Network Neuroscience

The Allen Mouse Brain Connectivity Atlas (AMBCA) is widely used to represent structural connectivity in the mouse brain. The AMBCA consists of tracer injection experiments where neuronal projections axonally connected to the initial injection site are labelled. The resulting whole-brain structural connectomes, derived from a subset of these experiments in C57BL/6 mice, have been used in several studies of connectomic architectures. However, through close inspection of n=437 distinct experiments used in a publicly-available connectome (Knox et al., 2018), we observed experiments with off-target injections, diffuse projections, unrealistically small injections and projections, and anatomical misalignments, affecting the accuracy and applicability of these connectivity experiments. We applied a combined automated and manual quality control (QC) and identified n=56 ([~]13% of the original n=437) experiments representing a wide variety of injection and projection failures across the brain. Automated QC was used to detect extreme injection and projection sizes and misalignments, while manual QC was used to detect subtle off-target tracer spreading. Using the remaining n=381 experiments, we rebuilt two different connectomes using previously-published methods; specifically: the regionalized voxel model from Knox et al. (2018), and the homogeneous model from Oh et al. (2014). Our rebuilt connectomes show strong losses in connectivity between regions with limited evidence of structural connectivity by other methods (e.g. hippocampus-medulla, cerebellum-isocortex) and gains in connectivity between regions with strong connectivity evidence (hypothalamus-cerebellum, hypothalamus-isocortex). Finally, we analyzed the rich club and community organization to demonstrate the potential downstream impacts on the representation of the overall structural connectome architectures of our QCd connectomes and observed subtle whole-brain organizational changes. We present our rebuilt connectomes, and particularly highlight the regionalized voxel model, as more accurate representations of structural connectivity derived from the AMBCA.

The brains intrinsic organization into resting-state networks has long been suggested to be fundamental for the offline support of mental processes. Extensive task-based evidence support the relevance of the crosstalk between network segregation, supporting systems specialization, and network integration, allowing to flexibly implement complex behavior. However, only scarce evidence focusing on few behavioral measures directly link changes in these network properties at rest with interindividual differences in behavior. In this work, we investigated whether the maintenance of behavior is associated with a segregated intrinsic resting-state networks organization. Using a comprehensive set of behavioral measures spanning cognition, emotion, and personality together with resting-state functional magnetic resonance imaging from the human connectome project, we performed functional connectivity prediction of behavior combined with model interpretability and latent connectivity-behavior factors extraction. We then assessed whether connectivity-behavior patterns were associated with changes in segregation or integration based on GINNA, a 33 resting-state-networks atlas with cognitive characterization, providing opportunities for comparison of the involved cognitive processes. We found that connectivity relevant for behavior organizes into 3 main latent dimensions, summarizing Cognition, Positive Affect and Negative Affect. Crucially, we show that only Cognition, but not Affect, was associated with global network segregation and reduced network integration, suggesting that Cognition is supported by an intrinsic segregated network architecture, necessary for modular specialization, while Affect may rely on distributed mechanisms across intrinsic brain networks. We further reveal differential resting-state-networks involvements, with Cognition associated with the segregation of higher-level resting-state-networks, and the integration of lower-level, visual networks. All in all, the present results reinforce the view that cognition rests upon a segregated intrinsic brain architecture, fostering the maintenance of specialized cognitive modules.

Dopamines influence on large-scale network dynamics, especially on the default mode network (DMN), remains uncertain, as fMRI studies have produced mixed results. One likely contributor to these discrepancies is reliance on traditional functional connectivity analyses, which typically derive a single metric (e.g., the Pearson correlation coefficient) from the entire time series and thus fail to capture network dynamics. To address this issue, we combined a dopaminergic challenge (mazindol, a dopamine transporter [DAT] reuptake inhibitor), PET, resting-state fMRI, and hidden Markov modeling (HMM) to characterize time-varying alterations in human large-scale functional networks following acute DAT blockade. We found that mazindol-induced increases in endogenous dopamine altered the balance between the brains functional "metastates," two recurrent higher-order network configurations that each encompass multiple HMM-derived brain states. Mazindol increased the time participants spent in an internally oriented cognitive metastate and decreased the time spent in a sensorimotor-perceptual metastate, with the DMN showing the most pronounced lengthening. In exploratory analyses, declines in [{superscript 1}{superscript 1}C]raclopride binding, a PET index of D2 dopamine receptor availability reflecting increased striatal extracellular dopamine levels, tended to show a positive correlation with the prolongation of these cognitive states. These findings indicate that dopamine is closely linked to shifts from sensorimotor and perceptual to cognitive brain metastates, potentially underpinning the prioritization of internally oriented over externally driven psychological processes. Our results highlight the importance of dynamic, time-resolved connectivity approaches for understanding neuromodulatory actions in the human brain and suggest that dopamine helps regulate the dynamic balance between functionally competing large-scale brain networks.

