Biology subjects

Vertes, P. E.

Publications and source records attributed to Vertes, P. E..

At least 19 recordsLinked to original sources

bioRxiv2026

Inflections in adolescent cortical maturation signal psychopathology risk

Cerebral cortical maturation supports emotional, social, and cognitive development during adolescence1. Neurodevelopmental theories of psychiatric illnesses, which peak in onset around age 182, implicate subtle alterations in adolescent cortical development3. However, mechanistic links between cortical maturation and emergent psychopathology have remained elusive. This gap likely reflects a need for more nuanced understanding of cortical development4, larger, longitudinal samples powered to detect small effects,5 and more uniform image quality control.6 Using 29,482 structural MRI scans from 11,720 adolescents in the Adolescent Brain Cognitive Development (ABCD) study, we demonstrate reproducible differences in the contour and tempo of maturation across the cortical surface. Regional trajectories are robust to image quality variance and align with increasing functional specialization over time. Thicker cortex in early-maturing sensory regions predicts psychiatric symptoms as early as four years prior to their emergence, a pattern that subsequently spreads to association cortex. Overlap with spatially and temporally organized gene co-expression networks implicates prenatal programming of this sequence of maturation and its links to psychopathology. These findings present a novel, versatile neurodevelopmental framework for cortical maturation during adolescence that sequentially implicates sensory and association cortex in risk for emergent psychiatric symptoms.

neuroscience↗

bioRxiv2026

Genetic insights on the mechanisms of human cortical folding

The unique and intricate pattern of human cortical folding is rooted in fetal neurodevelopmental processes and can now be comprehensively quantified by new neuroimaging-derived measures of sulcal complexity. Here, we provide the first genetic maps of human sulcal complexity. Beginning with large effects of rare variants, we survey nine different neurogenetic syndromes (n=615), detecting visible changes in sulcal complexity on a shared axis of sulcal change coupled to the prenatal timing of sulcation. Turning to common genetic variants, we use genome-wide association studies of complexity scores for 40 sulci in the UK Biobank (n[~]29,000) to (i) resolve variable heritability across sulci, (ii) reveal both local and remote shared genetic effects with cortical morphology, and (iii) identify complexity-associated genes and their embedding in brain maps of prenatal gene expression. These reference genetic maps uncover multiple new mechanistic pathways for cortical morphogenesis in health and disease.

neuroscience↗

bioRxiv2025

A gene expression atlas of a juvenile nervous system

Although the fundamental architecture of metazoan nervous systems is typically established in the embryo, substantial numbers of neurons are added during post-natal development while existing neurons expand in size, refine connectivity, and undergo additional differentiation. To reveal the underlying molecular determinants of postembryonic neurogenesis and maturation, we have produced gene expression profiles of all neuron types and their progenitors in the first larval stage (L1) of C. elegans. Comparisons of the L1 profile to the embryo and to the later L4 larval stage identified thousands of differentially expressed genes across individual neurons throughout the nervous system. Key neuropeptide signaling networks, for example, are remodeled during larval development. Gene regulatory network analysis revealed potential transcription factors driving the temporal changes in gene expression across the nervous system, including a broad role for the heterochronic transcription factor lin-14. We utilized available connectomic data of juvenile animals in combination with our neuron-specific atlas to identify potential molecular determinants of membrane contact and synaptic connectivity. These expression data are available through a user-friendly interface at CeNGEN.org for independent investigations of the maturation, connectivity and function of a developing nervous system.

neuroscience↗

bioRxiv2025

Right time, right place: Heterochronicity shapes brain network formation

Brain network formation unfolds on a non-uniform developmental timetable, with different cortical regions generating connections at different developmental phases. Generative network models (GNMs) aim to uncover the principles underpinning the organisation of connectomes by creating synthetic networks according to simple computational rules. These models capture the connectomes topology, operationalised here as the overall distributions of network metrics (e.g., modularity, small-worldness, rich-club structure). However, they typically ignore the differential timing of connectivity formation. By omitting this temporal programme, GNMs often misplace topological features in physical space. Here, we add a heterochronous growth term to GNMs and use a new model fitness function that weighs topology and topography equally. Topography refers to the spatial embedding of the network, the actual anatomical positions of tracts. With these advances, we can generate synthetic networks that more faithfully reproduce the spatial layout of diffusion-MRI connectomes from two independent adult cohorts. Compared with classical, temporally agnostic models, heterochronous simulations improve model fit, accurately locate cortical hubs and modules, and converge on a single caudal-to-rostral gradient of brain maturation. Integrating heterochronicity makes GNMs more faithful to brain development, setting the stage for using them to explain and ultimately predict individual differences in network formation.

