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Alzheimers Disease Neuroimaging Initiative (1U01AG024904-01)
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  • 2014-2023
  • Open Access
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  • CA
  • English

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    Authors: Steyer, Lisa; Stöcker, Almond; Greven, Sonja;

    We propose regression models for curve-valued responses in two or more dimensions, where only the image but not the parametrization of the curves is of interest. Examples of such data are handwritten letters, movement paths or outlines of objects. In the square-root-velocity framework, a parametrization invariant distance for curves is obtained as the quotient space metric with respect to the action of re-parametrization, which is by isometries. With this special case in mind, we discuss the generalization of 'linear' regression to quotient metric spaces more generally, before illustrating the usefulness of our approach for curves modulo re-parametrization. We address the issue of sparsely or irregularly sampled curves by using splines for modeling smooth conditional mean curves. We test this model in simulations and apply it to human hippocampal outlines, obtained from Magnetic Resonance Imaging scans. Here we model how the shape of the irregularly sampled hippocampus is related to age, Alzheimer's disease and sex.

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    arXiv.org e-Print Archive
    Other literature type . Preprint . 2023
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      arXiv.org e-Print Archive
      Other literature type . Preprint . 2023
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    Authors: Vromen, Eleonora M.; de Boer, Sterre C.M.; Teunissen, Charlotte E.; Rozemuller, Annemieke; +6 Authors

    Abstract: The biological definition of Alzheimer's disease using CSF biomarkers requires abnormal levels of both amyloid (A) and tau (T). However, biomarkers and corresponding cutoffs may not always reflect the presence or absence of pathology. Previous studies suggest that up to 32% of individuals with autopsy-confirmed Alzheimer's disease show normal CSF p-tau levels in vivo, but these studies are sparse and had small sample sizes. Therefore, in three independent autopsy cohorts, we studied whether or not CSF A+T- excluded Alzheimer's disease based on autopsy. We included 215 individuals, for whom ante-mortem CSF collection and autopsy had been performed, from three cohorts: (i) the Amsterdam Dementia Cohort (ADC) [n = 80, 37 (46%) Alzheimer's disease at autopsy, time between CSF collection and death 4.5 +/- 2.9 years]; (ii) the Antwerp Dementia Cohort (DEM) [n = 92, 84 (91%) Alzheimer's disease at autopsy, time CSF collection to death 1.7 +/- 2.3 years]; and (iii) the Alzheimer's Disease Neuroimaging Initiative (ADNI) [n = 43, 31 (72%) Alzheimer's disease at autopsy, time CSF collection to death 5.1 +/- 2.5 years]. Biomarker profiles were based on dichotomized CSF A beta(1-42) and p-tau levels. The accuracy of CSF AT profiles to detect autopsy-confirmed Alzheimer's disease was assessed. Lastly, we investigated whether the concordance of AT profiles with autopsy diagnosis improved when CSF was collected closer to death in 9 (10%) DEM and 30 (70%) ADNI individuals with repeated CSF measurements available. In total, 50-73% of A+T- individuals and 100% of A+T+ individuals had Alzheimer's disease at autopsy. Amyloid status showed the highest accuracy to detect autopsy-confirmed Alzheimer's disease (accuracy, sensitivity and specificity in the ADC: 88%, 92% and 84%; in the DEM: 87%, 94% and 12%; and in the ADNI cohort: 86%, 90% and 75%, respectively). The addition of CSF p-tau did not further improve these estimates. We observed no differences in demographics or degree of Alzheimer's disease neuropathology between A+T- and A+T+ individuals with autopsy-confirmed Alzheimer's disease. All individuals with repeated CSF measurements remained stable in A beta(1-42) status during follow-up. None of the Alzheimer's disease individuals with a normal p-tau status changed to abnormal; however, four (44%) DEM individuals and two (7%) ADNI individuals changed from abnormal to normal p-tau status over time, and all had Alzheimer's disease at autopsy. In summary, we found that up to 73% of A+T- individuals had Alzheimer's disease at autopsy. This should be taken into account in both research and clinical settings. The biological definition of Alzheimer's disease using CSF biomarkers requires both abnormal amyloid (A) and tau (T) levels. However, in a large multicentre cohort, Vromen et al. found that up to 73% of A+T- individuals had Alzheimer's disease at autopsy, with implications for both research and clinical settings.

