs between individuals and is particularly variable in the association cortex, implicated in a broad range of psychiatric disorders. However, it remains unknown whether this variation in functional topography is related to major dimensions of psychopathology in youth.The authors studied 790 youths ages 8 to 23 years who had 27 minutes of high-quality functional magnetic resonance imaging data as part of the Philadelphia Neurodevelopmental Cohort. Four correlated dimensions were estimated using a confirmatory correlated traits factor analysis on 112 item-level clinical symptoms, and one overall psychopathology factor with 4 orthogonal dimensions were extracted using a confirmatory factor analysis. Spatially regularized nonnegative matrix factorization was used to identify 17 individual-specific functional networks for each participant. Partial least square regression with split-half cross-validation was conducted to evaluate to what extent the topography of personalized functional networks encodes major dimensions of psychopathology.Personalized functional network topography significantly predicted unseen individuals' major dimensions of psychopathology, including fear, psychosis, externalizing, and anxious-misery. Reduced representation of association networks was among the most important features for the prediction of all 4 dimensions. Further analysis revealed that personalized functional network topography predicted overall psychopathology (r = 0.16, permutation testing p.001), which drove prediction of the 4 correlated dimensions.These results suggest that individual differences in functional network topography in association networks is related to overall psychopathology in youth. Such results underscore the importance of considering functional neuroanatomy for personalized diagnostics and therapeutics in psychiatry.

Prior work has shown that there is substantial interindividual variation in the spatial distribution of functional networks across the cerebral cortex, orfunctional topography. However, it remains unknown whether there are sex differences in the topography of individualized networks in youth. Here we leveraged an advanced machine learning method (sparsity-regularized nonnegative matrix factorization) to define individualized functional networks in 693 youth (ages 8-23 years) who underwent functional magnetic resonance imaging as part of the Philadelphia Neurodevelopmental Cohort. Multivariate pattern analysis using support vector machines classified participant sex based on functional topography with 83% accuracy (pSIGNIFICANCE STATEMENTWe identify normative developmental sex differences in the functional topography of personalized association networks including the ventral attention network and default mode network. Furthermore, chromosomal enrichment analyses revealed that sex differences in multivariate patterns of functional topography were spatially coupled to the expression of X-linked genes as well as astrocytic and excitatory neuronal cell-type signatures. These results highlight the role of sex as a biological variable in shaping functional brain development in youth.

Human learning is a complex process in which future behavior is altered via the reorganization of brain activity and connectivity. It remains unknown whether activity and connectivity differentially reorganize during learning, and, if so, how that differential reorganization tracks stages of learning across distinct brain areas. Here, we address this gap in knowledge by measuring brain activity and functional connectivity in a longitudinal fMRI experiment in which healthy adult human participants learn the values of novel objects over the course of four days. An increasing similarity in activity or functional connectivity across subjects during learning reflects reorganization toward a common functional architecture. We assessed the presence of reorganization in activity and connectivity both during value learning and during the resting-state, allowing us to differentiate common elicited processes from intrinsic processes. We found a complex and dynamic reorganization of brain connectivity and activity—as a function of time, space, and performance—that occurs while subjects learn. Spatially localized brain activity reorganizes across the brain to a common functional architecture early in learning, and this reorganization tracks early learning performance. In contrast, spatially distributed connectivity reorganizes across the brain to a common functional architecture as training progresses, and this reorganization tracks later learning performance. Particularly good performance is associated with a sticky connectivity, that persists into the resting state. Broadly, our work uncovers distinct principles of reorganization in activity and connectivity at different phases of value learning, which inform the ongoing study of learning processes more generally.

