Biology subjects

Misic, B.

Publications and source records attributed to Misic, B..

At least 109 records · Page 6Linked to original sources

bioRxiv2020

Early or late gestational exposure to maternal immune activation alters neurodevelopmental trajectories in mice: an integrated neuroimaging, behavioural, and transcriptional study

Prenatal maternal immune activation (MIA) is a risk factor for neurodevelopmental disorders. How gestational timing of MIA-exposure differentially impacts downstream development remains unclear. Here, we characterize neurodevelopmental trajectories of mice exposed to MIA induced by poly I:C either early (gestational day [GD]9) or late (GD17) in gestation using longitudinal structural magnetic resonance imaging from weaning to adulthood. Early MIA-exposure associated with accelerated brain volume increases in adolescence/early-adulthood that normalized in later adulthood, in regions including the striatum, hippocampus, and cingulate cortex. Similarly, alterations in anxiety, stereotypic, and sensorimotor gating behaviours observed in adolescence normalized in adulthood. In contrast, MIA-exposure in late gestation had less impact on anatomical and behavioural profiles. Using a multivariate technique to relate imaging and behavioural variables for the time of greatest alteration, i.e. adolescence/early adulthood, we demonstrate that variation in anxiety, social, and sensorimotor gating associates significantly with volume of regions including the dorsal and ventral hippocampus, and anterior cingulate cortex. Using RNA sequencing to explore the molecular underpinnings of region-specific alterations in early MIA-exposed mice in adolescence, we observed the most transcriptional changes in the dorsal hippocampus, with regulated genes enriched for fibroblast growth factor regulation, autistic behaviours, inflammatory pathways, and microRNA regulation. This indicates that MIA in early gestation perturbs brain development mechanisms implicated in neurodevelopmental disorders. Our findings demonstrate the inherent strength of an integrated hypothesis- and data-driven approach in linking brain-behavioural alterations to the transcriptome to understand how MIA confers risk for major mental illness.

neuroscience↗

bioRxiv2020

Microstructural underpinnings and macroscale functional implications of temporal lobe connectivity gradients

AO_SCPLOWBSTRACTC_SCPLOWThe temporal lobe is implicated in higher cognitive processes and is one of the regions that underwent substantial reorganization during primate evolution. Its functions are instantiated, in part, by its complex layout of structural connections. This study identified low-dimensional representations of structural connectivity variations in human temporal cortex and explored their microstructural underpinnings and associations to macroscale function. We identified three eigenmodes which described gradients in structural connectivity. These gradients reflected interregional variations in cortical microstructure derived from quantitative MRI and post-mortem histology. Gradient-informed models accurately predicted macroscale measures of temporal lobe function. Gradients aligned closely with established measures of functional reconfiguration and areal expansion between macaques and humans, highlighting the important role evolution has played in shaping temporal lobe function. Our results provide robust evidence for three axes of structural connectivity in human temporal cortex with consistent microstructural underpinnings and contributions to large-scale brain network function.

neuroscience↗

bioRxiv2020

Learning function from structure in neuromorphic networks

The connection patterns of neural circuits in the brain form a complex network. Collective signaling within the network manifests as patterned neural activity, and is thought to support human cognition and adaptive behavior. Recent technological advances permit macro-scale reconstructions of biological brain networks. These maps, termed connectomes, display multiple non-random architectural features, including heavy-tailed degree distributions, segregated communities and a densely interconnected core. Yet, how computation and functional specialization emerge from network architecture remains unknown. Here we reconstruct human brain connectomes using in vivo diffusion-weighted imaging, and use reservoir computing to implement these connectomes as artificial neural networks. We then train these neuromorphic networks to learn a cognitive task. We show that biologically realistic neural architectures perform optimally when they display critical dynamics. We find that performance is driven by network topology, and that the modular organization of large-scale functional systems is computationally relevant. Throughout, we observe a prominent interaction between network structure and dynamics, such that the same underlying architecture can support a wide range of learning capacities across dynamical regimes. This work opens new opportunities to discover how the network organization of the brain optimizes cognitive capacity, conceptually bridging neuroscience and artificial intelligence.

