Biology subjects

Misic, B.

Publications and source records attributed to Misic, B..

At least 19 recordsLinked to original sources

bioRxiv2026

Distinct transcriptomic and hierarchical organization associated with brain hyperconnectivity and hypoconnectivity in autism spectrum disorder

Functional hyperconnectivity and hypoconnectivity in autism spectrum disorder (ASD) are typically treated as opposing expressions of a single circuit-level disturbance, but their molecular and hierarchical basis remains unclear. We combined resting-state fMRI from 1,737 individuals from the Autism Brain Imaging Data Exchange (ABIDE I/II) with gene-expression maps from the Allen Human Brain Atlas and show that hyperconnectivity and hypoconnectivity are dissociable neurobiological phenomena, differing in molecular signatures, cortical-hierarchical embedding, age-group profile, and cognitive associations, rather than a single connectivity axis. Hyperconnectivity was concentrated in higher-order cortical and cerebellar regions and was greater in older participants, while hypoconnectivity was consistent across age groups and localized to subcortical and orbitofrontal systems. ASD was associated with reorganization of the sensory-to-transmodal cortical gradient, most pronounced in association networks. Hyperconnectivity- and hypoconnectivity-associated genes showed partially distinct neurotransmitter profiles and differential embedding within cortical hierarchy, both enriched in transmodal cortex and linked to social-cognitive, perceptual, attentional, and reward-related functions. This dissociation was preserved across developmental stage, sex, and symptom severity. These findings indicate hyperconnectivity and hypoconnectivity are not two poles of one process but two separable components of a reproducible molecular-hierarchical architecture, offering a multi-scale framework linking transcriptomic organization to systems-level brain dysfunction in ASD.

neuroscience↗

bioRxiv2026

Cerebral vasculature shapes the spatial patterning of brain metastases

Primary cancer cells that originate in diverse tissues in the body can spread to the brain through various physiological pathways and form metastatic tumors. The spatial patterning of brain metastases is highly stereotyped across individuals, but the physiological factors that confer regional vulnerability to tumor colonization are poorly understood. Here we map the brain metastases associated with primary breast cancer, lung cancer, and malignant melanoma to the multiscale organization of the brain. We analyze structural magnetic resonance imaging (MRI) data from more than 2, 300 cancer patients with approximately 10, 000 brain metastases to estimate metastatic frequency maps. We then examine whether transcriptional (microarray profiling), functional (functional MRI), and vascular (arterial spin labeling) features can predict the spatial pattern of each brain metastasis type. Our analyses highlight vascular anatomy as an integral determinant of metastatic patterning, particularly in breast and lung cancers. We find arterial border-zones as sites of elevated vulnerability to metastatic invasion. These areas, characterized by slow flow and small-caliber vessels, create a hemodynamic environment that favors the arrest and extravasation of circulating tumor cells. Together, these results provide quantitative support for the long-standing hypothesis that vascular architecture and its biomechanical properties constrain metastatic seeding in the brain. Identifying vascular topology as a key determinant of metastatic patterning suggests that systemic vascular and metabolic factors may contribute to metastatic risk.

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bioRxiv2026

Homophily-informed generative models of brain maps

The structural and functional organization of the brain can be studied across multiple scales, yielding richly detailed topographic brain maps of biological features. What are the underlying forces that shape their spatial patterning? Here we introduce a simple generative model of multiscale brain maps based on the concept of inter-regional homophily: the tendency for regions that are proximal in a given physical, molecular or functional space to display similar biological features. We evaluate the model with respect to six definitions of inter-regional homophily, including physical proximity, structural and functional connectivity, and laminar, receptor and transcriptional similarity, and across 43 empirical brain maps estimated using multiple imaging, electrophysiological and histological technologies. We show that homophilic principles are sufficient to accurately reconstruct many maps, with biological similarity and functional connectivity often contributing more than the brains geometry. We also identify consistent patterns of unexplained variation in maps with low homophily, revealing axes of cortical organization not captured by canonical inter-regional relationships. Finally, we show that homophily-informed generative models can be used to disentangle complex relationships between brain features and make new inferences on how they fit together. Collectively, this work highlights the fundamental contribution of homophily to the topographic layout of numerous biological features of the brain.