Network analysis of human brain connectivity provides a fundamental framework for identifying the neurobiological mechanisms that cause cognitive variations and neurological disorders. However, existing diagnostic models often treat structural connectivity (SC) as a fixed or optimal topological scaffold for functional connectivity (FC). This consequently overlooks the higher-order dependencies between brain regions that are critical for characterizing pathological alterations. Moreover, the distinct spatial organizations of SC and FC complicate their direct integration, as naive alignment methods may distort the inherent nonlinear patterns of brain connectivity. To address these limitations, we propose the Graph Diffusion Optimal Transport Network (GDOT-Net), which models disease-related topological evolution and achieves precise alignment between SC and FC. Unlike existing diffusion studies, the proposed model introduces an evolvable brain connectome modeling approach to infer the complex topological structure of brain networks, unveiling higher-order connectivity patterns linked to specific neuropsychiatric disorders. Furthermore, GDOT-Net incorporates a Pattern-Specific Alignment mechanism, leveraging optimal transport to align structural and functional topological representations in a geometry-aware manner. To capture nonlinear topological relationships between brain regions, a Neural Graph Aggregator Module was developed, which adaptively learns complex node interaction patterns in brain networks. By leveraging this module, GDOT-Net generates highly discriminative representations that form a robust basis for the precision diagnosis of brain disorders. Experiments on REST-meta-MDD and ADNI demonstrate that GDOT-Net surpasses SOTA methods in uncovering structural-functional misalignments and disorder-specific subnetworks. The source code is publicly available at this Link.

Linking structural connectivity (SC) to functional connectivity (FC) through mechanistic models remains challenging in network neuroscience. In this study, empirical data of diffusion magnetic resonance imaging (dMRI) and resting-state functional MRI (rs-fMRI) were used to reconstruct SC and FC of a swine connectome. We evaluated a structurally constrained Kuramoto phase-oscillator framework to reproduce resting-state FC and then assessed the models sensitivity to traumatic brain injury (TBI) and its longitudinal progression post-TBI. A joint tuning procedure was implemented to calibrate data-informed natural frequencies and global coupling strength. The tuned Kuramoto model was then used to evolve oscillator phases constrained by the SC, followed by a Balloon-Windkessel hemodynamic model. The optimized model produced significant edge-wise correspondence between averaged simulated FC and the empirical FC (r = 0.61, p < 0.001). Graph-theoretical analysis across network densities (30-50%) showed strong agreement for global efficiency, characteristic path length, and clustering coefficient, while modularity and small-worldness exhibited deviations. Longitudinal analysis of the swine TBI dataset revealed modest reductions in structure-function coupling over time but no significant differences across injury severities. These results demonstrate that optimized Kuramoto models can reproduce key functional network features while preserving inter-subject variability.

