Biology subjects

Smith, S. M.

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

At least 73 records · Page 4Linked to original sources

bioRxiv2020

Anatomically precise relationship between specific amygdala connections and selective markers of mental well-being in humans

There has been increasing interest in using neuroimaging measures to predict psychiatric disorders. However, predictions usually rely on large numbers of brain connections and large disorder heterogeneity, thus lacking both anatomical and behavioural specificity, preventing the advancement of targeted interventions. Here, we address both challenges. First, using resting-state functional MRI, we parcellated the amygdala, a region implicated in mood disorders but difficult to image with high fidelity, into seven nuclei. Next, a questionnaire factor analysis provided four sub-clinical latent behaviours frequently found in anxious-depressive individuals, such as negative emotions and sleep problems. Finally, for each latent behaviour, we identified the most predictive connections between individual amygdala nuclei and highly specific regions of interest e.g. dorsal raphe nucleus in the brainstem or medial prefrontal cortical regions. A small number of distinct connections predicted behaviours, providing unprecedented levels of specificity, in humans, for relating mental well-being to precise anatomical connections.

neuroscience↗

bioRxiv2020

Phenotype Discovery from Population Brain Imaging

Neuroimaging allows for the non-invasive study of the brain in rich detail. Data-driven discovery of patterns of population variability in the brain has the potential to be extremely valuable for early disease diagnosis and understanding the brain. The resulting patterns can be used as imaging-derived phenotypes (IDPs), and may complement existing expert-curated IDPs. However, population datasets, comprising many different structural and functional imaging modalities from thousands of subjects, provide a computational challenge not previously addressed. Here, for the first time, a multimodal independent component analysis approach is presented that is scalable for data fusion of voxel-level neuroimaging data in the full UK Biobank (UKB) dataset, that will soon reach 100,000 imaged subjects. This new computational approach can estimate modes of population variability that enhance the ability to predict thousands of phenotypic and behavioural variables using data from UKB and the Human Connectome Project. A high-dimensional decomposition achieved improved predictive power compared with widely-used analysis strategies, single-modality decompositions and existing IDPs. In UKB data (14,503 subjects with 47 different data modalities), many interpretable associations with non-imaging phenotypes were identified, including multimodal spatial maps related to fluid intelligence, handedness and disease, in some cases where IDP-based approaches failed.

neuroscience↗

bioRxiv2020

The Developing Human Connectome Project: typical and disrupted functional connectivity across the perinatal period

The Developing Human Connectome Project (dHCP) is an Open Science project which provides the first large sample of neonatal functional MRI (fMRI) data with high temporal and spatial resolution. This data enables mapping of intrinsic functional connectivity between spatially distributed brain regions under normal and adverse perinatal circumstances, offering a framework to study the ontogeny of large-scale brain organisation in humans. Here, we characterise in unprecedented detail the maturation and integrity of resting-state networks (RSNs) at normal term age in 337 infants (including 65 born preterm). First, we applied group independent component analysis (ICA) to define 11 RSNs in term-born infants scanned at 43.5-44.5 weeks postmenstrual age (PMA). Adult-like topography was observed in RSNs encompassing primary sensorimotor, visual and auditory cortices. Among six higher-order, association RSNs, analogues of the adult networks for language and ocular control were identified, but a complete default mode network precursor was not. Next, we regressed the subject-level datasets from an independent cohort of infants scanned at 37-43.5 weeks PMA against the group-level RSNs to test for the effects of age, sex and preterm birth. Brain mapping in term-born infants revealed areas of positive association with age across four of six association RSNs, indicating active maturation in functional connectivity from 37 to 43.5 weeks PMA. Female infants showed increased connectivity in inferotemporal regions of the visual association network. Preterm birth was associated with striking impairments of functional connectivity across all RSNs in a dose-dependent manner; conversely, connectivity of the superior parietal lobules within the lateral motor network was abnormally increased in preterm infants, suggesting a possible mechanism for specific difficulties such as developmental coordination disorder which occur frequently in preterm children. Overall, we find a robust, modular, symmetrical functional brain organisation at normal term age. A complete set of adult-equivalent primary RSNs is already instated, alongside emerging connectivity in immature association RSNs, consistent with a primary-to-higher-order ontogenetic sequence of brain development. The early developmental disruption imposed by preterm birth is associated with extensive alterations in functional connectivity.