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bioRxiv2026

Symptom Dimension-Specific Neurotransmitter Correlates of Psychopathology and Cognition in Early Psychosis

Extended duration of under-treated psychosis (DUP) is among the strongest predictors of poor outcome, yet diagnostic heterogeneity impedes treatment matching, with approximately 50% of patients failing to respond to first-line antipsychotics. Negative symptoms and cognitive impairment are particularly refractory, lacking effective pharmacological treatments. Identifying neurotransmitter systems associated with specific symptom dimensions could accelerate targeted therapeutic development and reduce DUP. We applied Partial Least Squares correlation (PLSc) to derive whole-brain resting-state functional connectivity (RSFC) and anatomical (cortical thickness and subcortical volume) signatures associated with five psychopathology dimensions (positive symptoms, negative symptoms, general psychopathology, mania, and cognition) in a transdiagnostic sample from the Human Connectome Project-Early Psychosis (HCP-EP; n=124). We tested associations with potential confounds including antipsychotic medication dosage and substance use. Signatures were spatially correlated with 21 Positron Emission Tomography (PET)-derived receptor and transporter maps across 9 neurotransmitter systems using the neuromaps toolbox. Significant RSFC signatures emerged for positive symptoms, negative symptoms, general psychopathology, and cognition, but not mania. The negative symptom RSFC signature correlated with norepinephrine transporter (NET; {rho}=.40, q=.030) and vesicular acetylcholine transporter (VAChT; {rho}=.38, q=.048) distributions. The cognition signature similarly correlated with VAChT ({rho}=.48, q=.025). Anatomical signatures were associated with positive symptoms, general psychopathology, and cognition, but were more susceptible to confounding by medication and substance use. No significant receptor associations were detected for anatomical signatures. These findings implicate cholinergic and noradrenergic systems as molecular targets for negative symptoms and cognitive impairment, supporting prioritization of these systems in pharmacotherapy development in early psychosis.

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bioRxiv2026

Linking human brain functional connectivity to underlying neurotransmission

The human brain is organized into interacting functional systems. Their underlying neurobiological mechanisms remain difficult to study in vivo. Here, we adopt a topological framework to quantify the association between neurobiology and brain functional connectivity derived from both resting-state fMRI and MEG. Across six healthy adult cohorts (n = 19-112), regional variation in fMRI connectivity robustly aligns with the distribution of neurotransmitter receptors and transporters. These patterns are present in every single subject, replicate across all cohorts, and are mirrored in MEG. Most prominently, noradrenergic modulation of connectivity in a sensorimotor-insular network is consistently detected across individuals and linked to autonomic arousal. In pharmacological and clinical samples, associations are sensitive to neurotransmitter system manipulation and are altered in early psychosis, aligning with clinical symptomatology. These findings provide biological insight into typical and atypical functional organization of the human brain using a framework linking underlying neurobiology to the functional connectome (NEOFC).

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bioRxiv2026

Benchmarking spatial interpolation methods for brain maps

The human brain is a unique biological space that hosts complex processes unfolding at multiple scales. To study these processes, an abundance of imaging technologies evolved over many decades to produce large-scale, dense mappings of structural and functional features. In parallel, a rich universe of techniques for cellular and molecular biology supplies us with fine-scale, highly specific and reliable measurements in sparse tissue samples. To represent cortical processes integratively across scales, spatial interpolation is necessary for bridging dense and sparse data. The absence of a field consensus for realistic interpolation of features over the whole brain prompts a comprehensive comparison of existing frameworks from the broader scientific literature. Here, we benchmark the performance of multiple deterministic (Inverse Distance Weighting, K-Nearest Neighbours, and Radial Basis Function) and stochastic (Spatially-Weighted Regression, Ordinary Kriging, and Regression Kriging) strategies first with simulated or empirical ground truths. We then demonstrate two use cases with de novo sparse brain data (intracranial EEG and microarray gene expression). In these experiments, we investigate how differences in data characteristics, such as spatial dependency structure and sampling distribution, impact the performance of different interpolation methods. Throughout the results, we consistently find that maps interpolated through spatially-informed stochastic frameworks such as Ordinary Kriging and Regression Kriging are more accurate and biologically realistic across geometric constraints, data modalities, and sampling conditions. This invites continued development of spatially-informed statistical frameworks for analyzing brain data and, more fundamentally, the biological processes that produce them.

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bioRxiv2026

Comparative lifespan trajectories of brain energy metabolism in human and macaque

The brains extraordinary energy demands are met by a suite of metabolic pathways that selectively appropriate glucose across development, yet how this metabolic strategy changes across the lifespan and whether it is conserved across species remains incompletely understood. We previously mapped five core energy metabolism pathways across the human cortex and lifespan, revealing a fundamental dichotomy between anabolic and energy-producing pathway expression: the pentose phosphate pathway, which contributes to biomass, peaks prenatally, while glycolysis, the TCA cycle, and oxidative phosphorylation rise postnatally. Here, we extend the analysis to the rhesus macaque and show that the prenatal-to-postnatal transition is similar across the two species. Using the MitoCarta3.0 annotation framework, we further identify two consistent mitochondria-specific programs: a progressive decline in mitochondrial genome maintenance pathways, and a postnatal rise in mitochondrial energy production and substrate utilization, replicated across human and macaque. Extending to mouse, rat, and chicken, we find this shifting metabolic strategy is a conserved feature of vertebrate brain development. Finally, we map the cortical expression of mitochondria-localized pathway genes in the adult human cortex, finding that the anabolic-oxidative dichotomy follows in the mature brain. Together, these findings provide a comparative transcriptomic framework for studying brain energy metabolism across the lifespan.

