Biology subjects

Smith, S. M.

Publications and source records attributed to Smith, S. M..

At least 55 records · Page 3Linked to original sources

bioRxiv2021

Graded variation in cortical T1w/T2w myelination during adolescence

Myelination influences brain connectivity during sensitive periods of development by enhancing neural signaling speed and regulating synapse formation to reduce plasticity. However, in vivo studies characterizing the maturational timing of cortical myelination during human development remain scant. Here, we take advantage of recent advances in high-resolution cortical T1w/T2w myelin mapping methods, including principled correction of B1+ transmit field effects, using data from the Human Connectome Project in Development (N=628, ages 8-21) to characterize the maturational timing of myelination from childhood through early adulthood throughout the cerebral neocortex. We apply Bayesian spline models and functional latent clustering analysis to demonstrate graded variation in the rate of cortical T1w/T2w myelin growth in neocortical areas that is strongly correlated with the sensorimotor-association (S-A) axis of cortical organization reported by others. In sensorimotor areas T1w/T2w myelin starts at high levels at early ages, increases at a fast pace, and decelerates at later ages (18-21). In intermediate multimodal areas along the S-A axis, T1w/T2w myelin tends to start at intermediate levels and increase linearly at an intermediate pace. In transmodal/paralimbic association areas high along the S-A axis, T1w/T2w myelin tends to start at low levels and increase linearly at the slowest pace. These data provide evidence for graded variation along the S-A axis in the rate of cortical myelination during adolescence, which could reflect ongoing plasticity underlying the development of complex information processing and psychological functioning. Significance StatementMyelin is a lipid membrane that is essential to healthy brain function. Myelin wraps axons to increase neural signaling speed, enabling complex neuronal functioning underlying learning and cognition. Here we characterize the developmental timing of myelination across the cerebral cortex during adolescence using recent advances in non-invasive myelin mapping. Our results provide new evidence demonstrating graded variation across the cortex in the timing of myelination during adolescence, with rapid myelination in lower-order sensory areas and gradual myelination in higher-order association areas. This spatial pattern of microstructural brain development closely parallels the sensorimotor-to-association axis of cortical organization and plasticity during ontogeny.

neuroscience↗

bioRxiv2021

Structural and functional asymmetry of the neonatal cerebral cortex

Features of brain asymmetry have been implicated in a broad range of cognitive processes; however, their origins are still poorly understood. Using a new left-right symmetric, spatiotemporal cortical surface atlas, we investigated cortical asymmetries in 442 healthy term-born neonates using structural and functional magnetic resonance images from the Developing Human Connectome Project. Cortical asymmetries observed in the term cohort were contextualised in two ways: by comparing them against cortical asymmetries observed in 103 preterm neonates scanned at term-equivalent age, and by comparing structural asymmetries against those observed in 1110 healthy young adults from the Human Connectome Project. Our results demonstrate that the neonatal cortex is markedly asymmetric in both structure and function, and while associations with preterm birth and biological sex were minimal, significant differences exist between birth and adulthood. Although these changes may represent experience-dependent developmental milestones, longitudinal studies across the lifespan are required to fully address these hypotheses.

neuroscience↗

bioRxiv2021

Supervised Phenotype Discovery from Multimodal Brain Imaging

Data-driven discovery of image-derived phenotypes (IDPs) from large-scale multimodal brain imaging data has enormous potential for neuroscientific and clinical research by linking IDPs to subjects demographic, behavioural, clinical and cognitive measures (i.e., non-imaging derived phenotypes or nIDPs). However, current approaches are primarily based on unsupervised approaches, without the use of information in nIDPs. In this paper, we proposed a semi-supervised, multimodal, and multi-task fusion approach, termed SuperBigFLICA, for IDP discovery, which simultaneously integrates information from multiple imaging modalities as well as multiple nIDPs. SuperBigFLICA is computationally efficient and largely avoids the need for parameter tuning. Using the UK Biobank brain imaging dataset with around 40,000 subjects and 47 modalities, along with more than 17,000 nIDPs, we showed that SuperBigFLICA enhances the prediction power of nIDPs, benchmarked against IDPs derived by conventional expert-knowledge and unsupervised-learning approaches (with average nIDP prediction accuracy improvements of up to 46%). It also enables the learning of generic imaging features that can predict new nIDPs. Further empirical analysis of the SuperBigFLICA algorithm demonstrates its robustness in different prediction tasks and the ability to derive biologically meaningful IDPs in predicting health outcomes and cognitive nIDPs, such as fluid intelligence and hypertension.