neuroscience↗

bioRxiv2019

Accurate brain age prediction with lightweight deep neural networks

Deep learning has huge potential for accurate disease prediction with neuroimaging data, but the prediction performance is often limited by training-dataset size and computing memory requirements. To address this, we propose a deep convolutional neural network model, Simple Fully Convolutional Network (SFCN), for accurate prediction of brain age using T1-weighted structural MRI data. Compared with other popular deep network architectures, SFCN has fewer parameters, so is more compatible with small dataset size and 3D volume data. The network architecture was combined with several techniques for boosting performance, including data augmentation, pre-training, model regularization, model ensemble and prediction bias correction. We compared our overall SFCN approach with several widely-used machine learning models. It achieved state-of-the-art performance in UK Biobank data (N = 14,503), with mean absolute error (MAE) = 2.14y in brain age prediction and 99.5% in sex classification. SFCN also won (both parts of) the 2019 Predictive Analysis Challenge for brain age prediction, involving 79 competing teams (N = 2,638, MAE = 2.90y). We describe here the details of our approach, and its optimisation and validation. Our approach can easily be generalised to other tasks using different image modalities, and is released on GitHub. HighlightsO_LIA lightweight deep learning model, Simple Fully Convolutional Network (SFCN), is presented, achieving state-of-the-art brain age prediction and sex classification performance in UK Biobank MRI brain imaging data. C_LIO_LIEven with limited number of training subjects (e.g., 50), SFCN performs better than widely-used regression models. C_LIO_LIA semi-multimodal ensemble strategy is proposed and achieved first place in the PAC 2019 brain age prediction challenge. C_LIO_LILinear regression can remove brain age prediction bias (even on unlabelled data) while maintaining state-of-the-art performance. C_LI

neuroscience↗

bioRxiv2019

Patterns of socio-cognitive stratification and perinatal risk in the child brain

The expanding behavioral repertoire of the developing brain during childhood and adolescence is shaped by complex brain-environment interactions and flavored by unique life experiences. The transition into young adulthood offer opportunities for adaptation and growth, but also increased susceptibility to environmental perturbations, such as the characteristics of social relationships, family environment, quality of schools and activities, financial security, urbanization and pollution, drugs, cultural practices, and values, that all act in concert with our genetic architecture and biology. Our multivariate brain-behavior mapping in 7,577 children aged 9-11 years across 585 brain imaging phenotypes, and 617 cognitive, behavioral, psychosocial and socioeconomic measures revealed three population modes of brain co-variation, which were robust as assessed by cross-validation and permutation testing, taking into account siblings and twins, identified using genetic data. The first mode revealed traces of perinatal complications, including pre-term and twin-birth, eclampsia and toxemia, shorter period of breast feeding and lower cognitive scores, with higher cortical thickness and lower cortical areas and volumes. The second mode reflected a pattern of socio-cognitive stratification, linking lower cognitive ability and socioeconomic status to lower cortical thickness, area and volumes. The third mote captured a pattern related to urbanicity, with particulate matter pollution (PM25) inversely related to home value, walkability and population density, associated with diffusion properties of white matter tracts. These results underscore the importance of a multidimensional and interdisciplinary understanding, integrating social, psychological and biological sciences, to map the constituents of healthy development and to identify factors that may precede maladjustment and mental illness.

neuroscience↗

bioRxiv2019

Brain aging comprises multiple modes of structural and functional change with distinct genetic and biophysical associations

Brain imaging can be used to study how individuals brains are aging, compared against population norms. This can inform on aspects of brain health; for example, smoking and blood pressure can be seen to accelerate brain aging. Typically, a single "brain age" is estimated per subject, whereas here we we identified 62 modes of subject variability, from 21,407 subjects multimodal brain imaging data in UK Biobank. The modes represent different aspects of brain aging, showing distinct patterns of functional and structural brain change, and distinct patterns of association with genetics, lifestyle, cognition, physical measures and disease. While conventional brain-age modelling found no genetic associations, 34 modes had genetic associations. We suggest that it is important not to treat brain aging as a single homogeneous process, and that modelling of distinct patterns of structural and functional change will reveal more biologically meaningful markers of brain aging in health and disease.

