Biology subjects

Beare, R.

Publications and source records attributed to Beare, R..

4 recordsLinked to original sources

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↗