Network Neuroscience

Micro-electrode array (MEA) recordings are widely used to characterize functional connectivity in neural cultures and have gained traction for the analysis of human brain slices. However, the impact of graph construction methodology on the resulting network topology has not been systematically quantified. Here, we benchmark three methods - shared spiking activity, Pearson cross-correlation, and the spike time tiling coefficient (STTC) - across 37 recordings from human cortical slice cultures classified into low, moderate, and high activity groups. We show that method choice alone produces large topological differences (Cohens d = 0.86-1.14 for clustering coefficient, d > 1.0 for node count), while higher-order features such as modularity remain stable. Each method exhibits a distinct sensitivity profile: shared spiking detects activity-dependent changes primarily through network size, correlation uniquely captures clustering differences, and STTC combines strong biological sensitivity with negligible parameter dependence across lag windows (all d < 0.1). Within shared spiking, z-score normalization dominates all other parameter choices (d > 1.0 versus bin size effects of d < 0.23), functioning as an implicit analytical null model that fundamentally reshapes the edge set rather than merely rescaling weights. Inter-method edge overlap is low (Jaccard index 0.08-0.45) and activity dependent, demonstrating that these methods identify substantially different connections from identical data. Our results reveal that methodological choices including construction method, threshold, and normalization introduce hidden degrees of freedom with effect sizes comparable to the biological signals being measured. We provide practical recommendations for parameter selection, reporting, and cross-method validation in MEA-based network neuroscience. Author SummaryWhen we record electrical activity from brain tissue using grids of electrodes, we can ask how different sites influence one another and map the tissue as a network of connections. Thanks to novel culturing methods, this approach is increasingly used to study human brain slices. However, deciding what is "connected" is not well defined. Researchers use several different methods, and it has never been clear how much this choice shapes the network they end up describing. Here we compared three widely used methods on 37 recordings from human cortical slices spanning a range of activity levels. We found that the method alone can change the apparent structure of the network as much as real biological differences do. The methods frequently disagreed about which connections exist and some technical choices, including normalization techniques, had surprisingly large effects. Because these hidden choices can rival the biological signal, we provide this benchmarking work with practical recommendations for selecting, reporting, and cross-checking methods, so that network studies of brain tissue become more transparent, comparable, and reproducible.

Whole-brain connectomes are increasingly used as wiring diagrams for spiking simulations and as structural priors for artificial networks, usually after an ad hoc synapse-count threshold whose effect on validated function has never been measured. We define function-preserving sparsification - removing edges under a fixed budget while preserving validated behaviour and population responses - and study it in the leaky integrate-and-fire whole-brain model of Shiu et al. (2024). Six operators - weak-synapse pruning, uniform and weight-proportional random removal, degree-preserving rewiring, weight shuffling and a task-aware criterion - were compared at identical edge budgets across three validated circuits (sugar-evoked proboscis extension, its bitter suppression, grooming pathway specificity), silent and spontaneously active network states, and multiple seeds. Weak-synapse pruning preserves 79% of feeding output with 6% of edges, with a cliff at k* of approximately 9 synapses, whereas random, degree-preserving and weight-shuffled controls at the same budget abolish output, and weight-proportional random sampling survives only while it retains the strong-edge set: function resides in the conjunction of wiring placement and strength, not in degree statistics or synaptic mass. The cliff is task-dependent - grooming specificity collapses below k = 2 through disinhibition. In the active state failure changes character: output over-excites instead of vanishing, and bitter suppression is lost progressively from k = 5 and reverses at k = 31. A task-aware criterion preserves its training stimulus at 0.4% of edges but loses held-out inhibition first. These results provide a first benchmark for connectome sparsification and show that what must be kept depends on task and network state.

