Biology subjects

Seal, M. L.

Publications and source records attributed to Seal, M. L..

11 recordsLinked to original sources

bioRxiv2025

Oestradiol and Progesterone Variability in Adolescent Females: Associations with Brain Structure and Mental Health Problems

ObjectiveAdolescent females are at increased risk for adverse mental health problems, and puberty is thought to be a critical factor. Pubertal onset involves substantial variability in oestradiol (E2) and progesterone (P4), yet the impact of these hormone dynamics remains unclear. We investigated whether within-individual E2 and P4 variability across one month relates to brain structure and mental health problems in adolescent females. MethodsParticipants were 147 females (11-17 years) from the community-based, cross-sectional Puberty and NeuroDevelopment in Adolescents (PANDA) study in Australia. Salivary E2 and P4 were collected weekly for one month, with variability indexed as within-subject standard deviation. Structural MRI was acquired on the final sampling day. Mental health problems were assessed via self- and parent-report. Linear regression and mediation analyses tested associations between hormone variability, brain structure, and mental health, adjusting for demographics. ResultsGreater P4 variability was associated with smaller left thalamus volume (Cohens d = -0.26, pFDR = 0.031). Higher E2 variability was associated with fewer anxiety (d = -0.170, pFDR = 0.043) and depressive symptoms (d = -0.179, pFDR = 0.036), lower emotion dysregulation (d = -0.179, pFDR = 0.036), and smaller right hippocampus volume in pre-menarchal adolescents (d = -0.192, p = 0.02). Weekly reductions in E2 predicted lower positive affect (d = 0.084, p = 0.048). ConclusionThese findings indicate that hormone variability may meaningfully shape brain and behavioural development in adolescent girls differently from hormone-related transitions in adulthood. Larger, replicable, and longer-term studies are needed to confirm these patterns. Oestradiol and Progesterone Variability in Adolescent Females: Associations with Brain Structure and Mental Health Problems. Muskan Khetan 1, Ye Ella Tian 2, Nandita Vijayakumar 3,4, Sarah Whittle 1,4 O_LICentre for Youth Mental Health, University of Melbourne, Australia C_LIO_LIDepartment of Psychiatry, University of Melbourne, Australia C_LIO_LIMurdoch Childrens Research Institute, Australia C_LIO_LISchool of Psychology, Deakin University, Australia C_LI

neuroscience↗

bioRxiv2024

Mild traumatic brain injury is associated with increased thalamic subregion volume in the subacute period following injury

Structural vulnerability of the thalamus remains under investigated in mild traumatic brain injury (mTBI), and few studies have addressed its constituent nuclei using robust segmentation methods. This study aimed to investigate thalamic subnuclei volume in the subacute period following mTBI. Trauma control (TC) and mTBI patients aged 18 - 60 years old completed an MRI neuroimaging protocol including both high resolution structural (T1w) and diffusion weighted sequences at 6 - 11 weeks following injury (mean: 57 days; sd 11). Each thalamus was segmented into its constituent subnuclei, which were grouped into eight lateralised subregions. Volumes of the subregions were calculated. Neurite Orientation Dispersion and Density (NODDI) maps with parameters optimised for grey matter were computed for the same subregions. Group differences in subregion volumes and NODDI parameters were investigated using Bayesian linear modelling, with age, sex, and intracranial volume included as covariates. Comparisons of mTBI (n = 39) and TC (n = 28) groups revealed evidence of relatively increased grey matter volume in the mTBI group for the bilateral medial and right intralaminar subregions (BF10 > 3). Of the subregions which showed volume differences, there was no evidence for differences in NODDI metrics between groups. This study demonstrates that in the subacute period following mTBI, there is evidence of increased volume in specific thalamic subregions. Putative mechanisms underpinning the increased volume observed here are disordered remyelination, or myelin debris yet to be cleared. Significance statementDespite the prevalence of continued cognitive, somatic, and vestibular symptoms in the subacute period (6 - 12 weeks) following an mTBI, a clear neuropathophysiological profile is yet to be determined. One key vulnerable structure in mTBI could be the thalamus, a subcortical grey matter structure which comprises numerous subregions. The present study investigated whether changes in thalamic subregion volume are evident in the subacute period. For the first time, we show that at approximately 8 weeks following injury, mTBI is associated with increased volume in specific thalamus subregions. This provides an important avenue for continued investigation into the clinical significance of these findings.