neuroscience↗

bioRxiv2021

Improved Interpretability of Brain-Behavior CCA with Domain-driven Dimension Reduction

Canonical Correlation Analysis (CCA) has been widely applied to study correlations between neuroimaging data and behavioral data. Practical use of CCA typically requires dimensionality reduction with, for example, Principal Components Analysis (PCA), however, this can result in CCA results that are difficult to interpret. In this paper, we introduce a Domain-driven Dimension Reduction (DDR) method, reducing the dimensionality of the original datasets combining human knowledge of the structure of the variables studied. We apply the method to the Human Connectome Project S1200 release and compare standard PCA across all variables with DDR applied to individual classes of variables, finding that DDR-CCA results are more stable and interpretable, allowing the contribution of each class of variable to be better understood. By carefully designing the analysis pipeline and cross-validating the results, we offer more insights on the interpretation of CCA applied to brain-behaviour data.

neuroscience↗

bioRxiv2021

Phenotypic and genetic associations of quantitative magnetic susceptibility in UK Biobank brain imaging

A key aim in epidemiological neuroscience is identification of markers to assess brain health and monitor therapeutic interventions. Quantitative susceptibility mapping (QSM) is an emerging MRI technique that measures tissue magnetic susceptibility and has been shown to detect pathological changes in tissue iron, myelin and calcification. We developed a QSM processing pipeline to estimate magnetic susceptibility of multiple brain structures in 35,885 subjects from the UK Biobank prospective epidemiological study. We identified phenotypic associations of magnetic susceptibility that include body iron, disease, diet, and alcohol consumption. Genome-wide associations related magnetic susceptibility to genetic variants with biological functions involving iron, calcium, myelin, and extracellular matrix. These patterns of associations include relationships that are unique to QSM, in particular being complementary to T2* measures. These new imaging phenotypes are being integrated into the core UK Biobank measures provided to researchers world-wide, creating potential to discover novel, non-invasive markers of brain health.

neuroscience↗

bioRxiv2021

The Forget-Me-Not dHCP study: 7 Tesla high resolution diffusion imaging in the unfixed post-mortem neonatal brain

Diffusion MRI of the neonatal brain allows investigation of the organisational structure of maturing fibres during brain development. Post-mortem imaging has the potential to achieve high resolution by using long scan times, enabling precise assessment of small structures. The Forget-Me-Not study, part of the Developing Human Connectome Project (dHCP), aims to acquire and publicly distribute high-resolution diffusion MRI data for unfixed post-mortem neonatal brain at 7T with a custom-built head coil. This paper describes how the study addressed logistical, technical and ethical challenges relating to recruitment pipeline, care pathway, tissue preservation, scan setup and protocol optimisation. Results from the first subject recruited to the study demonstrate high-quality diffusion MRI data. Preliminary voxel-wise and tractography-based analyses are presented for the cortical plate, subplate and white matter pathways, with comparison to age-matched in vivo dHCP data. These results demonstrate that high quality post-mortem data can be acquired and provide a sensitive means to explore the developing human brain, as well as altered diffusion properties consistent with post-mortem changes, at high resolution.