Lesion network mapping (LNM) has provided a compelling framework in clinical neuroscience, linking focal brain damage at heterogeneous anatomical locations to shared functional networks across behavioural syndromes. Recently, the methodological specificity of LNM has come under discussion following observations that derived network maps tend to converge onto the normative connectome's generic degree distribution. While current efforts largely treat this convergence as a statistical inference challenge addressable via post-hoc spatial null models or individual-level analyses, we identify a foundational upstream measurement artifact operating within individual patients: the lesion "bystander effect". Because pathological lesions follow biological constraints rather than functional brain parcellations, a typical lesion encapsulates both a symptom-driving critical node and surrounding asymptomatic bystander tissue. By averaging connectivity profiles across the entire lesion mask, LNM mechanically dilutes focal clinical signals. Across empirical clinical cohorts, individual-level machine learning classifiers, synthetic within-cohort lesion unions, and whole-brain spherical simulations, we demonstrate that increasing lesion extent deterministically drives network maps toward the connectome degree vector following a mathematical power law. Fulfilling the clinical promise of LNM will therefore require computational strategies that isolate the causal functional core from surrounding bystander tissue prior to network projection.

Brain activity can be understood as a sequence of neuronal avalanches, i.e., transient episodes of coordinated activation that emerge across scales, from individual neurons and local networks to whole-brain dynamics. Avalanches are typically characterized by features such as size, duration, number of active components, and the silent time separating consecutive events. Although these features have been extensively characterized through their marginal distributions, their temporal organization and dependence on the underlying brain architecture remain poorly understood, leaving us without a framework for embedding fast neuronal avalanches within slower brain dynamics. Here, we analyzed eyes-closed resting-state magnetoencephalography recordings and the corresponding structural connectomes from 30 healthy participants to investigate the dynamics of avalanche sizes. We found that large avalanches preferentially followed short silent times, whereas small avalanches were more likely to occur after long silent periods. Based on the empirical joint distributions of avalanche size and silent time, we could define four types of events occurring above chance levels (avalanche large or small, preceding pause long or short). Mixed categories--combining a small value of one feature with a large value of the other--occurred more frequently than expected, while same-category events happened less often than chance. Furthermore, consecutive events tended to remain in the same category, a phenomenon referred to as persistence. We next investigated whether a brain regions connectivity profile shapes its propensity to participate in avalanches of different sizes. More strongly connected regions participated most often in small avalanches, whereas weakly connected regions were preferentially recruited during large avalanches. This pattern may reflect the greater sensitivity of highly connected hubs to fluctuations propagating through the network, resulting in frequent but spatially contained events. By contrast, the recruitment of more peripheral regions may require broader and stronger collective activity, occurring only during rarer, large-scale avalanches. In contrast, regional participation showed no clear association with the silent time preceding an avalanche. Together, these findings show that neuronal avalanches are neither temporally independent nor anatomically unconstrained: their sequence retains a memory of preceding events, while structural topology shapes which regions are recruited as avalanches grow. By connecting fast avalanche dynamics with slower temporal organization and the structural connectome, our results provide a multiscale framework for understanding how transient events are embedded within ongoing brain activity.

Large-scale whole-brain network models rely on structural connectomes to constrain simulated neural dynamics, yet the optimal processing of these anatomical scaffolds is not fully established. Here, we investigate the impact of structural connectome thresholding on whole-brain model performance to advance precision individual modelling. We measure model goodness-of-fit to both static functional connectivity and dynamic functional connectivity across a comprehensive spectrum of network densities, spatial parcellations, and model complexities using neuroimaging datasets of healthy individuals and a clinical cohort of post-stroke patients. We demonstrate that the optimal structural sparsity is highly resolution dependent. In coarse-grained parcellations, proportional thresholding enhances the model's fit to static functional connectivity but relies on denser connectome to maintain dynamic functional fits. Conversely, fine-grained models require stringent connectome thresholding which simultaneously optimizes both static and dynamic functional fits. Taken together, these results indicate that the uncritical use of raw structural connectomes introduces suboptimal dynamical regimes, establishing resolution-tailored thresholding as an indispensable step for constructing precision brain network models.

Objective: Brain network controllability provides a framework for understanding how structural organization shapes brain dynamics, yet current models mainly rely on white-matter connectivity and may overlook the contribution of gray-matter architecture. Approach: We constructed a fusion network combining diffusion tensor imaging-derived white-matter connectivity with gray-matter morphological similarity and investigated its controllability, biological associations, heritability, phenotype prediction, and control energy. Main results: Controllability derived from the fusion network preserved key topological properties of the white-matter network and was associated with neurotransmitter systems and cerebral metabolism. Compared with the white-matter connectivity-based network, fusion-based controllability showed a systematic shift toward higher heritability, improved prediction of several individual characteristics and cognitive functions, and lower modeled control energy for activating resting-state networks. Significance: These findings suggest that incorporating gray-matter morphological information into a DTI-supported network provides a complementary structural representation for studying brain network controllability and state transitions. The lower control energy represents a model-derived transition cost and should not be interpreted as a direct measure of physiological energy expenditure.

