Biology subjects

Jenkinson, M.

Publications and source records attributed to Jenkinson, M..

27 records · Page 2Linked to original sources

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

White matter changes in the perforant path in patients with amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is a progressive and incurable motor neuron disease. Some ALS patients are affected by a level of cognitive or behavioural decline that meets the criteria for frontotemporal dementia (FTD). ALS and FTD share genetic and pathological features; for example, the deposition of phosphorylated 43 kDa TAR DNA-binding protein (pTDP-43) in the brain. Spreading of pTDP-43 pathology in ALS towards brain areas that connect via the Papez circuit is a possible indicator of progression towards FTD. For example, pTDP-43 aggregates in the granule cells of the hippocampus correlate well with clinically manifest FTD. Here, we test the hypothesis that white matter degeneration of the perforant path - as part of the Papez circuit - in the hippocampus is a feature of ALS, even in the absence of fully developed FTD or deposition of pTDP-43 inclusions in hippocampal granule cells. We used diffusion MRI (dMRI), polarized light imaging (PLI) and immunohistochemical analysis of hippocampus sections from controls (n=5) and ALS patients (n=14) to perform an in-depth study of white matter in the perforant path. The dMRI results show a significant decrease in fractional anisotropy (p=0.01) and an increase in mean diffusivity (p=0.01), axial diffusivity (p=0.03) and radial diffusivity (p=0.03) in the perforant path in ALS patients compared to controls, possibly indicating a loss of white matter fibres. Myelin density (measured with PLI retardance) was lower in ALS patients compared to controls (p=0.05) and correlated with dMRI fractional anisotropy (r=0.52, p=0.03). The dMRI and PLI results were confirmed by the immunohistochemistry; both myelin (proteolipid protein, p=0.03) and neurofilaments (SMI-312, p=0.02) were lower in ALS patients. The activated microglial (CD68) density was similar in ALS and controls. Only two out of the fourteen ALS cases showed pTDP-43 pathology in the dentate gyrus; however, while these two ALS-FTD cases showed reduced myelination in the perforant path, the values were comparable to other ALS cases. We conclude that degeneration of the perforant path occurs in ALS patients and that this may occur before, or independent of, pTDP-43 aggregation in the dentate gyrus of the hippocampus. Future research should focus on correlating the degree of clinically observed cognitive decline to the amount of white matter atrophy in the perforant path.

neuroscience↗

bioRxiv2018

Automated lesion segmentation with BIANCA: impact of population-level features, classification algorithm and locally adaptive thresholding

White matter hyperintensities (WMH) or white matter lesions exhibit high variability in their characteristics both at population- and subject-level, making their detection a challenging task. Population-level factors such as age, vascular risk factors and neurode-generative diseases affect lesion load and spatial distribution. At the individual level, WMH vary in contrast, amount and distribution in different white matter regions.\n\nIn this work, we aimed to improve BIANCA, the FSL tool for WMH segmentation, in order to better deal with these sources of variability. We worked on two stages of BIANCA by improving the lesion probability map estimation (classification stage) and making the lesion probability map thresholding stage automated and adaptive to local lesion probabilities. Firstly, in order to take into account the effect of population-level factors, we included population-level lesion probabilities, modelled with respect to a parametric factor (e.g. age), in the classification stage. Secondly, we tested BIANCA performance when using four alternative classifiers commonly used in the literature, with respect to K-nearest neighbour algorithm currently used for lesion probability map estimation in BIANCA. Finally, we propose LOCally Adaptive Threshold Estimation (LOCATE), a supervised method for determining optimal local thresholds to apply to the estimated lesion probability map, as an alternative option to global thresholding (i.e. applying the same threshold to the entire lesion probability map). For these experiments we used data from a neurodegenerative cohort and a vascular cohort.\n\nWe observed that including population-level parametric lesion probabilities with re-spect to age and using alternative machine learning techniques provided negligible im-provement. However, LOCATE provided a substantial improvement in the lesion segmentation performance when compared to the global thresholding currently used in BIANCA. We further validated LOCATE on a cohort of CADASIL (Cerebral autoso-mal dominant arteriopathy with subcortical infarcts and leukoencephalopathy) patients, a genetic form of cerebral small vessel disease characterised by extensive WMH burden, and healthy controls showing that LOCATE adapts well to wide variations in lesion load and spatial distribution.

neuroscience↗

bioRxiv2018

Modelling the distribution of white matter hyperintensities due to ageing on MRI images using Bayesian inference