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    Authors: Eitel, Fabian;

    Deep learning and especially convolutional neural networks (CNNs) have a high potential of being implemented into clinical decision support software for tasks such as diagnosis and prediction of disease courses. This thesis has studied the application of CNNs on structural MRI data for diagnosing neurological diseases. Specifically, multiple sclerosis and Alzheimer’s disease were used as classification targets due to their high prevalence, data availability and apparent biomarkers in structural MRI data. The classification task is challenging since pathology can be highly individual and difficult for human experts to detect and due to small sample sizes, which are caused by the high acquisition cost and sensitivity of medical imaging data. A roadblock in adopting CNNs to clinical practice is their lack of interpretability. Therefore, after optimizing the machine learning models for predictive performance (e.g. balanced accuracy), we have employed explainability methods to study the reliability and validity of the trained models. The deep learning models achieved good predictive performance of over 87% balanced accuracy on all tasks and the explainability heatmaps showed coherence with known clinical biomarkers for both disorders. Explainability methods were compared quantitatively using brain atlases and shortcomings regarding their robustness were revealed. Further investigations showed clear benefits of transfer-learning and image registration on the model performance. Lastly, a new CNN layer type was introduced, which incorporates a prior on the spatial homogeneity of neuro-MRI data. CNNs excel when used on natural images which possess spatial heterogeneity, and even though MRI data and natural images share computational similarities, the composition and orientation of neuro-MRI is very distinct. The introduced patch-individual filter (PIF) layer breaks the assumption of spatial invariance of CNNs and reduces convergence time on different data sets without reducing predictive performance. The presented work highlights many challenges that CNNs for disease diagnosis face on MRI data and defines as well as tests strategies to overcome those. In dieser Doktorarbeit wird die Frage untersucht, wie erfolgreich deep learning bei der Diagnostik von neurodegenerativen Erkrankungen unterstützen kann. In 5 experimentellen Studien wird die Anwendung von Convolutional Neural Networks (CNNs) auf Daten der Magnetresonanztomographie (MRT) untersucht. Ein Schwerpunkt wird dabei auf die Erklärbarkeit der eigentlich intransparenten Modelle gelegt. Mit Hilfe von Methoden der erklärbaren künstlichen Intelligenz (KI) werden Heatmaps erstellt, die die Relevanz einzelner Bildbereiche für das Modell darstellen. Die 5 Studien dieser Dissertation zeigen das Potenzial von CNNs zur Krankheitserkennung auf neurologischen MRT, insbesondere bei der Kombination mit Methoden der erklärbaren KI. Mehrere Herausforderungen wurden in den Studien aufgezeigt und Lösungsansätze in den Experimenten evaluiert. Über alle Studien hinweg haben CNNs gute Klassifikationsgenauigkeiten erzielt und konnten durch den Vergleich von Heatmaps zur klinischen Literatur validiert werden. Weiterhin wurde eine neue CNN Architektur entwickelt, spezialisiert auf die räumlichen Eigenschaften von Gehirn MRT Bildern.

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    Authors: Bouayed, Aymene Mohammed; Deslauriers-Gauthier, Samuel; Zucchelli, Mauro; Deriche, Rachid;

    Recently, a general analytical formula to extract all the Rotation Invariant Features (RIFs) of the diffusion Magnetic Resonance Imaging (dMRI) signal was proposed. The features extracted using this formula represent a generalisation of the usual second degree RIFs such as the mean diffusivity. In this work, we study the usefulness of all the 12 algebraically independent RIFs extracted from 4th degree spherical harmonics that model the dMRI signal per voxel in the context of Alzheimer Disease (AD) identification. To do so, and since we are working with imbalanced data sets, we first introduce a non-linear metric to evaluate the performance of the models, the (B-score). This proposed metric allows high score only when both classes are distinguished correctly. We use the proposed metric in conjunction with a deep Convolutional Neural Network that operates on subject slices to identify if a subject has AD or not. We find that micro-structure information communicated by RIFs is indeed useful to AD identification and that not all RIFs are equivalently useful. We also identify the two best RIF combinations for the ADNI-SIEMENS and the ADNI-GE medical data sets respectively. The combination of these RIFs achieves a classification B-score of 73.62% and 72.31% on the previous data sets respectively. We note the importance of combining high degree RIFs with low degree ones to improve the classification performance. International audience

