Author name cluster

Marc Modat

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22 papers

1 author row

YNIMG Journal 2021 Journal Article

Robust parametric modeling of Alzheimer’s disease progression

Mostafa Mehdipour Ghazi
Mads Nielsen
Akshay Pai
Marc Modat
M. Jorge Cardoso
Sébastien Ourselin
Lauge Sørensen

Quantitative characterization of disease progression using longitudinal data can provide long-term predictions for the pathological stages of individuals. This work studies the robust modeling of Alzheimer's disease progression using parametric methods. The proposed method linearly maps the individual's age to a disease progression score (DPS) and jointly fits constrained generalized logistic functions to the longitudinal dynamics of biomarkers as functions of the DPS using M-estimation. Robustness of the estimates is quantified using bootstrapping via Monte Carlo resampling, and the estimated inflection points of the fitted functions are used to temporally order the modeled biomarkers in the disease course. Kernel density estimation is applied to the obtained DPSs for clinical status classification using a Bayesian classifier. Different M-estimators and logistic functions, including a novel type proposed in this study, called modified Stannard, are evaluated on the data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) for robust modeling of volumetric magnetic resonance imaging (MRI) and positron emission tomography (PET) biomarkers, cerebrospinal fluid (CSF) measurements, as well as cognitive tests. The results show that the modified Stannard function fitted using the logistic loss achieves the best modeling performance with an average normalized mean absolute error (NMAE) of 0.991 across all biomarkers and bootstraps. Applied to the ADNI test set, this model achieves a multiclass area under the ROC curve (AUC) of 0.934 in clinical status classification. The obtained results for the proposed model outperform almost all state-of-the-art results in predicting biomarker values and classifying clinical status. Finally, the experiments show that the proposed model, trained using abundant ADNI data, generalizes well to data from the National Alzheimer's Coordinating Center (NACC) with an average NMAE of 1.182 and a multiclass AUC of 0.929.

YNIMG Journal 2021 Journal Article

Uncertainty analysis of MR-PET image registration for precision neuro-PET imaging

Pawel J. Markiewicz
Julian C. Matthews
John Ashburner
David M. Cash
David L. Thomas
Enrico De Vita
Anna Barnes
M. Jorge Cardoso

Accurate regional brain quantitative PET measurements, particularly when using partial volume correction, rely on robust image registration between PET and MR images. We argue here that the precision, and hence the uncertainty, of MR-PET image registration is mainly driven by the registration implementation and the quality of PET images due to their lower resolution and higher noise compared to the structural MR images. We propose a dedicated uncertainty analysis for quantifying the precision of MR-PET registration, centred around the bootstrap resampling of PET list-mode events to generate multiple PET image realisations with different noise (count) levels. The effects of PET image reconstruction parameters, such as the use of attenuation and scatter corrections and different number of iterations, on the precision and accuracy of MR-PET registration were investigated. In addition, the performance of four software packages with their default settings for rigid inter-modality image registration were considered: NiftyReg, Vinci, FSL and SPM. Four distinct PET image distributions made of two early time frames (similar to cortical FDG) and two late frames using two amyloid PET dynamic acquisitions of one amyloid positive and one amyloid negative participants were investigated. For the investigated four PET frames, the biggest impact on the uncertainty was observed between registration software packages (up to 10-fold difference in precision) followed by the reconstruction parameters. On average, the lowest uncertainty for different PET frames and brain regions was observed with SPM and two iterations of fully quantitative image reconstruction. The observed uncertainty for the varying PET count-level (from 5% to 60%) was slightly lower than for the reconstruction parameters. We also observed that the registration uncertainty in quantitative PET analysis depends on amyloid status of the considered PET frames, with increased uncertainty (up to three times) when using post-reconstruction partial volume correction. This analysis is applicable for PET data obtained from either PET/MR or PET/CT scanners.

YNICL Journal 2020 Journal Article

Basal forebrain atrophy in frontotemporal dementia

Rhian S. Convery
Mollie R. Neason
David M. Cash
M. Jorge Cardoso
Marc Modat
Sebastien Ourselin
Jason D. Warren
Jonathan D. Rohrer

