Biology subjects

Bazinet, V.

Publications and source records attributed to Bazinet, V..

15 recordsLinked to original sources

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.

neuroscience↗

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

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.

neuroscience↗

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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bioRxiv2025

netneurotools: a trainee-oriented approach to network neuroscience

Brain imaging is an increasingly inter-disciplinary field, encompassing multiple data types and multiple analytic traditions. Projects typically involve many moving parts, such as building customized preprocessing pipelines, transforming between data formats, preparing datasets for analysis, and ultimately displaying results. The field is conventionally built on highly specialized software packages that solve these individual challenges well, but are not necessarily designed to be interoperable. Trainees new to the field are therefore often left to come up with isolated heuristics and workarounds to complete a project. Here we present a way to navigate the increasingly complex informatics ecosystem of brain imaging. netneurotools is our labs internal Python toolkit that has been continuously developed and maintained by the labs trainees. The philosophy of the toolkit is that it should be the Swiss army knife of the lab: functions and routines that we often use but that are not part of any established pipeline or package. Since its inception, the toolkit has been open and welcomes contribution from neuroscientists across the globe. netneurotools presents a necessary counterweight to out-of-the-box software packages and highlights the importance of smaller, ad hoc functions for implementing projects. By opening a window into the inner workings of a lab, netneurotools also presents an opportunity to begin a new type of discourse among groups and establish tangible links within the community.

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bioRxiv2025

Brain network dynamics determine tau presence while regional vulnerability governs tau load in Alzheimer's disease

In Alzheimers disease (AD), tau pathology accumulates gradually throughout the brain, with clinical decline reflecting tau progression. A comprehensive understanding of, first, whether tau propagation is predominantly governed by connectome-based diffusion, regional vulnerability, or an interplay of both, and second, which types of brain connectivity or regional factors best explain tau propagation, remains crucial for advancing our understanding of AD progression. Here, we apply multi-scale, biologically informed disease progression simulations to human data, to disentangle the influence of local mechanisms on global tau progression patterns in AD. We find that whether tau reaches a brain region (presence) and how much tau accumulates there (load) are governed by different mechanisms. Tau presence patterns are highly consistent across the population, and can be largely explained through synaptic spread through white-matter networks and excitatory-inhibitory dynamics. Meanwhile tau load differs across people, and is driven by a combination of synaptic spread and intrinsic or extrinsic regional properties, including regional {beta}-amyloid load, MAPT gene expression and regional blood flow. Finally, while distinct tau patterns in the population could each be explained by established AD mechanisms, our models highlight a role of distinct brain networks (parietal networks in MTL-sparing AD tau subtype) and neurotransmitter systems (cholinergic system in posterior subtype). Together, this work suggests that network dynamics likely determine the sequence of regional tau progression, while individual-specific tissue-vulnerability factors influence regional tau load.

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bioRxiv2024

The effect of spherical projection on spin tests for brain maps

Statistical comparison between brain maps is a standard procedure in neuroimaging. Numerous inferential methods have been developed to account for the effect of spatial autocorrelation when evaluating map-to-map similarity. A popular method to generate surrogate maps with preserved spatial autocorrelation is the spin test. Here we show that a key component of the procedure -- projecting brain maps to a spherical surface -- distorts distance relationships between vertices. These distortions result in surrogate maps that imperfectly preserve spatial autocorrelation, yielding inflated false positive rates. We then confirm that targeted removal of individual spins with high distortion reduces false positive rates. Collectively, this work highlights the importance of accurately representing and manipulating cortical geometry when generating surrogate maps for use in map-to-map comparisons.

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bioRxiv2024

Network Spreading and Local Biological Vulnerability in Amyotrophic Lateral Sclerosis

Amyotrophic Lateral Sclerosis (ALS) is a progressive neurodegenerative disease that predominantly targets the motor system. Spread of pathology is thought to be driven by both local vulnerability and network architecture. Namely, molecular and cellular features may confer vulnerability to specific neuronal populations, while synaptic contacts may also increase exposure to pathology in connected neuronal populations. However, these principles are typically studied in isolation and it remains unknown how local vulnerability and network spreading interact to shape cortical atrophy. Here we investigate how network structure and local biological features jointly shape the spatial patterning of atrophy in ALS. We analyze the Canadian ALS Neuroimaging Consortium (CALSNIC) dataset and estimate cortical atrophy using deformation-based morphometry (DBM). We find that structural connectivity closely aligns with the course of atrophy. Atrophy is also more likely to occur in regions that share similar transcriptomic, neurotransmitter receptor and metabolic profiles. We identify disease epicenters in motor cortex. Epicenter probability maps show transcriptomic enrichment for biological pathways involved in mitochondrial function as well as support cells, including endothelial cells and pericytes. Finally, individual differences in epicenter location correspond to individual differences in clinical and cognitive symptoms, and differentiate patient subtypes.