neuroscience↗

bioRxiv2025

A neuro-immune axis of transcriptomic dysregulation within the subgenual anterior cingulate cortex in schizophrenia

Many genes are linked to psychiatric disorders, but genome-wide association studies (GWAS) and differential gene expression (DGE) analyses in post-mortem brain tissue often implicate distinct gene sets. This disconnect impedes therapeutic development, which relies on integrating genetic and genomic insights. We address this issue using a novel multivariate technique that reduces DGE bias by leveraging gene co-expression networks and controlling for confounds such as drug exposure. Deep RNA sequencing was performed in bulk post-mortem sgACC from individuals with bipolar disorder (BD; N=35), major depression (MDD; N=51), schizophrenia (SCZ; N=44), and controls (N=55). Toxicology data dimensionality was reduced using multiple correspondence analysis; case-control gene expression was then analyzed using 1) traditional DGE and 2) group regularized canonical correlation analysis (GRCCA) - a multivariate regression method that accounts for feature interdependence. Gene set enrichment analyses compared results with established neuropsychiatric risk genes, gene ontology pathways, and cell type enrichments. GRCCA revealed a significant association with SCZ (Pperm=0.001; no significant BD or MDD association), and the resulting gene weight vector correlated with DGE SCZ-control t-statistics (R=0.53; P<0.05). Both methods indicated down-regulation of immune and microglial genes and upregulation of ion transport and excitatory neuron genes. However, GRCCA - at both the gene and transcript level - showed stronger enrichments (FDR<0.05). Notably, GRCCA results were enriched for SCZ GWAS-implicated genes (FDR<0.05), while DGE results were not. These findings identify a SCZ-specific sgACC gene expression pattern that highlights SCZ risk genes and implicates neuro-immune pathways, thus demonstrating the utility of multivariate approaches to integrate genetic and genomic signals.

neuroscience↗

bioRxiv2024

Development and early life stress sensitivity of the rat cortical microstructural similarity network

Understanding how early life experiences shape brain network development is a key challenge in the neuroscience of mental health disorders. To address this, we used magnetic resonance imaging (MRI) similarity network analysis to study the effects of stress in the rat, an important animal model in neuropsychiatry. We measured magnetization transfer ratio (MTR) at each of 53 distinct cortical areas and estimated a cortical similarity network for each individual scan, in two independent experimental datasets. We first characterized normative network development in rats scanned repeatedly between postnatal days 20 (weanling) and 290 (mid-adulthood) (N=47), and then contrasted these findings with a cohort exposed to early life stress in the form of repeated maternal separation (RMS, N=40). The normative rat cortical similarity network exhibited biologically meaningful organization, aligning with prior cytoarchitectonic and tract-tracing data, and displayed complex topological features, including rich club organization. During postnatal and adolescent development, brain regions became more similar, including an early phase of fronto-hippocampal convergence. Early increases in inter-areal similarity were reversed in a later phase of fronto-hippocampal divergence in mid-adulthood. RMS exposure altered inter-areal similarity, especially between frontal and parahippocampal regions, that were also most active developmentally and in aging. Our results reveal how normative cortical network changes in the developing brain are influenced by early life stress. These findings suggest a new translational framework for elucidating how environmental risk factors lead to atypical development of cortical networks.

neuroscience↗

bioRxiv2024

Molecular patterns of evolutionary changes throughout the whole nervous system of multiple nematode species