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bioRxiv2020

Heritability of cortical morphology reflects a sensory-fugal plasticity gradient

Human brain plastically adapts to environmental demands. Here, we propose that naturally occuring plasticity in certain brain areas should be reflected by higher environmental influence and therefore lower heritability of the structure of those brain areas. Mesulams (1998) seminal overview proposed a hierarchy of plasticity, where higher-order multimodal areas should be more plastic than lower-order sensory areas. Using microstructural and functional gradients as proxies for Mesulams hierarchy, we seek to test whether these gradients predict heritability of brain structure. We test this model simultaneously across multiple measures of cortical structure and microstructure derived from structural magnet resonance imaging. We also account for multiple other explanations of heritability differences, such as signal-to-noise ratio and spatial autocorrelation. We estimated heritability of brain areas using 984 participants from the Human Connectome Project. Multi-level modelling of heritability differences demonstrated that heritability is explained by both signal quality, as well as by the primary microstructural gradient. Namely, sensory areas had higher heritability and limbic/heteromodal areas had lower heritability. Given the increasing availability of genetically informed imaging data, heritability could be a quick method assess brain plasticity. Highlights (up to 85 chars)Cortical areas vary in heritability. This is seen across structural measures. Heritability differences could be explained by plasticity, topography, or noise. We build a comprehensive model testing many explanations across 5 measures. Heritability is explained by noise and 1st structural gradient reflecting plasticity. Heritability could be a method to study brain plasticity.

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bioRxiv2020

Numerical Instabilities in Analytical Pipelines Lead to Large and Meaningful Variability in Brain Networks

The analysis of brain-imaging data requires complex processing pipelines to support findings on brain function or pathologies. Recent work has shown that variability in analytical decisions, small amounts of noise, or computational environments can lead to substantial differences in the results, endangering the trust in conclusions1-7. We explored the instability of results by instrumenting a connectome estimation pipeline with Monte Carlo Arithmetic8,9 to introduce random noise throughout. We evaluated the reliability of the connectomes, their features10,11, and the impact on analysis12,13. The stability of results was found to range from perfectly stable to highly unstable. This paper highlights the potential of leveraging induced variance in estimates of brain connectivity to reduce the bias in networks alongside increasing the robustness of their applications in the classification of individual differences. We demonstrate that stability evaluations are necessary for understanding error inherent to brain imaging experiments, and how numerical analysis can be applied to typical analytical workflows both in brain imaging and other domains of computational science. Overall, while the extreme variability in results due to analytical instabilities could severely hamper our understanding of brain organization, it also leads to an increase in the reliability of datasets.

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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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bioRxiv2020

Comparing spatially-constrained null models for parcellated brain maps

Technological and data sharing advances have led to a proliferation of high-resolution structural and functional maps of the brain. Modern neuroimaging research increasingly depends on identifying correspondences between the topographies of these maps; however, most standard methods for statistical inference fail to account for their spatial properties. Recently, multiple methods have been developed to generate null distributions that preserve the spatial autocorrelation of brain maps and yield more accurate statistical estimates. Here, we comprehensively assess the performance of ten published null frameworks in statistical analyses of neuroimaging data. To test the efficacy of these frameworks in situations with a known ground truth, we first apply them to a series of controlled simulations and examine the impact of data resolution and spatial autocorrelation on their family-wise error rates. Next, we use each framework with two empirical neuroimaging datasets, investigating their performance when testing (1) the correspondence between brain maps (e.g., correlating two activation maps) and (2) the spatial distribution of a feature within a partition (e.g., quantifying the specificity of an activation map within an intrinsic functional network). Finally, we investigate how differences in the implementation of these null models may impact their performance. In agreement with previous reports, we find that naive null models that do not preserve spatial autocorrelation consistently yield elevated false positive rates and unrealistically liberal statistical estimates. While spatially-constrained null models yielded more realistic, conservative estimates, even these frameworks suffer from inflated false positive rates and variable performance across analyses. Throughout our results, we observe minimal impact of parcellation and resolution on null model performance. Altogether, our findings highlight the need for continued development of statistically-rigorous methods for comparing brain maps. The present report provides a harmonised framework for benchmarking and comparing future advancements.

neuroscience↗

bioRxiv2020

The R1-weighted connectome: complementing brain networks with a myelin-sensitive measure