BACKGROUND: The basal forebrain is a subcortical structure that plays an important role in learning, attention, and memory. Despite the known subcortical involvement in frontotemporal dementia (FTD), there is little research into the role of the basal forebrain in this disease. We aimed to investigate differences in basal forebrain volumes between clinical, genetic, and pathological diagnoses of FTD. METHODS: 356 patients with FTD were recruited from the UCL Dementia Research Centre and matched on age and gender with 83 cognitively normal controls. All subjects had a T1-weighted MR scan suitable for analysis. Basal forebrain volumes were calculated using the Geodesic Information Flow (GIF) parcellation method and were compared between clinical (148 bvFTD, 82 svPPA, 103 nfvPPA, 14 PPA-NOS, 9 FTD-MND), genetic (24 MAPT, 15 GRN, 26 C9orf72) and pathological groups (28 tau, 3 FUS, 35 TDP-43) and controls. A subanalysis was also performed comparing pathological subgroups of tau (11 Pick's disease, 6 FTDP-17, 7 CBD, 4 PSP) and TDP-43 (12 type A, 2 type B, 21 type C). RESULTS: All clinical subtypes of FTD showed significantly smaller volumes than controls (p ≤ 0.010, ANCOVA), with svPPA (10% volumetric difference) and bvFTD (9%) displaying the smallest volumes. Reduced basal forebrain volumes were also seen in MAPT mutations (18%, p < 0.0005) and in individuals with pathologically confirmed FTDP-17 (17%), Pick's disease (12%), and TDP-43 type C (8%) (p < 0.001). CONCLUSION: Involvement of the basal forebrain is a common feature in FTD, although the extent of volume reduction differs between clinical, genetic, and pathological diagnoses. Tauopathies, particularly those with MAPT mutations, had the smallest volumes. However, atrophy was also seen in those with TDP-43 type C pathology (most of whom have svPPA clinically). This suggests that the basal forebrain is vulnerable to multiple types of FTD-associated protein inclusions.

YNIMG Journal 2020 Journal Article

Substantially thinner internal granular layer and reduced molecular layer surface in the cerebellar cortex of the Tc1 mouse model of down syndrome – a comprehensive morphometric analysis with active staining contrast-enhanced MRI

Da Ma
Manuel J. Cardoso
Maria A. Zuluaga
Marc Modat
Nick M. Powell
Frances K. Wiseman
Jon O. Cleary
Benjamin Sinclair

Down Syndrome is a chromosomal disorder that affects the development of cerebellar cortical lobules. Impaired neurogenesis in the cerebellum varies among different types of neuronal cells and neuronal layers. In this study, we developed an imaging analysis framework that utilizes gadolinium-enhanced ex vivo mouse brain MRI. We extracted the middle Purkinje layer of the mouse cerebellar cortex, enabling the estimation of the volume, thickness, and surface area of the entire cerebellar cortex, the internal granular layer, and the molecular layer in the Tc1 mouse model of Down Syndrome. The morphometric analysis of our method revealed that a larger proportion of the cerebellar thinning in this model of Down Syndrome resided in the inner granule cell layer, while a larger proportion of the surface area shrinkage was in the molecular layer.

YNICL Journal 2019 Journal Article

Prion disease diagnosis using subject-specific imaging biomarkers within a multi-kernel Gaussian process

Liane S. Canas
Carole H. Sudre
Enrico De Vita
Akin Nihat
Tze How Mok
Catherine F. Slattery
Ross W. Paterson
Alexander J.M. Foulkes

Prion diseases are a group of rare neurodegenerative conditions characterised by a high rate of progression and highly heterogeneous phenotypes. Whilst the most common form of prion disease occurs sporadically (sporadic Creutzfeldt-Jakob disease, sCJD), other forms are caused by prion protein gene mutations, or exposure to prions in the diet or by medical procedures, such us surgeries. To date, there are no accurate quantitative imaging biomarkers that can be used to predict the future clinical diagnosis of a healthy subject, or to quantify the progression of symptoms over time. Besides, CJD is commonly mistaken for other forms of dementia. Due to the heterogeneity of phenotypes and the lack of a consistent geometrical pattern of disease progression, the approaches used to study other types of neurodegenerative diseases are not satisfactory to capture the progression of human form of prion disease. In this paper, using a tailored framework, we aim to classify and stratify patients with prion disease, according to the severity of their illness. The framework is initialised with the extraction of subject-specific imaging biomarkers. The extracted biomakers are then combined with genetic and demographic information within a Gaussian Process classifier, used to calculate the probability of a subject to be diagnosed with prion disease in the next year. We evaluate the effectiveness of the proposed method in a cohort of patients with inherited and sporadic forms of prion disease. The model has shown to be effective in the prediction of both inherited CJD (92% of accuracy) and sporadic CJD (95% of accuracy). However the model has shown to be less effective when used to stratify the different stages of the disease, in which the average accuracy is 85%, whilst the recall is 59%. Finally, our framework was extended as a differential diagnosis tool to identify both forms of CJD among another neurodegenerative disease. In summary we have developed a novel method for prion disease diagnosis and prediction of clinical onset using multiple sources of features, which may have use in other disorders with heterogeneous imaging features.

