L’objectif de cette thèse était la validation de l’existence ainsi que la découverte de nouveaux sous-types au sein de la maladie d’Alzheimer, première cause de démence au monde. Afin d’explorer son hétérogénéité, nous avons employé des méthodes d’apprentissage profond appliquées à une modalité de neuroimagerie, l’imagerie par résonance magnétique structurelle.Cependant, la découverte de biais méthodologiques importants dans de nombreuses études de notre domaine, ainsi que l’absence de consensus de la communauté sur la manière d’interpréter les résultats des méthodes d’apprentissage profond a fait en partie dévier la thèse de son objectif principal pour s’orienter d’avantage vers des problématiques de validation, de robustesse et d’interprétabilité de l’apprentissage profond. Ainsi, trois études expérimentales ont été menées pour s’assurer de la capacité des réseaux profonds de correctement détecter la maladie. La première est une étude expérimentale de méthodes d’apprentissage profond pour la classification de la maladie d’Alzheimer et a permis d’établir une juste comparaison des méthodes. La seconde étude a permis de constater un manque de robustesse de la classification avec l’apprentissage profond en termes de motifs d’atrophie découverts à l’aide de méthodes d’interprétabilité. Enfin, la dernière étude propose une méthode de découverte de sous-types aidée par l’augmentation de données. Bien que fonctionnant sur des données synthétiques, celle-ci ne généralise pas aux données réelles.Une contribution majeure de la thèse est la librairie ClinicaDL, grâce à laquelle les résultats expérimentaux de la thèse ont été produits de manière à être reproductibles. The goal of this PhD was the validation of the existence and the discovery of new subtypes of Alzheimer’s disease, the first cause of dementia worldwide. Indeed, despite its discovery more than a century ago, this disease is still not well defined and existing treatments are only weakly effective, possibly because several phenotypes exist within the disease. In order to explore its heterogeneity, we employed deep learning methods applied to a neuroimaging modality, structural magnetic resonance imaging.However, the discovery of important methodological biases in many studies in our field, as well as the lack of consensus regarding deep learning interpretability, partly changed the main objective of the PhD to focus more on issues of validation, robustness and interpretability of deep learning. Then, to correctly assess the ability of deep learning to detect Alzheimer’s disease, three experimental studies were conducted. The first one is a study of deep learning methods for Alzheimer’s classification and allowed a fair comparison of the methods. The second study found a lack of robustness of classification with deep learning in terms of atrophy patterns discovered using interpretability methods. Finally, the last study proposed a subtype discovery method aided by data augmentation. Although it works on synthetic data, it does not generalize to real data.Experimental results of this PhD were obtained thanks to ClinicaDL, one major contribution of this PhD. It is an open source Python library that was used to improve the reproducibility of deep learning experiments.
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Example code for the multivariate template creation process used for Myelin imaging in the central nervous system: Comparison of multi-echo T2 relaxation and steady-state approaches
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LORIS is an open-source data management platform for neuroscience research and data sharing, built by the McGill Centre for Integrative Neuroscience (MCIN.ca) at the Montreal Neurological Institute-Hospital and led by Dr. Alan Evans and Samir Das. For more information on LORIS -- * Visit LORIS.ca * Fork github.com/aces/loris * Try Demo.loris.ca LORIS (Longitudinal Online Research and Imaging System) supports many Open Science projects, storing and processing and sharing behavioural, clinical, neuroimaging, electrophysiology and genetic data. LORIS makes it easy to manage large datasets acquired over time in a longitudinal study, or at different locations in a large multi-site study.