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    Authors: ÖZKAYA, Anıl; CEBECİ, Ufuk;

    Alzheimer hastalığı çağın en büyük sağlık problemlerinden biridir. Bir tedavisi bulunmaması nedeniyle hastalığın erken evrelerde teşhis edilmesi ve önleyici tedavilerin uygulanması gerekmektedir. Ancak hastalığın erken teşhisi oldukça zordur, bu nedenle çoğu kişide belirgin ve geri dönüşsüz etkiler oluştuktan sonra teşhis yapılabilmektedir. Hastalığın erken teşhis edilmesi için dünyada araştırmacılar tarafından çeşitli çalışmalar yapılmaktadır. Deep learning, Alzheimer hastalığının erken teşhisinde son zamanlarda oldukça önem kazanmıştır. Deep learning ile oluşturulmuş modellerin kullanılmasıyla erken teşhis yapılabilme başarısı yüksek seviyelere ulaşmıştır. Bu çalışmada Alzheimer hastalığının oluşum evreleri ve oluşan değişiklikler incelenmiştir. Alzheimer’s teşhisinde kullanılan çeşitli teknikler için literatür taraması yapılmış ve görüntüleme tekniklerinin Alzheimer’s erken teşhisinde kullanımı araştırılmıştır. Yaygın kullanımı nedeniyle MRI tekniği üzerinde durulmuş, çoğunlukla MRI kullanılan çalışmalar incelenmiştir. Deep learning’te kullanılan kavramlar açıklanmış, yenilikler ve sonuçlar ortaya konmuştur. Deep learning’te kullanılan mimariler ve bu alanda getirdikleri yenilikler ortaya konmuş, mevcut çalışmalarda oluşturulmuş ve test edilmiş deep learning modelleri incelenmiştir. Yapılan çeşitli çalışmaların getirdiği yenilikler ve başarı oranları ortaya konmuştur. Kullanım kolaylığı sağlayan ve hızlı, performanslı ve başaırılı bir model geliştirilmesi için çalışılmıştır. Bunun için scheduler yapısı, MONAI yapısı, “Data loader” yapısı ve çeşitli teknikler basit bir kullanımla sunulmuştur. Ayrıca model Google Colab üzerinde sorunsuz şekilde çalışması için optimize edilmiştir. Ayrıca görüntü önişlemede oldukça önemli olan FSL kütüphanesindeki toollar ile çalışılmış ve "Bias field and Neck Clean Up", “Standard Brain Extraction Using BET2” ve "Robust Brain Center Estimation" toolları için optimal parametreler bulunmuştur. Bu kütüphane ile herhangi bir model için optimal beyin görüntüleri elde edilebilmektedir. Modelde temel olarak DenseNet121 modeli kullanılmıştır ve kolaylıkla model değiştirilebilen bir yapıda sunulmuştur. Model 3 boyutlu MR görüntülerini doğrudan kullanabilmektedir ve bu sayede çeşitli uzaysal bilginin kaybının önüne geçilmiştir. Alzheimer's disease is one of the greatest health problems of our time. Since there is no cure, it is necessary to diagnose the disease in the early stages and to apply preventive treatments. However, early diagnosis of the disease is very difficult, so most people can be diagnosed after significant and irreversible effects occur. Various studies are carried out by researchers around the world for the early diagnosis of the disease. Deep learning has recently gained importance in the early diagnosis of Alzheimer's disease. With the use of models created using deep learning, the success of early diagnosis has reached high levels. In this study, the stages of Alzheimer's disease and the changes that occur were examined. A literature review was conducted for various techniques used in the diagnosis of Alzheimer's and the use of imaging techniques in the early diagnosis of Alzheimer's was investigated. Due to its widespread use, the MRI technique has been emphasized, and mostly studies using MRI have been examined. Concepts used in deep learning are explained, innovations and results are presented. The architectures used in deep learning and the innovations they bring to this field are revealed, and deep learning models that have been created and tested in current studies are examined. The innovations and success rates brought by various studies have been revealed. Efforts have been made to develop a fast, efficient and successful model that provides ease of use. For this, the scheduler structure, MONAI framework, Data loader structure and various techniques are presented with simple use. Also, the model is optimized to run smoothly on Google Colab. In addition, the tools in the FSL library, which are very important in preprocessing, were studied and optimal parameters were found for the "Bias field and Neck Clean Up", "Standard Brain Extraction Using BET2" and "Robust Brain Center Estimation" tools. By using this library, optimal brain images can be obtained for any model. The DenseNet121 model was used as a basis in the model and it was presented in a structure that can be easily changed. The model can directly use 3D MR images, thus preventing the loss of various spatial information.

