Biology subjects

Oldham, S.

Publications and source records attributed to Oldham, S..

At least 19 recordsLinked to original sources

bioRxiv2026

Early development of cerebellar functional connectivity

The cerebellum makes a critical contribution to cognitive and behavioural function, extending well beyond motor control, and is recognised as an important determinant of neurodevelopmental outcome. Despite this, little is known about the foundations of cerebellar functional organisation in early life. Addressing this gap is essential for establishing typical developmental trajectories against which atypical development can be assessed. Here, we analysed fMRI data from term-born neonates (n=155) scanned between 37 and 44 weeks postmenstrual age and mapped intra-cerebellar and cerebro-cerebellar functional connectivity across multiple levels of organisation. We identified 13 intrinsic connectivity networks (ICNs) within the neonatal cerebellum that recapitulated adult subdivisions, with two ICNs showing age-related increases in coverage and strength. Analysis of cerebro-cerebellar connectivity identified 15 sensory, motor and association cerebral clusters, seven increasing connectivity with age. Functional gradient analysis identified a continuous axis from somatomotor to association regions within intra-cerebellar and cerebro-cerebellar organisation, which became more pronounced with age. Together, these findings suggest that the foundations of adult cerebellar functional organisation are present at birth but undergo rapid and regionally heterogeneous maturation over the first weeks of postnatal life. This developmental framework provides a normative reference for understanding cerebellar development and its implications for later neurodevelopmental variation.

neuroscience↗

bioRxiv2026

Emergence of long- and short-range functional connectivity shapes neonatal brain gradients

The emergence of macroscale brain organisation during the perinatal period represents a critical but poorly understood process. Using resting-state fMRI in 325 term-born neonates within the first month of postnatal life, we characterised the early emergence of macroscale brain organisation with functional gradients. Neonatal functional gradients showed a distinct pattern compared with the canonical adult gradients: the principal gradient in neonates, separating the primary sensorimotor and visual networks, corresponded to the secondary gradient in adults (sensorimotor-visual axis), whereas the secondary gradient in neonates, spanning from primary visual to association networks, aligned with the principal gradient in adults (sensorimotor-association axis). We identified distance-dependent functional connectivity as a key mechanism shaping perinatal functional gradients and potentially underlying the protracted development of the sensorimotor-association hierarchy in neonates. Regions with a higher proportion of short-range functional connectivity contributed more to the sensorimotor-visual gradient in neonates, while regions with a greater proportion of middle-to long-range functional connectivity drive the sensorimotor-association hierarchy. Together, these findings highlight the critical role of short- and long-range functional connectivity in shaping neonatal macroscale functional hierarchy. Disruptions to this process during the perinatal period may alter macroscale brain organisation and contribute to the early origins of neurodevelopmental conditions. Significance statementMacroscale brain organisation, characterised by functional gradients, is a fundamental feature of the adult brain and is closely linked to neurodevelopmental conditions. Characterising its emergence in early life and the underlying mechanisms is therefore critical for understanding typical and atypical brain development. Here, we showed that macroscale brain organisation is present at birth, with neonatal functional gradients showing a reversed pattern compared to adults, highlighting the early maturation of primary visual and sensorimotor networks. We further demonstrated that the perinatal development of long- and short-range functional connectivity shapes neonatal functional gradients, identifying a potential mechanism through which early disruptions in functional connectivity may contribute to later neurodevelopmental outcomes.

neuroscience↗

bioRxiv2026

Thalamocortical constraints on areal connectivity in the developing human brain

The thalamus plays a central role in cortical development, organisation and function. Thalamic nuclei acquire distinct molecular identities during gestation, with first-order relays maturing before higher-order nuclei. Thalamic afferents innervate the cortical plate with a precise order, disruptions to which alter cortical function. Recent models propose that thalamic input to primary sensory cortex constrains the development of wider cortical networks, promoting the formation of highly-connected hubs in association cortex. Here, we combine neuroimaging, post mortem gene expression data and network modelling to examine how the timing and spatial distribution of thalamocortical innervation influences the formation of cortical networks during gestation. We find that the maturation rates of thalamic nuclei align with predicted timing and distribution of afferent outgrowth. While higher order nuclei connect widely across the cortex, they do not preferentially target high-degree hubs. Instead, hubs emerge from interdependent spatiotemporal constraints imposed by both wiring distance and thalamocortical maturation.

neuroscience↗

bioRxiv2025

Human cortical networks trade communication efficiency for computational reliability.

