Biology subjects

Ball, G.

Publications and source records attributed to Ball, G..

At least 37 records · Page 2Linked to original sources

bioRxiv2022

CbpD crystal structure adds intrigue to substrate-specificity motifs in chitin-active lytic polysaccharide monooxygenases

Pseudomonas aeruginosa secretes diverse proteins via its Type 2 Secretion System, including a 39 KDa Chitin-Binding Protein, CbpD. CbpD was recently shown to be a lytic polysaccharide monooxygenase active on chitin, and to contribute substantially to virulence. To-date no structure of this virulence factor has been reported. Its first two domains are homologous to those found in the crystal structure of Vibrio cholerae GbpA, while the third domain is homologous to the NMR structure of the Cellvibrio japonicus CjLPMO10A CBM73 domain. We report the 3.0 [A] resolution crystal structure of CbpD solved by molecular replacement, which required ab initio models of each CbpD domain generated by the artificial intelligence deep learning structure prediction algorithm RoseTTAFold. The structure of CbpD confirms previously postulated chitin-specific motifs in the AA10 domain while challenging the deterministic effects of other postulated substrate specificity motifs. Additionally, the structure of CbpD shows that post translational modifications occur on the chitin binding surface. Moreover, the structure raises interesting possibilities about how Type 2 Secretion System substrates may interact with the secretion machinery and demonstrates the utility of new artificial intelligence protein structure prediction algorithms in making challenging structural targets tractable.

microbiology↗

bioRxiv2021

Benchmarking Geometric Deep Learning for Cortical Segmentation and Neurodevelopmental Phenotype Prediction

The emerging field of geometric deep learning extends the application of convolutional neural networks to irregular domains such as graphs, meshes and surfaces. Several recent studies have explored the potential for using these techniques to analyse and segment the cortical surface. However, there has been no comprehensive comparison of these approaches to one another, nor to existing Euclidean methods, to date. This paper benchmarks a collection of geometric and traditional deep learning models on phenotype prediction and segmentation of sphericalised neonatal cortical surface data, from the publicly available Developing Human Connectome Project (dHCP). Tasks include prediction of postmenstrual age at scan, gestational age at birth and segmentation of the cortical surface into anatomical regions defined by the M-CRIB-S atlas. Performance was assessed not only in terms of model precision, but also in terms of network dependence on image registration, and model interpretation via occlusion. Networks were trained both on sphericalised and anatomical cortical meshes. Findings suggest that the utility of geometric deep learning over traditional deep learning is highly task-specific, which has implications for the design of future deep learning models on the cortical surface. The code, and instructions for data access, are available from https://github.com/Abdulah-Fawaz/Benchmarking-Surface-DL.

neuroscience↗

bioRxiv2021

Founder cell configuration drives competitive outcome within colony biofilms

Bacteria typically form dense communities called biofilms, where cells are embedded in a self-produced extracellular matrix. Competitive interactions between strains within the biofilm context are studied due to their potential applications in biological, medical, and industrial systems. Combining mathematical modelling with experimental assays, we reveal that the spatial structure and the competitive dynamics within biofilms are significantly affected by the location and density of founder cells. Using an isogenic pair of Bacillus subtilis strains, we show that the observed spatial structure and relative strain biomass in a mature biofilm can be mapped directly to the locations of founder cells. Moreover, we define a predictor of competitive outcome that accurately forecasts relative abundance of strains based solely on the founder cells access to free space. Consequently, we reveal that variability of competitive outcome in biofilms inoculated at low founder density is a natural consequence of the random positioning of founding cells in the inoculum. Extending our study to non-isogenic strain pairs of B. subtilis, we show that even for strains with different antagonistic strengths, a race for space remains the dominant mode of competition in biofilms inoculated at low founder densities. Our results highlight the importance of spatial dynamics on competitive interactions within biofilms and hence to related applications.

microbiology↗

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

Multidisciplinary Interrogation of a Crucial Protein Interface in the Type II Secretion System

The type IV filament superfamily comprises widespread membrane-associated polymers in prokaryotes. The Type II secretion system (T2SS), a significant virulence pathway in many pathogens, belongs to this superfamily. A knowledge gap in the understanding of the T2SS is the molecular role of a small pseudopilin protein. Using multiple biophysical techniques, we have deciphered how this missing component of the Xcp T2SS architecture is structurally integrated, and thereby also unlocked its function. We demonstrate that the low abundance XcpH is the adapter that bridges a trimeric initiating tip complex XcpIJK with a periplasmic filament of XcpG subunits. Our model reveals that each pseudopilin protein caps an XcpG protofilament in an overall pseudopilus compatible with the dimensions of the periplasm and the outer membrane-spanning secretin through which substrates of the T2SS pass. Unexpectedly, to fulfill its adapter function, the XcpH N-terminal helix must be unwound, a property shared with the XcpG subunits. We provide the first complete structural model of a type IV filament, a result immediately transferable to understanding of other T2SS and the type IV pili.