YNIMG Journal 2019 Journal Article

Spatiotemporal analysis for detection of pre-symptomatic shape changes in neurodegenerative diseases: Initial application to the GENFI cohort

Claire Cury
Stanley Durrleman
David M. Cash
Marco Lorenzi
Jennifer M. Nicholas
Martina Bocchetta
John C. van Swieten
Barbara Borroni

Brain atrophy as measured from structural MR images, is one of the primary imaging biomarkers used to track neurodegenerative disease progression. In diseases such as frontotemporal dementia or Alzheimer's disease, atrophy can be observed in key brain structures years before any clinical symptoms are present. Atrophy is most commonly captured as volume change of key structures and the shape changes of these structures are typically not analysed despite being potentially more sensitive than summary volume statistics over the entire structure. In this paper we propose a spatiotemporal analysis pipeline based on Large Diffeomorphic Deformation Metric Mapping (LDDMM) to detect shape changes from volumetric MRI scans. We applied our framework to a cohort of individuals with genetic variants of frontotemporal dementia and healthy controls from the Genetic FTD Initiative (GENFI) study. Our method, take full advantage of the LDDMM framework, and relies on the creation of a population specific average spatiotemporal trajectory of a relevant brain structure of interest, the thalamus in our case. The residuals from each patient data to the average spatiotemporal trajectory are then clustered and studied to assess when presymptomatic mutation carriers differ from healthy control subjects. We found statistical differences in shape in the anterior region of the thalamus at least five years before the mutation carrier subjects develop any clinical symptoms. This region of the thalamus has been shown to be predominantly connected to the frontal lobe, consistent with the pattern of cortical atrophy seen in the disease.

YNIMG Journal 2018 Journal Article

A magnetic resonance multi-atlas for the neonatal rabbit brain

Sebastiano Ferraris
Johannes van der Merwe
Lennart Van Der Veeken
Ferran Prados
Juan-Eugenio Iglesias
Andrew Melbourne
Marco Lorenzi
Marc Modat

The rabbit model has become increasingly popular in neurodevelopmental studies as it is best suited to bridge the gap in translational research between small and large animals. In the context of preclinical studies, high-resolution magnetic resonance imaging (MRI) is often the best modality to investigate structural and functional variability of the brain, both in vivo and ex vivo. In most of the MRI-based studies, an important requirement to analyze the acquisitions is an accurate parcellation of the considered anatomical structures. Manual segmentation is time-consuming and typically poorly reproducible, while state-of-the-art automated segmentation algorithms rely on available atlases. In this work we introduce the first digital neonatal rabbit brain atlas consisting of 12 multi-modal acquisitions, parcellated into 89 areas according to a hierarchical taxonomy. Delineations were performed iteratively, alternating between segmentation propagation, label fusion and manual refinements, with the aim of controlling the quality while minimizing the bias introduced by the chosen sequence. Reliability and accuracy were assessed with cross-validation and intra- and inter-operator test-retests. Multi-atlas, versioned controlled segmentations repository and supplementary materials download links are available from the software repository documentation at https: //github. com/gift-surg/SPOT-A-NeonatalRabbit.

YNICL Journal 2018 Journal Article

Thalamic atrophy in frontotemporal dementia — Not just a C9orf72 problem

Martina Bocchetta
Elizabeth Gordon
M. Jorge Cardoso
Marc Modat
Sebastien Ourselin
Jason D. Warren
Jonathan D. Rohrer

Background: expansion. However thalamic involvement has not been comprehensively investigated across the FTD spectrum. Methods: mutations), and pathological diagnosis (40 tauopathy, 61 TDP-43opathy, 3 FUSopathy). We assessed the diagnostic accuracy based on thalamic volume. Results: , PPA-NOS and SD were the subgroups showing the highest asymmetry in volumes. Conclusions: genetically, TDP-43opathies pathologically and FTD-MND clinically. However, thalamic atrophy is a common feature across all FTD groups.

YNIMG Journal 2015 Journal Article

Assessing atrophy measurement techniques in dementia: Results from the MIRIAD atrophy challenge

David M. Cash
Chris Frost
Leonardo O. Iheme
Devrim Ünay
Melek Kandemir
Jurgen Fripp
Olivier Salvado
Pierrick Bourgeat