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↗

bioRxiv2020

Individual variation underlying brain age estimates in typical development

Typical brain development follows a protracted trajectory throughout childhood and adolescence. Deviations from typical growth trajectories have been implicated in neurodevelopmental and psychiatric disorders. Recently, the use of machine learning algorithms to model age as a function of structural or functional brain properties has been used to examine advanced or delayed brain maturation in healthy and clinical populations. Termed brain age, this approach often relies on complex, nonlinear models that can be difficult to interpret. In this study, we use model explanation methods to examine the cortical features that contribute to brain age modelling on an individual basis. In a large cohort of n=768 typically-developing children (aged 3-21 years), we build models of brain development using three different machine learning approaches. We employ SHAP, a model-agnostic technique to estimate sample-specific feature importance, to identify regional cortical metrics that explain errors in brain age prediction. We find that, on average, brain age prediction and the cortical features that explain model predictions are consistent across model types and reflect previously reported patterns of regional brain development. However, while several regions are found to contribute to brain age prediction, we find little spatial correspondence between individual estimates of feature importance, even when matched for age, sex and brain age prediction error. We also find no association between brain age error and cognitive performance in this typically-developing sample. Overall, this study shows that, while brain age estimates based on cortical development are relatively robust and consistent across model types and preprocessing strategies, significant between-subject variation exists in the features that explain erroneous brain age predictions on an individual level.

neuroscience↗

bioRxiv2020

A longitudinal analysis of puberty-related cortical development

The brain undergoes extensive structural changes during adolescence, concurrent to puberty-related physical and hormonal changes. While animal research suggests these biological processes are related to one another, our knowledge of brain development in humans is largely based on age-related processes. Thus, the current study characterized puberty-related changes in human brain structure, by combining data from two longitudinal neuroimaging cohorts. Beyond normative changes in cortical thickness, we examined whether individual differences in the rate of pubertal maturation (or "pubertal tempo") was associated with variations in cortical trajectories. Participants (N = 192; scans = 366) completed up to three waves of MRI assessments between 8.5 and 14.5 years of age, as well as questionnaire assessments of pubertal stage at each wave. Generalized additive mixture models were used to characterize trajectories of cortical development. Results revealed widespread linear puberty-related changes across much of the cortex. Many of these changes, particularly within the frontal and parietal cortices, were independent of age-related development. Males exhibiting faster pubertal tempo demonstrated greater thinning in the precuneus and frontal cortices than same-aged and -sex peers. Findings suggest that the unique influence of puberty on cortical development may be more extensive than previously identified, and also emphasize important individual differences in the coupling of these developmental processes.

neuroscience↗

bioRxiv2019

Cortical remodelling in childhood is associated with genes enriched for neurodevelopmental disorders

Cortical development during childhood and adolescence has been characterised in recent years using metrics derived from Magnetic Resonance Imaging (MRI). Changes in cortical thickness are greatest in the first two decades of life and recapitulate the genetic organisation of the cortex, highlighting the potential early impact of gene expression on differences in cortical architecture over the lifespan. It is important to further our understanding of the possible neurobiological mechanisms that underlie these changes as differences in cortical thickness may act as a potential phenotypic marker of several common neurodevelopmental and psychiatric disorders.\n\nIn this study, we combine MRI acquired from a large typically-developing childhood population (n=768) with comprehensive human gene expression databases to test the hypothesis that disrupted mechanisms common to neurodevelopmental disorders are encoded by genes expressed early in development and nested within those associated with typical cortical remodelling in childhood.\n\nWe find that differential rates of thinning across the developing cortex are associated with spatially-varying gradients of gene expression. Genes that are expressed highly in regions of accelerated thinning are expressed predominantly in cortical neurons, involved in synaptic remodeling, and associated with common cognitive and neurodevelopmental disorders. Further, we identify subsets of genes that are highly expressed in the prenatal period and jointly associated with both developmental cortical morphology and neurodevelopmental disorders.