microbiology↗

bioRxiv2020

Pharmacological rescue of impaired mitophagy in Parkinson's disease-related LRRK2 G2019S knock-in mice

Parkinsons disease (PD) is a major and progressive neurodegenerative disorder, yet the biological mechanisms involved in its aetiology are poorly understood. Evidence links this disorder with mitochondrial dysfunction and/or impaired lysosomal degradation - key features of the autophagy of mitochondria, known as mitophagy. Here we investigated the role of LRRK2, a protein kinase frequently mutated in PD, on this process in vivo. Using mitophagy and autophagy reporter mice, bearing either knockout of LRRK2 or expressing the pathogenic kinase-activating G2019S LRRK2 mutation, we found that basal mitophagy was specifically altered in clinically relevant cells and tissues. Our data show that basal mitophagy inversely correlates with LRRK2 kinase activity in vivo. In support of this, use of distinct LRRK2 kinase inhibitors in cells increased basal mitophagy, and a CNS penetrant LRRK2 kinase inhibitor, GSK3357679A, rescued the mitophagy defects observed in LRRK2 G2019S mice. This study provides the first in vivo evidence that pathogenic LRRK2 directly impairs basal mitophagy, a process with strong links to idiopathic Parkinsons disease, and demonstrates that pharmacological inhibition of LRRK2 is a rational mitophagy-rescue approach and potential PD therapy.

cell biology↗

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

Involvement of the Pseudomonas aeruginosa MexAB-OprM efflux pump in the secretion of the metallophore pseudopaline

The ability for all organisms to acquire metals from their environment is essential for life. To overcome the metal restriction imposed by the hosts nutritional immunity, bacterial pathogens exploits the use of small high metal affinity molecules called metallophores. Metallophores are first synthetized in the cytoplasm, then secreted into the medium where they sequester the metal. The metal-metallophore complex is then imported into the bacterium following binding to dedicated cell surface receptors. Recently, a new family of metallophores has been discovered in pathogenic bacteria called staphylopine in Staphylococcus aureus and pseudopaline in Pseudomonas aeruginosa. Here, we are expending the molecular understanding of pseudopaline secretion/recovery cycle across the double-membraned envelope of the Gram-negative bacterium Pseudomonas aeruginosa. We first revealed that pseudopaline is secreted in a two-step process including export across the inner membrane by the CntI exporter followed by a specific transport across the outer membrane by the MexAB-OprM efflux pump. Such involvement of MexAB-OprM in pseudopaline secretion, reveal a new natural function that extends its spectrum of functions and therefore reasserts its interest as antibacterial target. We then addressed the fate of the recovered metal-loaded pseudopaline by combining in vitro reconstitution experiments using radio-labeled pseudopaline subjected to bacterial lysates, and in vivo phenotyping in absence of pseudopaline transporters. Our data support the existence of a pseudopaline degradation/modification mechanism, possibly involved in metal release following pseudopaline recovery. All together our data allowed us to provide an improved molecular model of secretion, recovery and fate of this important metallophore by P. aeruginosa. IMPORTANCEPseudopaline is a broad spectrum metallophore produced and used by Pseudomonas aeruginosa to supply the bacterium in metal in metal scarce environments. Here we are investigating the pseudopaline transport/recovery cycle across the bacterial envelope. We are first demonstrating that pseudopaline secretion in the medium is achieved by a specific efflux pump, usually dedicated to the release of toxic compounds such as antibiotics, thus revealing a new natural function for this efflux pump reasserting its interest as antibacterial target. Additional experiments also revealing the existence of an intracellular pseudopaline degradation mechanism providing new clues to another obscure step of the pseudopaline cycle which is the intracellular metal liberation from the imported metal-pseudopaline complex. All together our data allowed us to disclose important aspects of the secretion, recovery and fate of this essential molecule used by P. aeruginosa to survive during infections thus constituting new potential targets for antibacterial development.

microbiology↗

bioRxiv2020

Cortical morphology at birth reflects spatio-temporal patterns of gene expression in the fetal brain