neuroscience↗

bioRxiv2019

Behavioural relevance of spontaneous, transient brain network interactions in fMRI

How spontaneously fluctuating functional magnetic resonance imaging (fMRI) signals in different brain regions relate to behaviour has been an open question for decades. Correlations in these signals, known as functional connectivity, can be averaged over several minutes of data to provide a stable representation of the functional network architecture for an individual. However, associations between these stable features and behavioural traits have been shown to be dominated by individual differences in anatomy. Here, using kernel learning tools, we propose methods to assess and compare the relation between time-varying functional connectivity, time-averaged functional connectivity, structural brain data, and non-imaging subject behavioural traits. We applied these methods on Human Connectome Project resting-state fMRI data to show that time-varying fMRI functional connectivity, detected at time-scales of a few seconds, has associations with some behavioural traits that are not dominated by anatomy. Despite time-averaged functional connectivity accounting for the largest proportion of variability in the fMRI signal between individuals, we found that some aspects of intelligence could only be explained by time-varying functional connectivity. The finding that time-varying fMRI functional connectivity has a unique relationship to population behavioural variability suggests that it might reflect transient neuronal communication fluctuating around a stable neural architecture. Significance statementComplex cognition is dynamic and emerges from the interaction between multiple areas across the whole brain, i.e. from brain networks. Hence, the utility of functional MRI to investigate brain activity depends on how well it can capture time-varying network interactions. Here, we develop methods to predict behavioural traits of individuals from either time-varying functional connectivity, time-averaged functional connectivity, or structural brain data. We use these to show that the time-varying nature of functional brain networks in fMRI can be reliably measured and can explain aspects of behaviour not captured by structural data or time-averaged functional connectivity. These results provide important insights to the question of how the brain represents information and how these representations can be measured with fMRI.

neuroscience↗

bioRxiv2019

The developing Human Connectome Project (dHCP) automated resting-state functional processing framework for newborn infants.

The developing Human Connectome Project (dHCP) aims to create a detailed 4-dimensional connectome of early life spanning 20 to 45 weeks post-menstrual age. This is being achieved through the acquisition of multi-modal MRI data from over 1000 in- and ex-utero subjects combined with the development of optimised pre-processing pipelines. In this paper we present an automated and robust pipeline to minimally pre-process highly confounded neonatal resting-state fMRI data, robustly, with low failure rates and high quality-assurance. The pipeline has been designed to specifically address the challenges that neonatal data presents including low and variable contrast and high levels of head motion. We provide a detailed description and evaluation of the pipeline which includes integrated slice-to-volume motion correction and dynamic susceptibility distortion correction, a robust multimodal registration approach, bespoke ICA-based denoising, and an automated QC framework. We assess these components on a large cohort of dHCP subjects and demonstrate that processing refinements integrated into the pipeline provide substantial reduction in movement related distortions, resulting in significant improvements in SNR, and detection of high quality RSNs from neonates. HighlightsO_LIAn automated and robust pipeline to minimally pre-process highly confounded neonatal fMRI data C_LIO_LIIncludes integrated dynamic distortion and slice-to-volume motion correction C_LIO_LIA robust multimodal registration approach which includes custom neonatal templates C_LIO_LIIncorporates an automated and self-reporting QC framework to quantify data quality and identify issues for further inspection C_LIO_LIData analysis of 538 infants imaged at 26-45 weeks post-menstrual age C_LI