BackgroundLesion network mapping (LNM) has emerged as a powerful tool for linking focal brain lesions to distributed functional networks. However, the biological specificity of these networks has been questioned. Recent mathematical derivations suggest that LNM-derived maps may trivially track the normative connectomes global degree vector rather than specific symptom-related topography, potentially rendering them biologically nonspecific. MethodsWe introduced a rigorous validation pipeline to distinguish true network specificity from low-dimensional connectome artifacts. We projected lesion connectivity maps into a low-dimensional feature space defined by the principal gradients and eigenmodes of the normative connectome. We applied this framework to a large-scale dataset of 858 lesions associated with four distinct clinical cohorts: obsessive-compulsive disorder (OCD), schizophrenia, aphasia, and epilepsy. We performed multivariate classification to determine if symptom-associated lesions occupied distinct regions of the functional manifold compared to null distributions. ResultsOur analysis revealed a sharp dissociation in network specificity across disorders. While schizophrenia-associated lesions were indistinguishable from null models (Accuracy=0.51, p=0.412), confirming the "degree artifact" hypothesis for this cohort, other disorders displayed significant network specificity. Lesions associated with OCD (Accuracy=0.58, p=0.036), aphasia (Accuracy=0.60, p=0.007), and epilepsy (Accuracy=0.61, p=0.002) occupied distinct regions of the functional manifold significantly different from the normative connectome baseline. ConclusionsThese findings demonstrate that while LNM is sensitive to connectome-level artifacts, it retains genuine biological specificity for distinct clinical phenotypes. The proposed linear projection framework offers a standardized, computationally efficient benchmark for assessing network specificity against methodological noise.

Functional brain imaging data can provide a window into the task-driven network states that shape brain function (or dysfunction). Conventionally, these network states can be represented as bivariate correlation matrices (which are formed from fMRI time series from multiple brain regions/nodes within any task window). Here, we treat these conventional connectivity matrices as connectivity terrains in order to recover local structure. In principle, any such terrain can be traversed node by node, where from any node, one can move towards its nearest functional neighbor (i.e., its maximally correlated node). In terrains with meaningful structure, such traversals across multiple nodes should converge to attractor nodes; here, the nodes that flow into a shared attractor form an attractor basin, which effectively is a sub-network within the system. Extant methods (e.g., degree distribution and characteristic path length) can summarize global network properties but cannot identify attractor nodes and basins. Here, we construct a new relation, called transitive maximal correlation (TMC) that can recover attractors and attractor basins in connectivity terrains. Node A is said to be transitively maximally correlated to node B if and only if B is an attractor into which A flows. We first develop the mathematical basis for deriving a TMC matrix TMC(M) from a bivariate correlation matrix M (before explaining this with hypothetical data). We next apply the TMC relation to connectivity terrains derived from real fMRI time series data, where these data were acquired in two distinct task-domains (that varied in their extent of cross-cerebral demand): i) associative learning and ii) visually guided motor control. We show that TMC is remarkably sensitive to inter-hemispheric structure in the connectivity terrain; here, attractor pairs that were inter-hemispheric homologues were more likely to be observed for the cross-cerebral learning task, than the more circumscribed motor-control data. We confirm the condition-specific sensitivity of TMC showing that observed attractor basins differed significantly across conditions of the learning task. Finally, we demonstrate that TMC complements graph theoretic constructions like path length and betweenness centrality. We suggest that TMC is a mathematically sound and novel method for capturing functional properties of brain networks.

Autism spectrum disorder (ASD) is increasingly conceptualized as a disorder of large-scale functional brain network organization rather than isolated regional abnormalities. Graph-theoretical analysis provides a principled framework for characterizing such distributed network reconfiguration. Here, we investigated global, nodal, and system-level functional network topology in ASD using a large, multi-site resting-state fMRI dataset. Resting-state fMRI data from 996 participants (428 ASD, 568 healthy controls) were obtained from the ABIDE I and II data repositories. Whole-brain weighted resting state functional networks were constructed using Pearson correlation. To improve robustness and reduce threshold-selection bias, graph-theoretical metrics were computed across a range of network sparsity thresholds and summarized using an area-under-the-curve (AUC) approach. At the global level, ASD was associated with reduced assortativity, and local efficiency, along with altered normalized characteristic path length ({lambda}), indicating local information processing and subtle deviations in network integration relative to an optimal small-world topology. Nodal analyses revealed non-random, region-specific alterations predominantly affecting higher-order associative systems. Increased nodal centrality and hub-like properties were observed in frontal and parietal regions within the frontoparietal control and dorsal attention networks, whereas reduced nodal efficiency and centrality were primarily localized to limbic and anterior temporal regions, including the temporal pole. System-level analyses, controlling for age, sex, and acquisition site, further demonstrated network-specific topological reorganization across multiple functional systems. Clinical correlation analyses identified modest but significant associations between nodal topology and core ASD symptom severity, particularly within default mode, limbic, and attention networks. Together, these findings indicate that ASD is characterized by subtle yet reproducible multi-scale reorganization of functional brain network topology, supporting a systems-level account of ASD neurobiology and highlighting the clinical relevance of large-scale network architecture.