Structural MRI is widely used for investigating brain atrophy in many neurodegenerative disorders, with several research groups developing and publishing techniques to provide quantitative assessments of this longitudinal change. Often techniques are compared through computation of required sample size estimates for future clinical trials. However interpretation of such comparisons is rendered complex because, despite using the same publicly available cohorts, the various techniques have been assessed with different data exclusions and different statistical analysis models. We created the MIRIAD atrophy challenge in order to test various capabilities of atrophy measurement techniques. The data consisted of 69 subjects (46 Alzheimer's disease, 23 control) who were scanned multiple (up to twelve) times at nine visits over a follow-up period of one to two years, resulting in 708 total image sets. Nine participating groups from 6 countries completed the challenge by providing volumetric measurements of key structures (whole brain, lateral ventricle, left and right hippocampi) for each dataset and atrophy measurements of these structures for each time point pair (both forward and backward) of a given subject. From these results, we formally compared techniques using exactly the same dataset. First, we assessed the repeatability of each technique using rates obtained from short intervals where no measurable atrophy is expected. For those measures that provided direct measures of atrophy between pairs of images, we also assessed symmetry and transitivity. Then, we performed a statistical analysis in a consistent manner using linear mixed effect models. The models, one for repeated measures of volume made at multiple time-points and a second for repeated “direct” measures of change in brain volume, appropriately allowed for the correlation between measures made on the same subject and were shown to fit the data well. From these models, we obtained estimates of the distribution of atrophy rates in the Alzheimer's disease (AD) and control groups and of required sample sizes to detect a 25% treatment effect, in relation to healthy ageing, with 95% significance and 80% power over follow-up periods of 6, 12, and 24months. Uncertainty in these estimates, and head-to-head comparisons between techniques, were carried out using the bootstrap. The lateral ventricles provided the most stable measurements, followed by the brain. The hippocampi had much more variability across participants, likely because of differences in segmentation protocol and less distinct boundaries. Most methods showed no indication of bias based on the short-term interval results, and direct measures provided good consistency in terms of symmetry and transitivity. The resulting annualized rates of change derived from the model ranged from, for whole brain: −1. 4% to −2. 2% (AD) and −0. 35% to −0. 67% (control), for ventricles: 4. 6% to 10. 2% (AD) and 1. 2% to 3. 4% (control), and for hippocampi: −1. 5% to −7. 0% (AD) and −0. 4% to −1. 4% (control). There were large and statistically significant differences in the sample size requirements between many of the techniques. The lowest sample sizes for each of these structures, for a trial with a 12month follow-up period, were 242 (95% CI: 154 to 422) for whole brain, 168 (95% CI: 112 to 282) for ventricles, 190 (95% CI: 146 to 268) for left hippocampi, and 158 (95% CI: 116 to 228) for right hippocampi. This analysis represents one of the most extensive statistical comparisons of a large number of different atrophy measurement techniques from around the globe. The challenge data will remain online and publicly available so that other groups can assess their methods.

YNIMG Journal 2015 Journal Article

Longitudinal measurement of the developing grey matter in preterm subjects using multi-modal MRI

Zach Eaton-Rosen
Andrew Melbourne
Eliza Orasanu
M. Jorge Cardoso
Marc Modat
Alan Bainbridge
Giles S. Kendall
Nicola J. Robertson

Preterm birth is a major public health concern, with the severity and occurrence of adverse outcome increasing with earlier delivery. Being born preterm disrupts a time of rapid brain development: in addition to volumetric growth, the cortex folds, myelination is occurring and there are changes on the cellular level. These neurological events have been imaged non-invasively using diffusion-weighted (DW) MRI. In this population, there has been a focus on examining diffusion in the white matter, but the grey matter is also critically important for neurological health. We acquired multi-shell high-resolution diffusion data on 12 infants born at ≤28weeks of gestational age at two time-points: once when stable after birth, and again at term-equivalent age. We used the Neurite Orientation Dispersion and Density Imaging model (NODDI) (Zhang et al. , 2012) to analyse the changes in the cerebral cortex and the thalamus, both grey matter regions. We showed region-dependent changes in NODDI parameters over the preterm period, highlighting underlying changes specific to the microstructure. This work is the first time that NODDI parameters have been evaluated in both the cortical and the thalamic grey matter as a function of age in preterm infants, offering a unique insight into neuro-development in this at-risk population.

YNICL Journal 2014 Journal Article

Brain volume estimation from post-mortem newborn and fetal MRI

Eliza Orasanu
Andrew Melbourne
M. Jorge Cardoso
Marc Modat
Andrew M. Taylor
Sudhin Thayyil
Sebastien Ourselin

OBJECTIVE: Minimally invasive autopsy using post-mortem magnetic resonance imaging (MRI) is a valid alternative to conventional autopsy in fetuses and infants. Estimation of brain weight is an integral part of autopsy, but manual segmentation of organ volumes on MRI is labor intensive and prone to errors, therefore unsuitable for routine clinical practice. In this paper we aim to show that volumetric measurements of the post-mortem fetal and neonatal brain can be accurately estimated using semi-automatic techniques and a high correlation can be found with the weights measured from conventional autopsy results. METHODS: The brains of 17 newborn subjects, part of Magnetic Resonance Imaging Autopsy Study (MaRIAS), were segmented from post-mortem MR images into cerebrum, cerebellum and brainstem using a publicly available neonate brain atlas and semi-automatic segmentation algorithm. The results of the segmentation were averaged to create a new atlas, which was then used for the automated atlas-based segmentation of 17 MaRIAS fetus subjects. As validation, we manually segmented the MR images from 8 subjects of each cohort and compared them with the automatic ones. The semi-automatic estimation of cerebrum weight was compared with the results of the conventional autopsy. RESULTS: The Dice overlaps between the manual and automatic segmentations are 0.991 and 0.992 for cerebrum, 0.873 and 0.888 for cerebellum and 0.819 and 0.815 for brainstem, for newborns and fetuses, respectively. Excellent agreement was obtained between the estimated MR weights and autopsy gold standard ones: mean absolute difference of 5 g and 2% maximum error for the fetus cohort and mean absolute difference of 20 g and 11% maximum error for the newborn one. CONCLUSIONS: The high correlation between the obtained segmentation and autopsy weights strengthens the idea of using post-mortem MRI as an alternative for conventional autopsy of the brain.