neuroscience↗

bioRxiv2019

Optimising network modelling methods for fMRI

A major goal of neuroimaging studies is to develop predictive models to analyse the relationship between whole brain functional connectivity patterns and behavioural traits. However, there is no single widely-accepted standard pipeline for analyzing functional connectivity. The common procedure for designing functional connectivity based predictive models entails three main steps: parcellating the brain, estimating the interaction between defined parcels, and lastly, using these integrated associations between brain parcels as features fed to a classifier for predicting non-imaging variables e.g., behavioural traits, demographics, emotional measures, etc. There are also additional considerations when using correlation-based measures of functional connectivity, resulting in three supplementary steps: utilising Riemannian geometry tangent space parameterization to preserve the geometry of functional connectivity; penalizing the connectivity estimates with shrinkage approaches to handle challenges related to short time-series (and noisy) data; and removing confounding variables from brain-behaviour data. These six steps are contingent on each-other, and to optimise a general framework one should ideally examine these various methods simultaneously. In this paper, we investigated strengths and short-comings, both independently and jointly, of the following measures: parcellation techniques of four kinds (categorized further depending upon number of parcels), five measures of functional connectivity, the decision of staying in the ambient space of connectivity matrices or in tangent space, the choice of applying shrinkage estimators, six alternative techniques for handling confounds and finally four novel classifiers/predictors. For performance evaluation, we have selected two of the largest datasets, UK Biobank and the Human Connectome Project resting state fMRI data, and have run more than 9000 different pipeline variants on a total of [~]14000 individuals to determine the optimum pipeline. For independent performance validation, we have run some best-performing pipeline variants on ABIDE and ACPI datasets ([~]1000 subjects) to evaluate the generalisability of proposed network modelling methods.

neuroscience↗

bioRxiv2019

A data-driven approach to optimising the encoding for multi-shell diffusion MRI with application to neonatal imaging

Diffusion MRI has the potential to provide important information about the connectivity and microstructure of the human brain during normal and abnormal development, non-invasively and in vivo. Recent developments in MRI hardware and reconstruction methods now permit the acquisition of large amounts of data within relatively short scan times. This makes it possible to acquire more informative multi-shell data, with diffusion-sensitisation applied along many directions over multiple b-value shells. Such schemes are characterised by the number of shells acquired, and the specific b-value and number of directions sampled for each shell. However, there is currently no clear consensus as to how to optimise these parameters. In this work, we propose a means of optimising multi-shell acquisition schemes by estimating the information content of the diffusion MRI signal, and optimising the acquisition parameters for sensitivity to the observed effects, in a manner agnostic to any particular diffusion analysis method that might subsequently be applied to the data. This method was used to design the acquisition scheme for the neonatal diffusion MRI sequence used in the developing Human Connectome Project, which aims to acquire high quality data and make it freely available to the research community. The final protocol selected by the algorithm, and currently in use within the dHCP, consists of b = 0, 400, 1000, 2600 s/mm2 with 20, 64, 88 & 128 DW directions per shell respectively.\n\nHighlightsO_LIA data driven method is presented to design multi-shell diffusion MRI acquisition schemes (b-values and no. directions).\nC_LIO_LIThis method optimises the multi-shell scheme for maximum sensitivity to the information content in the signal.\nC_LIO_LIWhen applied in neonates, the data suggest that a b=0 + 3 shell strategy is appropriate\nC_LI

neuroscience↗

bioRxiv2019

A Symmetric Prior for the Regularisation of Elastic Deformations: Improved Anatomical Plausibility in Nonlinear Image Registration

Nonlinear registration is critical to many aspects of Neuroimaging research. It facilitates averaging and comparisons across multiple subjects, as well as reporting of data in a common anatomical frame of reference. It is, however, a fundamentally ill-posed problem, with many possible solutions which minimise a given dissimilarity metric equally well. We present a novel regularisation method that aims to selectively drive solutions towards those which would be considered anatomically plausible by penalising unlikely lineal, areal and volumetric deformations. In addition, our penalty is symmetric in the sense that geometric expansions and contractions are penalised equally, which encourages inverse-consistency. We demonstrate that our method is able to significantly reduce volume and shape distortions compared to state-of-the-art elastic (FNIRT) and plastic (ANTs) registration frameworks. Crucially, this is achieved whilst matching or exceeding the registration quality of these methods, as measured by overlap scores of labelled cortical regions. Furthermore, extensive use of GPU parallelisation has allowed us to implement what is a highly computationally intensive optimisation strategy while maintaining reasonable run times of under half an hour.