Engaging with others in social scenarios can result in the alignment of brain activity between individuals. Dyadic (i.e., hyperscanning) studies typically estimate average region-specific levels of connectivity between brains (as opposed to within brains) across a given task to quantify brain activity alignment. This approach assumes symmetric interactions with equal and mutual adaptation from both dyad members, excluding asymmetric (e.g., leader-follower) contexts. Such approaches also obscure spatial dynamics (i.e., relationship between different brain regions) and temporal dynamics during unfolding interactions. To overcome these challenges, we took a data-driven approach to quantify within- and between-brain connectivity during collaborative drawing among dyads. Specifically, we used sliding windows and Riemannian-geometry-based k-means clustering to identify recurrent two-brain states while 61 dyads drew alone and together at 6 weekly timepoints. Thirty dyads comprised young adults only (same generation) and 31 dyads comprised one older and one younger individual (intergenerational). We identified 7 two-brain states, 3 of which were specific to real (not pseudo) dyads. One two-brain state showed convincing evidence of sensitivity to collaboration context: During collaborative drawing, low-to-medium between-brain connectivity and prominent within-brain connectivity in bilateral IFG in a single dyad member arose for longer periods in intergenerational than same generation dyads. No two-brain state showed evidence of longitudinal changes across sessions. These findings inform recent accounts of neural dynamics that emphasise the complementary roles of within-brain and between-brain connectivity. Furthermore, they suggest that state-based analyses can inform neural dynamics in a way not captured by traditional analysis techniques. HighlightsO_LITwo-brain states can characterise within- and between-brain connectivity. C_LIO_LISeven two-brain states identified using data-driven approach. C_LIO_LIIntergenerational collaborative drawing linked to asymmetric connectivity. C_LI

In studying the brains functional connectivity and its associations with clinically observed assessments, novel learning frameworks modeling its network properties in conjunction with assessment variables are crucial to uncover variable-specific patterns via meaningful encoding and reconstruction. We present a generative framework for modeling human brain functional connectivity features while retaining key network metrics and differences associated with demographic and cognitive variables. A conditional graph variational autoencoder is employed to encode static functional network connectivity (sFNC) features into a latent representation, which is then utilized for the dual purpose of reconstructing sFNC data conditioned on variables such as biological sex or fluid intelligence, and identifying discriminative connectivity features associated with the conditioning variables in the latent space. Using over 20,000 subjects from the UK Biobank, our model demonstrates high-fidelity reconstructions that preserve condition-specific network patterns, while the latent space captures interpretable patterns associated with these variables. The group differences in latent space are highlighted by one-hot probing of the latent dimensions and forward mapping to connectivity patterns. This approach provides a scalable, network-informed framework for studying brain functional connectivity and its associations with individual differences, offering potential applications in characterizing functional signatures for mental health conditions via clinically observed assessment variables. AUTHOR SUMMARYTo enable the modeling of the brain functional connectivity network for encoding and reconstructing assessment-specific differences, we propose a conditional graph-based generative framework for modeling human brain functional connectivity while accounting for demographic and cognitive differences. Using a conditional graph variational autoencoder, our approach learns interpretable latent representations of functional connectivity networks derived from fMRI data. Evaluated on over 20,000 UK Biobank subjects, the model accurately reconstructs connectivity patterns outperforming baseline architectures and preserves differences associated with biological sex and fluid intelligence. By probing the latent space and mapping latent dimensions back to brain networks, we identify condition-specific connectivity features in an interpretable manner. This work provides a scalable, network-informed approach for studying individual differences in functional brain organization.

