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•Brain age is estimated using a 3D CNN from 12,802 full T1-weighted images.•Regions used to drive predictions are different for linearly and nonlinearly registered data.•Linear registrations utilise a greater diversity of biologically meaningful areas.•Correlations with IDPs and non-imaging variables are consistent with other publications.•Excluding subjects with various health conditions had minimal impact on main correlations. 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 ΔBrainAge=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 ΔBrainAge 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 ΔBrainAge from models such as this network were predictive of any health outcomes.

PurposeVolumetric and health assessment of the liver is crucial to avoid poor post-operative outcomes following liver resection surgery. No current methods allow for concurrent and accurate measurement of both Couinaud segmental volumes for future liver remnant estimation and liver health using non-invasive imaging. In this study, we demonstrate the accuracy and precision of segmental volume measurements using new medical software, Hepatica™.MethodsMRI scans from 48 volunteers from three previous studies were used in this analysis. Measurements obtained from Hepatica™ were compared with OsiriX. Time required per case with each software was also compared. The performance of technicians and experienced radiologists as well as the repeatability and reproducibility were compared using Bland–Altman plots and limits of agreement.ResultsHigh levels of agreement and lower inter-operator variability for liver volume measurements were shown between Hepatica™ and existing methods for liver volumetry (mean Dice score 0.947 ± 0.010). A high consistency between technicians and experienced radiologists using the device for volumetry was shown (± 3.5% of total liver volume) as well as low inter-observer and intra-observer variability. Tight limits of agreement were shown between repeated Couinaud segment volume (+ 3.4% of whole liver), segmental liver fibroinflammation and segmental liver fat measurements in the same participant on the same scanner and between different scanners. An underestimation of whole-liver volume was observed between three non-reference scanners.ConclusionHepatica™ produces accurate and precise whole-liver and Couinaud segment volume and liver tissue characteristic measurements. Measurements are consistent between trained technicians and experienced radiologists.Graphic abstract

Studies exploring the brain correlates of behavioral symptoms in the frontotemporal dementia spectrum (FTD) have mainly searched for linear correlations with single modality neuroimaging data, either structural magnetic resonance imaging (MRI) or fluoro-deoxy-D-glucose positron emission tomography (FDG-PET). We aimed at studying the two imaging modalities in combination to identify nonlinear co-occurring patterns of atrophy and hypometabolism related to behavioral symptoms. We analyzed data from 93 FTD patients who underwent T1-weighted MRI, FDG-PET imaging, and neuropsychological assessment including the Neuropsychiatric Inventory, Frontal Systems Behavior Scale, and Neurobehavioral Rating Scale. We used a data-driven approach to identify the principal components underlying behavioral variability, then related the identified components to brain variability using a newly developed method fusing maps of grey matter volume and FDG metabolism. A component representing apathy, executive dysfunction, and emotional withdrawal was associated with atrophy in bilateral anterior insula and putamen, and with hypometabolism in the right prefrontal cortex. Another component representing the disinhibition versus depression/mutism continuum was associated with atrophy in the right striatum and ventromedial prefrontal cortex for disinhibition, and hypometabolism in the left fronto-opercular region and sensorimotor cortices for depression/mutism. A component representing psychosis was associated with hypometabolism in the prefrontal cortex and hypermetabolism in auditory and visual cortices. Behavioral symptoms in FTD are associated with atrophy and altered metabolism of specific brain regions, especially located in the frontal lobes, in a hierarchical way: apathy and disinhibition are mostly associated with grey matter atrophy, whereas psychotic symptoms are mostly associated with hyper-/hypo-metabolism.

In this thesis, we present three novel methods based on machine learning for use with MRI-derived neuroimaging data, all with the aim of aiding biomarker development for neurodegeneration. Resting-state functional MRI (rfMRI) can potentially detect early functional changes in disease. Therefore, the first method is a novel supervised learning algorithm for use in classifying rfMRI data into two groups. The main advantage over existing rfMRI-based classification approaches is that the entire voxel by time data is fed in without any prior decomposition or parcellation of the data into brain regions, and it does not require any prior knowledge of potential discriminatory networks. We show that the algorithm can give interpretable results for simulated data, performs better than two existing approaches for a Parkinson's disease dataset and gives results consistent with those reported previously for an anaesthesia dataset. Ageing is strongly linked with neurodegeneration. Hence, the second method is an errors-in-variables model for estimating the brain age of individuals. It takes a fundamentally different approach compared to existing methods, which gives it the advantages of being able to capture inverted U-shaped brain ageing trajectories, provide visualisable trajectories and potentially model interactions with disease. Using simulated data, we show that it is effective at estimating brain age, and can match and in some cases outperform current approaches. Using the UK biobank dataset, we show that the difference between chronological age and predicted brain age for an individual correlates with variables that are consistent with those reported previously. We also show that it can potentially provide information that is complementary to existing methods. The final method is a tool for decomposing multimodal neuroimaging data into spatial maps and associated trajectories. The main differences compared to existing methods are that the decomposition is driven by a variable of interest, which in our case was age, and each brain region is associated with only one trajectory as opposed to a mixture of different trajectories. This means that the components do not have to be associated with age post-hoc and the results are potentially more interpretable. Using simulated data, we show that, compared to two existing methods, our method can more accurately reproduce trajectories and associated spatial maps. Using the UK biobank dataset, we show that our method estimates trajectories and associated regions that are in agreement with previously published studies.

Abstract 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 ΔBrainAge = 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 ΔBrainAge 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 ΔBrainAge from models such as this network were predictive of any health outcomes. Highlights * Brain age is estimated using a 3D CNN from 12,802 full T1-weighted images. * Regions used to drive predictions are different for linearly and nonlinearly registered data. * Linear registrations utilise a greater diversity of biologically meaningful areas. * Correlations with IDPs and non-imaging variables are consistent with other publications. * Excluding subjects with various health conditions had minimal impact on main correlations. Competing Interest Statement The authors have declared no competing interest.