bioengineering↗

bioRxiv2021

Multi-dynamic Modelling Reveals Strongly Time-varying Resting fMRI Correlations

The activity of functional brain networks is responsible for the emergence of time-varying cognition and behaviour. Accordingly, time-varying correlations (Functional Connectivity) in resting fMRI have been shown to be predictive of behavioural traits, and psychiatric and neurological conditions. Typically, methods that measure time varying Functional Connectivity (FC), such as sliding windows approaches, do not separately model when changes occur in the mean activity levels from when changes occur in the FC, therefore conflating these two distinct types of modulation. We show that this can bias the estimation of time-varying FC to appear more stable over time than it actually is. Here, we propose an alternative approach that models changes in the mean brain activity and in the FC as being able to occur at different times to each other. We refer to this method as the Multi-dynamic Adversarial Generator Encoder (MAGE) model, which includes a model of the network dynamics that captures long-range time dependencies, and is estimated on fMRI data using principles of Generative Adversarial Networks. We evaluated the approach across several simulation studies and resting fMRI data from the Human Connectome Project (1003 subjects), as well as from UK Biobank (13301 subjects). Importantly, we find that separating fluctuations in the mean activity levels from those in the FC reveals much stronger changes in FC over time, and is a better predictor of individual behavioural variability Statement of SignificanceO_LIMAGE is multi-dynamic in that it models temporal fluctuations in FC independently from fluctuations in the mean of the activity. C_LIO_LIMAGE reveals stronger changes in FC over time than single-dynamic approaches, such as sliding window correlations. C_LIO_LIMulti-dynamic modelling provides an explanation and a solution as to why resting fMRI FC has previously looked so stable. C_LIO_LIMAGE models fMRI data as a set of reoccurring brain states, and importantly, these states do not have to be binary and mutually exclusive (e.g., multiple states can be active at one time-point). C_LIO_LIMAGE estimated time-varying FC is a better predictor of behavioural variability in the resting-state fMRI data than established methods. C_LI

neuroscience↗

bioRxiv2021

Multi-modal adaptor-clathrin contacts drive coated vesicleassembly

Clathrin-coated pits are formed by the recognition of membrane and cargo by the heterotetrameric AP2 complex and the subsequent recruitment of clathrin triskelia. A potential role for AP2 in coated-pit assembly beyond initial clathrin recruitment has not been explored. Clathrin binds the {beta}2 subunit of AP2, and several binding sites on {beta}2 and on the clathrin heavy chain have been identified, but our structural knowledge of these interactions is incomplete and their functional importance during endocytosis is unclear. Here, we analysed the cryo-EM structure of clathrin cages assembled in the presence of {beta}2 hinge and appendage ({beta}2HA) domains. We find that the {beta}2-appendage binds in at least two positions in the cage, demonstrating that multi-modal binding is a fundamental property of clathrin-AP2 interactions. In one position, {beta}2-appendage cross-links two adjacent terminal domains from different triskelia below the vertex. Functional analysis of {beta}2HA-clathrin interactions reveals that endocytosis requires two clathrin interaction sites: a clathrin-box motif on the hinge and the "sandwich site" on the appendage, with the appendage "platform site" having less importance. From these studies and the work of others, we propose that {beta}2-appendage binding to more than one clathrin triskelion is a key feature of the system and likely explains why clathrin assembly is driven by AP2.

biochemistry↗

bioRxiv2021

CaSR modulates sodium channel-mediated Ca 2+ -dependent excitability

Increasing extracellular [Ca2+] ([Ca2+]o) strongly decreases intrinsic excitability in neurons but the mechanism is unclear. By one hypothesis, [Ca2+]o screens surface charge reducing voltage-dependent sodium channel (VGSC) activation and by another [Ca2+]o activates Calcium-sensing receptor (CaSR) closing the sodium-leak channel (NALCN). Here we report that action potential (AP) firing rates increased in wild-type (WT), but not CaSR null mutant (Casr-/-) neocortical neurons, following the switch from physiological to reduced Ca2+-containing Tyrode. However, after membrane potential correction, AP firing increased similarly in both genotypes inconsistent with CaSR regulation of NALCN. Activation of VGSCs was the dominant contributor to the increase in excitability after the [Ca2+]o change. VGSC conductance-voltage relationships were hyperpolarized by decreasing [Ca2+]o for Casr-/- neurons indicating CaSR contributes to [Ca2+]o-dependent excitability via VGSCs. Regulation of VGSC gating by [Ca2+]o is the key mechanism mediating [Ca2+]o-dependent changes in neocortical neuron excitability and CaSR influences neuronal excitability by its effects on VGSC gating.