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    Authors: Timsina, Jigyasha; Gomez-Fonseca, Duber; Xiong, Chengjie; Schindler, Suzanne E; +22 Authors

    BACKGROUND: The SOMAscan assay has an advantage over immunoassay-based methods because it measures a large number of proteins in a cost-effective manner. However, the performance of this technology compared to the routinely used immunoassay techniques needs to be evaluated. OBJECTIVE: We performed comparative analyses of SOMAscan and immunoassay-based protein measurements for five cerebrospinal fluid (CSF) proteins associated with Alzheimer’s disease (AD) and neurodegeration: NfL, Neurogranin, sTREM2, VILIP-1 and SNAP-25. METHODS: We compared biomarkers measured in ADNI (N=689), Knight-ADRC (N=870), DIAN (N=115), and Barcelona-1 (N=92) cohorts. Raw protein values were transformed using z-score in order to combine measures from the different studies. sTREM2 and VILIP-1 had more than one analyte in SOMAscan; all available analytes were evaluated. Pearson’s correlation coefficients between SOMAscan and immunoassays were calculated. Receiver operating characteristic curve and area under the curve were used to compare prediction accuracy of these biomarkers between the two platforms. RESULTS: Neurogranin, VILIP-1 and NfL showed high correlation between SOMAscan and immunoassay measures (r > 0.9). sTREM2 had a fair correlation (r > 0.6), whereas SNAP-25 showed weak correlation (r = 0.06). Measures in both platforms provided similar predicted performance for all biomarkers except SNAP-25 and one of the sTREM2 analytes. sTREM2 showed higher AUC for SOMAscan based measures. CONCLUSION: Our data indicate that SOMAscan performs as well as immunoassay approaches for NfL, Neurogranin, VILIP-1 and sTREM2. Our study shows promise for using SOMAscan as an alternative to traditional immunoassay-based measures. Follow-up investigation will be required for SNAP-25 and additional established biomarkers.

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    Authors: Khorramyar, Pooriya;

    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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    Authors: Mehdipour Ghazi, Mostafa; Nielsen, Mads;

    Medical images used in clinical practice are heterogeneous and not the same quality as scans studied in academic research. Preprocessing breaks down in extreme cases when anatomy, artifacts, or imaging parameters are unusual or protocols are different. Methods robust to these variations are most needed. A novel deep learning method is proposed for fast and accurate segmentation of the human brain into 132 regions. The proposed model uses an efficient U-Net-like network and benefits from the intersection points of different views and hierarchical relations for the fusion of the orthogonal 2D planes and brain labels during the end-to-end training. Weakly supervised learning is deployed to take the advantage of partially labeled data for the whole brain segmentation and estimation of the intracranial volume (ICV). Moreover, data augmentation is used to expand the magnetic resonance imaging (MRI) data by generating realistic brain scans with high variability for robust training of the model while preserving data privacy. The proposed method can be applied to brain MRI data including skull or any other artifacts without preprocessing the images or a drop in performance. Several experiments using different atlases are conducted to evaluate the segmentation performance of the trained model compared to the state-of-the-art, and the results show higher segmentation accuracy and robustness of the proposed model compared to the existing methods across different intra- and inter-domain datasets.