YNICL Journal 2013 Journal Article

Accurate multimodal probabilistic prediction of conversion to Alzheimer's disease in patients with mild cognitive impairment

Jonathan Young
Marc Modat
Manuel J. Cardoso
Alex Mendelson
Dave Cash
Sebastien Ourselin

Accurately identifying the patients that have mild cognitive impairment (MCI) who will go on to develop Alzheimer's disease (AD) will become essential as new treatments will require identification of AD patients at earlier stages in the disease process. Most previous work in this area has centred around the same automated techniques used to diagnose AD patients from healthy controls, by coupling high dimensional brain image data or other relevant biomarker data to modern machine learning techniques. Such studies can now distinguish between AD patients and controls as accurately as an experienced clinician. Models trained on patients with AD and control subjects can also distinguish between MCI patients that will convert to AD within a given timeframe (MCI-c) and those that remain stable (MCI-s), although differences between these groups are smaller and thus, the corresponding accuracy is lower. The most common type of classifier used in these studies is the support vector machine, which gives categorical class decisions. In this paper, we introduce Gaussian process (GP) classification to the problem. This fully Bayesian method produces naturally probabilistic predictions, which we show correlate well with the actual chances of converting to AD within 3 years in a population of 96 MCI-s and 47 MCI-c subjects. Furthermore, we show that GPs can integrate multimodal data (in this study volumetric MRI, FDG-PET, cerebrospinal fluid, and APOE genotype with the classification process through the use of a mixed kernel). The GP approach aids combination of different data sources by learning parameters automatically from training data via type-II maximum likelihood, which we compare to a more conventional method based on cross validation and an SVM classifier. When the resulting probabilities from the GP are dichotomised to produce a binary classification, the results for predicting MCI conversion based on the combination of all three types of data show a balanced accuracy of 74%. This is a substantially higher accuracy than could be obtained using any individual modality or using a multikernel SVM, and is competitive with the highest accuracy yet achieved for predicting conversion within three years on the widely used ADNI dataset.

YNIMG Journal 2013 Journal Article

AdaPT: An adaptive preterm segmentation algorithm for neonatal brain MRI

M. Jorge Cardoso
Andrew Melbourne
Giles S. Kendall
Marc Modat
Nicola J. Robertson
Neil Marlow
Sebastien Ourselin

Advances in neonatal care have improved the survival of infants born prematurely although these infants remain at increased risk of adverse neurodevelopmental outcome. The measurement of white matter structure and features of the cortical surface can help define biomarkers that predict this risk. The measurement of these structures relies upon accurate automated segmentation routines, but these are often confounded by neonatal-specific imaging difficulties including poor contrast, low resolution, partial volume effects and the presence of significant natural and pathological anatomical variability. In this work we develop and evaluate an adaptive preterm multi-modal maximum a posteriori expectation-maximisation segmentation algorithm (AdaPT) incorporating an iterative relaxation strategy that adapts the tissue proportion priors toward the subject data. Also incorporated are intensity non-uniformity correction, a spatial homogeneity term in the form of a Markov random field and furthermore, the proposed method explicitly models the partial volume effect specifically mitigating the neonatal specific grey and white matter contrast inversion. Spatial priors are iteratively relaxed, enabling the segmentation of images with high anatomical disparity from a normal population. Experiments performed on a clinical cohort of 92 infants are validated against manual segmentation of normal and pathological cortical grey matter, cerebellum and ventricular volumes. Dice overlap scores increase significantly when compared to a widely-used maximum likelihood expectation maximisation algorithm for pathological cortical grey matter, cerebellum and ventricular volumes. Adaptive maximum a posteriori expectation maximisation is shown to be a useful tool for accurate and robust neonatal brain segmentation.