Myelin plays a crucial role in how well information travels between brain regions. Many neurological diseases affect the myelin in the white matter, making myelin-sensitive metrics derived from quantitative MRI of potential interest for early detection and prognosis of those conditions. Complementing the structural connectome, obtained with diffusion MRI tractography, with a myelin sensitive measure could result in a more complete model of structural brain connectivity and give better insight into how the myeloarchitecture relates to brain function. In this work we weight the connectome by the longitudinal relaxation rate (R1) as a measure sensitive to myelin, and then we assess its added value by comparing it with connectomes weighted by the number of streamlines (NOS). Our analysis reveals differences between the two connectomes both in the distribution of their weights and the modular organization. Additionally, the rank-based analysis shows that R1 is able to separate different classes (unimodal and transmodal), following a functional gradient. Overall, the R1-weighted connectome provides a different perspective on structural connectivity taking into account white matter myeloarchitecture. Author summaryIn the present work, we integrate a myelin sensitive MRI metric into the connectome and compare it with a connectome weighted with a standard diffusion-derived metric, number of streamlines (NOS). Our analysis shows that the R1-weighted connectome complements the NOS-weighted connectome. We show that the R1-weighted average distribution does not follow the same trend as the NOS strength distribution, and the two connectomes exhibit different modular organization. We also show that unimodal cortical regions tend to be connected by more streamlines, but the connections exhibit a lower R1-weighted average, while the transmodal regions tend to have a higher R1-weighted average but fewer streamlines. In terms of network communication, this could imply that the unimodal regions require more connections with lower myelination, whereas the transmodal regions take more myelinated, but fewer, connections for a reliable transfer of information.

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bioRxiv2020

Molecular signatures of cognition and affect

Regulation of gene expression drives protein interactions that govern synaptic wiring and neuronal activity. The resulting coordinated activity among neuronal populations supports complex psychological processes, yet how gene expression shapes cognition and emotion remains unknown. Here we directly bridge the microscale and macroscale by mapping gene expression patterns to functional activation patterns across the cortical sheet. Applying unsupervised learning to the Allen Human Brain Atlas and Neurosynth databases, we identify a ventromedial-dorsolateral gradient of gene assemblies that separate affective and cognitive domains. This topographic molecular-psychological signature reflects the hierarchical organization of the neocortex, including systematic variations in cell type, myeloarchitecture, laminar differentiation, and intrinsic network affiliation. In addition, this molecular-psychological signature is related to individual differences in cognitive performance, strengthens over neurodevelopment, and can be replicated in two independent repositories. Collectively, our results reveal spatially covarying transcriptomic and cognitive architectures, highlighting the influence that molecular mechanisms exert on psychological processes.

neuroscience↗

bioRxiv2020

Topographic gradients of intrinsic dynamics across neocortex

The intrinsic dynamics of neuronal populations are shaped by both macroscale connectome architecture and microscale attributes. Neural activity arising from the interplay of these local and global factors therefore varies from moment to moment, with rich temporal patterns. Here we comprehensively characterize intrinsic dynamics throughout the human brain. Applying massive temporal feature extraction to regional haemodynamic activity, we estimate over 6,000 statistical properties of individual brain regions time series across the neocortex. We identify two robust topographic gradients of intrinsic dynamics, one spanning a ventromedial-dorsolateral axis and the other spanning a unimodal-transmodal axis. These gradients are distinct in terms of their temporal composition and reflect spatial patterns of microarray gene expression, intracortical myelin and cortical thickness, as well as structural and functional network embedding. Importantly, these gradients are closely correlated with patterns of functional activation, differentiating cognitive versus affective processing and sensory versus higher-order cognitive processing. Altogether, these findings demonstrate a link between microscale and macroscale architecture, intrinsic dynamics, and cognition.

neuroscience↗

bioRxiv2020

Understanding the impact of preprocessing pipelines on neuroimaging cortical surface analyses

The choice of preprocessing pipeline introduces variability in neuroimaging analyses that affects the reproducibility of scientific findings. Features derived from structural and functional MR imaging data are sensitive to the algorithmic or parametric differences of preprocessing tasks, such as image normalization, registration, and segmentation to name a few. Therefore it is critical to understand and potentially mitigate the cumulative biases of pipelines in order to distinguish biological effects from methodological variance. Here we use an open structural MR imaging dataset (ABIDE), supplemented with the Human Connectome Project (HCP), to highlight the impact of pipeline selection on cortical thickness measures. Specifically, we investigate the effect of 1) software tool (e.g. ANTs, CIVET, FreeSurfer), 2) cortical parcellation (DKT, Destrieux, Glasser), and 3) quality control procedure (manual, automatic). We divide our statistical analyses by 1) method type, i.e. task-free (unsupervised) versus task-driven (supervised), and 2) inference objective, i.e. neurobiological group differences versus individual prediction. Results show that software, parcellation, and quality control significantly impact task-driven neurobiological inference. Additionally, software selection strongly impacts neurobiological and individual task-free analyses, and quality control alters the performance for the individual-centric prediction tasks. This comparative performance evaluation partially explains the source of inconsistencies in neuroimaging findings. Furthermore, it underscores the need for more rigorous scientific workflows and accessible informatics resources to replicate and compare preprocessing pipelines to address the compounding problem of reproducibility in the age of large-scale, data-driven computational neuroscience.Competing Interest StatementThe authors have declared no competing interest.View Full Text

bioinformatics↗

bioRxiv2020

Integrated morphometric, molecular, and clinical characterization of Parkinson's disease pathology