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{"references": ["Bautista, T. et al. (2021) 'Removal of Gibbs ringing artefacts for 3D acquisitions using subvoxel shifts', in Proc. Intl. Soc. Mag. Reson. Med., p. 3535.", "Chakravarty, M. M. et al. (2013) 'Performing label-fusion-based segmentation using multiple automatically generated templates', Human Brain Mapping. doi: 10.1002/hbm.22092.", "Eckstein, K. et al. (2018) 'Computationally Efficient Combination of Multi-channel Phase Data From Multi-echo Acquisitions (ASPIRE)', Magnetic Resonance in Medicine, 79(6), pp. 2996\u20133006. doi: 10.1002/mrm.26963.", "Friedel, M. et al. (2014) 'Pydpiper: A flexible toolkit for constructing novel registration pipelines', Frontiers in Neuroinformatics. doi: 10.3389/fninf.2014.00067.", "Gudbjartsson, H. and Patz, S. (1995) 'The rician distribution of noisy MRI data', Magnetic Resonance in Medicine, 34(6), pp. 910\u2013914. doi: 10.1002/mrm.1910340618.", "Jenkinson, M. (2003) 'Fast, automated, N-dimensional phase-unwrapping algorithm', Magnetic Resonance in Medicine, 49(1), pp. 193\u2013197. doi: 10.1002/mrm.10354.", "Kellner, E. et al. (2016) 'Gibbs-ringing artifact removal based on local subvoxel-shifts', Magnetic Resonance in Medicine. doi: 10.1002/mrm.26054.", "Li, W. et al. (2015) 'A method for estimating and removing streaking artifacts in quantitative susceptibility mapping', NeuroImage. doi: 10.1016/j.neuroimage.2014.12.043.", "Li, W., Wu, B. and Liu, C. (2011) 'Quantitative susceptibility mapping of human brain reflects spatial variation in tissue composition', NeuroImage. doi: 10.1016/j.neuroimage.2010.11.088.", "Robinson, S. D. et al. (2017) 'Combining phase images from array coils using a short echo time reference scan (COMPOSER)', Magnetic Resonance in Medicine. doi: 10.1002/mrm.26093.", "Schweser, F. et al. (2011) 'Quantitative imaging of intrinsic magnetic tissue properties using MRI signal phase: An approach to in vivo brain iron metabolism?', NeuroImage. doi: 10.1016/j.neuroimage.2010.10.070.", "Tisca, C. et al. (2021) 'Vcan mutation induces sex-specific changes in white matter microstructure in mice', in Proc. Intl. Soc. Mag. Reson. Med. 29, p. 1226. Available at: https://index.mirasmart.com/ISMRM2021/PDFfiles/1226.html.", "Tisca, C. et al. (2022) 'White matter microstructure changes in a Bcan knockout mouse model', in Proc. Intl. Soc. Mag. Reson. Med. 31.", "Wang, C. et al. (2020) 'Methods for quantitative susceptibility and R2* mapping in whole post-mortem brains at 7T applied to amyotrophic lateral sclerosis', NeuroImage. Elsevier Inc., 222(May), p. 117216. doi: 10.1016/j.neuroimage.2020.117216.", "Wang, C. et al. (2022) 'Phenotypic and genetic associations of quantitative magnetic susceptibility in UK Biobank brain imaging', Nature Neuroscience, 25(6), pp. 818\u2013831. doi: 10.1038/s41593-022-01074-w."]} This repository contains all scripts to run the ex vivo R2*- and QSM post-processing pipelines on data acquired at WIN's 7T Bruker facility. It should be compatible with any other data acquired on a similar Bruker scanner using an equivalent protocol. This resource contains anonymised file-paths which will need to be edited to enable running on a cluster facility. The commands for submitting jobs to the cluster also need to be edited. This pipeline is based on the code developed by Chaoyue Wang and Benjamin Tendler and published here: https://doi.org/10.1016/j.neuroimage.2020.117216. The scripts were either written or adapted by Cristiana Tisca. The outputs of the pipeline include QSM and R2* maps. Additional funding sources: Wellcome Trust Senior Research Fellowship (Renewal), Prof Karla Miller, 224573/Z/21/Z
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Chronic systemic inflammatory conditions, such as atherosclerosis, diabetes and obesity are associated with increased risk of stroke, which suggests that systemic inflammation may contribute to the development of stroke in humans. The hypothesis that systemic inflammation may induce brain pathology can be tested in animals, and this was the key objective of the present study. First, we assessed inflammatory changes in the brain in rodent models of chronic, systemic inflammation. PET imaging revealed increased microglia activation in the brain of JCR-LA (corpulent) rats, which develop atherosclerosis and obesity, compared to the control lean strain. Immunostaining against Iba1 confirmed reactive microgliosis in these animals. An atherogenic diet in apolipoprotein E knock-out (ApoE−/−) mice induced microglial activation in the brain parenchyma within 8 weeks and increased expression of vascular adhesion molecules. Focal lipid deposition and neuroinflammation in periventricular and cortical areas and profound recruitment of activated myeloid phagocytes, T cells and granulocytes into the choroid plexus were also observed. In a small, preliminary study, patients at risk of stroke (multiple risk factors for stroke, with chronically elevated C-reactive protein, but negative MRI for brain pathology) exhibited increased inflammation in the brain, as indicated by PET imaging. These findings show that brain inflammation occurs in animals, and tentatively in humans, harbouring risk factors for stroke associated with elevated systemic inflammation. Thus a “primed” inflammatory environment in the brain may exist in individuals at risk of stroke and this can be adequately recapitulated in appropriate co-morbid animal models.