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bioRxiv2024

A simulated annealing algorithm for randomizing weighted networks

Scientific discovery in connectomics relies on the use of network null models. To systematically evaluate the prominence of brain network features, empirical measures are compared against null statistics computed in randomized networks. Modern imaging and tracing technologies provide an increasingly rich repertoire of biologically meaningful edge weights. Despite the prevalence of weighted graph analysis in connectomics, randomization models that only preserve binary node degree remain most widely used. Here, to adapt network null models to weighted network inference, we propose a simulated annealing procedure for generating strength sequence-preserving randomized networks. This model outperforms other commonly used rewiring algorithms in preserving weighted degree (strength). We show that these results generalize to directed networks as well as a wide range of real-world networks, making them generically applicable in neuroscience and in other scientific disciplines. Furthermore, we introduce morphospace representation as a tool for the assessment of null network ensemble variability and feature preservation. Finally, we show how the choice of a network null model can yield fundamentally different inferences about established organizational features of the brain such as the rich-club phenomenon and lay out best practices for the use of rewiring algorithms in brain network inference. Collectively, this work provides a simple but powerful inferential method to meet the challenges of analyzing richly detailed next-generation connectomics datasets.

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bioRxiv2024

Controlling the human connectome with spatially diffuse input signals

The human brain is never at "rest"; its activity is constantly fluctuating over time, transitioning from one brain state-a whole-brain pattern of activity-to another. Network control theory offers a framework for understanding the effort - energy - associated with these transitions. One branch of control theory that is especially useful in this context is "optimal control", in which input signals are used to selectively drive the brain into a target state. Typically, these inputs are introduced independently to the nodes of the network (each input signal is associated with exactly one node). Though convenient, this input strategy ignores the continuity of cerebral cortex - geometrically, each region is connected to its spatial neighbors, allowing control signals, both exogenous and endogenous, to spread from their foci to nearby regions. Additionally, the spatial specificity of brain stimulation techniques is limited, such that the effects of a perturbation are measurable in tissue surrounding the stimulation site. Here, we adapt the network control model so that input signals have a spatial extent that decays exponentially from the input site. We show that this more realistic strategy takes advantage of spatial dependencies in structural connectivity and activity to reduce the energy (effort) associated with brain state transitions. We further leverage these dependencies to explore near-optimal control strategies such that, on a per-transition basis, the number of input signals required for a given control task is reduced, in some cases by two orders of magnitude. This approximation yields network-wide maps of input site density, which we compare to an existing database of functional, metabolic, genetic, and neurochemical maps, finding a close correspondence. Ultimately, not only do we propose a more efficient framework that is also more adherent to well-established brain organizational principles, but we also posit neurobiologically grounded bases for optimal control.

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bioRxiv2022

Resolving inter-regional communication capacity in the human connectome

Applications of graph theory to the connectome have inspired several models of how neural signaling unfolds atop its structure. Analytic measures derived from these communication models have mainly been used to extract global characteristics of brain networks, obscuring potentially informative interregional relationships. Here we develop a simple standardization method to investigate polysynaptic communication pathways between pairs of cortical regions. This procedure allows us to determine which pairs of nodes are topologically closer and which are further than expected on the basis of their degree. We find that communication pathways delineate canonical functional systems. Relating nodal communication capacity to meta-analytic probabilistic patterns of functional specialization, we also show that areas that are most closely integrated within the network are associated with higher-order cognitive functions. We find that these regions proclivity towards functional integration could naturally arise from the brains anatomical configuration through evenly distributed connections among multiple specialized communities. Throughout, we consider two increasingly constrained null models to disentangle the effects of the networks topology from those passively endowed by spatial embedding. Altogether, the present findings uncover relationships between polysynaptic communication pathways and the brains functional organization across multiple topological levels of analysis and demonstrate that network integration facilitates cognitive integration.