Neural activity is organized in low-dimensional structure, yet functional connectivity in fMRI typically represents each brain parcel by a single voxel-averaged time series. This scalar representation makes whole-brain connectivity tractable but discards potentially informative dimensions of within-parcel BOLD activity. Here, we represent each parcel by a low-dimensional temporal subspace derived from its principal-component time series and use the RV coefficient to quantify connectivity between regional subspaces. Across Human Connectome Project resting-state and working-memory data, progressively expanding these subspaces reveals reproducible connectivity regimes with distinct network and identifiability profiles. At rest, connectivity constructed from the first principal component identifies individuals more strongly than either voxel-averaged functional connectivity or higher-dimensional subspace representations. During working memory, identifiability instead peaks after secondary components are included, indicating that the distribution of individual-specific information across the regional PCA spectrum depends on cognitive state. These patterns replicate across independent samples and remain robust across multiple parcellation resolutions. Together, our findings show that within-parcel BOLD structure contains identity- and statedependent information that is obscured by scalar regional summaries. Functional connectivity may therefore be better understood as a family of related connectomes indexed by the regional subspace retained, providing a general framework for mapping interactions between low-dimensional neural representations.

Understanding dynamic mechanisms underlying cognition remains a major challenge in human neuroscience. Here, we develop, validate, and apply Multivariate Dynamical Systems Identification with Amortized Variational Inference (MDSI-AVI), a novel computational framework designed to address critical challenges in capturing asymmetric, context-dependent, whole-brain directed interactions while accounting for regional hemodynamic response variability in fMRI data. MDSI-AVI leverages simulation-based inference through forward and reverse variational inference to address the limitations of conventional variational methods in high-dimensional settings. By averaging over uncertainty in hemodynamic response parameters using forward simulation, MDSI-AVI provides well-calibrated posteriors of directed connectivity that scale efficiently to networks with hundreds of nodes. Applied to Human Connectome Project data (N=728), MDSI-AVI reveals new insights into working memory mechanisms, identifying the dorsal anterior insula as a critical hub influencing activity at the whole-brain level. We demonstrate task-dependent modulation of causal influences, where the salience network drives frontoparietal network activity, which differentially influences the default mode and sensorimotor networks depending on working memory load. These whole-brain causal interactions distinguish task conditions with high accuracy and predict working memory performance. Our framework demonstrates reproducible results across whole-brain parcellations, establishing MDSI-AVI as a robust tool for advancing our understanding of circuit dynamics in cognition and disease.

A brain's wiring must satisfy conflicting pressures, from the outside world (be capable) and from the inside (stay within finite resources). These pressures often have opposing requirements. Prior work studies these trade-offs mostly between 2-3 pressures at a time, and thus cannot say how many pressures shape brain wiring or for which trade-off brains decide. To see all possible trade-offs at once and to uncover the set of most important pressures, we generate 25,000 synthetic brain networks and describe each by 29 network properties. We then place 635 human (ages 6-22) and 224 mammalian (12 taxonomic orders) brain networks in this property space. The space is triangular, and follows three pressures: computational capability, robustness to damage, and metabolic efficiency. No network is optimal at all three, and biological brains cluster in one small niche of this triangle. There, they accept higher metabolic cost to gain both robustness and capability. Development and evolution move brains in the same direction; additionally, human maturation first raises capability, and later optimizes for efficiency. This study underscores the triangular trade-off between metabolic efficiency, computational capacity and robustness, and the narrow region brains occupy in this trade-off triangle, visible only from a multi-property, cross-species perspective.