YNIMG Journal 2013 Journal Article

An unbiased longitudinal analysis framework for tracking white matter changes using diffusion tensor imaging with application to Alzheimer's disease

Shiva Keihaninejad
Hui Zhang
Natalie S. Ryan
Ian B. Malone
Marc Modat
M. Jorge Cardoso
David M. Cash
Nick C. Fox

We introduce a novel image-processing framework for tracking longitudinal changes in white matter microstructure using diffusion tensor imaging (DTI). Charting the trajectory of such temporal changes offers new insight into disease progression but to do so accurately faces a number of challenges. Recent developments have highlighted the importance of processing each subject's data at multiple time points in an unbiased way. In this paper, we aim to highlight a different challenge critical to the processing of longitudinal DTI data, namely the approach to image alignment. Standard approaches in the literature align DTI data by registering the corresponding scalar-valued fractional anisotropy (FA) maps. We propose instead a DTI registration algorithm that leverages full tensor information to drive improved alignment. This proposed pipeline is evaluated against the standard FA-based approach using a DTI dataset from an ongoing study of Alzheimer's disease (AD). The dataset consists of subjects scanned at two time points and at each time point the DTI acquisition consists of two back-to-back repeats in the same scanning session. The repeated scans allow us to evaluate the specificity of each pipeline, using a test–retest design, and assess precision, using bootstrap-based method. The results show that the tensor-based pipeline achieves both higher specificity and precision than the standard FA-based approach. Tensor-based registration for longitudinal processing of DTI data in clinical studies may be of particular value in studies assessing disease progression.

YNIMG Journal 2012 Journal Article

An event-based model for disease progression and its application in familial Alzheimer's disease and Huntington's disease

Hubert M. Fonteijn
Marc Modat
Matthew J. Clarkson
Josephine Barnes
Manja Lehmann
Nicola Z. Hobbs
Rachael I. Scahill
Sarah J. Tabrizi

Understanding the progression of neurological diseases is vital for accurate and early diagnosis and treatment planning. We introduce a new characterization of disease progression, which describes the disease as a series of events, each comprising a significant change in patient state. We provide novel algorithms to learn the event ordering from heterogeneous measurements over a whole patient cohort and demonstrate using combined imaging and clinical data from familial Alzheimer's and Huntington's disease cohorts. Results provide new detail in the progression pattern of these diseases, while confirming known features, and give unique insight into the variability of progression over the cohort. The key advantage of the new model and algorithms over previous progression models is that they do not require a priori division of the patients into clinical stages. The model and its formulation extend naturally to a wide range of other diseases and developmental processes and accommodate cross-sectional and longitudinal input data.

YNIMG Journal 2011 Journal Article

A comparison of voxel and surface based cortical thickness estimation methods

Matthew J. Clarkson
M. Jorge Cardoso
Gerard R. Ridgway
Marc Modat
Kelvin K. Leung
Jonathan D. Rohrer
Nick C. Fox
Sébastien Ourselin

Cortical thickness estimation performed in-vivo via magnetic resonance imaging is an important technique for the diagnosis and understanding of the progression of neurodegenerative diseases. Currently, two different computational paradigms exist, with methods generally classified as either surface or voxel-based. This paper provides a much needed comparison of the surface-based method FreeSurfer and two voxel-based methods using clinical data. We test the effects of computing regional statistics using two different atlases and demonstrate that this makes a significant difference to the cortical thickness results. We assess reproducibility, and show that FreeSurfer has a regional standard deviation of thickness difference on same day scans that is significantly lower than either a Laplacian or Registration based method and discuss the trade off between reproducibility and segmentation accuracy caused by bending energy constraints. We demonstrate that voxel-based methods can detect similar patterns of group-wise differences as well as FreeSurfer in typical applications such as producing group-wise maps of statistically significant thickness change, but that regional statistics can vary between methods. We use a Support Vector Machine to classify patients against controls and did not find statistically significantly different results with voxel based methods compared to FreeSurfer. Finally we assessed longitudinal performance and concluded that currently FreeSurfer provides the most plausible measure of change over time, with further work required for voxel based methods.

YNIMG Journal 2011 Journal Article

Brain MAPS: An automated, accurate and robust brain extraction technique using a template library

Kelvin K. Leung
Josephine Barnes
Marc Modat
Gerard R. Ridgway
Jonathan W. Bartlett
Nick C. Fox
Sébastien Ourselin