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    Convolutional neural networks have enabled significant improvements in medical image-based diagnosis. It is, however, increasingly clear that these models are susceptible to performance degradation when facing spurious correlations and dataset shift, leading, e.g., to underperformance on underrepresented patient groups. In this paper, we compare two classification schemes on the ADNI MRI dataset: a simple logistic regression model using manually selected volumetric features, and a convolutional neural network trained on 3D MRI data. We assess the robustness of the trained models in the face of varying dataset splits, training set sex composition, and stage of disease. In contrast to earlier work in other imaging modalities, we do not observe a clear pattern of improved model performance for the majority group in the training dataset. Instead, while logistic regression is fully robust to dataset composition, we find that CNN performance is generally improved for both male and female subjects when including more female subjects in the training dataset. We hypothesize that this might be due to inherent differences in the pathology of the two sexes. Moreover, in our analysis, the logistic regression model outperforms the 3D CNN, emphasizing the utility of manual feature specification based on prior knowledge, and the need for more robust automatic feature selection.

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    Online Research Database In Technology
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    Authors: Thibeau-Sutre, Elina;

    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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Alzheimers Disease Neuroimaging Initiative (1U01AG024904-01)
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    Authors: Steyer, Lisa; Stöcker, Almond; Greven, Sonja;

    We propose regression models for curve-valued responses in two or more dimensions, where only the image but not the parametrization of the curves is of interest. Examples of such data are handwritten letters, movement paths or outlines of objects. In the square-root-velocity framework, a parametrization invariant distance for curves is obtained as the quotient space metric with respect to the action of re-parametrization, which is by isometries. With this special case in mind, we discuss the generalization of 'linear' regression to quotient metric spaces more generally, before illustrating the usefulness of our approach for curves modulo re-parametrization. We address the issue of sparsely or irregularly sampled curves by using splines for modeling smooth conditional mean curves. We test this model in simulations and apply it to human hippocampal outlines, obtained from Magnetic Resonance Imaging scans. Here we model how the shape of the irregularly sampled hippocampus is related to age, Alzheimer's disease and sex.

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    arXiv.org e-Print Archive
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      arXiv.org e-Print Archive
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    Authors: Vromen, Eleonora M.; de Boer, Sterre C.M.; Teunissen, Charlotte E.; Rozemuller, Annemieke; +6 Authors

    Abstract: The biological definition of Alzheimer's disease using CSF biomarkers requires abnormal levels of both amyloid (A) and tau (T). However, biomarkers and corresponding cutoffs may not always reflect the presence or absence of pathology. Previous studies suggest that up to 32% of individuals with autopsy-confirmed Alzheimer's disease show normal CSF p-tau levels in vivo, but these studies are sparse and had small sample sizes. Therefore, in three independent autopsy cohorts, we studied whether or not CSF A+T- excluded Alzheimer's disease based on autopsy. We included 215 individuals, for whom ante-mortem CSF collection and autopsy had been performed, from three cohorts: (i) the Amsterdam Dementia Cohort (ADC) [n = 80, 37 (46%) Alzheimer's disease at autopsy, time between CSF collection and death 4.5 +/- 2.9 years]; (ii) the Antwerp Dementia Cohort (DEM) [n = 92, 84 (91%) Alzheimer's disease at autopsy, time CSF collection to death 1.7 +/- 2.3 years]; and (iii) the Alzheimer's Disease Neuroimaging Initiative (ADNI) [n = 43, 31 (72%) Alzheimer's disease at autopsy, time CSF collection to death 5.1 +/- 2.5 years]. Biomarker profiles were based on dichotomized CSF A beta(1-42) and p-tau levels. The accuracy of CSF AT profiles to detect autopsy-confirmed Alzheimer's disease was assessed. Lastly, we investigated whether the concordance of AT profiles with autopsy diagnosis improved when CSF was collected closer to death in 9 (10%) DEM and 30 (70%) ADNI individuals with repeated CSF measurements available. In total, 50-73% of A+T- individuals and 100% of A+T+ individuals had Alzheimer's disease at autopsy. Amyloid status showed the highest accuracy to detect autopsy-confirmed Alzheimer's disease (accuracy, sensitivity and specificity in the ADC: 88%, 92% and 84%; in the DEM: 87%, 94% and 12%; and in the ADNI cohort: 86%, 90% and 75%, respectively). The addition of CSF p-tau did not further improve these estimates. We observed no differences in demographics or degree of Alzheimer's disease neuropathology between A+T- and A+T+ individuals with autopsy-confirmed Alzheimer's disease. All individuals with repeated CSF measurements remained stable in A beta(1-42) status during follow-up. None of the Alzheimer's disease individuals with a normal p-tau status changed to abnormal; however, four (44%) DEM individuals and two (7%) ADNI individuals changed from abnormal to normal p-tau status over time, and all had Alzheimer's disease at autopsy. In summary, we found that up to 73% of A+T- individuals had Alzheimer's disease at autopsy. This should be taken into account in both research and clinical settings. The biological definition of Alzheimer's disease using CSF biomarkers requires both abnormal amyloid (A) and tau (T) levels. However, in a large multicentre cohort, Vromen et al. found that up to 73% of A+T- individuals had Alzheimer's disease at autopsy, with implications for both research and clinical settings.