One avenue to better understand brain evolution is to map molecular patterns of evolutionary changes in neuronal cell types across entire nervous systems of distantly related species. Generating whole-animal single-cell transcriptomes of three nematode species from the Caenorhabditis genus, we observed a remarkable stability of neuronal cell type identities over more than 45 million years of evolution. Conserved patterns of combinatorial expression of homeodomain transcription factors are among the best classifiers of homologous neuron classes. Unexpectedly, we discover an extensive divergence in neuronal signaling pathways. While identities of neurotransmitter-producing neurons (glutamate, acetylcholine, GABA and several monoamines) remain stable, ionotropic and metabotropic receptors for all these neurotransmitter systems show substantial divergence, resulting in more than half of all neuron classes changing their capacity to be receptive to specific neurotransmitters. Neuropeptidergic signaling is also remarkably divergent, both at the level of neuropeptide expression and receptor expression, yet the overall dense network topology of the wireless neuropeptidergic connectome remains stable. Novel neuronal signaling pathways are suggested by our discovery of small secreted proteins that show no obvious hallmarks of conventional neuropeptides, but show similar patterns of highly neuron-type-specific and highly evolvable expression profiles. In conclusion, by investigating the evolution of entire nervous systems at the resolution of single neuron classes, we uncover patterns that may reflect basic principles governing evolutionary novelty in neuronal circuits.

evolutionary biology↗

bioRxiv2023

A bipolar taxonomy of adult human brain sulcal morphology related to timing of fetalsulcation and trans-sulcal gene expression gradients

We developed a computational pipeline (now provided as a resource) for measuring morphological similarity between cortical surface sulci to construct a sulcal phenotype network (SPN) from each magnetic resonance imaging (MRI) scan in an adult cohort (N=34,725; 45-82 years). Networks estimated from pairwise similarities of 40 sulci on 5 morphological metrics comprised two clusters of sulci, represented also by the bipolar distribution of sulci on a linear-to-complex dimension. Linear sulci were more heritable and typically located in unimodal cortex; complex sulci were less heritable and typically located in heteromodal cortex. Aligning these results with an independent fetal brain MRI cohort (N=228; 21-36 gestational weeks), we found that linear sulci formed earlier, and the earliest and latest-forming sulci had the least between-adult variation. Using high-resolution maps of cortical gene expression, we found that linear sulcation is mechanistically underpinned by trans-sulcal gene expression gradients enriched for developmental processes.

neuroscience↗

bioRxiv2023

conn2res: A toolbox for connectome-based reservoir computing

The connection patterns of neural circuits form a complex network. How signaling in these circuits manifests as complex cognition and adaptive behaviour remains the central question in neuroscience. Concomitant advances in connectomics and artificial intelligence open fundamentally new opportunities to understand how connection patterns shape computational capacity in biological brain networks. Reservoir computing is a versatile paradigm that uses nonlinear dynamics of high-dimensional dynamical systems to perform computations and approximate cognitive functions. Here we present conn2res: an open-source Python toolbox for implementing biological neural networks as artificial neural networks. conn2res is modular, allowing arbitrary architectures and arbitrary dynamics to be imposed. The toolbox allows researchers to input connectomes reconstructed using multiple techniques, from tract tracing to noninvasive diffusion imaging, and to impose multiple dynamical systems, from simple spiking neurons to memristive dynamics. The versatility of the conn2res toolbox allows us to ask new questions at the confluence of neuroscience and artificial intelligence. By reconceptualizing function as computation, conn2res sets the stage for a more mechanistic understanding of structure-function relationships in brain networks.