Whole brain extraction is an important pre-processing step in neuroimage analysis. Manual or semi-automated brain delineations are labour-intensive and thus not desirable in large studies, meaning that automated techniques are preferable. The accuracy and robustness of automated methods are crucial because human expertise may be required to correct any suboptimal results, which can be very time consuming. We compared the accuracy of four automated brain extraction methods: Brain Extraction Tool (BET), Brain Surface Extractor (BSE), Hybrid Watershed Algorithm (HWA) and a Multi-Atlas Propagation and Segmentation (MAPS) technique we have previously developed for hippocampal segmentation. The four methods were applied to extract whole brains from 682 1. 5T and 157 3T T1-weighted MR baseline images from the Alzheimer's Disease Neuroimaging Initiative database. Semi-automated brain segmentations with manual editing and checking were used as the gold-standard to compare with the results. The median Jaccard index of MAPS was higher than HWA, BET and BSE in 1. 5T and 3T scans (p <0. 05, all tests), and the 1st to 99th centile range of the Jaccard index of MAPS was smaller than HWA, BET and BSE in 1. 5T and 3T scans ( p <0. 05, all tests). HWA and MAPS were found to be best at including all brain tissues (median false negative rate ≤0. 010% for 1. 5T scans and ≤0. 019% for 3T scans, both methods). The median Jaccard index of MAPS were similar in both 1. 5T and 3T scans, whereas those of BET, BSE and HWA were higher in 1. 5T scans than 3T scans (p <0. 05, all tests). We found that the diagnostic group had a small effect on the median Jaccard index of all four methods. In conclusion, MAPS had relatively high accuracy and low variability compared to HWA, BET and BSE in MR scans with and without atrophy.

YNIMG Journal 2011 Journal Article

LoAd: A locally adaptive cortical segmentation algorithm

M. Jorge Cardoso
Matthew J. Clarkson
Gerard R. Ridgway
Marc Modat
Nick C. Fox
Sebastien Ourselin

Thickness measurements of the cerebral cortex can aid diagnosis and provide valuable information about the temporal evolution of diseases such as Alzheimer's, Huntington's, and schizophrenia. Methods that measure the thickness of the cerebral cortex from in-vivo magnetic resonance (MR) images rely on an accurate segmentation of the MR data. However, segmenting the cortex in a robust and accurate way still poses a challenge due to the presence of noise, intensity non-uniformity, partial volume effects, the limited resolution of MRI and the highly convoluted shape of the cortical folds. Beginning with a well-established probabilistic segmentation model with anatomical tissue priors, we propose three post-processing refinements: a novel modification of the prior information to reduce segmentation bias; introduction of explicit partial volume classes; and a locally varying MRF-based model for enhancement of sulci and gyri. Experiments performed on a new digital phantom, on BrainWeb data and on data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) show statistically significant improvements in Dice scores and PV estimation (p<10−3) and also increased thickness estimation accuracy when compared to three well established techniques.

YNIMG Journal 2011 Journal Article

Magnetic resonance virtual histology for embryos: 3D atlases for automated high-throughput phenotyping

Jon O. Cleary
Marc Modat
Francesca C. Norris
Anthony N. Price
Sujatha A. Jayakody
Juan Pedro Martinez-Barbera
Nicholas D.E. Greene
David J. Hawkes

Ambitious international efforts are underway to produce gene-knockout mice for each of the 25, 000 mouse genes, providing a new platform to study mammalian development and disease. Robust, large-scale methods for morphological assessment of prenatal mice will be essential to this work. Embryo phenotyping currently relies on histological techniques but these are not well suited to large volume screening. The qualitative nature of these approaches also limits the potential for detailed group analysis. Advances in non-invasive imaging techniques such as magnetic resonance imaging (MRI) may surmount these barriers. We present a high-throughput approach to generate detailed virtual histology of the whole embryo, combined with the novel use of a whole-embryo atlas for automated phenotypic assessment. Using individual 3D embryo MRI histology, we identified new pituitary phenotypes in Hesx1 mutant mice. Subsequently, we used advanced computational techniques to produce a whole-body embryo atlas from 6 CD-1 embryos, creating an average image with greatly enhanced anatomical detail, particularly in CNS structures. This methodology enabled unsupervised assessment of morphological differences between CD-1 embryos and Chd7 knockout mice (n=5 Chd7 +/+ and n=8 Chd7 +/-, C57BL/6 background). Using a new atlas generated from these three groups, quantitative organ volumes were automatically measured. We demonstrated a difference in mean brain volumes between Chd7 +/+ and Chd7 +/- mice (42. 0 vs. 39. 1mm3, p<0. 05). Differences in whole-body, olfactory and normalised pituitary gland volumes were also found between CD-1 and Chd7 +/+ mice (C57BL/6 background). Our work demonstrates the feasibility of combining high-throughput embryo MRI with automated analysis techniques to distinguish novel mouse phenotypes.