The connectivity of brain networks is extremely sparse due to metabolic, physical and spatial constraints. Although wiring sparsity can confer computational advantages for biological and artificial neural networks, sparse networks require fine parameter tuning and exhibit strong sensitivity to perturbations. How brains achieve their efficiency and robustness is unclear. Here we addressed this by analysing the dynamical properties of Echo State Networks with wiring based on the Drosophila melanogaster fruit fly connectome, compared with sparsity-matched random-wiring networks. We evaluated these networks on a set of eight cognitive tasks, and found that connectome-based neural networks (CoNNs) typically showed narrowly distributed task engagement across their neurons. The importance of a neuron for task performance correlated with its node degree, local clustering, and selfrecurrency, and these correlations were stronger in CoNNs than in random networks. CoNNs were more robust to neuronal loss, retaining their task performance and beneficial dynamical properties such as criticality and spectral radius better than random networks. Similarly, CoNNs were more robust to hyperparameter variations in both input and recurrent weight scaling. Using theoretical arguments and numerical simulations, we show that excess CoNN node self-recurrency is sufficient to explain this enhanced robustness. Overall, these results identify non-random features of connectome wiring that allow brains to reconcile extreme sparsity with reliable computation. SignificanceBrain networks support robust computation even though they operate under extreme wiring sparsity due to metabolic and spatial constraints. While sparse networks typically require fine-tuning and are sensitive to perturbations, we show that biological connectomes support specialised, efficient task engagement and remain robust to neuron loss and parameter variation. We identify excess neuronal selfrecurrency as a key structural feature underlying this stability. These results reveal how non-random connectivity stabilises computation in extremely sparse networks, providing principles for understanding brain function and designing robust, efficient artificial neural systems.

Functional brain networks are conventionally constructed using measures of direct temporal synchrony between neural signals, implicitly restricting connectivity to scale-specific interactions. Here, we introduce an alternative framework in which interregional similarity is defined through correlations between multiscale entropy (MSE) profiles, enabling network construction based on scale-dependent dynamical structure rather than instantaneous alignment. Using resting-state fMRI data from the Human Connectome Project (N = 1003), we systematically compare MSE-based networks with conventional time-series-based networks across conventional/spectral graph-theoretical, and information-theoretic measures. We show that MSE-based networks exhibit stronger modular organization, enhanced local segregation, and distinct global integration patterns, reflecting a reorganization of functional architecture when multiscale dynamics are taken into account. Importantly, MSE-based networks demonstrate substantially greater sensitivity to biologically meaningful variability, revealing robust and reproducible sex differences across multiple network measures, in contrast to the limited and inconsistent effects observed in conventional networks. These findings suggest that multiscale representations provide a more informative and biologically grounded basis for functional brain network construction, capturing aspects of neural organization that are not accessible through direct synchrony alone.

White matter structural connectivity constrains large-scale brain communication, yet most network models do not account for biologically meaningful differences between connections. Although axonal diameter and myelination influence neural signaling at the microscale, how these features shape systems-level functional connectivity remains unclear. Here, we test whether structural connectomes weighted by white matter microstructure give rise to distinct communication regimes that differentially predict multimodal functional connectivity. Combining quantitative MRI and advanced diffusion modeling, we constructed whole-brain networks weighted by tract caliber and multiple myelin-sensitive measures. To these, we applied routing- and diffusion-based communication models and used the resulting communication metrics to predict haemodynamic and frequency-resolved electromagnetic connectivity. Myelin-weighted networks preferentially enhanced long-range communication efficiency and redistributed spectral energy toward globally integrative topological eigenmodes. In contrast, caliber-weighted networks emphasized mesoscale organization and short-range communication. Across nested regression models controlling for geometric embedding and network topology, myelin-sensitive communication explained unique variance in functional connectivity with effects varying systematically across cortical systems and frequency bands. The strongest coupling was observed for alpha-band connectivity in association and attentional networks, consistent with a role for myelin-dependent communication delays in supporting long-range alpha synchrony. These findings demonstrate how distinct white matter microstructural features give rise to heterogeneous large-scale communication regimes: tract caliber and myelin bias communication toward locally specialized and globally integrative architectures, respectively. By integrating biologically informed connectomics with communication modeling and multimodal functional data, this work advances a mechanistic account of how white matter microstructure shapes macroscale brain dynamics.