Interruption to gestation through preterm birth can significantly impact cortical development and have long-lasting adverse effects on neurodevelopmental outcome. We compared cortical morphology captured by high-resolution, multimodal MRI in n=292 healthy newborn infants (mean age at birth=39.9 weeks) with regional patterns of gene expression in the fetal cortex across gestation (n=156 samples from 16 brains, aged 12 to 37 post-conceptional weeks). We tested the hypothesis that noninvasive measures of cortical structure at birth mirror areal differences in cortical gene expression across gestation, and in a cohort of n=64 preterm infants (mean age at birth = 32.0 weeks), we tested whether cortical alterations observed after preterm birth were associated with altered gene expression in specific developmental cell populations. Neonatal cortical structure was aligned to differential patterns of cell-specific gene expression in the fetal cortex. Principal component analysis of six measures of cortical morphology and microstructure showed that cortical regions were ordered along a principal axis, with primary cortex clearly separated from heteromodal cortex. This axis was correlated to estimated tissue maturity, indexed by differential expression of genes expressed by progenitor cells and neurons; and engaged in stem cell differentiation, neuron migration and forebrain development. Preterm birth was associated with altered regional MRI metrics and patterns of differential gene expression in glial cell populations. The spatial patterning of gene expression in the developing cortex was thus mirrored by regional variation in cortical morphology and microstructure at term, and this was disrupted by preterm birth. This work provides a framework to link molecular mechanisms to noninvasive measures of cortical development in early life and highlights novel pathways to injury in neonatal populations at increased risk of neurodevelopmental disorder.

neuroscience↗

bioRxiv2019

BIAFLOWS: A collaborative framework to benchmark bioimage analysis workflows

Automated image analysis has become key to extract quantitative information from scientific microscopy bioimages, but the methods involved are now often so refined that they can no longer be unambiguously described using written protocols. We introduce BIAFLOWS, a software tool with web services and a user interface specifically designed to document, interface, reproducibly deploy, and benchmark image analysis workflows. BIAFLOWS allows image analysis workflows to be compared fairly and shared in a reproducible manner, safeguarding research results and promoting the highest quality standards in bioimage analysis. A curated instance of BIAFLOWS is available online; it is currently populated with 34 workflows that can be triggered to process image datasets illustrating 15 common bioimage analysis problems organized in 9 major classes. As a complete case study, the open benchmarking of 7 nuclei segmentation workflows, including classical and deep learning techniques, was performed on this online instance. All the results presented can be reproduced online.

bioinformatics↗

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↗

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

FAM83D directs protein kinase CK1α to the mitotic spindle for proper spindle positioning

The concerted action of many protein kinases helps orchestrate the error-free progression through mitosis of mammalian cells. The roles and regulation of some prominent mitotic kinases, such as cyclin-dependent kinases, are well-established. However, these and other known mitotic kinases alone cannot account for the extent of protein phosphorylation that has been reported during mammalian mitosis. Here we demonstrate that CK1, of the casein kinase 1 family of protein kinases, localises to the spindle and is required for proper spindle-positioning and timely cell division. CK1 is recruited to the spindle by FAM83D, and cells devoid of FAM83D, or those harbouring CK1-binding-deficient FAM83DF283A/F283A knockin mutation, display pronounced spindle-positioning defects, and a prolonged mitosis. Restoring FAM83D at the endogenous locus in FAM83D-/- cells, or artificially delivering CK1 to the spindle in FAM83DF283A/F283A cells, rescues these defects. These findings implicate CK1 as new mitotic kinase that orchestrates the kinetics and orientation of cell division.

cell biology↗

bioRxiv2018

Quantifying individual differences in brain morphometry underlying symptom severity in Autism Spectrum Disorders

The neurobiology of heterogeneous neurodevelopmental disorders such as autism spectrum disorders (ASD) are still unclear. Despite extensive efforts, most findings are difficult to reproduce due to high levels of individual variance in phenotypic expression. To quantify individual differences in brain morphometry in ASD, we implemented a novel subject-level, distance-based method on subject-specific attributes. In a large multi-cohort sample, each subject with ASD (n=100; n=84 males; mean age: 11.43 years; mean IQ: 110.58) was strictly matched to a control participant (n=100; n=84 males; mean age: 11.43 years; mean IQ: 110.70). Intrapair Euclidean distance of MRI brain morphometry and symptom severity measures were entered into a regularised machine learning pipeline for feature selection, with rigorous out-of-sample validation and bootstrapped permutation testing. Subject-specific structural morphometry features significantly predicted individual variation in ASD symptom severity (19 cortical thickness features, p=0.01, n=5000 permutations; 10 surface area features, p=0.006, n=5000 permutations). Findings remained robust across subjects and were replicated in validation samples. Identified cortical regions implicate key hubs of the salience and default mode networks as neuroanatomical features of social impairment in ASD. Present results highlight the importance of subject-level markers in ASD, and offer an important step forward in understanding the neurobiology of heterogeneous disorders.

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↗