neuroscience↗

bioRxiv2021

Hierarchical modelling of functional brain networks in population and individuals from big fMRI data

A major goal of large-scale brain imaging datasets is to provide resources for investigating heterogeneous populations. Characterisation of functional brain networks for individual subjects from these datasets will have an enormous potential for prediction of cognitive or clinical traits. We propose for the first time a technique, Stochastic Probabilistic Functional Modes (sPROFUMO), that is scalable to UK Biobank (UKB) with expected 100,000 participants, and hierarchically estimates functional brain networks in individuals and the population, while allowing for bidirectional flow of information between the two. Using simulations, we show the models utility, especially in scenarios that involve significant cross-subject variability, or require delineation of fine-grained differences between the networks. Subsequently, by applying the model to resting-state fMRI from 4999 UKB subjects, we mapped resting state networks (RSNs) in single subjects with greater detail than has been possible previously in UKB (>100 RSNs), and demonstrate that these RSNs can predict a range of sensorimotor and higher-level cognitive functions. Furthermore, we demonstrate several advantages of the model over independent component analysis combined with dual-regression (ICA-DR), particularly with respect to the estimation of the spatial configuration of the RSNs and the predictive power for cognitive traits. The proposed model and results can open a new door for future investigations into individualised profiles of brain function from big data. HighlightsO_LIWe introduce stochastic PROFUMO (sPROFUMO) for inferring functional brain networks from big data C_LIO_LIsPROFUMO hierarchically estimates fMRI networks for the population and every individual C_LIO_LIWe characterised high dimensional resting state fMRI networks from UK Biobank C_LIO_LIModel outperforms ICA and dual regression for estimation of individual-specific network topography C_LIO_LIWe demonstrate the models utility for predicting cognitive traits, and capturing subject variability in network topographies versus connectivity C_LI

neuroscience↗

bioRxiv2020

Optimising a Simple Fully Convolutional Network (SFCN) for accurate brain age prediction in the PAC 2019 challenge

Brain age prediction from brain MRI scans not only helps improve brain ageing modelling generally, but also provides benchmarks for predictive analysis methods. Brain-age delta, which is the difference between a subjects predicted age and true age, has become a meaningful biomarker for the health of the brain. Here, we report the details of our brain age prediction models and results in the Predictive Analysis Challenge 2019. The aim of the challenge was to use T1-weighted brain MRIs to predict a subjects age in multicentre datasets. We apply a lightweight deep convolutional neural network architecture, Simple Fully Convolutional Neural Network (SFCN), and combined several techniques including data augmentation, transfer learning, model ensemble, and bias correction for brain age prediction. The model achieved first places in both of the two objectives in the PAC 2019 brain age prediction challenge: Mean absolute error (MAE) = 2.90 years without bias removal, and MAE = 2.95 years with bias removal.

neuroscience↗

bioRxiv2020

Large, stable spikes exhibit differential broadening in excitatory and inhibitory neocortical boutons

Presynaptic action potential spikes control neurotransmitter release and thus interneuronal communication. However, the properties and the dynamics of presynaptic spikes in the neocortex remain enigmatic because boutons in the neocortex are small and direct patch-clamp recordings have not been performed. Here we report direct recordings from boutons of neocortical pyramidal neurons and interneurons. Our data reveal rapid and large presynaptic action potentials in layer 5 neurons and fast-spiking interneurons reliably propagating into axon collaterals. For in-depth analyses we validate boutons of mature cultured neurons as models for excitatory neocortical boutons, demonstrating that the presynaptic spike amplitude was unaffected by potassium channels, homeostatic long-term plasticity, and high-frequency firing. In contrast to the stable amplitude, presynaptic spikes profoundly broadened for example during high-frequency firing in layer 5 pyramidal neurons but not in fast-spiking interneurons. Thus, our data demonstrate large presynaptic spikes and fundamental differences between excitatory and inhibitory boutons in the neocortex.