neuroscience↗

bioRxiv2019

High resolution nonlinear registration with simultaneous modelling of intensities

This paper describes and evaluates FMRIBs nonlinear image registration tool (FNIRT), that is part of the FMRIB software library (FSL). It is a small deformation framework using sum of squared differences (SSD) as its cost function and Gauss-Newton for minimisation. The framework uses a joint shape and intensity model that attempts to explain the observed differences between two images in terms of having different shape and/or contrast, being differently affected by intensity bias-fields etc. Thus the estimation of the warps will be relatively unaffected by intensity differences that would otherwise violate the assumptions behind the SSD cost function. It uses a projection onto a manifold defined by a specified range of allowed Jacobian determinants to ensure that the warps are diffeomorphic. The utility of the model is demonstrated on a variety of simulated and experimental data with good results. FNIRT is also quantitatively evaluated using previously published datasets consisting of scans from multiple subjects, all with anatomically defined brain regions that are manually outlined. In this evaluation FNIRT performs well in comparison to previously published results with other registration algorithms.

neuroscience↗

bioRxiv2019

Hippocampal volume across age: Nomograms derived from over 19,700 people in UK Biobank

Measurement of hippocampal volume has proven useful to diagnose and track progression in several brain disorders, most notably in Alzheimers disease (AD). For example, an objective evaluation of a patients hippocampal volume status may provide important information that can assist diagnosis or risk stratification of AD. However, clinicians and researchers require access to age-related normative percentiles to reliably categorise a patients hippocampal volume as being pathologically small. Here we analysed effects of age, sex, and hemisphere on the hippocampus and neighbouring temporal lobe volumes, in 19,793 generally healthy participants in the UK Biobank. A key finding of the current study is a significant acceleration in the rate of hippocampal volume loss in middle age, more pronounced in females than in males. In this report, we provide normative values for hippocampal and total grey matter volume as a function of age for reference in clinical and research settings. These normative values may be used in combination with our online, automated percentile estimation tool to provide a rapid, objective evaluation of an individuals hippocampal volume status. The data provide a large-scale normative database to facilitate easy age-adjusted determination of where an individual hippocampal and temporal lobe volume lies within the normal distribution.

neuroscience↗

bioRxiv2019

Estimation of Brain Age Delta from Brain Imaging

It is of increasing interest to study "brain age" - the apparent age of a subject, as inferred from brain imaging data. The difference between brain age and actual age (the "delta") is typically computed, reflecting deviation from the population norm. This therefore may reflect accelerated aging (positive delta) or resilience (negative delta) and has been found to be a useful correlate with factors such as disease and cognitive decline. However, although there has been a range of methods proposed for estimating brain age, there has been little study of the optimal ways of computing the delta. In this technical note we describe problems with the most common current approach, and present potential improvements. We evaluate different estimation methods on simulated and real data. We also find the strongest correlations of corrected brain age delta with 5,792 non-imaging variables (non-brain physical measures, life-factor measures, cognitive test scores, etc.), and also with 2,641 multimodal brain imaging-derived phenotypes, with data from 19,000 participants in UK Biobank.

neuroscience↗

bioRxiv2019

Modelling Subject Variability in the Spatial and Temporal Characteristics of Functional Modes

Recent work has highlighted the scale and ubiquity of subject variability in observations from functional MRI data (fMRI). Furthermore, it is highly likely that errors in the estimation of either the spatial presentation of, or the coupling between, functional regions can confound cross-subject analyses, making accurate and unbiased representations of functional data essential for interpreting any downstream analyses. Here, we extend the framework of probabilistic functional modes (PFMs) [Harrison et al. 2015] to capture cross-subject variability not only in the mode spatial maps, but also in the functional coupling between modes and in mode amplitudes. A new implementation of the inference now also allows for the analysis of modern, large-scale data sets, and the combined inference and analysis package, PROFUMO, is available from git.fmrib.ox.ac.uk/samh/profumo. Using simulated data, resting-state data from 1,000 subjects collected as part of the Human Connectome Project [Van Essen et al. 2013], and an analysis of 14 subjects in a variety of continuous task-states [Kieliba et al. 2019], we demonstrate how PFMs are able to capture, within a single model, a rich description of how the spatio-temporal structure of resting-state fMRI activity varies across subjects. We also compare the new PFM model to the well established independent component analysis with dual regression (ICA-DR) pipeline. This reveals that, under PFM assumptions, much more of the (behaviorally relevant) cross-subject variability in fMRI activity should be attributed to the variability in spatial maps, and that, after accounting for this, functional coupling between modes primarily reflects current cognitive state. This has fundamental implications for the interpretation of cross-sectional studies of functional connectivity that do not capture cross-subject variability to the same extent as PFMs.