Intrinsic neural timescale (INT) quantifies the persistence of spontaneous neural dynamics and offers a principled metric for characterizing brain-wide temporal organization. Although a hierarchy of INTs has been established during rest, how task engagement reconfigures this organization and how it is constrained by the structural connectome (SC) remain poorly understood. Here, we systematically mapped whole-brain INT using high-resolution fMRI data from the Human Connectome Project during rest and seven tasks spanning working memory, gambling, motor, language, social, relational, and emotion domains. Task engagement induced robust, regionally heterogeneous changes in INT while largely preserving the brain-wide temporal hierarchy across cognitive states. SC-INT coupling remained strong but consistently decreased during tasks, indicating that anatomical architecture continues to constrain INT, although its influence is attenuated under task demands. To investigate these findings mechanistically, we employed a multiscale, whole-brain neuronal-network model, which revealed that INT increase and peak within a broad critical-like regime. Strong SC-INT coupling, as observed empirically, emerged in the subcritical regime, weakened progressively with increasing network excitability, and reversed in the supercritical regime. These results demonstrate that task engagement reconfigures INTs while maintaining their hierarchical organization, suggesting that both resting and task states operate largely within a common subcritical dynamical regime.

A central goal of neuroscience is to establish how similar (or different) brain networks are across individuals, development, psychiatric conditions, and even between biological and artificial brains. Whenever one such comparison is made, it relies on the assumption that we have a measure that produces an accurate and plausible similarity score. Yet, the field lacks one such measure, and the wide variety of existing measures often produces conflicting results. To address this problem, we perform a systematic comparison of 16 established similarity measures. First, we show that different measures consistently disagree on which brain networks are most similar. Second, we rank all measures according to multiple criteria such as biological plausibility, computational efficiency, and sensitivity. We show that DeltaCon is the most accurate measure in terms of parameter recovery, and that it ranks among the fastest and most noise-tolerant. For this reason, we conclude that DeltaCon is the best general-purpose similarity measure. However, no measure ranks best on more than two of the five criteria we assessed, and different measures should be preferred depending on the exact research question. Measure selection can now be made on evidence and should be a crucial decision in any study that compares brain networks.

Brain network organization emerges from ongoing network dynamics that continuously create, maintain, and reorganize connectivity patterns. At both the structural and functional levels, these dynamics shape network evolution throughout the lifespan. Here, we introduce a data-driven framework based on Stochastic Actor-Oriented Models (SAOMs), which model longitudinal network changes as a process of local edge formation and dissolution driven by local-connectivity factors. Applied to functional and structural brain networks from an aging cohort, the resulting models recovered a set of interacting mechanisms that collectively promote hub formation, short-range connectivity, and modular organization. These factors were derived through a novel data-driven factor-selection procedure rather than specified a priori, allowing network mechanisms underlying longitudinal change to be identified directly from observations. Functional networks exhibited substantially higher rates of change than structural networks, suggesting more frequent opportunities for network maintenance and reorganization. Structural networks additionally showed despecification in the hub-forming mechanisms with cognitive impairment. These results support a view of brain network organization as an actively maintained process emerging from ongoing local connectivity dynamics. More broadly, this framework provides a general approach for studying longitudinal network dynamics through the estimation of network-generating mechanisms over time.

A defining feature of the human brain is its multiscale organization, in which regional processes are integrated into coherent whole-brain network architecture. How local functional organization adapts to variation in this global architecture, however, remains largely unknown. Here, we addressed this question by applying an allometric scaling framework to resting-state functional magnetic resonance imaging data from four large-scale datasets spanning different cultures, cognitive states, developmental stages, and clinical conditions. We found that human functional networks were organized according to a robust and spatially heterogeneous connectivity allometry, whereby regional functional connectivity scales nonlinearly with whole-brain connectivity strength. This scaling architecture closely followed the cortical sensorimotor-association hierarchy, with lower-order systems exhibiting supralinear scaling and higher-order systems exhibiting sublinear scaling. Connectivity allometry was further associated with multiscale neurobiological organization, including regional metabolic properties, neurotransmitter receptor distributions, and transcriptomic landscapes, indicating that local-global functional scaling is embedded within fundamental biological gradients of cortical organization. Moreover, the scaling pattern and its biological associations were highly reproducible across independent cross-cultural cohorts. Importantly, connectivity allometry remained remarkably stable across working memory conditions regardless of cognitive load, emerged by late childhood, and was broadly preserved in children and adolescents with autism spectrum disorder despite region-specific alterations. Together, these findings establish connectivity allometry as a previously unrecognized organizational principle of the human functional connectome and provide a quantitative framework linking regional functional organization to whole-brain network architecture in health and disease.

