Systematic comparison of different techniques to measure hippocampal subfield volumes in ADNI2

OBJECTIVE: Subfield-specific measurements provide superior information in the early stages of neurodegenerative diseases compared to global hippocampal measurements. The overall goal was to systematically compare the performance of five representative manual and automated T1 and T2 based subfield labeling techniques in a sub-set of the ADNI2 population. METHODS: The high resolution T2 weighted hippocampal images (T2-HighRes) and the corresponding T1 images from 106 ADNI2 subjects (41 controls, 57 MCI, 8 AD) were processed as follows. A. T1-based: 1. Freesurfer + Large-Diffeomorphic-Metric-Mapping in combination with shape analysis. 2. FreeSurfer 5.1 subfields using in-vivo atlas. B. T2-HighRes: 1. Model-based subfield segmentation using ex-vivo atlas (FreeSurfer 6.0). 2. T2-based automated multi-atlas segmentation combined with similarity-weighted voting (ASHS). 3. Manual subfield parcellation. Multiple regression analyses were used to calculate effect sizes (ES) for group, amyloid positivity in controls, and associations with cognitive/memory performance for each approach. RESULTS: Subfield volumetry was better than whole hippocampal volumetry for the detection of the mild atrophy differences between controls and MCI (ES: 0.27 vs 0.11). T2-HighRes approaches outperformed T1 approaches for the detection of early stage atrophy (ES: 0.27 vs.0.10), amyloid positivity (ES: 0.11 vs 0.04), and cognitive associations (ES: 0.22 vs 0.19). CONCLUSIONS: T2-HighRes subfield approaches outperformed whole hippocampus and T1 subfield approaches. None of the different T2-HghRes methods tested had a clear advantage over the other methods. Each has strengths and weaknesses that need to be taken into account when deciding which one to use to get the best results from subfield volumetry

Applicability of in vivo staging of regional amyloid burden in a cognitively normal cohort with subjective memory complaints: the INSIGHT-preAD study.

BACKGROUND:Current methods of amyloid PET interpretation based on the binary classification of global amyloid signal fail to identify early phases of amyloid deposition. A recent analysis of 18F-florbetapir PET data from the Alzheimer's disease Neuroimaging Initiative cohort suggested a hierarchical four-stage model of regional amyloid deposition that resembles neuropathologic estimates and can be used to stage an individual's amyloid burden in vivo. Here, we evaluated the validity of this in vivo amyloid staging model in an independent cohort of older people with subjective memory complaints (SMC). We further examined its potential association with subtle cognitive impairments in this population at elevated risk for Alzheimer's disease (AD). METHODS:The monocentric INSIGHT-preAD cohort includes 318 cognitively intact older individuals with SMC. All individuals underwent 18F-florbetapir PET scanning and extensive neuropsychological testing. We projected the regional amyloid uptake signal into the previously proposed hierarchical staging model of in vivo amyloid progression. We determined the adherence to this model across all cases and tested the association between increasing in vivo amyloid stage and cognitive performance using ANCOVA models. RESULTS:In total, 156 participants (49%) showed evidence of regional amyloid deposition, and all but 2 of these (99%) adhered to the hierarchical regional pattern implied by the in vivo amyloid progression model. According to a conventional binary classification based on global signal (SUVRCereb = 1.10), individuals in stages III and IV were classified as amyloid-positive (except one in stage III), but 99% of individuals in stage I and even 28% of individuals in stage II were classified as amyloid-negative. Neither in vivo amyloid stage nor conventional binary amyloid status was significantly associated with cognitive performance in this preclinical cohort. CONCLUSIONS:The proposed hierarchical staging scheme of PET-evidenced amyloid deposition generalizes well to data from an independent cohort of older people at elevated risk for AD. Future studies will determine the prognostic value of the staging approach for predicting longitudinal cognitive decline in older individuals at increased risk for AD