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    Authors: Eitel, Fabian;

    Deep learning and especially convolutional neural networks (CNNs) have a high potential of being implemented into clinical decision support software for tasks such as diagnosis and prediction of disease courses. This thesis has studied the application of CNNs on structural MRI data for diagnosing neurological diseases. Specifically, multiple sclerosis and Alzheimer’s disease were used as classification targets due to their high prevalence, data availability and apparent biomarkers in structural MRI data. The classification task is challenging since pathology can be highly individual and difficult for human experts to detect and due to small sample sizes, which are caused by the high acquisition cost and sensitivity of medical imaging data. A roadblock in adopting CNNs to clinical practice is their lack of interpretability. Therefore, after optimizing the machine learning models for predictive performance (e.g. balanced accuracy), we have employed explainability methods to study the reliability and validity of the trained models. The deep learning models achieved good predictive performance of over 87% balanced accuracy on all tasks and the explainability heatmaps showed coherence with known clinical biomarkers for both disorders. Explainability methods were compared quantitatively using brain atlases and shortcomings regarding their robustness were revealed. Further investigations showed clear benefits of transfer-learning and image registration on the model performance. Lastly, a new CNN layer type was introduced, which incorporates a prior on the spatial homogeneity of neuro-MRI data. CNNs excel when used on natural images which possess spatial heterogeneity, and even though MRI data and natural images share computational similarities, the composition and orientation of neuro-MRI is very distinct. The introduced patch-individual filter (PIF) layer breaks the assumption of spatial invariance of CNNs and reduces convergence time on different data sets without reducing predictive performance. The presented work highlights many challenges that CNNs for disease diagnosis face on MRI data and defines as well as tests strategies to overcome those. In dieser Doktorarbeit wird die Frage untersucht, wie erfolgreich deep learning bei der Diagnostik von neurodegenerativen Erkrankungen unterstützen kann. In 5 experimentellen Studien wird die Anwendung von Convolutional Neural Networks (CNNs) auf Daten der Magnetresonanztomographie (MRT) untersucht. Ein Schwerpunkt wird dabei auf die Erklärbarkeit der eigentlich intransparenten Modelle gelegt. Mit Hilfe von Methoden der erklärbaren künstlichen Intelligenz (KI) werden Heatmaps erstellt, die die Relevanz einzelner Bildbereiche für das Modell darstellen. Die 5 Studien dieser Dissertation zeigen das Potenzial von CNNs zur Krankheitserkennung auf neurologischen MRT, insbesondere bei der Kombination mit Methoden der erklärbaren KI. Mehrere Herausforderungen wurden in den Studien aufgezeigt und Lösungsansätze in den Experimenten evaluiert. Über alle Studien hinweg haben CNNs gute Klassifikationsgenauigkeiten erzielt und konnten durch den Vergleich von Heatmaps zur klinischen Literatur validiert werden. Weiterhin wurde eine neue CNN Architektur entwickelt, spezialisiert auf die räumlichen Eigenschaften von Gehirn MRT Bildern.

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    Authors: Bouayed, Aymene Mohammed; Deslauriers-Gauthier, Samuel; Zucchelli, Mauro; Deriche, Rachid;

