Biology subjects

Bozek, J.

Publications and source records attributed to Bozek, J..

7 recordsLinked to original sources

bioRxiv2022

Normative models for neuroimaging markers: Impact of model selection, sample size and evaluation criteria

Modelling population reference curves or normative modelling is increasingly used with the advent of large neuroimaging studies. In this paper we assess the performance of fitting methods from the perspective of clinical applications and investigate the influence of the sample size. Further, we evaluate linear and nonlinear models for percentile curve estimation and highlight how the bias-variance trade-off manifests in typical neuroimaging data. We created plausible ground truth distributions of hippocampal volumes in the age range of 45 to 80 years, as an example application. Based on these distributions we repeatedly simulated samples for sizes between 50 and 50,000 data points, and for each simulated sample we fitted a range of normative models. We compared the fitted models and their variability across repetitions to the ground truth, with specific focus on the outer percentiles (1th, 5th, 10th) as these are the most clinically relevant. Our results quantify the expected decreasing trend in variance of the volume estimates with increasing sample size. However, bias in the volume estimates only decreases a modest amount, without much improvement at large sample sizes. The uncertainty of model performance is substantial for what would often be considered large samples in a neuroimaging context and rises dramatically at the ends of the age range, where fewer data points exist. Flexible models perform better across sample sizes, especially for nonlinear ground truth. Surprisingly large samples of several thousand data points are needed to accurately capture outlying percentiles across the age range for applications in research and clinical settings. Performance evaluation methods should assess both, bias and variance. Furthermore, extreme caution is needed when attempting to extrapolate beyond the age range included in the source dataset. To help with such evaluations of normative models we have made our code available to guide researchers developing or utilising normative models.

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

Neonatal multi-modal cortical profiles predict 18-month developmental outcomes

Developmental delays in infanthood often persist, turning into life-long difficulties, and coming at great cost for the individual and community. By examining the developing brain and its relation to developmental outcomes we can start to elucidate how the emergence of brain circuits is manifested in variability of infant motor, cognitive and behavioural capacities. In this study, we examined if cortical structural covariance at birth, indexing coordinated development, is related to later infant behaviour. We included 193 healthy term-born infants from the Developing Human Connectome Project (dHCP). An individual cortical connectivity matrix derived from morphological and microstructural features was computed for each subject (morphometric similarity networks, MSNs) and was used as input for the prediction of behavioural scores at 18 months using Connectome-Based Predictive Modeling (CPM). Neonatal MSNs successfully predicted social-emotional performance. Predictive edges were distributed between and within known functional cortical divisions with a specific important role for primary and posterior cortical regions. These results reveal that multi-modal neonatal cortical profiles showing coordinated maturation are related to developmental outcomes and that network organization at birth provides an early infrastructure for future functional skills.

neuroscience↗

bioRxiv2020

Development of Microstructural and Morphological Cortical Profiles in the Neonatal Brain

In the perinatal brain, regional cortical architecture and connectivity lay the foundations for functional circuits and emerging behaviour. Interruptions or atypical development during or before this period may therefore have long-lasting consequences. However, to be able to investigate these deviations, we need a measure of how this architecture evolves in the typically developing brain. To this end, in a large cohort of 241 term-born infants we used Magnetic Resonance Imaging to estimate cortical profiles based on morphometry and microstructure over the perinatal period (37-44 weeks post-menstrual age, PMA). Using the covariance of these profiles as a measure of inter-areal network similarity (Morphometric Similarity Networks; MSN), we clustered these networks into distinct modules. The resulting modules were consistent and symmetric, and corresponded to known functional distinctions, including sensory-motor, limbic and association regions and were spatially mapped onto known cytoarchitectonic tissue classes. Posterior (parietal, occipital) regions became more morphometrically similar with increasing PMA, while peri-cingulate and medial temporal regions became more dissimilar. Network strength was associated with PMA: Within-network similarity increased over PMA suggesting emerging network distinction. These changes in cortical network architecture over an eight-week period are consistent with, and likely underpin, the highly dynamic behavioural and cognitive development occurring during this critical period. The resulting cortical profiles might provide normative reference to investigate atypical early brain development.

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↗

bioRxiv2017

Multimodal Surface Matching with Higher-Order Smoothness Constraints

The accurate alignment of brains is fundamental to the statistical sensitivity and spatial localisation of group studies in brain imaging, and cortical surface-based alignment is generally accepted to be superior to volume-based approaches at aligning cortical areas. However, human subjects have considerable variation in cortical folding, and in the location of cortical areas relative to these folds, which makes aligning cortical areas based on folding alone a challenging problem. The Multimodal Surface Matching (MSM) tool is a flexible spherical registration approach that enables accurate registration of surfaces based on a variety of different features. Using MSM, we have previously shown that using areal features such as resting state-networks and myelin maps to drive cross-subject surface alignment improves group task fMRI statistics and map sharpness. However, the initial implementation of MSMs regularisation function did not penalize all forms of surface distortion evenly. In some cases, this allowed peak distortions to exceed neurobiologically plausible limits unless the regularisation strength was increased, in which case this prevented the algorithm from fully maximizing surface alignment. Here, we propose a new regularisation penalty, derived from physically relevant equations of strain (deformation) energy, and demonstrate that its use leads to improved and more robust alignment of multi-modal imaging data. In addition, since spherical warps incorporate projection distortions that are unavoidable when mapping from a convoluted cortical surface to the sphere, we also propose constraints to enforce smooth deformation of cortical anatomies. We test the impact of this approach for longitudinal modeling of cortical development for neonates (born between 32 and 45 weeks) and demonstrate that the proposed method increases the biological interpretability of the distortion fields and improves the statistical significance of population-based analysis relative to other spherical methods.

neuroscience↗

bioRxiv2017

The Developing Human Connectome Project: a Minimal Processing Pipeline for Neonatal Cortical Surface Reconstruction

The Developing Human Connectome Project (dHCP) seeks to create the first 4-dimensional connectome of early life. Understanding this connectome in detail may provide insights into normal as well as abnormal patterns of brain development. Following established best practices adopted by the WU-MINN Human Connectome Project (HCP), and pioneered by FreeSurfer, the project utilises cortical surface-based processing pipelines. In this paper, we propose a fully automated processing pipeline for the structural Magnetic Resonance Imaging (MRI) of the developing neonatal brain. This proposed pipeline consists of a refined framework for cortical and sub-cortical volume segmentation, cortical surface extraction, and cortical surface inflation, which has been specifically designed to address considerable differences between adult and neonatal brains, as imaged using MRI. Using the proposed pipeline our results demonstrate that images collected from 465 subjects ranging from 28 to 45 weeks post-menstrual age (PMA) can be processed fully automatically; generating cortical surface models that are topologically correct, and correspond well with manual evaluations of tissue boundaries in 85% of cases. Results improve on state-of-the-art neonatal tissue segmentation models and significant errors were found in only 2% of cases, where these corresponded to subjects with high motion. Downstream, these surfaces will enhance comparisons of functional and diffusion MRI datasets, supporting the modelling of emerging patterns of brain connectivity.

neuroscience↗