Individuals with Parkinsons disease present with a complex clinical phenotype, encompassing sleep, motor, cognitive, and affective disturbances. However, characterizations of PD are typically made for the "average" patient, ignoring patient heterogeneity and obscuring important individual differences. Modern large-scale data sharing efforts provide a unique opportunity to precisely investigate individual patient characteristics, but there exists no analytic framework for comprehensively integrating data modalities. Here we apply an unsupervised learning method--similarity network fusion--to objectively integrate MRI morphometry, dopamine active transporter binding, protein assays, and clinical measurements from n = 186 individuals with de novo Parkinsons disease from the Parkinsons Progression Markers Initiative. We show that multimodal fusion captures inter-dependencies among data modalities that would otherwise be overlooked by field standard techniques like data concatenation. We then examine how patient subgroups derived from fused data map onto clinical phenotypes, and how neuroimaging data is critical to this delineation. Finally, we identify a compact set of phenotypic axes that span the patient population, demonstrating that this continuous, low-dimensional projection of individual patients presents a more parsimonious representation of heterogeneity in the sample compared to discrete biotypes. Altogether, these findings showcase the potential of similarity network fusion for combining multimodal data in heterogeneous patient populations.

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bioRxiv2020

Signal propagation via cortical hierarchies

The wiring of the brain is organized around a putative unimodal-transmodal hierarchy. Here we investigate how this intrinsic hierarchical organization of the brain shapes the transmission of information among regions. The hierarchical positioning of individual regions was quantified by applying diffusion map embedding to resting state functional MRI networks. Structural networks were reconstructed from diffusion spectrum imaging and topological shortest paths among all brain regions were computed. Sequences of nodes encountered along a path were labelled by their hierarchical position, tracing out path motifs. We find that the cortical hierarchy guides communication in the network. Specifically, nodes are more likely to forward signals to nodes closer in the hierarchy and cover a range of unimodal and transmodal regions, potentially enriching or diversifying signals en route. We also find evidence of systematic detours, particularly in attention networks, where communication is re-routed. Altogether, the present work highlights how the cortical hierarchy shapes signal exchange and imparts behaviourally-relevant communication patterns in brain networks.

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bioRxiv2019

BrainSpace: a toolbox for the analysis of macroscale gradients in neuroimaging and connectomics datasets

Understanding how higher order cognitive function emerges from the underlying brain structure depends on quantifying how the behaviour of discrete regions are integrated within the broader cortical landscape. Recent work has established that this macroscale brain organization and function can be quantified in a compact manner through the use of multivariate machine learning approaches that identify manifolds often described as cortical gradients. By quantifying topographic principles of macroscale organization, cortical gradients lend an analytical framework to study structural and functional brain organization across species, throughout development and aging, and its perturbations in disease. More generally, its macroscale perspective on brain organization offers novel possibilities to investigate the complex relationships between brain structure, function, and cognition in a quantified manner. Here, we present a compact workflow and open-access toolbox that allows for (i) the identification of gradients (from structural or functional imaging data), (ii) their alignment (across subjects or modalities), and (iii) their visualization (in embedding or cortical space). Our toolbox also allows for controlled association studies between gradients with other brain-level features, adjusted with respect to several null models that account for spatial autocorrelation. The toolbox is implemented in both Python and Matlab, programming languages widely used by the neuroimaging and network neuroscience communities. Several use-case examples and validation experiments demonstrate the usage and consistency of our tools for the analysis of functional and microstructural gradients across different spatial scales.