    Recently, a general analytical formula to extract all the Rotation Invariant Features (RIFs) of the diffusion Magnetic Resonance Imaging (dMRI) signal was proposed. The features extracted using this formula represent a generalisation of the usual second degree RIFs such as the mean diffusivity. In this work, we study the usefulness of all the 12 algebraically independent RIFs extracted from 4th degree spherical harmonics that model the dMRI signal per voxel in the context of Alzheimer Disease (AD) identification. To do so, and since we are working with imbalanced data sets, we first introduce a non-linear metric to evaluate the performance of the models, the (B-score). This proposed metric allows high score only when both classes are distinguished correctly. We use the proposed metric in conjunction with a deep Convolutional Neural Network that operates on subject slices to identify if a subject has AD or not. We find that micro-structure information communicated by RIFs is indeed useful to AD identification and that not all RIFs are equivalently useful. We also identify the two best RIF combinations for the ADNI-SIEMENS and the ADNI-GE medical data sets respectively. The combination of these RIFs achieves a classification B-score of 73.62% and 72.31% on the previous data sets respectively. We note the importance of combining high degree RIFs with low degree ones to improve the classification performance. International audience

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    Authors: ÖZKAYA, Anıl; CEBECİ, Ufuk;

    Alzheimer hastalığı çağın en büyük sağlık problemlerinden biridir. Bir tedavisi bulunmaması nedeniyle hastalığın erken evrelerde teşhis edilmesi ve önleyici tedavilerin uygulanması gerekmektedir. Ancak hastalığın erken teşhisi oldukça zordur, bu nedenle çoğu kişide belirgin ve geri dönüşsüz etkiler oluştuktan sonra teşhis yapılabilmektedir. Hastalığın erken teşhis edilmesi için dünyada araştırmacılar tarafından çeşitli çalışmalar yapılmaktadır. Deep learning, Alzheimer hastalığının erken teşhisinde son zamanlarda oldukça önem kazanmıştır. Deep learning ile oluşturulmuş modellerin kullanılmasıyla erken teşhis yapılabilme başarısı yüksek seviyelere ulaşmıştır. Bu çalışmada Alzheimer hastalığının oluşum evreleri ve oluşan değişiklikler incelenmiştir. Alzheimer’s teşhisinde kullanılan çeşitli teknikler için literatür taraması yapılmış ve görüntüleme tekniklerinin Alzheimer’s erken teşhisinde kullanımı araştırılmıştır. Yaygın kullanımı nedeniyle MRI tekniği üzerinde durulmuş, çoğunlukla MRI kullanılan çalışmalar incelenmiştir. Deep learning’te kullanılan kavramlar açıklanmış, yenilikler ve sonuçlar ortaya konmuştur. Deep learning’te kullanılan mimariler ve bu alanda getirdikleri yenilikler ortaya konmuş, mevcut çalışmalarda oluşturulmuş ve test edilmiş deep learning modelleri incelenmiştir. Yapılan çeşitli çalışmaların getirdiği yenilikler ve başarı oranları ortaya konmuştur. Kullanım kolaylığı sağlayan ve hızlı, performanslı ve başaırılı bir model geliştirilmesi için çalışılmıştır. Bunun için scheduler yapısı, MONAI yapısı, “Data loader” yapısı ve çeşitli teknikler basit bir kullanımla sunulmuştur. Ayrıca model Google Colab üzerinde sorunsuz şekilde çalışması için optimize edilmiştir. Ayrıca görüntü önişlemede oldukça önemli olan FSL kütüphanesindeki toollar ile çalışılmış ve "Bias field and Neck Clean Up", “Standard Brain Extraction Using BET2” ve "Robust Brain Center Estimation" toolları için optimal parametreler bulunmuştur. Bu kütüphane ile herhangi bir model için optimal beyin görüntüleri elde edilebilmektedir. Modelde temel olarak DenseNet121 modeli kullanılmıştır ve kolaylıkla model değiştirilebilen bir yapıda sunulmuştur. Model 3 boyutlu MR görüntülerini doğrudan kullanabilmektedir ve bu sayede çeşitli uzaysal bilginin kaybının önüne geçilmiştir. Alzheimer's disease is one of the greatest health problems of our time. Since there is no cure, it is necessary to diagnose the disease in the early stages and to apply preventive treatments. However, early diagnosis of the disease is very difficult, so most people can be diagnosed after significant and irreversible effects occur. Various studies are carried out by researchers around the world for the early diagnosis of the disease. Deep learning has recently gained importance in the early diagnosis of Alzheimer's disease. With the use of models created using deep learning, the success of early diagnosis has reached high levels. In this study, the stages of Alzheimer's disease and the changes that occur were examined. A literature review was conducted for various techniques used in the diagnosis of Alzheimer's and the use of imaging techniques in the early diagnosis of Alzheimer's was investigated. Due to its widespread use, the MRI technique has been emphasized, and mostly studies using MRI have been examined. Concepts used in deep learning are explained, innovations and results are presented. The architectures used in deep learning and the innovations they bring to this field are revealed, and deep learning models that have been created and tested in current studies are examined. The innovations and success rates brought by various studies have been revealed. Efforts have been made to develop a fast, efficient and successful model that provides ease of use. For this, the scheduler structure, MONAI framework, Data loader structure and various techniques are presented with simple use. Also, the model is optimized to run smoothly on Google Colab. In addition, the tools in the FSL library, which are very important in preprocessing, were studied and optimal parameters were found for the "Bias field and Neck Clean Up", "Standard Brain Extraction Using BET2" and "Robust Brain Center Estimation" tools. By using this library, optimal brain images can be obtained for any model. The DenseNet121 model was used as a basis in the model and it was presented in a structure that can be easily changed. The model can directly use 3D MR images, thus preventing the loss of various spatial information.