Brain decoding from fMRI data using artificial neural networks traditionally operates at the regional level, identifying which brain areas activate during tasks but ignoring how these regions interact through structural networks. While Graph Neural Networks can capture connectivity, they require prohibitively large datasets for typical neuroscience studies. We introduce a message-passing mechanism that allows a shallow neural network to incorporate structural connectivity, enabling network-level interpretation from limited data. Using motor task data from 30 Human Connectome Project subjects, we evaluate seven structural connectivity matrices derived from deterministic and probabilistic tractography. Our approach achieves 83.0% classification accuracy while revealing functional network organization. We demonstrate that sparser, anatomy-driven connectivity matrices outperform dense alternatives, and that normalizing for network size improves model performance. Critically, our method is capable of exposing structural pathways contributing towards classification, distinguishing between complete network recruitment and selective regional activation. This approach bridges the gap between high-performance brain decoding and biological fidelity of the model, enhancing neuroscientific understanding, with implications for analyzing network dysfunctions in neurological disorders such as Alzheimers disease (AD), attention deficit hyperactivity disorder (ADHD), autism spectrum disorder (ASD), bipolar disorder, mild cognitive impairment (MCI), and schizophrenia.

Reading is supported by rapid and flexible coordination of neural activity across distributed brain regions. We have previously shown that left fusiform gyrus (FusG) provides a bridge flexibly linking the visual form analysis required for reading with the language system. Here, we investigate the dynamic organisation of an extending reading network encompassing classical perisylvian language areas and FusG. We do so by applying multilayer network modelling to language fMRI data acquired through the Australian Epilepsy Project, using a paradigm that contrasts reading with visuospatial judgements. The dataset included 201 participants with left dominant language, both with and without seizure disorders. We hypothesised that the relative strength of dynamic inter-actions within this extended language network is associated with reading ability. Time resolved functional connectivity was estimated using a sliding window Pearsons correlation approach, and the resulting connectivity matrices were entered into a multilayer community detection algorithm to quantify spatiotemporal community structure within the reading network. We concentrate our analyses on allegiance, the probability that a pair of regions is assigned to the same community over time. Our results show that community structure within the reading network is characterised by a preference for within hemisphere assignment over cross hemisphere assignment, as well as higher nodal allegiance among left language regions compared with their right hemisphere homologues. As anticipated, within versus between network allegiance followed a similar gradient in both language and attention networks: lowest between left language and right attentional regions, intermediate between the left FusG and each respective network, and highest within-network (left language or right attention). Importantly, as hypothesised, reading ability was associated with FusG-inferior frontal gyrus (IFG) interactions: higher left FusG-left IFG allegiance correlated with better reading performance, whereas increased right FusG-left IFG allegiance correlated with poorer reading. These findings highlight hemispheric asymmetries in the dynamic organisation of the reading system and provide novel evidence linking individual differences in reading ability to network level dynamics. Our findings align with a developmental literature suggesting that as reading proficiency improves, there is a shift from bilateral to unilateral left occipitotemporal engagement.