neuroscience↗

bioRxiv2019

The relationship between spatial configuration and functional connectivity of brain regions revisited

In our previous paper (Bijsterbosch et al., 2018), we showed that network-based modelling of brain connectivity interacts strongly with the shape and exact location of brain regions, such that cross-subject variations in the spatial configuration of functional brain regions are being interpreted as changes in functional connectivity. Here we show that these spatial effects on connectivity estimates actually occur as a result of spatial overlap between brain networks. This is shown to systematically bias connectivity estimates obtained from group spatial ICA followed by dual regression. We introduce an extended method that addresses the bias and achieves more accurate connectivity estimates.\n\nImpact statementWe show that functional connectivity network matrices as estimated from resting state functional MRI are biased by spatially overlapping network structure.

neuroscience↗

bioRxiv2018

Effective Degrees of Freedom of the Pearson’s Correlation Coefficient under Serial Correlation

The dependence between pairs of time series is commonly quantified by Pearsons correlation. However, if the time series are themselves dependent (i.e. exhibit temporal autocorrelation), the effective degrees of freedom (EDF) are reduced, the standard error of the sample correlation coefficient is biased, and Fishers transformation fails to stabilise the variance. Since fMRI time series are notoriously autocorrelated, the issue of biased standard errors - before or after Fishers transformation - becomes vital in individual-level analysis of resting-state functional connectivity (rsFC) and must be addressed anytime a standardized Z-score is computed. We find that the severity of autocorrelation is highly dependent on spatial characteristics of brain regions, such as the size of regions of interest and the spatial location of those regions. We further show that the available EDF estimators make restrictive assumptions that are not supported by the data, resulting in biased rsFC inferences that lead to distorted topological descriptions of the connectome on the individual level. We propose a practical \"xDF\" method that accounts not only for distinct autocorrelation in each time series, but instantaneous and lagged cross-correlation. We find the xDF correction varies substantially over node pairs, indicating the limitations of global EDF corrections used previously. In addition to extensive synthetic and real data validations, we investigate the impact of this correction on rsFC measures in data from the Young Adult Human Connectome Project, showing that accounting for autocorrelation dramatically changes fundamental graph theoretical measures relative to no correction.

neuroscience↗

bioRxiv2018

Divergent receptor proteins confer responses to different karrikins in two ephemeral weeds

Wildfires can encourage the establishment of invasive plants by releasing potent germination stimulants, such as karrikins. Seed germination of Brassica tournefortii, a noxious weed of Mediterranean climates, is strongly stimulated by KAR1, which is the archetypal karrikin produced from burning vegetation. In contrast, the closely-related yet non-fire-associated ephemeral Arabidopsis thaliana is unusual because it responds preferentially to KAR2. The /{beta}-hydrolase KARRIKIN INSENSITIVE2 (KAI2) is the putative karrikin receptor identified in Arabidopsis. Here we show that B. tournefortii differentially expresses three KAI2 homologues, and the most highly-expressed homologue is sufficient to confer enhanced responses to KAR1 relative to KAR2 when expressed in Arabidopsis. We further identify two variant amino acid residues near the KAI2 active site that explain the ligand selectivity, and show that this combination has arisen independently multiple times within dicots. Our results suggest that duplication and diversification of KAI2 proteins could confer upon weedy ephemerals and potentially other angiosperms differential responses to chemical cues produced by environmental disturbance, including fire.

plant biology↗