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    Authors: Timsina, Jigyasha; Gomez-Fonseca, Duber; Xiong, Chengjie; Schindler, Suzanne E; +22 Authors

    BACKGROUND: The SOMAscan assay has an advantage over immunoassay-based methods because it measures a large number of proteins in a cost-effective manner. However, the performance of this technology compared to the routinely used immunoassay techniques needs to be evaluated. OBJECTIVE: We performed comparative analyses of SOMAscan and immunoassay-based protein measurements for five cerebrospinal fluid (CSF) proteins associated with Alzheimer’s disease (AD) and neurodegeration: NfL, Neurogranin, sTREM2, VILIP-1 and SNAP-25. METHODS: We compared biomarkers measured in ADNI (N=689), Knight-ADRC (N=870), DIAN (N=115), and Barcelona-1 (N=92) cohorts. Raw protein values were transformed using z-score in order to combine measures from the different studies. sTREM2 and VILIP-1 had more than one analyte in SOMAscan; all available analytes were evaluated. Pearson’s correlation coefficients between SOMAscan and immunoassays were calculated. Receiver operating characteristic curve and area under the curve were used to compare prediction accuracy of these biomarkers between the two platforms. RESULTS: Neurogranin, VILIP-1 and NfL showed high correlation between SOMAscan and immunoassay measures (r > 0.9). sTREM2 had a fair correlation (r > 0.6), whereas SNAP-25 showed weak correlation (r = 0.06). Measures in both platforms provided similar predicted performance for all biomarkers except SNAP-25 and one of the sTREM2 analytes. sTREM2 showed higher AUC for SOMAscan based measures. CONCLUSION: Our data indicate that SOMAscan performs as well as immunoassay approaches for NfL, Neurogranin, VILIP-1 and sTREM2. Our study shows promise for using SOMAscan as an alternative to traditional immunoassay-based measures. Follow-up investigation will be required for SNAP-25 and additional established biomarkers.

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    Authors: Khorramyar, Pooriya;

    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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    Authors: Mehdipour Ghazi, Mostafa; Nielsen, Mads;

    Medical images used in clinical practice are heterogeneous and not the same quality as scans studied in academic research. Preprocessing breaks down in extreme cases when anatomy, artifacts, or imaging parameters are unusual or protocols are different. Methods robust to these variations are most needed. A novel deep learning method is proposed for fast and accurate segmentation of the human brain into 132 regions. The proposed model uses an efficient U-Net-like network and benefits from the intersection points of different views and hierarchical relations for the fusion of the orthogonal 2D planes and brain labels during the end-to-end training. Weakly supervised learning is deployed to take the advantage of partially labeled data for the whole brain segmentation and estimation of the intracranial volume (ICV). Moreover, data augmentation is used to expand the magnetic resonance imaging (MRI) data by generating realistic brain scans with high variability for robust training of the model while preserving data privacy. The proposed method can be applied to brain MRI data including skull or any other artifacts without preprocessing the images or a drop in performance. Several experiments using different atlases are conducted to evaluate the segmentation performance of the trained model compared to the state-of-the-art, and the results show higher segmentation accuracy and robustness of the proposed model compared to the existing methods across different intra- and inter-domain datasets.

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