Brains are often described as cost-efficient communication networks, optimally balancing the cost of long connections with the benefits of fast communication. Here, inspired by the "use it or lose it" principle, we present a novel game-theoretic model of self-organizing neural units and show that the brain is, in fact, sub-optimal in both regards: First, we demonstrate that regional competition for connectivity under propagative communication dynamics naturally gives rise to network configurations similar to those derived from the human cortex while being even more efficient and economical. Next, we use a reservoir computing framework to compare the information processing capacity of these networks against those of the brain. Although comparable in performance, the more optimal trade-off comes with a tax on computational reliability. Through synthetic lesions, we show that these networks are fragile because, to optimize for communication, they funnel information through a spatially clustered "oligarchy" comprising a tight set of transmodal hubs. In contrast, the human brain uses a more distributed "rich club" backbone that better resists breakdown following targeted attacks, even when it means higher wiring costs and less efficient communication. This reveals a previously overlooked principle: cortical networks trade both cost and efficiency for reliable computation. Thus, our findings highlight computational reliability as another, and even more prominent driver of brain connectivity compared to wiring cost and communication efficiency. TeaserThe brain avoids fragile efficiency: brain networks favor reliable computation, revealing resilience as a hidden design rule.

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

Geometric constraints on the architecture of mammalian cortical connectomes

The intricate network of axonal fibres that forms the mammalian cortical connectome has a complex topology, being organized in a way that is neither completely regular nor random, as well as a characteristic topography, in which specific anatomical locations are imbued with distinctive connectivity profiles. The mechanisms that give rise to such properties remain a mystery. Here, we formulate a simple analytic model derived from neural field theory that prioritizes physical constraints on connectome architecture by assuming that connectivity is preferentially concentrated between pairs of cortical locations that facilitate the excitation of resonant geometric modes of the cortex. We show that the model outperforms existing approaches in reproducing multiple topological and topographical properties of cortical connectomes mapped at spatial scales spanning orders of magnitude in humans, chimpanzees, macaques, marmosets, and mice, as mapped with either non-invasive diffusion magnetic resonance imaging or invasive viral tract-tracing. Our findings thus point to a fundamental role of geometry in shaping the multiscale architecture of cortical connectomes that has been conserved across 90 million years of evolution.

neuroscience↗

bioRxiv2025

Disentangling the influences of pre- and postnatal periods on human cortical microstructure

During late gestation and early postnatal development a combination of intrinsic and extrinsic factors drive the maturation of the human cortex. This process is regionally heterogeneous, with cortical areas developing at different paces and trajectories. Leveraging submillimetre T1-weighted/T2w-weighted (T1w/T2w) magnetic resonance imaging (MRI) from pre- and full-term neonates (n = 599, 0-7 weeks), we sampled intracortical microstructure profiles across the cortex and characterised the profiles shapes according to their central moments. We found that gestational age at birth dominated the effects on early cortical development, with significant, global increases in microstructural density, increasing intracortical homogeneity and a bimodal change of the microstructural balance between superficial and deeper cortical layers. On the other hand, weeks since birth (i.e. postnatal age) exhibited different effects on microstructure, with density increasing at a slower pace, increasing intracortical heterogeneity, and intracortical balance only shifting towards deeper layers in posterior temporal, occipital, medial parietal areas and some prefrontal areas. These effects align with low spatial-frequency geometric eigenmodes of the human cortex, specifically the anterior-posterior, superior-inferior and central-polar axes. Our findings demonstrate that separating prenatal from postnatal influences, and analysing intracortical profiles rather than macroscale features, provides finer-grained insights into how human cortical microstructure changes during perinatal development and lays the groundwork for investigating the biological underpinnings that govern normative cortical maturation.

neuroscience↗

bioRxiv2025

Transcriptomic divergence of network hubs in the prenatal human brain

Connections in the human brain are not uniformly distributed; instead, a dense network of long-range projections converges on highly connected hub regions located in transmodal, paralimbic, and association cortices. Hub connectivity is strongly influenced by genetic factors but the molecular cues guiding the foundation of these structures remain poorly understood. Here, we combined high-resolution diffusion MRI tractography from 208 term-born neonates with spatially resolved prenatal gene expression data to investigate the molecular correlates of network hub formation at mid-gestation. We identified robust hub architecture in the neonatal connectome and mapped these structural hubs to corresponding cortical regions in the Brain prenatal digital brain atlas. Transcriptomic analysis revealed differential gene expression in network hubs prior to the time of birth, with genes positively associated with hub status supporting the establishment of early neuronal circuitry and predominantly expressed in the transient subplate and intermediate zones. Hub genes were expressed by excitatory neurons, including subplate neurons and intratelencephalic projection neurons in deep cortical layers, and overlapped with markers of cortical expansion and interhemispheric connectivity in adulthood. Our study identifies prenatal transcriptomic signatures of network hubs in the neonatal human brain, demonstrating how early gene expression programs can shape brain network connectivity from mid-gestation.