neuroscience↗

bioRxiv2019

White Matter Extension of the Melbourne Children’s Regional Infant Brain Atlas: M-CRIB-WM

BackgroundUnderstanding typically developing infant brain structure is crucial in investigating neurological disorders of early childhood. Brain atlases providing standardised identification of neonatal brain regions are key in such investigations. Our previously developed Melbourne Childrens Regional Infant Brain (M-CRIB) and M-CRIB 2.0 neonatal brain atlases provide standardised parcellation of 100 and 94 brain regions respectively, including cortical, subcortical, and cerebellar regions. The aim of this study was to extend the M-CRIB atlas coverage to include 54 white matter regions.\n\nMethodsParticipants were ten healthy term-born neonates who comprised the sample for the M-CRIB and M-CRIB 2.0 atlases. WM regions were manually segmented on T2 images and co-registered diffusion tensor imaging-based, direction-encoded colour maps. Our labelled regions are based on those in the JHU-neonate-SS atlas, but differ in the following ways: 1) we included five corpus callosum subdivisions instead of a left / right division; 2) we included a left / right division for the middle cerebellar peduncle; and 3) we excluded the three brainstem divisions. All segmentations were reviewed and approved by a paediatric radiologist and a neurosurgery research fellow for anatomical accuracy.\n\nResultsThe resulting neonatal WM atlas comprises 54 WM regions: 24 paired regions, and six unpaired regions comprising five corpus callosum subdivisions and one pontine crossing tract. Detailed protocols for manual WM parcellations are provided, and the M-CRIB-WM atlas is presented together with the existing M-CRIB and M-CRIB 2.0 cortical, subcortical and cerebellar parcellations in ten individual neonatal MRI datasets.\n\nConclusionThe updated M-CRIB atlas including the WM parcellations will be made publicly available. The atlas will be a valuable tool that will help facilitate neuroimaging research into neonatal WM development in both healthy and diseased states.

neuroscience↗

bioRxiv2019

Charting shared developmental trajectories of cortical thickness and structural connectivity in childhood and adolescence

The cortex is organised into broadly hierarchical functional systems with distinct neuroanatomical characteristics reflected by macroscopic measures of cortical morphology. Diffusion-weighted MRI allows the delineation of areal connectivity, changes to which reflect the ongoing maturation of white matter tracts. These developmental processes are intrinsically linked with timing coincident with the development of cognitive function.\n\nIn this study, we use a data-driven multivariate approach, non-negative matrix factorisation, to define cortical regions that co-vary together across a large paediatric cohort (n=456) and are associated with specific subnetworks of cortical connectivity.\n\nWe find that age between 3 and 21 years is associated with accelerated cortical thinning in fronto-parietal regions, whereas relative thinning of primary motor and sensory regions is slower. Together, the subject-specific weights of the derived set of components can be combined to predict chronological age. Structural connectivity networks reveal a relative increase in strength in connection within, as opposed to between hemispheres that vary in line with cortical changes. We confirm our findings in an independent sample.

neuroscience↗

bioRxiv2018

Desikan-Killiany-Tourville Atlas Compatible Version of M-CRIB Neonatal Parcellated Whole Brain Atlas: The M-CRIB 2.0