A central objective in neuroscience is to elucidate how the brain generates complex dynamic activity through the interactions of brain areas. In this study, we utilized Interaction Network, a graph neural network model, to develop a computational framework for predicting whole-brain cortical blood oxygenation level dependent (BOLD) signals. We derived an Inter-Regional Interaction (IRI) metric to quantify information exchange among brain areas probing the underlying dynamical mechanisms. In addition, the total IRI emitted from each brain region was calculated and defined as the IRI sent by region (RS-IRI). Our model predicted the following 10 time points BOLD activity from initial BOLD signals, and achieved a mean absolute error of 0.04. The predicted functional connectivity (FC) achieves a correlation coefficient of 0.97 compared to the empirical FC. The fluctuation amplitude of the IRI increases with the length of the connection and the largest RS-IRI oscillation amplitude is observed in visual areas. The RS-IRI demonstrates a hierarchical organization, characterized by more concentrated distributions in association regions and larger fluctuation amplitudes in unimodal regions. Applying our approach to Alzheimers disease (AD), we demonstrate that the frequency-specific amplitudes of IRI oscillations discriminate AD patients from healthy controls and correlate with Mini-Mental State Examination scores. Together, this work presents a deep learning-based framework for modeling brain dynamics as well a quantitative index of inter-areal interactions, and offers a new perspective for disease characterization. Author SummaryThe human brain comprises distinct regions that interact through complex fiber tracts, forming the functional dynamics for diverse cognitive processes. We employed fMRI to assess functional activity and DTI to reconstruct fiber tract connectivity. To elucidate how brain function emerges from these inter-regional interactions, we developed a novel computational framework based on Graph Neural Network (GNN) to model the brains interactive dynamics for its capacity to uncover hidden and intricate patterns within data. From this model, we derived a quantitative metric termed Inter-Regional Interaction (IRI), which characterized the fine-grained, dynamic fluctuations in communication between brain areas. Our results suggest that this GNN-based model can accurately simulate brain functional activity and provide a quantitative description of neural interaction patterns. Applying this model to a cohort of Alzheimers disease patients, we demonstrated that the IRI metric not only effectively distinguished patients from healthy controls but also significantly correlated with clinical cognitive performance (MMSE scores). This approach advances our understanding of the fundamental principles of brain function and offers a promising tool for identifying the underlying mechanisms of neurological disorders.

Diffusion MRI provides multiple quantitative descriptors of neural tissue derived from distinct physical and biophysical models, each probing different aspects of cerebral microstructure. While these metrics are often interpreted independently, their joint statistical structure may encode information about latent biophysical properties that constrain and organize brain microarchitecture. Here, we investigate whether stable patterns of multimodal diffusion MRI covariation reveal underlying biophysical diffusion properties of cerebral microstructure in the squirrel monkey (Saimiri sciureus). Using a high-resolution multimodal diffusion MRI dataset acquired at 11.7T (400 m isotropic resolution) from 15 adult subjects, we analyzed tensor-based metrics alongside advanced multicompartment and axonal models, including NODDI and ActiveAx. Voxelwise and region-level correlation analyses were performed across multiple spatial scales to examine the structure, redundancy, and stability of relationships among diffusion-derived maps. We show that metrics originating from conceptually distinct models consistently organize into a low-dimensional structure characterized by stable clusters of covariation, robust to changes in parcellation and spatial aggregation. These patterns cannot be explained by trivial metric redundancy alone, but instead suggest convergence toward a reduced set of effective biophysical degrees of freedom governing diffusion behavior in neural tissue, including axonal organization, neurite density, orientation dispersion, and isotropic diffusion components. We interpret these covariation structures as manifestations of latent biophysical diffusion properties--emergent tissue states that are not directly observable through any single metric but become apparent through their structured relationships. From a systems neuroscience perspective, the stability of these latent dimensions supports the view that adult cerebral microstructure reflects the steady-state outcome of neurodevelopmental dynamics operating under biophysical constraints. Rather than providing a regional atlas, this work proposes a conceptual framework for interpreting multimodal diffusion MRI as a projection of an underlying low-dimensional biophysical state space organizing cerebral microstructure in primate brains. Key pointsO_LIMultimodal diffusion MRI metrics derived from distinct biophysical models exhibit stable and non-trivial patterns of covariation, revealing a low-dimensional organization of cerebral microstructure. C_LIO_LIThese covariation structures suggest the existence of latent biophysical diffusion properties that are not directly observable through individual metrics but emerge from their structured relationships across spatial scales. C_LIO_LIThe stability of these latent dimensions supports the interpretation of adult cerebral microstructure as a structurally stable outcome of neurodevelopmental dynamics governed by biophysical constraints. C_LI