neuroscience↗

bioRxiv2022

System-wide mapping of neuropeptide-GPCR interactions in C. elegans

Neuropeptides are ancient, widespread signaling molecules that underpin almost all brain functions. They constitute a broad ligand-receptor network, mainly by binding to G protein-coupled receptors (GPCRs). However, the organization of the peptidergic network and roles of many neuropeptides remain elusive, as our insight into neuropeptide-receptor interactions is limited and many peptide GPCRs in animal models and humans are still orphan receptors. Here we report a genome-wide neuropeptide-GPCR interaction map in C. elegans. By reverse pharmacology screening of over 55,384 possible interactions, we identify 461 cognate peptide-GPCR couples that uncover a broad signaling network with specific and complex combinatorial interactions encoded across and within single peptidergic genes. These interactions provide insights into neuropeptide functions and evolution. Combining our dataset with phylogenetic analysis supports peptide-receptor co-evolution and conservation of at least 14 bilaterian peptidergic systems in C. elegans. This resource lays a foundation for system-wide analysis of the peptidergic network. HighlightsO_LISystem-wide reverse pharmacology deorphanizes 68 C. elegans peptide GPCRs C_LIO_LIDiscovery of 461 peptide-GPCR pairs and additional ligands for characterized GPCRs C_LIO_LINeuropeptides show specific and complex combinatorial receptor interactions C_LIO_LIPeptide-GPCR pairs support long-range conservation and expansion of peptide systems C_LI

neuroscience↗

bioRxiv2022

Transcriptional Cartography Integrates Multiscale Biology of the Human Cortex

The cerebral cortex underlies many of our unique strengths and vulnerabilities - but efforts to understand human cortical organization are challenged by reliance on incompatible measurement methods at different spatial scales. Macroscale features such as cortical folding and functional activation are accessed through spatially dense neuroimaging maps, whereas microscale cellular and molecular features are typically measured with sparse postmortem sampling. Here, we integrate these distinct windows on brain organization by building upon existing postmortem data to impute, validate and analyze a library of spatially dense neuroimaging-like maps of human cortical gene expression. These maps allow spatially unbiased discovery of cortical zones with extreme transcriptional profiles or unusually rapid transcriptional change which index distinct microstructure and predict neuroimaging measures of cortical folding and functional activation. Modules of spatially coexpressed genes define a family of canonical expression maps that integrate diverse spatial scales and temporal epochs of human brain organization - ranging from protein-protein interactions to large-scale systems for cognitive processing. These module maps also parse neuropsychiatric risk genes into subsets which tag distinct cyto-laminar features and differentially predict the location of altered cortical anatomy and gene expression in patients. Taken together, the methods, resources and findings described here advance our understanding of human cortical organization and offer flexible bridges to connect scientific fields operating at different spatial scales of human brain research.

neuroscience↗

bioRxiv2022

Early adversity changes the economic conditions of structural brain network organisation

Early adversity can change educational, cognitive, and mental health outcomes. However, the neural processes through which early adversity exerts these effects remain largely unknown. We used generative network modelling of the mouse connectome to test whether unpredictable postnatal stress shifts the constraints that govern the formation of the structural connectome. A model that trades off the wiring cost of long-distance connections with topological homophily (i.e. links between regions with shared neighbours) generated simulations that replicate the organisation of the rodent connectome. The imposition of early life adversity significantly shifted the best-performing parameter combinations toward zero, heightening the stochastic nature of the generative process. Put simply, unpredictable postnatal stress changes the economic constraints that shape network formation, introducing greater randomness into the structural development of the brain. While this change may constrain the development of cognitive abilities, it could also reflect an adaptive mechanism. In other words, neural development could harness heightened stochasticity to make networks more robust to perturbation, thereby facilitating effective responses to future threats and challenges. Significance statementChildren who experience adversity early in life - such as chronic poverty or abuse - show numerous neural differences that are linked to poorer cognition and mental health later in life. To effectively mitigate the burden of adversity, it is critical to identify how these differences arise. In this paper, we use computational modelling to test whether growing up in an impoverished and unpredictable environment changes the development of structural connections in the mouse brain. We found that early adversity appears to introduce more stochasticity in the formation of neural architecture. Our findings point to a potential mechanism for how early adversity could change the course of child development.

