Biology subjects

Beare, R.

Publications and source records attributed to Beare, R..

10 recordsLinked to original sources

bioRxiv2021

Brain charts for the human lifespan

Over the past few decades, neuroimaging has become a ubiquitous tool in basic research and clinical studies of the human brain. However, no reference standards currently exist to quantify individual differences in neuroimaging metrics over time, in contrast to growth charts for anthropometric traits such as height and weight1. Here, we built an interactive resource to benchmark brain morphology, www.brainchart.io, derived from any current or future sample of magnetic resonance imaging (MRI) data. With the goal of basing these reference charts on the largest and most inclusive dataset available, we aggregated 123,984 MRI scans from 101,457 participants aged from 115 days post-conception through 100 postnatal years, across more than 100 primary research studies. Cerebrum tissue volumes and other global or regional MRI metrics were quantified by centile scores, relative to non-linear trajectories2 of brain structural changes, and rates of change, over the lifespan. Brain charts identified previously unreported neurodevelopmental milestones3; showed high stability of individual centile scores over longitudinal assessments; and demonstrated robustness to technical and methodological differences between primary studies. Centile scores showed increased heritability compared to non-centiled MRI phenotypes, and provided a standardised measure of atypical brain structure that revealed patterns of neuroanatomical variation across neurological and psychiatric disorders. In sum, brain charts are an essential first step towards robust quantification of individual deviations from normative trajectories in multiple, commonly-used neuroimaging phenotypes. Our collaborative study proves the principle that brain charts are achievable on a global scale over the entire lifespan, and applicable to analysis of diverse developmental and clinical effects on human brain structure. Furthermore, we provide open resources to support future advances towards adoption of brain charts as standards for quantitative benchmarking of typical or atypical brain MRI scans.

neuroscience↗

bioRxiv2021

Participant followup rate can bias structural imaging measures in longitudinal studies

Longitudinal MRI analysis is essential to accurately describe neuroanatomical changes over time. Loss of participants to followup (dropout) in longitudinal studies is inevitable and can lead to great difficulty in interpretation of statistical results if dropout is correlated with a study outcome or exposure. Beyond this, technical aspects of longitudinal MRI analysis require specialised processing pipelines to improve reliability while avoiding bias towards individual timepoints. In this article we test whether there is an additional problem that must be considered in longitudinal imaging studies, namely whether dropout has an impact on the function of FreeSurfer, a popular software pipeline used to estimate important structural brain metrics. We find that the number of acquisitions available per individual can impact the estimation of cortical thickness and brain volume using the FreeSurfer longitudinal pipeline, and can induce group differences in brain metrics. The effect on trajectories of brain metrics is smaller than the effect on brain metrics. HighlightsO_LILongitudinal MRI analysis is essential to accurately track neuroanatomical changes over time C_LIO_LILongitudinal MRI analysis requires specialised processing pipelines to reduce bias towards single timepoints C_LIO_LIParticipant drop out or loss can bias neuroanatomical measures derived from longitudinal pipelines C_LIO_LIWe find that group differences in the number of acquisitions available to analyse can cause group differences in estimated cortical thickness and brain volume C_LIO_LIThis bias appears to be due to the number of scans used to create individualised templates in the Freesurfer longitudinal pipeline C_LIO_LIThe effect on estimates of brain metric trajectories appears smaller than the effect on the estimates of brain metrics C_LI

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↗

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

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↗

bioRxiv2019

Parcellation of the cortex using Surface-based Melbourne Children's Regional Infant Brain atlases (M-CRIB-S)