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bioRxiv2026

Structure-function coupling in the human brainstem

The nuclei of the brainstem fundamentally modulate neuronal activity throughout the central nervous system. Yet connectome reconstructions typically do not include the brainstem because it is notoriously difficult to image. As a result, the influence of the brainstem on structure-function coupling in the brain is unknown. Here we use high-resolution 7 Tesla magnetic resonance imaging (MRI) to reconstruct structural and functional brain connectomes encompassing cortex and 58 brainstem nuclei spanning the midbrain, pons, and medulla. We identify structural connectional profiles of individual brainstem nuclei to the cortex and find that they align with a spectrum of functions, spanning sensory and motor processing to higher-order cognition. Structural and functional connectivity in brainstem-augmented connectomes are positively correlated, and pairs of regions with direct anatomical projections display greater functional connectivity than pairs without. Structure-function coupling is heterogeneous across brainstem nuclei, with greatest coupling in both modulatory and relay nuclei. Collectively, this work presents an initial step towards understanding how the brainstem shapes structure-function relationships in the brain.

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bioRxiv2026

White Matter Myelin Shapes Macroscale Functional Connectivity Through Integrative Communication

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.

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bioRxiv2026

Multiscale reorganization of brain and behavior under large-scale electrical perturbation

Large-scale electrical perturbation of the human brain provides a unique model for understanding how multiscale biological constraints shape behaviorally relevant reorganization. Here, we integrate longitudinal neuroimaging coordinates from 148 experiments ({approx}2,300 subjects) with normative connectomics, chemoarchitecture, intrinsic electrophysiology, and transcriptomics to identify cross-scale principles governing human brain reconfiguration under strong perturbation. Convergent hubs of structural and functional plasticity embed within default-mode and salience systems and show complementary coupling to visual networks, linking perturbation-induced change to large-scale circuits supporting affective regulation, memory, interoception, and psychosis-relevant processes. These macroscopic patterns align with intrinsic cortical dynamics and chemoarchitectural gradients dominated by 5-HT1A receptors, with additional contributions from D2, -opioid and GABAA systems, and are enriched for astrocytic and microglial gene expression, implicating glial plasticity in systems-level reorganization. Finally, in a separate intervention dataset, regularized statistical-learning models demonstrate that this multiscale signature tracks behaviorally relevant symptom change specifically under strong electrical perturbation. Together, these results outline general organizing principles linking molecular, cellular and network-level constraints to human behavioral adaptation, providing a computational framework for understanding how large-scale perturbations reshape brain systems across levels of biological organization.

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bioRxiv2026

Spatial organization of AQP4 channels in the human brain: links with perfusion, edema, and disease vulnerability

Aquaporin-4 (AQP4) water channels support the glymphatic system, a brain-wide pathway that clears cerebral waste products. Despite its importance, the whole-brain organization of this system in humans remains underexplored. Here we use AQP4 gene expression as a molecular anchor to reconstruct a whole-brain glymphatic-related topography and link it to vascular physiology, edema, and neurode-generative vulnerability. We find that AQP4 expression is highly organized across the brain, peaking in subcortical, ventral, and periventricular territories, consistent with a clearance axis near cerebrospinal fluid reservoirs and perivascular interfaces. Linking AQP4 expression to vascular organization, we find that AQP4-enriched regions show lower normative blood perfusion and lower vein density, suggesting that this axis is not simply explained by vascular supply or large-vessel anatomy. Turning to neurodegeneration, we find that atrophy patterns across multiple neurodegenerative diseases co-localize with AQP4 expression--most strongly for tau and TDP-43 proteinopathies--and high-atrophy regions lie close to AQP4 hotspots in both anatomical space and structural connectome space. Furthermore, incorporating normative PET markers of neuroinflammation typically strengthens the spatial alignment between AQP4 expression and disease atrophy, suggesting that inflammatory tone and glymphatic-related architecture jointly shape vulnerability. Finally, we show that peritumoral edema is most frequent in AQP4-enriched regions and is further shaped by regional inflammatory tone. Collectively, this work highlights a whole-brain glymphatic organization that relates to diverse aspects of brain physiology and vulnerability.