neuroscience↗

bioRxiv2020

Learning Patterns of the Ageing Brain in MRI using Deep Convolutional Networks

Both normal ageing and neurodegenerative diseases cause morphological changes to the brain. Age-related brain changes are subtle, nonlinear, and spatially and temporally heterogenous, both within a subject and across a population. Machine learning models are particularly suited to capture these patterns and can produce a model that is sensitive to changes of interest, despite the large variety in healthy brain appearance. In this paper, the power of convolutional neural networks (CNNs) and the rich UK Biobank dataset, the largest database currently available, are harnessed to address the problem of predicting brain age. We developed a 3D CNN architecture to predict chronological age, using a training dataset of 12, 802 T1-weighted MRI images and a further 6, 885 images for testing. The proposed method shows competitive performance on age prediction, but, most importantly, the CNN prediction errors {Delta}BrainAge = AgePredicted - AgeTrue correlated significantly with many clinical measurements from the UK Biobank in the female and male groups. In addition, having used images from only one imaging modality in this experiment, we examined the relationship between {Delta}BrainAge and the image-derived phenotypes (IDPs) from all other imaging modalities in the UK Biobank, showing correlations consistent with known patterns of ageing. Furthermore, we show that the use of nonlinearly registered images to train CNNs can lead to the network being driven by artefacts of the registration process and missing subtle indicators of ageing, limiting the clinical relevance. Due to the longitudinal aspect of the UK Biobank study, in the future it will be possible to explore whether the {Delta}BrainAge from models such as this network were predictive of any health outcomes. HighlightsO_LIBrain age is estimated using a 3D CNN from 12,802 full T1-weighted images. C_LIO_LIRegions used to drive predictions are different for linearly and nonlinearly registered data. C_LIO_LILinear registrations utilise a greater diversity of biologically meaningful areas. C_LIO_LICorrelations with IDPs and non-imaging variables are consistent with other publications. C_LIO_LIExcluding subjects with various health conditions had minimal impact on main correlations. C_LI

neuroscience↗

bioRxiv2020

Individualized characterization of volumetric development in the preterm brain

The diverse cerebral consequences of preterm birth create significant challenges for understanding pathogenesis or predicting later outcome. Instead of focusing on describing effects common to the group, comparing individual infants against robust normative data offers a powerful alternative to study brain maturation. Here we used Gaussian process regression to create normative curves characterising brain volumetric development in 274 term-born infants, modelling for age at scan and sex. We then compared 89 preterm infants scanned at termequivalent age to these normative charts, relating individual deviations from typical volumetric development to perinatal risk factors and later neurocognitive scores. To test generalisability, we used a second independent dataset comprising of 253 preterm infants scanned using different acquisition parameters and scanner. We describe rapid, non-uniform brain growth during the neonatal period. In both preterm cohorts, cerebral atypicalities were widespread, often multiple, and varied highly between individuals. Deviations from normative development were associated with respiratory support, nutrition, birth weight, and later neurocognition, demonstrating their clinical relevance. Group-level understanding of the preterm brain disguise a large degree of individual differences. We provide a method and normative dataset that offer a more precise characterisation of the cerebral consequences of preterm birth by profiling the individual neonatal brain.