neuroscience↗

bioRxiv2024

Coming up short: generative network models fail to accurately capture long-range connectivity

Generative network models (GNMs) have been proposed to identify the mechanisms/constraints that shape the organisation of the connectome. These models parameterise the formation of inter-regional connections using a trade-off between connection cost and topological complexity or biophysical similarity. Despite their simplicity, GNMs can generate synthetic networks that capture many topological properties of empirical brain networks. However, current models often fail to capture the topography (i.e., spatial embedding) of many such properties, such as the anatomical location of network hubs. In this study, we investigate a diverse array of generative network model formulations and find that none can accurately capture empirical patterns of long-range connectivity. We demonstrate that the spatial embedding of longer-range connections is critical in defining hub locations and that it is precisely these connections that are poorly captured by extant models. We further show how standard measures used for model optimisation and evaluation mask these and other differences between synthetic and empirical brain networks, highlighting the need for care when interpreting generative network models and metrics. Overall, our findings demonstrate common failure modes of GNMs, identify why these models do not fully capture brain network organisation, and suggest ways the field can move forward to address these challenges. Author summaryGenerative network models aim to explain the organisation of connectomes using simple wiring rules. While these models replicate topological features of brain networks, they do not capture key topographical properties, like the anatomical location of network hubs. We show that this failure occurs because the models are unable to accurately capture the spatial position of long-range inter-regional connections. Moreover, standard evaluation measures fail to accurately quantify the similarity between model and empirical networks. This study identifies how and why limitations of current generative models occur and suggests ways forward for improved practices.

neuroscience↗

bioRxiv2024

Multiscale heterogeneity of white matter morphometry in psychiatric disorders

BackgroundInter-individual variability in neurobiological and clinical characteristics in mental illness is often overlooked by classical group-mean case-control studies. Studies using normative modelling to infer person-specific deviations of grey matter volume have indicated that group means are not representative of most individuals. The extent to which this variability is present in white matter morphometry, which is integral to brain function, remains unclear. MethodsWe applied Warped Bayesian Linear Regression normative models to T1- weighted magnetic resonance imaging data and mapped inter-individual variability in person-specific white matter volume deviations in 1,294 cases (58% male) diagnosed with one of six disorders (attention-deficit/hyperactivity, autism, bipolar, major depressive, obsessive-compulsive and schizophrenia) and 1,465 matched controls (54% male) recruited across 25 scan sites. We developed a framework to characterize deviation heterogeneity at multiple spatial scales, from individual voxels, through inter- regional connections, specific brain regions, and spatially extended brain networks. ResultsThe specific locations of white matter volume deviations were highly heterogeneous across participants, affecting the same voxel in fewer than 8% of individuals with the same diagnosis. For autism and schizophrenia, negative deviations (i.e., areas where volume is lower than normative expectations) aggregated into common tracts, regions and large-scale networks in up to 35% of individuals. ConclusionsThe prevalence of white matter volume deviations was lower than previously observed in grey matter, and the specific location of these deviations was highly heterogeneous when considering voxel-wise spatial resolution. Evidence of aggregation within common pathways and networks was apparent in schizophrenia and autism but not other disorders.

neuroscience↗

bioRxiv2024

Development of thalamocortical connectivity during the third trimester

Thalamocortical connections are crucial for relaying sensory information in the brain and facilitate essential functions including motor skills, emotion, and cognition. Emerging evidence suggests that thalamocortical connections are organised along spatial gradients that may reflect their sequential formation during early brain development. However, this has not been extensively characterised in humans. To examine early thalamocortical development, we analysed diffusion MRI data from 345 infants, scanned between 29-45 weeks gestational age. Using diffusion tractography, we mapped thalamocortical connectivity in each neonate and used Principal Component Analysis to extract shared spatial patterns of connectivity. We identified a primary axis of connectivity that varied along an anterior/medial to posterior/lateral gradient within the thalamus, with corresponding projections to cortical areas varying along a rostral-caudal direction. The primary patterns of thalamocortical connectivity were present at 30 weeks gestational age and gradually refined during gestation. This refinement was largely driven by the maturation of connections between the thalamus and cortical association areas. Differences in thalamocortical connectivity between preterm and term neonates were only weakly related to primary thalamocortical gradients, suggesting a relative preservation of these features following premature birth. Overall, our results indicate that the organisation of structural thalamocortical connections are highly conserved across individuals, develop early in gestation and gradually mature with age.