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bioRxiv2026

Integrating Functional Transdiagnostic Dimensions of Psychopathology with Cortical Organization

Childhood and adolescence represent a critical period in which neurodevelopmental psychiatric conditions emerge; traditional case-control approaches often underestimate the complexity and co-occurrence of psychiatric conditions, calling for a transdiagnostic approach as a complementary measure. Open-access data sharing initiatives provide an opportunity to decipher structural- and functional-based organisational constraints on the relationship between brain connectivity and psychopathology. Using a highly heterogenous and comorbid neurodevelopmental sample of children and adolescents aged between 6 and 17 years old at-risk of neurodevelopmental conditions (N = 174, 114 males, mean age 10.72 {+/-} 2.21 years), and age-matched neurotypical controls (N = 27, 12 males, mean age 10.65 {+/-} 2.07 years), we identified a multivariate association, or latent variable, between resting-state functional connectivity and psychopathology. We extensively benchmarked this latent construct to develop a more parsimonious account of childhood psychopathology though an analytical framework spanning biological maps, brain connectivity, and behaviour. Participant-level expression of this latent brain-behaviour association differed by diagnostic burden and symptomatology, and pre-empted longitudinal psychopathology. Whilst diagnostic status was useful for interpretation, the latent construct transcended traditional diagnostic borders, revealing a neurotypical-neurodivergent continuum. The relationship between functional connectivity and neurodevelopmental psychopathology was circumscribed by functional connectivity networks (visual, fronto-parietal, and dorsal attention) and cytoarchitectonic class (primary/secondary sensory and primary sensory cortices). The latent variable aligned with magnetoencephalography-defined electrophysiological alpha (), high-gamma ({gamma}), and theta ({theta}) frequency bands, and was enriched across cortical distributions of astrocytes and excitatory neurones. The connectivity signature was significantly aligned with the archetypal sensorimotor-to-association axis and validated in an independent sample of pre-adolescents (N = 3504), with the strongest alignment with the principal component of gene expression and myelination and were relatively less enriched in cortical regions related to language, indicating a protective effect, and more positively enriched in regions related to executive functioning, conferring greater risk of psychopathology. Together, our findings suggest that the predictive link between functional connectivity and common symptoms of neurodevelopmental psychopathology are circumscribed by underlying macroscale anatomical, functional, and cognitive-related hierarchies.

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bioRxiv2025

Anatomical White Matter Tracts Span the Cortical Hierarchy to Support Cognitive Diversity

Long-range white matter (WM) tracts support cognition by enabling communication between distant cortical regions, which are organized along a hierarchy defined by the sensorimotor-to-association (S-A) axis. However, it remains unknown how WM tracts are positioned within the cortical hierarchy to support cognition. Here, we show that WM tracts are differentially positioned in the cortical hierarchy to support specific cognitive functions, and that tracts spanning the hierarchy connect regions with greater cognitive diversity. Moreover, tracts situated within the same hierarchical level connect biologically similar regions, while those crossing the hierarchy bridge distinct biological milieux to support diverse cognitive functions. The placement of tracts in the cortical hierarchy also reflects developmental variation in tract microstructure and individual differences in cognition. Together, these findings provide a framework that moves beyond conventional categories of association or projection tracts and links WM tract anatomy to cortical organization, cognitive function, cortical neurobiology, and neurodevelopment. We anticipate that this cortex-anchored framework for describing WM tracts may aid the interpretation of individual differences in WM structure related to development and behavior.

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bioRxiv2025

Hierarchical Community Structure of the Adult Drosophila Connectome Reveals Conserved Circuit Archetypes

The community structure of connectomes supports functional specialization, adaptability, and cost-efficient wiring. However, little is known about communities in connectomes mapped at the level of individual neurons and synapses. Here, we analyze a whole-brain Drosophila adult connectome using a nested stochastic blockmodel to uncover its hierarchical community structure. Most of the roughly 1500 fine-scale communities-the smallest, and best resolved level of the hierarchy- are spatially compact, mostly assortative, and aligned with biological features. Nonetheless, we find evidence of nonassortative communities, spatially co-localized within the optic lobe and vision-processing pathways. Seeking "functional primitives"-small circuits with functionally narrow feature profiles-we use data-driven clustering to group communities into 45 archetypical meta-clusters based on their spatial, functional, and molecular properties, revealing modular building blocks from which larger, functionally diverse communities are composed. This work advances our understanding of how structure and function are organized in the fruit fly brain and highlights the value of statistical network models in interpreting nanoscale connectomes.