YNIMG Journal 2010 Journal Article

Atrophy patterns in Alzheimer's disease and semantic dementia: A comparison of FreeSurfer and manual volumetric measurements

Manja Lehmann
Abdel Douiri
Lois G. Kim
Marc Modat
Dennis Chan
Sebastien Ourselin
Josephine Barnes
Nick C. Fox

Alzheimer's disease (AD) and semantic dementia (SD) are characterized by different patterns of global and temporal lobe atrophy which can be studied using magnetic resonance imaging (MRI). Manual delineation of regions of interest is time-consuming. FreeSurfer is a freely available automated technique which has a facility to label cortical and subcortical brain regions automatically. As with all automated techniques comparison with existing methods is important. Eight temporal lobe structures in each hemisphere were delineated using FreeSurfer and compared with manual segmentations in 10 control, 10 AD, and 10 SD subjects. The reproducibility errors for the manual segmentations ranged from 3% to 6%. Differences in protocols between the two methods led to differences in absolute volumes with the greatest differences between methods found bilaterally in the hippocampus, entorhinal cortex and fusiform gyrus (p < 0. 005). However, good correlations between the methods were found for most regions, with the highest correlations shown for the ventricles, whole brain and left medial–inferior temporal gyrus (r > 0. 9), followed by the bilateral amygdala and hippocampus, left superior temporal gyrus, right medial–inferior temporal gyrus and left temporal lobe (r >0. 8). Overlap ratios differed between methods bilaterally in the amygdala, superior temporal gyrus, temporal lobe, left fusiform gyrus and right parahippocampal gyrus (p <0. 01). Despite differences in protocol and volumes, both methods showed similar atrophy patterns in the patient groups compared with controls, and similar right–left differences, suggesting that both methods accurately distinguish between the three groups.

YNIMG Journal 2010 Journal Article

Distinct profiles of brain atrophy in frontotemporal lobar degeneration caused by progranulin and tau mutations

Jonathan D. Rohrer
Gerard R. Ridgway
Marc Modat
Sebastien Ourselin
Simon Mead
Nick C. Fox
Martin N. Rossor
Jason D. Warren

Neural network breakdown is a key issue in neurodegenerative disease, but the mechanisms are poorly understood. Here we investigated patterns of brain atrophy produced by defined molecular lesions in the two common forms of genetically mediated frontotemporal lobar degeneration (FTLD). Nine patients with progranulin (GRN) mutations and eleven patients with microtubule-associated protein tau (MAPT) mutations had T1 MR brain imaging. Brain volumetry and grey and white matter voxel-based morphometry (VBM) were used to assess patterns of cross-sectional atrophy in the two groups. In a subset of patients with longitudinal MRI rates of whole-brain atrophy were derived using the brain-boundary-shift integral and a VBM-like analysis of voxel-wise longitudinal volume change was performed. The GRN mutation group showed asymmetrical atrophy whereas the MAPT group showed symmetrical atrophy. Brain volumes were smaller in the GRN group with a faster rate of whole-brain atrophy. VBM delineated a common anterior cingulate–prefrontal–insular pattern of atrophy in both disease groups. Additional disease-specific profiles of grey and white matter loss were identified on both cross-sectional and longitudinal imaging: GRN mutations were associated with asymmetrical inferior frontal, temporal and inferior parietal lobe grey matter atrophy and involvement of long intrahemispheric association white matter tracts, whereas MAPT mutations were associated with symmetrical anteromedial temporal lobe and orbitofrontal grey matter atrophy and fornix involvement. The findings suggest that the effects of GRN and MAPT mutations are expressed in partly overlapping but distinct anatomical networks that link specific molecular dysfunction with clinical phenotype.

YNIMG Journal 2008 Journal Article

Appearance modeling of 11C PiB PET images: Characterizing amyloid deposition in Alzheimer's disease, mild cognitive impairment and healthy aging

Jurgen Fripp
Pierrick Bourgeat
Oscar Acosta
Parnesh Raniga
Marc Modat
Kerryn E Pike
Gareth Jones
Graeme O'Keefe

β-amyloid (Aβ) deposition is one of the neuropathological hallmarks of Alzheimer's disease (AD), Aβ burden can be quantified using 11C PiB PET. Neuropathological studies have shown that the initial plaques are located in the temporal and orbitofrontal cortices, extending later to the cingulate, frontal and parietal cortices (Braak and Braak, 1997). Previous studies have shown an overlap in 11C PiB PET retention between AD, mild cognitive impairment (MCI) patients and normal elderly control (NC) participants. It has also been shown that there is a relationship between Aβ deposition and memory impairment in MCI patients. In this paper we explored the variability seen in 15 AD, 15 MCI and 18 NC by modeling the voxel data from spatially and uptake normalized PiB images using principal component analysis. The first two principal components accounted for 80% of the variability seen in the data, providing a clear separation between AD and NC, and allowing subsequent classification. The MCI cases were distributed along an apparent axis between the AD and NC group, closely aligned with the first principal component axis. The NC cases that were PiB + formed a distinct cluster that was between, but separated from the AD and PiB − NC clusters. The PiB + MCI were found to cluster with the AD cases, and exhibited a similar deposition pattern. The primary principal component score was found to correlate with episodic memory scores and mini mental status examination and it was observed that by varying the first principal component, a change in amyloid deposition could be derived that is similar to the expected progression of amyloid deposition observed from post mortem studies.