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This project publishes the code used in the following publication: Bron et al., Cross-Cohort Generalizability of Deep and Conventional Machine Learning for MRI-based diagnosis and prediction of Alzheimer’s disease, NeuroImage: Clinical, 2021 Link: https://doi.org/10.1016/j.nicl.2021.102712, arxiv.org/2012.08769 Starting point: Overview.ipynb {"references": ["Bron et al., Cross-Cohort Generalizability of Deep and Conventional Machine Learning for MRI-based diagnosis and prediction of Alzheimer's disease, NeuroImage: Clinical, 2021"]} https://gitlab.com/radiology/neuro/bron-cross-cohort/-/tree/v1.0
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A Neurodegenerative Disease (ND) is progressive damage to brain neurons, which the human body cannot repair or replace. The well-known examples of such conditions are Dementia and Alzheimer’s Disease (AD), which affect millions of lives each year. Although conducting numerous researches, there are no effective treatments for the mentioned diseases today. However, early diagnosis is crucial in disease management. Diagnosing NDs is challenging for neurologists and requires years of training and experience. So, there has been a trend to harness the power of deep learning, including state-of-the-art Convolutional Neural Network (CNN), to assist doctors in diagnosing such conditions using brain scans. The CNN models lead to promising results comparable to experienced neurologists in their diagnosis. But, the advent of transformers in the Natural Language Processing (NLP) domain and their outstanding performance persuaded Computer Vision (CV) researchers to adapt them to solve various CV tasks in multiple areas, including the medical field. This research aims to develop Vision Transformer (ViT) models using Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset to classify NDs. More specifically, the models can classify three categories (Cognitively Normal (CN), Mild Cognitive Impairment (MCI), Alzheimer’s Disease (AD)) using brain Fluorodeoxyglucose (18F-FDG) Positron Emission Tomography (PET) scans. Also, we take advantage of Automated Anatomical Labeling (AAL) brain atlas and attention maps to develop explainable models. We propose three ViTs, the best of which obtains an accuracy of 82% on the test dataset with the help of transfer learning. Also, we encode the AAL brain atlas information into the best performing ViT, so the model outputs the predicted label, the most critical region in its prediction, and overlaid attention map on the input scan with the crucial areas highlighted. Furthermore, we develop two CNN models with 2D and 3D convolutional kernels as baselines to classify NDs, which achieve accuracy of 77% and 73%, respectively, on the test dataset. We also conduct a study to find out the importance of brain regions and their combinations in classifying NDs using ViTs and the AAL brain atlas. This thesis was awarded a prize of 50,000 SEK by Getinge Sterilization for projects within Health Innovation.
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The intestine and the gut-associated lymphoid tissue (GALT) are essential components of whole body immune defense, protecting the body from foreign antigens and pathogens, while allowing tolerance to commensal bacteria and dietary antigens. The requirement for protein to support the immune system is well established. Less is known regarding the immune modifying properties of individual amino acids, particularly on the GALT. Both oral and parenteral feeding studies have established convincing evidence that not only the total protein intake, but the availability of specific dietary amino acids (in particular glutamine, glutamate, and arginine, and perhaps methionine, cysteine and threonine) are essential to optimizing the immune functions of the intestine and the proximal resident immune cells. These amino acids each have unique properties that include, maintaining the integrity, growth and function of the intestine, as well as normalizing inflammatory cytokine secretion and improving T-lymphocyte numbers, specific T cell functions, and the secretion of IgA by lamina propria cells. Our understanding of this area has come from studies that have supplemented single amino acids to a mixed protein diet and measuring the effect on specific immune parameters. Future studies should be designed using amino acid mixtures that target a number of specific functions of GALT in order to optimize immune function in domestic animals and humans during critical periods of development and various disease states.