BackgroundLongitudinal studies of cortical morphology are best facilitated by parcellation schemes that are compatible across all life stages. Until recently, neonatal atlases derived strictly from neonatal data, rather than warping brain labels from an adult atlas, did not exist. The Melbourne Childrens Regional Infant Brain (M-CRIB) and M-CRIB 2.0 atlases now provide voxel-based parcellations of the cerebral cortex within T2-weighted neonatal images. These atlases are compatible with the Desikan-Killiany (DK) and the Desikan-Killiany-Tourville (DKT) cortical labelling schemes commonly used for older children and adults. However, there is still a need for a surface-based approach for parcellating neonatal images using these atlases. AimsWe aimed to introduce surface-based versions of the M-CRIB and M-CRIB 2.0 atlases, termed M-CRIB-S(DK) and M-CRIB-S(DKT), along with a pipeline for automated parcellation with FreeSurfer tools. We also aimed to evaluate the automated parcellation accuracy of our M-CRIB-S atlases using cross-validation with manually labelled data; and to evaluate accuracy of the proposed automated parcellation pipeline for the M-CRIB-S labels against the publicly available surface-based University of North Carolina (UNC) 4D neonatal atlas, which has labels derived from the DK adult atlas. MethodsUsing datasets of 10 M-CRIB or M-CRIB 2.0 manually labelled ground truth images and 48 unlabelled T2-weighted magnetic resonance images (MRI) of healthy neonates, cortical surfaces were extracted using the Deformable module within MIRTK. Cortical regions from the labelled images were encoded into a spherical template space using FreeSurfer tools wherein M-CRIB-S(DK) and M-CRIB-S(DKT) atlases were constructed. Automatic parcellation of the labelled and unlabelled (L+U) images (n=58) using the M-CRIB-S(DK) and M-CRIB-S(DKT) atlases were computed with Bayesian labelling using FreeSurfer tools. Accuracy was assessed by comparison with manually labelled ground truth data (n=10) using Dice coefficients and within a Leave-One-Out (LOO) cross-validation framework. Agreement of the UNC atlas parcellations with manual M-CRIB labelling was also evaluated using Dice coefficients. ResultsWhen comparing automated parcellations from the M-CRIB-S atlases to manual labels, disagreements were mostly closely confined to region boundaries, suggesting high overlap overall. Quantitatively, average regional Dice coefficients were 0.88 (L+U) and 0.83 (LOO) for the M-CRIB 2.0 labels and 0.87 (L+U), 0.80 (LOO) for the M-CRIB labels, and 0.67 for the UNC atlas. ConclusionsThe M-CRIB-S atlases and automatic pipeline described in this paper allow infant cortical surfaces to be accurately parcellated according to the DK or DKT parcellation schemes. This will help facilitate surface-based investigations of brain function at the neonatal time point, and potentially longitudinally across the lifespan. The use of M-CRIB-S outperformed a technique that relies on an adult-derived atlas. The atlases, averaged white, inflated, pial and spherical surfaces, along with customised scripts for segmentation, cortical surface extraction and parcellation, are available for public download.

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

Marker-based watershed transform method for fully automatic mandibular segmentation from low-dose CBCT images

Introduction Introduction Methods and materials Results Discussion Conclusions Funding References Three-dimensional mandibular models are useful for planning maxillofacial surgery and orthodontic treatment.1,2 In studies of growth, mandibular models are important for assessing morphological changes over time.3,4 Such models are typically obtained from conventional computed tomography (CT), using high radiation dose to capture fine detail of the bony structure. Cone beam computed tomography (CBCT) shows promise for oral and craniofacial imaging applications due to lower radiation dose, lower cost and shorter acquisition time compared to CT. However, CBCT images have lower contrast and higher levels of ...

bioinformatics↗

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↗

bioRxiv2017

Network component analysis reveals developmental trajectories of structural connectivity and specific alterations in autism spectrum disorder

The structural organisation of the brain can be characterised as a hierarchical ensemble of segregated modules linked by densely interconnected hub regions that facilitate distributed functional interactions. Disturbances to this network may be an important marker of abnormal development. Recently, several neurodevelopmental disorders, including autism spectrum disorder (ASD), have been framed as disorders of connectivity but the full nature and timing of these disturbances remain unclear.\n\nIn this study, we use non-negative matrix factorisation, a data-driven, multivariate approach, to model the structural network architecture of the brain as a set of superposed subnetworks, or network components.\n\nIn an openly available dataset of 196 subjects scanned between 5 to 85 years we identify a set of robust and reliable subnetworks that develop in tandem with age and reflect both anatomically local and long-range, network hub connections. In a second experiment, we compare network components in a cohort of 51 high-functioning ASD adolescents to a group of age-matched controls. We identify a specific subnetwork representing an increase in local connection strength in the cingulate cortex in ASD (t=3.44, p<0.001).\n\nThis work highlights possible long-term implications of alterations to the developmental trajectories of specific cortical subnetworks.

neuroscience↗