neuroscience↗

bioRxiv2022

Homophilic wiring principles underpin neuronal network topology in vitro

Economic efficiency has been a popular explanation for how networks self-organize within the developing nervous system. However, the precise nature of the economic negotiations governing this putative organizational principle remains unclear. Here, we address this question further by combining large-scale electrophysiological recordings, to characterize the functional connectivity of developing neuronal networks in vitro, with a generative modeling approach capable of simulating network formation. We find that the best fitting model uses a homophilic generative wiring principle in which neurons form connections to other neurons which are spatially proximal and have similar connectivity patterns to themselves. Homophilic generative models outperform more canonical models in which neurons wire depending upon their spatial proximity either alone or in combination with the extent of their local connectivity. This homophily-based mechanism for neuronal network emergence accounts for a wide range of observations that are described, but not sufficiently explained, by traditional analyses of network topology. Using rodent and human monolayer and organoid cultures, we show that homophilic generative mechanisms can accurately recapitulate the topology of emerging cellular functional connectivity, representing an important wiring principle and determining factor of neuronal network formation in vitro.

neuroscience↗

bioRxiv2020

A generative network model of neurodevelopment

The emergence of large-scale brain networks, and their continual refinement, represent crucial developmental processes that can drive individual differences in cognition and which are associated with multiple neurodevelopmental conditions. But how does this organization arise, and what mechanisms govern the diversity of these developmental processes? There are many existing descriptive theories, but to date none are computationally formalized. We provide a mathematical framework that specifies the growth of a brain network over developmental time. Within this framework macroscopic brain organization, complete with spatial embedding of its organization, is an emergent property of a generative wiring equation that optimizes its connectivity by renegotiating its biological costs and topological values continuously over development. The rules that govern these iterative wiring properties are controlled by a set of tightly framed parameters, with subtle differences in these parameters steering network growth towards different neurodiverse outcomes. Regional expression of genes associated with the developmental simulations converge on biological processes and cellular components predominantly involved in synaptic signaling, neuronal projection, catabolic intracellular processes and protein transport. Together, this provides a unifying computational framework for conceptualizing the mechanisms and diversity of childhood brain development, capable of integrating different levels of analysis - from genes to cognition.

neuroscience↗

bioRxiv2020

Sexually dimorphic development of depression-related brain networks during healthy human adolescence

We hypothesized that there are sexual differences in human brain network development underlying the female > male divergence in adolescent depression. We tested for sex differences in parameters of brain network development (accelerated longitudinal fMRI, N=298 healthy adolescents, each scanned 1 to 3 times). Sexually divergent development of functional connectivity was located in default mode network (DMN), limbic cortex, and subcortical nuclei. Females had a more "disruptive" pattern of development, where weak functional connectivity at age 14 became stronger during adolescence. This fMRI-derived map of divergent adolescent development was co-located with (i) a map of functional dysconnectivity associated with adult major depressive disorder (MDD); and (ii) an adult brain gene expression pattern enriched for genes on the X chromosome, neurodevelopmental genes, and risk genes for MDD. Sexual divergence in disruptive development of DMN, limbic and subcortical functional networks is potentially relevant to the increased risk of depression in adolescent females.

neuroscience↗

bioRxiv2019

A moment of change: shifts in myeloarchitecture characterise adolescent development of cortical gradients

The biological processes underpinning adolescent brain maturation remain elusive. Expanding on previous work showing age-related changes in cortical morphology, we studied an accelerated longitudinal cohort of adolescents and young adults (n=223, two time points) to investigate dynamic reconfigurations in myeloarchitecture. Intracortical profiles were generated using magnetization transfer (MT) data, a myelin-sensitive magnetic resonance imaging contrast. Mixed-effect models of depth specific intracortical profiles demonstrated two separate processes i) related to overall increases in MT, and ii) showing a flattening of the MT profile related to enhanced signal in mid-to-deeper layers, especially in heteromodal and unimodal association cortices. This development was independent of morphological changes, and enhanced MT in mid-to-deeper layers was found to spatially co-localise specifically with gene expression markers of oligodendrocytes. Covariance analysis between all pairs of intracortical profiles revealed that these intracortical changes contributed to a gradual and dynamic differentiation from higher-order to lower-order systems. Depth-dependent trajectories of intracortical myeloarchitectural development contribute to the maturation of structural hierarchies in the human neocortex, providing a model for adolescent development that bridges microstructural and macroscopic scales of brain organization.\n\neLife digestIntracortical myelin imposes a spatial structure on cortico-cortical connections, yet little is known about how myeloarchitecture develops throughout youth. We formulated a novel approach to study cortical myeloarchitecture in individual humans and leveraged an accelerated longitudinal design to track age-related changes from 14-27 years. We discovered two unique processes: one involving increasing mean myelin and another characterised by the preferential accumulation of myelin in mid-to-deeper cortical layers. Both processes contributed to an increasing segregation of lower-order from higher-order systems along the macroscale cortical hierarchy. These findings illustrate how layer specific microstructural changes contribute to the maturation of cortical organization and suggest adolescent fine tuning of hierarchical gradients of cortical networks.