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This study describes the experiences of four groups of healthcare providers who facilitate exercise interventions for people with multiple sclerosis (MS)-related fatigue. Fatigue is a complex symptom frequently experienced by people with MS, yet it is poorly understood by clinicians and clinical researchers. Historically, clinicians have recommended less physical activity in order to limit fatigue; however, recent experimental studies suggest that regular exercise provides health benefits with little increase in fatigue. We used interpretive description methodology to guide data collection and analysis. Four groups of healthcare providers participated in either focus group discussions or individual interviews. Transcripts were analyzed for key meanings. Healthcare providers described their perceptions of the \"nature of fatigue\" and how this raised \"professional challenges,\" specifically \"barriers to implementation\" of interventions, \"stirring conflict\" among interdisciplinary members, and \"modifying roles.\" The nature of fatigue and professional challenges influenced clinician practice by \"demanding creativity\" with regard to exercise prescription and advice. Healthcare providers are encouraged to consider strategies of active listening and careful observation when providing individualized exercise programs for people with MS-related fatigue. In addition, recognition and understanding of the complex nature of fatigue by the interdisciplinary team might facilitate more positive exercise experiences for this population.
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Our objective is to examine how persons diagnosed with Multiple Sclerosis (MS) and Parkinson's disease (PD) change residence following disease onset. We hypothesize that persons choose to change residence (locally or regionally) in different ways depending on whether or not they have been diagnosed with MS/PD. We also estimate the effects of residence change on measures of disease prevalence made at several different levels of geography. METHODS: Using fee-for service and hospitalization data, we identify cases of MS and PD between 1994 and 2004. Both of these case groups are matched to controls based on age, sex, socioeconomic status and municipality of residence. We tabulate and compare the changes of residence among persons in the case and control groups. We also use these data to estimate the effects that changes in residence have on disease prevalence at three different levels of geography. RESULTS: Both MS and PD patients were more likely to change residence following disease onset compared to groups of matched controls (p<=0.001). Most changes of residence occur within the same municipality. The total magnitude of these changes is small, however, and is unlikely to affect estimates of disease prevalence; over our study period, the largest change in geographical prevalence estimates due to individual changes in residence was about 1%. CONCLUSIONS: Persons diagnosed with MS and PD both have mobility characteristics that differ from those of their respective control groups, and in general, are more likely to move to or between Edmonton and Calgary, and less likely to move out of province. However, the balance of mobility characteristics of persons with PD and MS appear unlikely to greatly affect the patterns observed on maps of disease prevalence.
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L’objectif de cette thèse était la validation de l’existence ainsi que la découverte de nouveaux sous-types au sein de la maladie d’Alzheimer, première cause de démence au monde. Afin d’explorer son hétérogénéité, nous avons employé des méthodes d’apprentissage profond appliquées à une modalité de neuroimagerie, l’imagerie par résonance magnétique structurelle.Cependant, la découverte de biais méthodologiques importants dans de nombreuses études de notre domaine, ainsi que l’absence de consensus de la communauté sur la manière d’interpréter les résultats des méthodes d’apprentissage profond a fait en partie dévier la thèse de son objectif principal pour s’orienter d’avantage vers des problématiques de validation, de robustesse et d’interprétabilité de l’apprentissage profond. Ainsi, trois études expérimentales ont été menées pour s’assurer de la capacité des réseaux profonds de correctement détecter la maladie. La première est une étude expérimentale de méthodes d’apprentissage profond pour la classification de la maladie d’Alzheimer et a permis d’établir une juste comparaison des méthodes. La seconde étude a permis