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bioRxiv2022

Assortative mixing in micro-architecturally annotated brain connectomes

The wiring of the brain connects micro-architecturally diverse neuronal populations. The conventional graph model encodes macroscale brain connectivity as a network of nodes and edges, but abstracts away the rich biological detail of each regional node. Regions are different in terms of their microscale attributes, many of which are readily available through modern technological advances and data-sharing initiatives. How is macroscale connectivity related to nodal attributes? Here we investigate the systematic arrangement of white-matter connectivity with respect to multiple biological annotations. Namely, we formally study assortative mixing in annotated connectomes by quantifying the tendency for regions to be connected with each other based on the similarity of their micro-architectural attributes. We perform all experiments using four cortico-cortical connectome datasets from three different species (human, macaque and mouse), and consider a range of molecular, cellular and laminar annotations, including gene expression, neurotransmitter receptors, neuron density, laminar thickness and intracortical myelin. Importantly, we disentangle the relationship between neural wiring, regional heterogeneity and spatial embedding using spatial autocorrelation-preserving null models. We show that mixing between micro-architecturally diverse neuronal populations is supported by long-distance connections. Using meta-analytic decoding, we find that the arrangement of connectivity patterns with respect to biological annotations shape patterns of regional functional specialization. Specifically, regions that connect to biologically similar regions are associated with executive function; conversely, regions that connect with biologically dissimilar regions are associated with memory function. By bridging scales of cortical organization, from microscale attributes to macroscale connectivity, this work lays the foundation for next-generation annotated connectomics.

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bioRxiv2022

Neuromaps: structural and functional interpretation of brain maps

Imaging technologies are increasingly used to generate high-resolution reference maps of brain structure and function. Modern scientific discovery relies on making comparisons between new maps (e.g. task activations, group structural differences) and these reference maps. Although recent data sharing initiatives have increased the accessibility of such brain maps, data are often shared in disparate coordinate systems (or "spaces"), precluding systematic and accurate comparisons among them. Here we introduce the neuromaps toolbox, an open-access software package for accessing, transforming, and analyzing structural and functional brain annotations. We implement two registration frameworks to generate high-quality transformations between four standard coordinate systems commonly used in neuroimaging research. The initial release of the toolbox features >40 curated reference maps and biological ontologies of the human brain, including maps of gene expression, neurotransmitter receptors, metabolism, neurophysiological oscillations, developmental and evolutionary expansion, functional hierarchy, individual functional variability, and cognitive specialization. Robust quantitative assessment of map-to-map similarity is enabled via a suite of spatial autocorrelation-preserving null models. By combining open-access data with transparent functionality for standardizing and comparing brain maps, the neuromaps software package provides a systematic workflow for comprehensive structural and functional annotation enrichment analysis of the human brain.

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bioRxiv2021

Global network structure and local transcriptomic vulnerability shape atrophy in sporadic and genetic behavioral variant frontotemporal dementia

Connections among brain regions allow pathological perturbations to spread from a single source region to multiple regions. Patterns of neurodegeneration in multiple diseases, including behavioral variant of frontotemporal dementia (bvFTD), resemble the large-scale functional systems, but how bvFTD-related atrophy patterns relate to structural network organization remains unknown. Here we investigate whether neurodegeneration patterns in sporadic and genetic bvFTD are conditioned by connectome architecture. Regional atrophy patterns were estimated in both genetic bvFTD (75 patients, 247 controls) and sporadic bvFTD (70 patients, 123 controls). We first identify distributed atrophy patterns in bvFTD, mainly targeting areas associated with the limbic intrinsic network and insular cytoarchitectonic class. Regional atrophy was significantly correlated with atrophy of structurally- and functionally-connected neighbors, demonstrating that network structure shapes atrophy patterns. The anterior insula was identified as the predominant group epicenter of brain atrophy using data-driven and simulation-based methods, with some secondary regions in frontal ventromedial and anteromedial temporal areas. Finally, we find that FTD-related genes, namely C9orf72 and TARDBP, confer local transcriptomic vulnerability to the disease, effectively modulating the propagation of pathology through the connectome. Collectively, our results demonstrate that atrophy patterns in sporadic and genetic bvFTD are jointly shaped by global connectome architecture and local transcriptomic vulnerability.

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bioRxiv2020

Multiscale communication in cortico-cortical networks

Signaling in brain networks unfolds over multiple topological scales. Areas may exchange information over local circuits, encompassing direct neighbours and areas with similar functions, or over global circuits, encompassing distant neighbours with dissimilar functions. Here we study how the organization of cortico-cortical networks mediate localized and global communication by parametrically tuning the range at which signals are transmitted on the white matter connectome. By investigating the propensity for brain areas to communicate with their neighbors across multiple scales, we naturally reveal their functional diversity. We find that unimodal regions show preference for local communication and multimodal regions show preferences for global communication. We show that these preferences manifest as region- and scale-specific structure-function coupling. Namely, the functional connectivity of unimodal regions emerges from monosynaptic communication in small-scale circuits, while the functional connectivity of transmodal regions emerges from polysynaptic communication in large-scale circuits. Altogether, the present findings reveal how functional hierarchies emerge from hidden but highly structured multiscale connection patterns.

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