Alzheimer Disease Detection Techniques and Methods: A Review

Brain pathological changes linked with Alzheimer's disease (AD) can be measured with Neuroimaging. In the past few years, these measures are rapidly integrated into the signatures of Alzheimer disease (AD) with the help of classification frameworks which are offering tools for diagnosis and prognosis. Here is the review study of Alzheimer's disease based on Neuroimaging and cognitive impairment classification. This work is a systematic review for the published work in the field of AD especially the computer-aided diagnosis. The imaging modalities include 1) Magnetic resonance imaging (MRI) 2) Functional MRI (fMRI) 3) Diffusion tensor imaging 4) Positron emission tomography (PET) and 5) amyloid-PET. The study revealed that the classification criterion based on the features shows promising results to diagnose the disease and helps in clinical progression. The most widely used machine learning classifiers for AD diagnosis include Support Vector Machine, Bayesian Classifiers, Linear Discriminant Analysis, and K-Nearest Neighbor along with Deep learning. The study revealed that the deep learning techniques and support vector machine give higher accuracies in the identification of Alzheimer’s disease. The possible challenges along with future directions are also discussed in the paper

DEEP-AD: The deep learning model for diagnostic classification and prognostic prediction of alzheimer's disease

In terms of context, the aim of this dissertation is to aid neuroradiologists in their clinical judgment regarding the early detection of AD by using DL. To that aim, the system design research methodology is suggested in this dissertation for achieving three goals. The first goal is to investigate the DL models that have performed well at identifying patterns associated with AD, as well as the accuracy so far attained, limitations, and gaps. A systematic review of the literature (SLR) revealed a shortage of empirical studies on the early identification of AD through DL. In this regard, thirteen empirical studies were identified and examined. We concluded that three-dimensional (3D) DL models have been generated far less often and that their performance is also inadequate to qualify them for clinical trials. The second goal is to provide the neuroradiologist with the computer-interpretable information they need to analyze neuroimaging biomarkers. Given this context, the next step in this dissertation is to find the optimum DL model to analyze neuroimaging biomarkers. It has been achieved in two steps. In the first step, eight state-of-the-art DL models have been implemented by training from scratch using end-to-end learning (E2EL) for two binary classification tasks (AD vs. CN and AD vs. stable MCI) and compared by utilizing MRI scans from the publicly accessible datasets of neuroimaging biomarkers. Comparative analysis is carried out by utilizing efficiency-effects graphs, comprehensive indicators, and ranking mechanisms. For the training of the AD vs. sMCI task, the EfficientNet-B0 model gets the highest value for the comprehensive indicator and has the fewest parameters. DenseNet264 performed better than the others in terms of evaluation matrices, but since it has the most parameters, it costs more to train. For the AD vs. CN task by DenseNet264, we achieved 100% accuracy for training and 99.56% accuracy for testing. However, the classification accuracy was still only 82.5% for the AD vs. sMCI task. In the second step, fusion of transfer learning (TL) with E2EL is applied to train the EfficientNet-B0 for the AD vs. sMCI task, which achieved 95.29% accuracy for training and 93.10% accuracy for testing. Additionally, we have also implemented EfficientNet-B0 for the multiclass AD vs. CN vs. sMCI classification task with E2EL to be used in ensemble of models and achieved 85.66% training accuracy and 87.38% testing accuracy. To evaluate the model’s robustness, neuroradiologists must validate the implemented model. As a result, the third goal of this dissertation is to create a tool that neuroradiologists may use at their convenience. To achieve this objective, this dissertation proposes a web-based application (DEEP-AD) that has been created by making an ensemble of Efficient-Net B0 and DenseNet 264 (based on the contribution of goal 2). The accuracy of a DEEP-AD prototype has undergone repeated evaluation and improvement. First, we validated 41 subjects of Spanish MRI datasets (acquired from HT Medica, Madrid, Spain), achieving an accuracy of 82.90%, which was later verified by neuroradiologists. The results of these evaluation studies showed the accomplishment of such goals and relevant directions for future research in applied DL for the early detection of AD in clinical settings.En términos de contexto, el objetivo de esta tesis es ayudar a los neurorradiólogos en su juicio clínico sobre la detección precoz de la AD mediante el uso de DL. Para ello, en esta tesis se propone la metodología de investigación de diseño de sistemas para lograr tres objetivos. El segundo objetivo es proporcionar al neurorradiólogo la información interpretable por ordenador que necesita para analizar los biomarcadores de neuroimagen. Dado este contexto, el siguiente paso en esta tesis es encontrar el modelo DL óptimo para analizar biomarcadores de neuroimagen. Esto se ha logrado en dos pasos. En el primer paso, se han implementado ocho modelos DL de última generación mediante entrenamiento desde cero utilizando aprendizaje de extremo a extremo (E2EL) para dos tareas de clasificación binarias (AD vs. CN y AD vs. MCI estable) y se han comparado utilizando escaneos MRI de los conjuntos de datos de biomarcadores de neuroimagen de acceso público. El análisis comparativo se lleva a cabo utilizando gráficos de efecto-eficacia, indicadores exhaustivos y mecanismos de clasificación. Para el entrenamiento de la tarea AD vs. sMCI, el modelo EfficientNet-B0 obtiene el valor más alto para el indicador exhaustivo y tiene el menor número de parámetros. DenseNet264 obtuvo mejores resultados que los demás en términos de matrices de evaluación, pero al ser el que tiene más parámetros, su entrenamiento es más costoso. Para la tarea AD vs. CN de DenseNet264, conseguimos una accuracy del 100% en el entrenamiento y del 99,56% en las pruebas. Sin embargo, la accuracy de la clasificación fue sólo del 82,5% para la tarea AD vs. sMCI. En el segundo paso, se aplica la fusión del aprendizaje por transferencia (TL) con E2EL para entrenar la EfficientNet-B0 para la tarea AD vs. sMCI, que alcanzó una accuracy del 95,29% en el entrenamiento y del 93,10% en las pruebas. Además, también hemos implementado EfficientNet-B0 para la tarea de clasificación multiclase AD vs. CN vs. sMCI con E2EL para su uso en conjuntos de modelos y hemos obtenido una accuracy de entrenamiento del 85,66% y una precisión de prueba del 87,38%. Para evaluar la solidez del modelo, los neurorradiólogos deben validar el modelo implementado. Como resultado, el tercer objetivo de esta disertación es crear una herramienta que los neurorradiólogos puedan utilizar a su conveniencia. Para lograr este objetivo, esta disertación propone una aplicación basada en web (DEEP-AD) que ha sido creada haciendo un ensemble de Efficient-Net B0 y DenseNet 264 (basado en la contribución del objetivo 2). La accuracy del prototipo DEEP-AD ha sido sometida a repetidas evaluaciones y mejoras. En primer lugar, validamos 41 sujetos de conjuntos de datos de MRI españoles (adquiridos de HT Medica, Madrid, España), logrando una accuracy del 82,90%, que posteriormente fue verificada por neurorradiólogos. Los resultados de estos estudios de evaluación mostraron el cumplimiento de dichos objetivos y las direcciones relevantes para futuras investigaciones en DL, aplicada en la detección precoz de la AD en entornos clínicos.Escuela de DoctoradoDoctorado en Tecnologías de la Información y las Telecomunicacione