de constater un manque de robustesse de la classification avec l’apprentissage profond en termes de motifs d’atrophie découverts à l’aide de méthodes d’interprétabilité. Enfin, la dernière étude propose une méthode de découverte de sous-types aidée par l’augmentation de données. Bien que fonctionnant sur des données synthétiques, celle-ci ne généralise pas aux données réelles.Une contribution majeure de la thèse est la librairie ClinicaDL, grâce à laquelle les résultats expérimentaux de la thèse ont été produits de manière à être reproductibles. The goal of this PhD was the validation of the existence and the discovery of new subtypes of Alzheimer’s disease, the first cause of dementia worldwide. Indeed, despite its discovery more than a century ago, this disease is still not well defined and existing treatments are only weakly effective, possibly because several phenotypes exist within the disease. In order to explore its heterogeneity, we employed deep learning methods applied to a neuroimaging modality, structural magnetic resonance imaging.However, the discovery of important methodological biases in many studies in our field, as well as the lack of consensus regarding deep learning interpretability, partly changed the main objective of the PhD to focus more on issues of validation, robustness and interpretability of deep learning. Then, to correctly assess the ability of deep learning to detect Alzheimer’s disease, three experimental studies were conducted. The first one is a study of deep learning methods for Alzheimer’s classification and allowed a fair comparison of the methods. The second study found a lack of robustness of classification with deep learning in terms of atrophy patterns discovered using interpretability methods. Finally, the last study proposed a subtype discovery method aided by data augmentation. Although it works on synthetic data, it does not generalize to real data.Experimental results of this PhD were obtained thanks to ClinicaDL, one major contribution of this PhD. It is an open source Python library that was used to improve the reproducibility of deep learning experiments.
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Example code for the multivariate template creation process used for Myelin imaging in the central nervous system: Comparison of multi-echo T2 relaxation and steady-state approaches
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LORIS is an open-source data management platform for neuroscience research and data sharing, built by the McGill Centre for Integrative Neuroscience (MCIN.ca) at the Montreal Neurological Institute-Hospital and led by Dr. Alan Evans and Samir Das. For more information on LORIS -- * Visit LORIS.ca * Fork github.com/aces/loris * Try Demo.loris.ca LORIS (Longitudinal Online Research and Imaging System) supports many Open Science projects, storing and processing and sharing behavioural, clinical, neuroimaging, electrophysiology and genetic data. LORIS makes it easy to manage large datasets acquired over time in a longitudinal study, or at different locations in a large multi-site study.
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{"references": ["Bautista, T. et al. (2021) 'Removal of Gibbs ringing artefacts for 3D acquisitions using subvoxel shifts', in Proc. Intl. Soc. Mag. Reson. Med., p. 3535.", "Chakravarty, M. M. et al. (2013) 'Performing label-fusion-based segmentation using multiple automatically generated templates', Human Brain Mapping. doi: 10.1002/hbm.22092.", "Eckstein, K. et al. (2018) 'Computationally Efficient Combination of Multi-channel Phase Data From Multi-echo Acquisitions (ASPIRE)', Magnetic Resonance in Medicine, 79(6), pp. 2996\u20133006. doi: 10.1002/mrm.26963.", "Friedel, M. et al. (2014) 'Pydpiper: A flexible toolkit for constructing novel registration pipelines', Frontiers in Neuroinformatics. doi: 10.3389/fninf.2014.00067.", "Gudbjartsson, H. and Patz, S. (1995) 'The rician distribution of noisy MRI data', Magnetic Resonance in Medicine, 34(6), pp. 910\u2013914. doi: 10.1002/mrm.1910340618.", "Jenkinson, M. (2003) 'Fast, automated, N-dimensional phase-unwrapping algorithm', Magnetic Resonance in Medicine, 49(1), pp. 193\u2013197. doi: 10.1002/mrm.10354.", "Kellner, E. et al. (2016) 'Gibbs-ringing artifact removal based on local subvoxel-shifts', Magnetic Resonance in Medicine. doi: 10.1002/mrm.26054.", "Li, W. et al. (2015) 'A method for estimating and removing streaking artifacts in quantitative susceptibility mapping', NeuroImage. doi: 10.1016/j.neuroimage.2014.12.043.", "Li, W., Wu, B. and Liu, C. (2011) 'Quantitative susceptibility