neuroscience↗

bioRxiv2019

Multi-Subject Stochastic Blockmodels for Adaptive Analysis of Individual Differences in Human Brain Network Cluster Structure.

There is great interest in elucidating the cluster structure of brain networks in terms of modules, blocks or clusters of similar nodes. However, it is currently challenging to handle data on multiple subjects since most of the existing methods are applicable only on a subject-by-subject basis or for analysis of a group average network. The main limitation of per-subject models is that there is no obvious way to combine the results for group comparisons, and of group-averaged models that they do not reflect the variability between subjects. Here, we propose two novel extensions of the classical Stochastic Blockmodel (SBM) that use a mixture model to estimate blocks or clusters of connected nodes, combined with a regression model to capture the effects on cluster structure of individual differences on subject-level covariates. Multi-subject Stochastic Blockmodels (MS-SBM) can flexibly account for between-subject variability in terms of a homogenous or heterogeneous effect on connectivity of covariates such as age or diagnostic status. Using synthetic data, representing a range of block sizes and cluster structures, we investigate the accuracy of the estimated MS-SBM parameters as well as the validity of inference procedures based on Wald, likelihood ratio and Monte Carlo permutation tests. We show that multi-subject SBMs recover the true cluster structure of synthetic networks more accurately and adaptively than standard methods for modular decomposition. Permutation tests of MS-SBM parameters were more robustly valid for statistical inference and Type I error control than tests based on standard asymptotic assumptions. Applied to analysis of multi-subject resting state fMRI networks (13 healthy volunteers; 12 people with schizophrenia; N = 268 brain regions), we show that the Heterogeneous Stochastic Blockmodel estimates core-on-modules architecture. The intra-block and inter-block connection weights vary between individual participants and can be modelled as a logistic function of subject-level covariates like age or diagnostic status. Multi-subject Stochastic Blockmodels are likely to be useful tools for statistical analysis of individual differences in human brain graphs and other networks whose prior cluster structure needs to be estimated from the data.

neuroscience↗

bioRxiv2019

Conservative and disruptive modes of adolescent change in brain functional connectivity

Adolescent changes in human brain function are not entirely understood. Here we used multi-echo functional magnetic resonance imaging (fMRI) to measure developmental change in functional connectivity (FC) of resting-state oscillations between pairs of 330 cortical regions and 16 subcortical regions in N=298 healthy adolescents. Participants were aged 14-26 years and were scanned on two or more occasions at least 6 months apart. We found two distinct modes of age-related change in FC: \"conservative\" and \"disruptive\". Conservative development was characteristic of primary cortex, which was strongly connected at 14 years and became even more connected in the period 14-26 years. Disruptive development was characteristic of association cortex, hippocampus and amygdala, which were not strongly connected at 14 years but became more strongly connected during adolescence. We defined the maturational index (MI) as the signed coefficient of the linear relationship between baseline FC (at 14 years, FC14) and adolescent change in FC ({triangleup}FC14-26). Disruptive systems (with negative MI) were functionally specialised for social cognition and autobiographical memory and were significantly co-located with prior maps of aerobic glycolysis (AG), AG-related gene expression, post-natal expansion of cortical surface area, and adolescent shrinkage of cortical depth. We conclude that human brain organization is disrupted during adolescence by the emergence of strong functional connectivity of subcortical nuclei and association cortical areas, representing metabolically expensive re-modelling of synaptic connectivity between brain regions that were not strongly connected in childhood. We suggest that this re-modelling process may support emergence of social skills and self-awareness during healthy human adolescence.

neuroscience↗