White matter hyperintensities (WMH), also known as white matter lesions, are localised white matter areas that appear hyperintense on MRI scans. WMH commonly occur in the ageing population, and are often associated with several factors such as cognitive disorders, cardiovascular risk factors, cerebrovascular and neurodegenerative diseases. Despite the fact that some links between lesion location and parametric factors such as age have already been established, the relationship between voxel-wise spatial distribution of lesions and these factors is not yet well understood. Hence, it would be of clinical importance to model the distribution of lesions at the population-level and quantitatively analyse the effect of various factors on the lesion distribution model.\n\nIn this work we compare various methods, including our proposed method, to generate voxel-wise distributions of WMH within a population with respect to various factors. Our proposed Bayesian spline method models the spatio-temporal distribution of WMH with respect to a parametric factor of interest, in this case age, within a population. Our probabilistic model takes as input the lesion segmentation binary maps of subjects belonging to various age groups and provides a population-level parametric lesion probability map as output. We used a spline representation to ensure a degree of smoothness in space and the dimension associated with the parameter, and formulated our model using a Bayesian framework.\n\nWe tested our algorithm output on simulated data and compared our results with those obtained using various existing methods with different levels of algorithmic and computational complexity. We then compared the better performing methods on a real dataset, consisting of 1000 subjects of the UK Biobank, divided in two groups based on hypertension diagnosis. Finally, we applied our method on a clinical dataset of patients with vascular disease.\n\nOn simulated dataset, the results from our algorithm showed a mean square error (MSE) value of 7.27 x 10-5, which was lower than the MSE value reported in the literature, with the advantage of being robust and computationally efficient. In the UK Biobank data, we found that the lesion probabilities are higher for the hypertension group compared to the non-hypertension group and further verified this finding using a statistical t-test. Finally, when applying our method on patients with vascular disease, we observed that the overall probability of lesions is significantly higher in later age groups, which is in line with the current literature.

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

Using Arterial Spin Labelling to Investigate Spontaneous and Evoked Ongoing Musculoskeletal Pain

Clinical pain is difficult to study using standard Blood Oxygenation Level Dependent (BOLD) magnetic resonance imaging because it is often ongoing and, if evoked, it is associated with stimulus-correlated motion. Arterial spin labelling (ASL) offers an attractive alternative. This study used arm repositioning to evoke clinically-relevant musculoskeletal pain in patients with shoulder impingement syndrome. Fifty-five patients were scanned using a multi post-labelling delay pseudo-continuous ASL (pCASL) sequence, first with both arms along the body and then with the affected arm raised into a painful position. Twenty healthy volunteers were scanned as a control group. Arm repositioning resulted in increased perfusion in brain regions involved in sensory processing and movement integration, such as the contralateral primary motor and primary somatosensory cortex, mid- and posterior cingulate cortex, and, bilaterally, in the insular cortex/operculum, putamen, thalamus, midbrain and cerebellum. Perfusion in the thalamus, midbrain and cerebellum was larger in the patient group. Results of a post hoc analysis suggested that the observed perfusion changes were related to pain rather than arm repositioning. This study showed that ASL can be useful in research on clinical ongoing musculoskeletal pain but the technique is not sensitive enough to detect small differences in perfusion.

neuroscience↗

bioRxiv2017

An integrated genomic analysis of anaplastic meningioma identifies prognostic molecular signatures

Anaplastic meningioma is a rare and aggressive brain tumor characterised by intractable recurrences and dismal outcomes. Here, we present an integrated analysis of the whole genome, transcriptome and methylation profiles of primary and recurrent anaplastic meningioma. A key finding was the delineation of two distinct molecular subgroups that were associated with diametrically opposed survival outcomes. Relative to lower grade meningiomas, anaplastic tumors harbored frequent driver mutations in SWI/SNF complex genes, which were confined to the poor prognosis subgroup. Our analyses discern two biologically distinct variants of anaplastic meningioma with potential prognostic and therapeutic significance.

genomics↗

bioRxiv2017

Image Processing and Quality Control for the first 10,000 Brain Imaging Datasets from UK Biobank

UK Biobank is a large-scale prospective epidemiological study with all data accessible to researchers worldwide. It is currently in the process of bringing back 100,000 of the original participants for brain, heart and body MRI, carotid ultrasound and low-dose bone/fat x-ray. The brain imaging component covers 6 modalities (T1, T2 FLAIR, susceptibility weighted MRI, Resting fMRI, Task fMRI and Diffusion MRI). Raw and processed data from the first 10,000 imaged subjects has recently been released for general research access. To help convert this data into useful summary information we have developed an automated processing and QC (Quality Control) pipeline that is available for use by other researchers. In this paper we describe the pipeline in detail, following a brief overview of UK Biobank brain imaging and the acquisition protocol. We also describe several quantitative investigations carried out as part of the development of both the imaging protocol and the processing pipeline.

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↗