neuroscience↗

bioRxiv2020

Common variants contribute to intrinsic functional architecture of human brain

The human brain remains active in the absence of explicit tasks and forms networks of correlated activity. Resting-state functional magnetic resonance imaging (rsfMRI) measures brain activity at rest, which has been linked with both cognitive and clinical outcomes. The genetic variants influencing human brain function are largely unknown. Here we utilized rsfMRI from 44,190 individuals of multiple ancestries (37,339 in the UK Biobank) to discover and validate the common genetic variants influencing intrinsic brain activity. We identified hundreds of novel genetic loci associated with intrinsic functional signatures (P < 2.8 x 10-11), including associations to the central executive, default mode, and salience networks involved in the triple network model of psychopathology. A number of intrinsic brain activity associated loci colocalized with brain disorder GWAS (e.g., Alzheimers disease, Parkinsons disease, schizophrenia) and cognition, such as 19q13.32, 17q21.31, and 2p16.1. Particularly, we detected a colocalization between one (rs429358) of the two variants in the APOE {varepsilon}4 locus and function of the default mode, central executive, attention, and visual networks. Genetic correlation analysis demonstrated shared genetic influences between brain function and brain structure in the same regions. We also detected significant genetic correlations with 26 other complex traits, such as ADHD, major depressive disorder, schizophrenia, intelligence, education, sleep, subjective well-being, and neuroticism. Common variants associated with intrinsic brain activity were enriched within regulatory element in brain tissues.

genetics↗

bioRxiv2020

Enhanced Brain Imaging Genetics in UK Biobank

UK Biobank is a major prospective epidemiological study that is carrying out detailed multimodal brain imaging on 100,000 participants, and includes genetics and ongoing health outcomes. As a step forwards in understanding genetic influence on brain structure and function, in 2018 we published genome-wide associations of 3,144 brain imaging-derived phenotypes, with a discovery sample of 8,428 UKB subjects. Here we present a new open resource of GWAS summary statistics, resulting from a greatly expanded set of genetic associations with brain phenotypes, using the 2020 UKB imaging data release of approximately 40,000 subjects. The discovery sample has now almost tripled (22,138), the number of phenotypes increased to 3,935 and the number of genetic variants with MAF[≥]1% increased to 10 million. For the first time, we include associations on the X chromosome, and several new classes of image derived phenotypes (primarily, more fine-grained subcortical volumes, and cortical grey-white intensity contrast). Previously we had found 148 replicated clusters of associations between genetic variants and imaging phenotypes; here we find 692 replicating clusters of associations, including 12 on the X chromosome. We describe some of the newly found associations, focussing particularly on the X chromosome and autosomal associations involving the new classes of image derived phenotypes. Our novel associations implicate pathways involved in the rare X-linked syndrome STAR (syndactyly, telecanthus and anogenital and renal malformations), Alzheimers disease and mitochondrial disorders. All summary statistics are openly available for interactive viewing and download on the "BIG40" open web server.

neuroscience↗

bioRxiv2020

Mid-life and late life activities and their relationship with MRI measures of brain structure and functional connectivity in the UK Biobank cohort

INTRODUCTIONThis study aimed to evaluate whether mid-life and late life participation in leisure activities is linked to measures of brain structure, functional connectivity and cognition in early old age. METHODSWe examined data collected from 7,152 participants of the UK Biobank study. Weekly participation in six leisure activities was assessed twice. A cognitive battery and 3T MRI brain scan were administered at the second visit. RESULTSWeekly computer use at mid-life associated with larger volumes of the left putamen and higher scores for fluid intelligence, alphanumeric and numeric trail making tasks and prospective memory. Frequent attendance at a sports club or gym at mid-life was associated with stronger connectivity of the sensorimotor network with the lateral visual and cerebellar networks. No other associations were significant. DISCUSSIONThis study demonstrates that not all leisure activities contribute to cognitive health equally, nor is there one unifying neural signature across leisure activities.

neuroscience↗

bioRxiv2020

Confound modelling in UK Biobank brain imaging

Dealing with confounds is an essential step in large cohort studies to address problems such as unexplained variance and spurious correlations. UK Biobank is a powerful resource for studying associations between imaging and nonimaging measures such as lifestyle factors and health outcomes, in part because of the large subject numbers. However, the resulting high statistical power also raises the sensitivity to confound effects, which therefore have to be carefully considered. In this work we describe a set of possible confounds (including non-linear effects and interactions) that researchers may wish to consider for their studies using such data. We include descriptions of how we can estimate the confounds, and study the extent to which each of these confounds affects the data, and the spurious correlations that may arise if they are not controlled. Finally, we discuss several issues that future studies should consider when dealing with confounds.

neuroscience↗