neuroscience↗

bioRxiv2024

A shifting role of thalamocortical connectivity in the emergence of large-scale functional brain organization across early lifespan development

While cortical patterning has been a perennial research topic in neuroscience, the mechanism for its consequence, namely functional specialization at the macro scale, remains an open question in the human brain. Here, we focused on age-dependent changes of resting-state thalamocortical connectivity to investigate its role in the emergence of large-scale functional networks across infancy, childhood and young adulthood. We found that the thalamocortical connectivity during infancy reflects an early differentiation of sensorimotor networks and genetically-influenced axonal projection. This initial role of the thalamus, however, seems to change during childhood, by establishing connectivity with the salience network and decoupling externally- and internally-oriented functional processes. Developmental simulation and perturbation analyses corroborated these findings, demonstrating the highest contribution of thalamic connectivity, especially in the later age of youth, in the formation of key characteristics of the mature brain, such as functional gradient and cortical hierarchy. Our study highlights a developmentally shifting role of the thalamus in orchestrating complex brain organization and its potential implications for developmental conditions characterized by compromised internal and external processing.

neuroscience↗

bioRxiv2024

Molecular signatures of cortical expansion in the human fetal brain

The third trimester of human gestation is characterised by rapid increases in brain volume and cortical surface area. A growing catalogue of cells in the prenatal brain has revealed remarkable molecular diversity across cortical areas.1,2 Despite this, little is known about how this translates into the patterns of differential cortical expansion observed in humans during the latter stages of gestation. Here we present a new resource, Brain, to facilitate knowledge translation between molecular and anatomical descriptions of the prenatal developing brain. Built using generative artificial intelligence, Brain is a three-dimensional cellular-resolution digital atlas combining publicly-available serial sections of the postmortem human brain at 21 weeks gestation3 with bulk tissue microarray data, sampled across 29 cortical regions and 5 transient tissue zones.4 Using Brain, we evaluate the molecular signatures of preferentially-expanded cortical regions during human gestation, quantified in utero using magnetic resonance imaging (MRI). We find that differences in the rates of expansion across cortical areas during gestation respect anatomical and evolutionary boundaries between cortical types5 and are founded upon extended periods of upper-layer cortical neuron migration that continue beyond mid-gestation. We identify a set of genes that are upregulated from mid-gestation and highly expressed in rapidly expanding neocortex, which are implicated in genetic disorders with cognitive sequelae. Our findings demonstrate a spatial coupling between areal differences in the timing of neurogenesis and rates of expansion across the neocortical sheet during the prenatal epoch. The Brain atlas is available from: https://garedaba.github.io/micro-brain/ and provides a new tool to comprehensively map early brain development across domains, model systems and resolution scales.

neuroscience↗

bioRxiv2022

A phylogenetically-conserved axis of thalamocortical connectivity in the human brain

The thalamus enables key sensory, motor, emotive, and cognitive processes via connections to the cortex. These projection patterns are traditionally considered to originate from discrete thalamic nuclei, however recent work showing gradients of molecular and connectivity features in the thalamus suggests the organisation of thalamocortical connections occurs along a continuous dimension. By performing a joint decomposition of densely sampled gene expression and non-invasive diffusion tractography in the adult human thalamus, we define a principal axis of genetic and connectomic variation along a medial-lateral thalamic gradient. Projections along this axis correspond to an anterior-posterior cortical pattern and are aligned with electrophysiological properties of the cortex. The medial-lateral axis demonstrates phylogenetic conservation, reflects transitions in neuronal subtypes, and shows associations with neurodevelopment and common brain disorders. This study provides evidence for a supra-nuclear axis of thalamocortical organisation characterised by a graded transition in molecular properties and anatomical connectivity.

neuroscience↗

bioRxiv2022

FGF21 protects against hepatic lipotoxicity and macrophage activation to attenuate fibrogenesis in nonalcoholic steatohepatitis