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bioRxiv2019

Spatial patterning of tissue volume deformation in schizophrenia reflects brain network architecture

BackgroundThere is growing recognition that connectome architecture shapes cortical and sub-cortical grey matter atrophy across a spectrum of neurological and psychiatric diseases. Whether connectivity contributes to tissue volume loss in schizophrenia in the same manner remains unknown.\n\nMethodsHere we relate tissue volume loss in patients with schizophrenia to patterns of structural and functional connectivity. Grey matter deformation was estimated in a sample of N = 133 individuals with chronic schizophrenia (48 female, 34.7 {+/-} 12.9 years) and N = 113 controls (64 female, 23.5 {+/-} 8.4 years). Deformation-based morphometry (DBM) was used to estimate cortical and subcortical grey matter deformation from T1-weighted MR images. Structural and functional connectivity patterns were derived from an independent sample of N = 70 healthy participants using diffusion spectrum imaging and resting-state functional MRI.\n\nResultsWe find that regional deformation is correlated with the deformation of structurally- and functionally-connected neighbours. Distributed deformation patterns are circumscribed by specific functional systems (the ventral attention network) and cytoarchitectonic classes (limbic class), with an epicenter in the anterior cingulate cortex.\n\nConclusionsAltogether, the present study demonstrates that brain tissue volume loss in schizophrenia is conditioned by structural and functional connectivity, accounting for 25-35% of regional variance in deformation.

neuroscience↗

bioRxiv2019

Gradients of structure-function tethering across neocortex

The white matter architecture of brain networks imparts a distinct signature on neuronal co-activation patterns. Inter-regional projections promote synchrony among distant neuronal populations, giving rise to richly patterned functional networks. A variety of statistical, communication and biophysical models have been proposed to study the relationship between brain structure and function, but the link is not yet known. In the present report we seek to relate the structural and functional connection profiles of individual brain areas. We apply a simple multilinear model that incorporates information about spatial proximity, routing and diffusion between brain regions to predict their functional connectivity. We find that structure-function relationships vary markedly across the neocortex. Structure and function correspond closely in unimodal, primary sensory and motor regions, but diverge in transmodal cortex, corresponding to the default mode and salience networks. The divergence between structure and function systematically follows functional and cytoarchitectonic hierarchies. Altogether, the present results demonstrate that structural and functional networks do not align uniformly across the brain, but gradually uncouple in higher-order polysensory areas.

neuroscience↗

bioRxiv2018

Dissociations between microstructural and functional hierarchies within regions of transmodal cortex

While the role of cortical microstructure in organising neural function is well established, it remains unclear how structural constraints can give rise to more flexible elements of cognition. While non-human primate research has demonstrated a close structure-function correspondence, the relationship between microstructure and function remains poorly understood in humans, in part because of the reliance on post mortem analyses which cannot be directly related to functional data. To overcome this barrier, we developed a novel approach to model the similarity of microstructural profiles sampled in the direction of cortical columns. Our approach was initially formulated based on an ultra-high-resolution 3D histological reconstruction of an entire human brain and then translated to myelin-sensitive MRI data in a large cohort of healthy adults. This novel method identified a system-level gradient of microstructural differentiation traversing from primary sensory to limbic regions that followed shifts in laminar differentiation and cytoarchitectural complexity. Importantly, while microstructural and functional gradients described a similar hierarchy, they became increasingly dissociated in transmodal default mode and fronto-parietal networks. Meta analytic decoding of these topographic dissociations highlighted involvement in higher-level aspects of cognition such as cognitive control and social cognition. Our findings demonstrate a relative decoupling of macroscale functional from microstructural gradients in transmodal regions, which likely contributes to the flexible role these regions play in human cognition.

neuroscience↗

bioRxiv2018

Connectome architecture, gene expression and functional co-activation shape the propagation of misfolded proteins in neurodegenerative disease

It is becoming increasingly clear that brain network organization shapes the course and expression of neurodegenerative diseases. Parkinsons disease (PD) is marked by progressive spread of atrophy from the midbrain to subcortical structures and eventually, to the cerebral cortex. Recent discoveries suggest that the neurodegenerative process involves the misfolding and prion-like propagation of endogenous -synuclein via axonal projections. However, the mechanisms that translate local \"synucleinopathy\" to large-scale network dysfunction and atrophy remain unknown. Here we use an agent-based epidemic spreading model to integrate structural connectivity, functional connectivity and gene expression, and to predict sequential volume loss due to neurodegeneration. The dynamic model replicates the spatial and temporal patterning of empirical atrophy in PD and implicates the substantia nigra as the disease epicenter. We reveal a significant role for both connectome topology and geometry in shaping the distribution of atrophy. The model also demonstrates that SNCA and GBA transcription influence -synuclein concentration and local regional vulnerability. Functional co-activation further amplifies the course set by connectome architecture and gene expression. Altogether, these results support the theory that the progression of PD is a multifactorial process that depends on both cell-to-cell spreading of misfolded proteins and regional vulnerability.

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