mapping of human brain reflects spatial variation in tissue composition', NeuroImage. doi: 10.1016/j.neuroimage.2010.11.088.", "Robinson, S. D. et al. (2017) 'Combining phase images from array coils using a short echo time reference scan (COMPOSER)', Magnetic Resonance in Medicine. doi: 10.1002/mrm.26093.", "Schweser, F. et al. (2011) 'Quantitative imaging of intrinsic magnetic tissue properties using MRI signal phase: An approach to in vivo brain iron metabolism?', NeuroImage. doi: 10.1016/j.neuroimage.2010.10.070.", "Tisca, C. et al. (2021) 'Vcan mutation induces sex-specific changes in white matter microstructure in mice', in Proc. Intl. Soc. Mag. Reson. Med. 29, p. 1226. Available at: https://index.mirasmart.com/ISMRM2021/PDFfiles/1226.html.", "Tisca, C. et al. (2022) 'White matter microstructure changes in a Bcan knockout mouse model', in Proc. Intl. Soc. Mag. Reson. Med. 31.", "Wang, C. et al. (2020) 'Methods for quantitative susceptibility and R2* mapping in whole post-mortem brains at 7T applied to amyotrophic lateral sclerosis', NeuroImage. Elsevier Inc., 222(May), p. 117216. doi: 10.1016/j.neuroimage.2020.117216.", "Wang, C. et al. (2022) 'Phenotypic and genetic associations of quantitative magnetic susceptibility in UK Biobank brain imaging', Nature Neuroscience, 25(6), pp. 818\u2013831. doi: 10.1038/s41593-022-01074-w."]} This repository contains all scripts to run the ex vivo R2*- and QSM post-processing pipelines on data acquired at WIN's 7T Bruker facility. It should be compatible with any other data acquired on a similar Bruker scanner using an equivalent protocol. This resource contains anonymised file-paths which will need to be edited to enable running on a cluster facility. The commands for submitting jobs to the cluster also need to be edited. This pipeline is based on the code developed by Chaoyue Wang and Benjamin Tendler and published here: https://doi.org/10.1016/j.neuroimage.2020.117216. The scripts were either written or adapted by Cristiana Tisca. The outputs of the pipeline include QSM and R2* maps. Additional funding sources: Wellcome Trust Senior Research Fellowship (Renewal), Prof Karla Miller, 224573/Z/21/Z
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Chronic systemic inflammatory conditions, such as atherosclerosis, diabetes and obesity are associated with increased risk of stroke, which suggests that systemic inflammation may contribute to the development of stroke in humans. The hypothesis that systemic inflammation may induce brain pathology can be tested in animals, and this was the key objective of the present study. First, we assessed inflammatory changes in the brain in rodent models of chronic, systemic inflammation. PET imaging revealed increased microglia activation in the brain of JCR-LA (corpulent) rats, which develop atherosclerosis and obesity, compared to the control lean strain. Immunostaining against Iba1 confirmed reactive microgliosis in these animals. An atherogenic diet in apolipoprotein E knock-out (ApoE−/−) mice induced microglial activation in the brain parenchyma within 8 weeks and increased expression of vascular adhesion molecules. Focal lipid deposition and neuroinflammation in periventricular and cortical areas and profound recruitment of activated myeloid phagocytes, T cells and granulocytes into the choroid plexus were also observed. In a small, preliminary study, patients at risk of stroke (multiple risk factors for stroke, with chronically elevated C-reactive protein, but negative MRI for brain pathology) exhibited increased inflammation in the brain, as indicated by PET imaging. These findings show that brain inflammation occurs in animals, and tentatively in humans, harbouring risk factors for stroke associated with elevated systemic inflammation. Thus a “primed” inflammatory environment in the brain may exist in individuals at risk of stroke and this can be adequately recapitulated in appropriate co-morbid animal models.
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This project publishes the code used in the following publication: Bron et al., Cross-Cohort Generalizability of Deep and Conventional Machine Learning for MRI-based diagnosis and prediction of Alzheimer’s disease, NeuroImage: Clinical, 2021 Link: https://doi.org/10.1016/j.nicl.2021.102712, arxiv.org/2012.08769 Starting point: Overview.ipynb {"references": ["Bron et al., Cross-Cohort Generalizability of Deep and Conventional Machine Learning for MRI-based diagnosis and prediction of Alzheimer's disease, NeuroImage: Clinical, 2021"]} https://gitlab.com/radiology/neuro/bron-cross-cohort/-/tree/v1.0