Despite decades of extensive research, the complex architecture underlying human brain signaling remains incompletely understood. Previous work investigating the relationship between brain glucose metabolism and functional connectivity (FC) employed whole-brain analysis, without accounting for network interactions, dependencies or hierarchies. Here, we assess network-specific differences in the relationship between FC and metabolic demand, using [18F]FDG PET/MR data from three independent datasets. The metabolic cost of FC, i.e., the change in glucose demand associated with corresponding changes in FC, was modeled at rest, during cognitive task performance and in Alzheimer's disease (AD). Our findings reveal network-specific differences in metabolic cost, with the default mode (DM), somatomotor (SM) and fronto-temporal networks accounting for highest, intermediate and lowest metabolic demands, respectively. Similarly, time-resolved variability of FC demonstrated highest costs for states with DM network involvement and lowest for SM network participation. This relationship was reversed in participants with AD, who exhibited decreased demands in the DM network and increased costs in the SM network. Cognitive performance consistently revealed cost reductions in the DM and SM networks, as well as increases in task relevant networks. Together, these results highlight the flexibility of functional network architecture associated with cognitive demands and neuropathology, and shed light on the complex interplay between glucose metabolism and network interactions.

Why are some individuals able to recollect past events and imagine future events more richly than others? Here we examine how structural connectivity within a 'core network' supporting mnemonic cognition, substantially overlapping with the canonical default network, underpins inter-individual variation in autobiographical thought. We analysed state-of-the-art magnetic resonance imaging data from 109 adults collected on a high-gradient 3T Connectom scanner, in relation to their Survey of Autobiographical Memory (SAM) scores - a psychometric measure of self-reported episodic autobiographical, semantic, spatial, and future thinking abilities. We anatomically defined a core network, employed streamline tractography to derive participant-specific structural brain networks, and weighted their connections with the myelin water fraction derived from quantitative multicomponent relaxometry. We investigated associations between graph theoretical metrics of the core network and self-reported mnemonic abilities. We found a negative association between future thinking abilities and characteristic path length, and a positive association between future thinking abilities and mean clustering coefficient. Further analysis showed that the node-specific clustering coefficient of most core network nodes was positively correlated with future thinking scores, with frontotemporal nodes showing the strongest associations. Our findings indicate that both efficient integration and local segregation of the core network are important for trait-level future thinking.

Much of our knowledge is structured in networks, but how the brain represents such networks remains unclear. In most nested knowledge structures a subset of the nodes will be highly connected to other nodes in the network. High levels of connection can exist either locally (high degree centrality) or at a global level (high closeness/betweenness centrality). Here, we explored how the human brain represents network structure using functional magnetic resonance imaging (fMRI) and a novel learning paradigm. During training, participants learned the transition structure for travel between a set of fictious partially connected alien planets. Despite encountering only individual connections, participants choices indicated they could infer the broader network structure. Next day, they viewed each stimulus during a cover task while undergoing fMRI. Representational-similarity analysis showed that shortest-path distances within the learned graph were encoded in the posterior hippocampus and right retrosplenial complex, whereas global connectivity (closeness/ betweenness centrality) was represented in the left retrosplenial complex. These findings extend the view that brain networks associated with spatial navigation also process abstract relational knowledge using principles akin to mapping physical space. The results are also consistent with proposals that the hippocampus represents distance information and that the retrosplenial cortex acts as hub for integrating recently acquired knowledge.