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bioRxiv2025

Cognitive cartography of mammalian brains using meta-analysis of AI experts

The complexity of the brain is increasingly mirrored by the complexity of the neuroscientific literature, yet no individual mind can fully grasp the diversity of scales, methodologies and model organisms. Where human experts flag, the latest AI models excel: large language models can seamlessly integrate knowledge across scientific domains. Here we show how large language models can systematically and quantitatively synthesise literature-wide neuroscientific knowledge about the cognitive operations and dysfunctions associated with each brain region. Meta-analysis of AI experts reveals structure-function mappings to which existing meta-analytic frameworks are blind, demonstrated by lesions and direct intracranial stimulation. It also unlocks the possibility of extending quantitative literature meta-analysis and decoding of brain maps to other model organisms beyond human. As proof of concept, we integrate LLM meta-analysis with species-specific transcriptomics in human, macaque, and mouse, to discover an evolutionarily conserved molecular circuit for cognition. Altogether, meta-analysis of AI experts can fundamentally catalyze neuroscientific discovery by overcoming the barrier of data aggregation from heterogeneous studies, finally bringing together a scattered literature to identify emergent patterns and latent insights across disparate subfields, modalities, and species.

neuroscience↗

bioRxiv2025

Joint multi-omics profiling of brain and body health in aging

The human brain and peripheral systems undergo coordinated changes throughout the lifespan, yet studies of aging have traditionally examined these systems as separate entities. Here we ask how brain health relates to peripheral biomarkers of bodily health including body mass index, blood pressure, and blood biochemistry results. We use partial least squares analysis to identify generalizable patterns of covariance between multi-modal neuroimaging data (structural, functional, diffusion, and arterial spin labeling MRI), demographic, and peripheral physiological markers in two large-scale deeply phenotyped datasets: the Human Connectome Project-Aging and UK Biobank. This data-driven pattern learning approach identifies two principal axes of brain-body associations in both biological sex groups. The first axis is driven by the dominant contribution of age. Across multiple brain measures, aging is associated with loss of brain structural integrity and cerebral vascular dysfunction. The second axis is driven by metabolic features, characterized by low high-density lipoprotein cholesterol, elevated body mass index, blood pressure, glycosylated hemoglobin, insulin, glucose, and alanine aminotransferase, and reduced cerebral blood perfusion. Finally, we show that deviations from a healthy metabolic profile are linked to cognitive deficits, particularly in females. Our study contributes to development of comprehensive translatable biomarkers for brain health assessment, and highlights the importance of metabolic health as a determinant of brain health.

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bioRxiv2025

Clinical profile impacts the replicability of multivariate brain-behavioural associations

Recent work suggests that thousands of individuals are required in multivariate brain-behaviour analyses to obtain consistently replicable results. Some believe, however, that smaller sample sizes may be sufficient if specific subpopulations are targeted. We investigate how sample size and cohort composition influence the replicability of Canonical Correlation Analysis (CCA) results using the UK Biobank (N=40,514). We apply CCA to diffusion-weighted magnetic resonance imaging (dMRI) phenotypes and cognitive assessment test scores. We define four participant cohorts based on clinical profile and find that, across all cohorts, sample sizes of {approx}500 are needed to obtain replicable canonical correlations and variable loadings. The most targeted cohort (comprising individuals with a history of psychoactive substance use) requires much fewer samples to achieve similar or greater correlations than the other cohorts. Our findings support the idea that moderate sample sizes from targeted cohorts can be sufficient for obtaining replicable brain-behaviour associations.

neuroscience↗

bioRxiv2025

Spinal cord structural and functional architecture and its shared organization with the brain across the adult lifespan

The spinal cord connects the brain to peripheral systems. Yet its integration with cerebral networks remains a key neuroscience question. Capturing structural and functional central nervous system (CNS) changes throughout the lifespan is essential for characterizing healthy and pathological aging. Leveraging a unique multimodal dataset combining spinal and cerebrospinal imaging, we jointly mapped the spinal cord structural and functional architecture across adulthood. Our results revealed age-related changes across these modalities and identified organizational principles shared with the brain. These changes were most pronounced in the somatosensory pathway, with microstructural decline coupled to shifts in functional connectivity and local spontaneous activity as aging progresses. Extending analyses to the brain uncovered convergent CNS-wide aging mechanisms, including gray matter loss, functional dedifferentiation, and increased spontaneous activity, highlighting shared neural aging trajectories. Together, our findings provide a systems-level view of alterations with age and lay the groundwork for early biomarkers of sensorimotor decline.

neuroscience↗