Understanding how cognitive demands and pathology reshape large-scale functional connectivity (FC) requires methods that are both multivariate and region-specific. Here we introduce One-class SVM-based Connectome Anomaly Recognition (OSCAR), a normative modelling framework that detects condition-related deviations in the multivariate connectivity profile of a brain region. OSCAR learns the distribution of region-to-whole-brain connectivity patterns given a reference state sample (e.g. resting-state data; RS) using a one-class support vector machine (OCSVM). The trained models are then applied to FC profiles from a target condition (e.g., task or patient group). The outlier proportions are used to quantify the difference between the reference and target condition. We validated OSCAR on three diverse tasks and a patient cohort with early psychosis. OSCAR consistently identified condition-sensitive regions in networks known to support conflict processing, object-location memory, lexical learning, and early psychosis, respectively, including thalamic and basal ganglia regions. Moreover, it detected additional well-established task- or disease-relevant parcels not captured by the comparison method permutation-based multivariate analysis of variance (perMANOVA). Regions flagged by OSCAR were at least as close, and often closer, to independent task-activation findings than those identified by perMANOVA. These results demonstrate that OSCAR provides an interpretable, region-centred normative modelling approach that is sensitive to subtle multivariate FC deviations, and offers a practical tool for mapping condition-specific reconfigurations of functional brain networks with high external validity, in both experimental and clinical settings.

PurposeIn this study, we characterize the spatiotemporal organization of resting-state metabolic connectivity (RSMC) in the human brain, as measured by [18F]- fluorodeoxyglucose (FDG) functional PET (fPET-FDG). We examine the relationship between RSMC organization and resting-state functional connectivity (RSFC) derived from functional magnetic resonance imaging and other known cortical organizational principles. MethodsResting-state fPET-FDG data from 24 individuals were obtained from a publicly available repository. We characterized local metabolic organization using connectivity-based boundary mapping, with adaptations to account for the low signal-to-noise ratio of fPET-FDG data. We then estimated global metabolic organization through community detection-based network and principal gradient analyses. Furthermore, we examined how metabolic connectivity is shaped by temporal-frequency-specific components of fPET-FDG signal. Finally, we contextualized metabolic organization by relating metabolic gradients to anatomical, functional, and energetic reference measures. ResultsAt the local scale, boundary mapping results indicated structured transitions shaped by a combination of both fast and slow fPET-FDG signals, partly overlapping with RSFC boundary maps. Globally, RSMC analyses revealed a robust metabolic structure organized along a superior-inferior cortical gradient. This pattern remained consistent across network community detection and principal gradient analyses and was primarily driven by low-frequency, minute-scale fPET-FDG dynamics. The identified large-scale metabolic profile aligns closely with several known anatomical and energetic constraints. ConclusionThis study characterizes the spatiotemporal organizational principles of RSMC, deepening insight into the brains energetic framework and providing a basis for future cognitive and clinical investigations of metabolic connectivity organization.

Resting-state functional connectivity (RSFC) and networks (RSNs) provide insight into large-scale brain organization and its disruption in neurological disease. RSNs are most commonly assessed using fMRI, yet its translational use is constrained by high cost, motion sensitivity, and limited feasibility for repeated measurements. Functional near-infrared spectroscopy (fNIRS) offers a portable alternative, but its reliability for RSFC and RSN mapping remains insufficiently established. Near whole-head fNIRS data and fMRI-BOLD signals of corresponding cortical regions were extracted, based on which RSN organization was compared across two independent cohorts of 31 participants each. Cross-modal convergence and divergence were assessed using bivariate and partial correlations across multiple network levels. Edgewise analyses revealed substantial modality differences with bivariate correlations (50-61% of edges), which were markedly reduced using partial correlations (<3%). Group-level connectivity patterns showed moderate cross-modal similarity (r {approx} 0.37). At nodal level, net strength, local efficiency, and path-length differed substantially between modalities, while normalized strength and assortativity were largely comparable. Across nodes, group-level graph-metric distributions were broadly similar for normalized strength, assortativity, local efficiency, and path length (rho {approx} 0.27-0.5). At network-level, fNIRS-derived modules significantly overlapped with fMRI modules, particularly based on bivariate correlations, identifying default mode, attentional, executive, salience, sensorimotor, and visual networks (Jaccard {approx} 0.27-0.5). Overall, fNIRS captured key features of large-scale RSFC and RSN organization observed with fMRI, supporting meaningful cross-modal correspondence and translational utility. While partial correlations enhanced edge-level agreement, they attenuated nodal and modular recovery, suggesting greater suitability for targeted connectivity analyses rather than whole-network characterization.