Our recently published M-CRIB atlas comprises 100 neonatal brain regions including 68 compatible with the widely-used Desikan-Killiany adult cortical atlas. A successor to the Desikan-Killiany atlas is the Desikan-Killiany-Tourville atlas, in which some regions with unclear boundaries were removed, and many existing boundaries were revised to conform to clearer landmarks in sulcal fundi. Our first aim here was to modify cortical M-CRIB regions to comply with the Desikan-Killiany-Tourville protocol, in order to offer: a) compatibility with this adult cortical atlas, b) greater labelling accuracy due to clearer landmarks, and c) optimisation of cortical regions for integration with surface-based infant parcellation pipelines. Secondly, we aimed to update subcortical regions in order to offer greater compatibility with subcortical segmentations produced in FreeSurfer. Data utilized were the T2-weighted MRI scans in our M-CRIB atlas, for ten healthy neonates (postmenstrual age at MRI 40-43 weeks, 4 female), and corresponding parcellated images. Edits were performed on the parcellated images in volume space using ITK-SNAP. Cortical updates included deletion of frontal and temporal poles and Banks STS, and modification of boundaries of many other regions. Changes to subcortical regions included the addition of ventral diencephalon, and deletion of subcortical matter labels. A detailed updated parcellation protocol was produced. The resulting whole-brain M-CRIB 2.0 atlas comprises 94 regions altogether. This atlas provides comparability with adult Desikan-Killiany-Tourville-labelled cortical data and FreeSurfer-labelled subcortical data, and is more readily adaptable for incorporation into surface-based neonatal parcellation pipelines. As such, it offers the ability to help facilitate a broad range of investigations into brain structure and function both at the neonatal time point and developmentally across the lifespan.

neuroscience↗

bioRxiv2018

Individual variation in longitudinal postnatal development of the primate brain

Quantifying individual variation in postnatal brain development can provide insight into cognitive diversity within a population and the aetiology of common neuropsychiatric and neurodevelopmental disorders that are associated with adverse conditions in early life. Non-invasive studies of the non-human primate can aid understanding of human brain development, facilitating longitudinal analysis during early postnatal development when comparative human populations are difficult to sample.\n\nIn this study, we perform analysis of a longitudinal MRI dataset of 32 macaques, each with up to five magnetic resonance imaging (MRI) scans acquired between 1 and 36 months of age. Using nonlinear mixed effects model we derive growth trajectories for whole brain, cortical and subcortical grey matter, cerebral white matter and cerebellar volume. We then test the association between individual variation in postnatal tissue volumes and birth weight.\n\nWe report nonlinear growth models for all tissue compartments, as well as significant variation in total intracranial volume between individuals. We also demonstrate that subcortical grey matter varies both in total volume and rate of change between individuals and is associated with differences in birth weight. This supports evidence that subcortical grey matter is specifically vulnerable to adverse conditions in utero and highlights the importance of longitudinal MRI analysis in developmental studies.

neuroscience↗

bioRxiv2017

Modelling Neuroanatomical Variation Due To Age And Sex During Childhood And Adolescence

Brain development is a dynamic process that follows a choreographed trajectory during childhood and adolescence with tissue-specific alterations that reflect complex and ongoing biological processes. Accurate identification and modelling of these anatomical processes in vivo with MRI may provide clinically useful imaging markers of individual variability in development. In this study, we build a model of age- and sex-related anatomical variation using multimodal imaging measures and manifold learning.\n\nUsing publicly-available data from two large, independent developmental cohorts (n=768 and 862), we apply a multimodal machine learning approach combining measures of tissue volume, cortical area and cortical thickness into a low-dimensional data representation.\n\nWe find that neuroanatomical variation due to age and sex can be captured by two orthogonal patterns of brain development and we use this model to predict age with a mean error of 1.6-2 years and sex with an accuracy of 80-84%.\n\nWe present a framework for modelling anatomical development during childhood using low-dimensional data representations. This model accurately predicts age and sex based on image-derived markers of cerebral morphology and generalises well to independent populations.

neuroscience↗