Two decades of functional connectivity research have established schizophrenia as a disorder of distributed dysconnectivity, with a robust thalamocortical signature: reduced prefrontal and increased sensory coupling. A fundamental issue is that functional connectivity is undirected, operates at a single slow timescale, and cannot reveal causal direction. Moreover, the mismatch between BOLD sampling speed and neural dynamics can hide edges, fabricate spurious ones, and reverse the apparent orientation of causal relationships. To overcome these limitations, we introduce a general framework for estimating directed causal graphs from fMRI that explicitly accounts for temporal undersampling. We apply RnR, a causal discovery method built on the rate-agnostic RASL framework, to resting-state fMRI from the multi-site FBIRN cohort. Rather than returning a single directed graph, RnR recovers an equivalence class of directed graphs consistent with the observed data, each annotated with the sampling rate that would produce it and classifies each estimated orientation by its stability across inferred rates. This provides a principled basis for distinguishing directed interpretations that are safe to trust from those that are timescale-contingent. Benchmarking against five single-timescale estimators on schizophrenia data, RnR recovered substantially more group-differentiating directed edges, reproducing the fields most replicated finding in directed form: a sensory-to-visual hyperconnectivity hub oriented from the post-central gyrus component to primary visual cortex. Through simulation, we show that coarse spatial resolution has shielded single-timescale methods from the full effects of undersampling, whereas finer parcellations will require explicit undersampling modeling. This work reframes fMRI effective connectivity estimation by treating undersampling as a fundamental property of the measurement, enabling directed interpretations that are grounded in the data-generating process.

Lesion Network Mapping (LNM) is a method used to map symptom-related circuits in the human brain. Recent work has raised concerns about the method and the specificity of LNM-derived networks, which often show high spatial overlap across unrelated conditions. Null models have been proposed to address the raised limitations1. Useful statistical tools in network neuroscience, they are reference procedures; in LNM, the normative connectome matrix C remains the source of the derived networks also under null modelling. Their broader role as a general solution for LNM therefore warrants careful evaluation. Here, we examine the proposed null models step-by-step and test them empirically across multiple datasets. We show that the proposed null-model correction effectively only replaces C with a transformed matrix C' (e.g., C' ~ C - mean(C)), one that is directly derived from C and retains much of its low-dimensional structure. Consequently, null-model-corrected LNM maps remain shaped by the same standard set of connectome-derived patterns and continue to show repetition and non-specificity across unrelated conditions. We discuss the scope and limitations of null models for LNM, showing that they can statistically benchmark LNMs, but that their application does not resolve the core limitation of non-specificity of LNM networks.

Neural sequences, characterized by neurons or groups of neurons that fire one after the other, have been observed in multiple brain regions, across species, and are known to underlie a diversity of brain functions. To flexibly support behaviour and cognition, neural sequences exhibit much variability in properties like their temporal width, baseline, and peak firing rate. Despite this variability and the central role that sequences play in supporting brain function, a framework that explains how flexible sequences are generated and dynamically maintained within a circuit is still missing. Here we go beyond traditional approaches that investigate a one-to-one relationship between network connectivity and specific sequential dynamics. Instead, we train recurrent neural network models to generate a repertoire of sequential dynamics and characterize the obtained connectivity matrices. We found that different connectivity matrices can generate the same neural sequence, yet all connectivity matrices that generate a specific sequence share a common connectivity profile, defined here as the average weight between pairs of neurons as a function of their distance in the sequence ordering. It is the connectivity profile, as opposed to the connectivity matrix, that serves as a fingerprint of the sequential dynamics and shapes the network response to perturbations of the neural activity. Our model predictions were consistent with results obtained from experimental data recorded across brain regions and across species. Finally, we demonstrated that neural sequences can facilitate and constrain the formation of a large repertoire of sequences in downstream brain regions, with the potential of acting as scaffolds for a wide range of computations. Altogether, our results explain how network connectivity can generate a diversity of neural sequences across circuits and how those sequences can be flexibly adapted. Our framework reveals sequences as a common algorithm to support brain function across brain regions and species.

Network measures of the ageing connectome are dominated by magnitude: connection strength and density decline, and the topological summaries built on them decline with them. Whether the geometry of the network follows the same course is not known, because the quantities in common use do not separate how strong a connection is from how it sits among the connections around it. We computed the Ollivier-Ricci curvature of every edge in structural connectomes from 307 participants spanning the adult lifespan, a quantity defined by optimal transport between the neighbourhoods of connected regions, and asked how it changes with age. The total geometric separation between within-network and between-network connections did not change across seven decades. Underneath that constancy, individual network pairs moved substantially and in opposite directions, gaining curvature around the salience and ventral attention system and losing it between the control and default mode networks. Curvature and connection strength reached half of their age-related variation almost four decades apart, and a small set of prefrontal nodes moved against the global trend. Ageing appears to conserve the local redundancy of the structural connectome in total while relocating it, on a timescale distinct from that of connection strength.