Analogues of the hepatokine FGF21 are in clinical development for type 2 diabetes and nonalcoholic steatohepatitis (NASH) treatment. Although their glucose-lowering and insulin-sensitizing effects have been largely unraveled, the mechanisms by which they alleviate liver injury have only been scarcely addressed. Here, we aimed to unveil the mechanisms underlying the protective effects of FGF21 on NASH using APOE*3-Leiden.CETP mice, a well-established model for human-like metabolic diseases. Liver-specific FGF21 overexpression was achieved in mice, followed by administration of a high-fat high-cholesterol diet for 23 weeks. FGF21 prevented hepatic lipotoxicity, accompanied by activation of thermogenic tissues and attenuation of adipose tissue inflammation, improvement of hyperglycemia and hypertriglyceridemia, and upregulation of hepatic programs involved in fatty acid oxidation and cholesterol removal. Furthermore, FGF21 inhibited hepatic inflammation, as evidenced by reduced Kupffer cell (KC) activation, diminished monocyte infiltration and lowered accumulation of monocyte-derived macrophages. Moreover, FGF21 decreased lipid- and scar-associated macrophages, which correlated with less hepatic fibrosis as demonstrated by reduced collagen accumulation. Collectively, hepatic FGF21 overexpression limits hepatic lipotoxicity, inflammation and fibrogenesis. Mechanistically, FGF21 blocks hepatic lipid influx and accumulation through combined endocrine and autocrine signaling, respectively, which prevents KC activation and lowers the presence of lipid- and scar-associated macrophages to inhibit fibrogenesis.

pathology↗

bioRxiv2021

Modeling spatial, developmental, physiological, and topological constraints on human brain connectivity

The complex connectivity of nervous systems is thought to have been shaped by competitive selection pressures to minimize wiring costs and support adaptive function. Accordingly, recent modeling work indicates that stochastic processes, shaped by putative trade-offs between the cost and value of each connection, can successfully reproduce many topological properties of macroscale human connectomes measured with diffusion magnetic resonance imaging. Here, we derive a new formalism with the aim to more accurately capture the competing pressures of wiring cost minimization and topological complexity. We further show that model performance can be improved by accounting for developmental changes in brain geometry and associated wiring costs, and by using inter-regional transcriptional or microstructural similarity rather than topological wiring-rules. However, all models struggled to capture topologies spatial embedding. Our findings highlight an important role for genetics in shaping macroscale brain connectivity and indicate that stochastic models offer an incomplete account of connectome organization.

neuroscience↗

bioRxiv2021

On the intersection between data quality and dynamical modelling of large-scale fMRI signals

Large-scale dynamics of the brain are routinely modelled using systems of nonlinear dynamical equations that describe the evolution of population-level activity, with distinct neural populations often coupled according to an empirically measured structural connection matrix. This modelling approach has been used to generate insights into the neural underpinnings of spontaneous brain dynamics, as recorded with techniques such as resting state functional MRI (fMRI). In fMRI, researchers have many degrees of freedom in the way that they can process the data and recent evidence indicates that the choice of pre-processing steps can have a major effect on empirical estimates of functional connectivity. However, the potential influence of such variations on modelling results are seldom considered. Here we show, using three popular whole-brain dynamical models, that different choices during fMRI preprocessing can dramatically affect model fits and interpretations of findings. Critically, we show that the ability of these models to accurately capture patterns in fMRI dynamics is mostly driven by the degree to which they fit global signals rather than interesting sources of coordinated neural dynamics. We show that widespread deflections can arise from simple global synchronisation. We introduce a simple two-parameter model that captures these fluctuations and which performs just as well as more complex, multi-parameter biophysical models. From our combined analyses of data and simulations, we describe benchmarks to evaluate model fit and validity. Although most models are not resilient to denoising, we show that relaxing the approximation of homogeneous neural populations by more explicitly modelling inter-regional effective connectivity can improve model accuracy at the expense of increased model complexity. Our results suggest that many complex biophysical models may be fitting relatively trivial properties of the data, and underscore a need for tighter integration between data quality assurance and model development.

neuroscience↗

bioRxiv2020

Dynamical consequences of regional heterogeneity in the brains transcriptional landscape

Brain regions vary in their molecular and cellular composition, but how this heterogeneity shapes neuronal dynamics is unclear. Here, we investigate the dynamical consequences of regional heterogeneity using a biophysical model of whole-brain functional magnetic resonance imaging (MRI) dynamics in humans. We show that models in which transcriptional variations in excitatory and inhibitory receptor (E:I) gene expression constrain regional heterogeneity more accurately reproduce the spatiotemporal structure of empirical functional connectivity estimates than do models constrained by global gene expression profiles and MRI-derived estimates of myeloarchitecture. We further show that regional heterogeneity is essential for yielding both ignition-like dynamics, which are thought to support conscious processing, and a wide variance of regional activity timescales, which supports a broad dynamical range. We thus identify a key role for E:I heterogeneity in generating complex neuronal dynamics and demonstrate the viability of using transcriptional data to constrain models of large-scale brain function.

neuroscience↗