The network organization of the human brain varies across individuals, changes with development and aging, and differs in disease. Discovering the major dimensions along which this variability is displayed remains a central goal of both neuroscience and clinical medicine. Such efforts can be usefully framed within the context of the brain’s modular network organization, which can be assessed quantitatively using powerful computational techniques and extended for the purposes of multi-scale analysis, dimensionality reduction, and biomarker generation. Though the concept of modularity and its utility in describing brain network organization is clear, principled methods for comparing multi-scale communities across individuals and time are surprisingly lacking. Here, we present a method that uses multi-layer networks to simultaneously discover the modular structure of many subjects at once. This method builds upon the well-known multi-layer modularity maximization technique, and provides a viable and principled tool for studying differences in network communities across individuals and within individuals across time. We test this method on two datasets and identify consistent patterns of inter-subject community variability, demonstrating that this variability – which would be undetectable using past approaches – is associated with measures of cognitive performance. In general, the multi-layer, multi-subject framework proposed here represents an advancement over current approaches by straighforwardly mapping community assignments across subjects and holds promise for future investigations of inter-subject community variation in clinical populations or as a result of task constraints.

Brain networks exhibit community structure that reconfigures during cognitively demanding tasks. Extant work has emphasized a single class of communities: those that are assortative, or internally dense and externally sparse. Other classes that may play key functional roles in brain function have largely been ignored, leading to an impoverished view in the best case and a mischaracterization in the worst case. Here, we leverage weighted stochastic blockmodeling, a community detection method capable of detecting diverse classes of communities, to study the community structure of functional brain networks while subjects either rest or perform cognitively demanding tasks. We find evidence that the resting brain is largely assortative, although higher order association areas exhibit non-assortative organization, forming cores and peripheries. Surprisingly, this assortative structure breaks down during tasks and is supplanted by core, periphery, and disassortative communities. Using measures derived from the community structure, we show that it is possible to classify an individual’s task state with an accuracy that is well above average. Finally, we show that inter-individual differences in the composition of assortative and non-assortative communities is correlated with subject performance on in-scanner cognitive tasks. These findings offer a new perspective on the community organization of functional brain networks and its relation to cognition.

The network organization of the human brain varies across individuals, changes with development and aging, and differs in disease. Discovering the major dimensions along which this variability is displayed remains a central goal of both neuroscience and clinical medicine. Such efforts can be usefully framed within the context of the brain's modular network organization, which can be assessed quantitatively using powerful computational techniques and extended for the purposes of multi-scale analysis, dimensionality reduction, and biomarker generation. Though the concept of modularity and its utility in describing brain network organization is clear, principled methods for comparing multi-scale communities across individuals and time are surprisingly lacking. Here, we present a method that uses multi-layer networks to simultaneously discover the modular structure of many subjects at once. This method builds upon the well-known multi-layer modularity maximization technique, and provides a viable and principled tool for studying differences in network communities across individuals and within individuals across time. We test this method on two datasets and identify consistent patterns of inter-subject community variability, demonstrating that this variability – which would be undetectable using past approaches – is associated with measures of cognitive performance. In general, the multi-layer, multi-subject framework proposed here represents an advancement over current approaches by straighforwardly mapping community assignments across subjects and holds promise for future investigations of inter-subject community variation in clinical populations or as a result of task constraints.

Network-based analyses of brain imaging data consistently reveal distinct modules and connector nodes with diverse global connectivity across the modules. How discrete the functions of modules are, how dependent the computational load of each module is to the other modules’ processing, and what the precise role of connector nodes is for between-module communication remains underspecified. Here, we use a network model of the brain derived from resting-state functional MRI (rs-fMRI) data and investigate the modular functional architecture of the human brain by analyzing activity at different types of nodes in the network across 9,208 experiments of 77 cognitive tasks in the BrainMap database. Using an author–topic model of cognitive functions, we find a strong spatial correspondence between the cognitive functions and the network’s modules, suggesting that each module performs a discrete cognitive function. Crucially, activity at local nodes within the modules does not increase in tasks that require more cognitive functions, demonstrating the autonomy of modules’ functions. However, connector nodes do exhibit increased activity when more cognitive functions are engaged in a task. Moreover, connector nodes are located where brain activity is associated with many different cognitive functions. Connector nodes potentially play a role in between-module communication that maintains the modular function of the brain. Together, these findings provide a network account of the brain’s modular yet integrated implementation of cognitive functions.