Pattern recognition and machine learning for magnetic resonance images with kernel methods

The aim of this thesis is to apply a particular category of machine learning and pattern recognition algorithms, namely the kernel methods, to both functional and anatomical magnetic resonance images (MRI). This work specifically focused on supervised learning methods. Both methodological and practical aspects are described in this thesis. Kernel methods have the computational advantage for high dimensional data, therefore they are idea for imaging data. The procedures can be broadly divided into two components: the construction of the kernels and the actual kernel algorithms themselves. Pre-processed functional or anatomical images can be computed into a linear kernel or a non-linear kernel. We introduce both kernel regression and kernel classification algorithms in two main categories: probabilistic methods and non-probabilistic methods. For practical applications, kernel classification methods were applied to decode the cognitive or sensory states of the subject from the fMRI signal and were also applied to discriminate patients with neurological diseases from normal people using anatomical MRI. Kernel regression methods were used to predict the regressors in the design of fMRI experiments, and clinical ratings from the anatomical scans

Machine learning for efficient recognition of anatomical structures and abnormalities in biomedical images

Three studies have been carried out to investigate new approaches to efficient image segmentation and anomaly detection. The first study investigates the use of deep learning in patch based segmentation. Current approaches to patch based segmentation use low level features such as the sum of squared differences between patches. We argue that better segmentation can be achieved by harnessing the power of deep neural networks. Currently these networks make extensive use of convolutional layers. However, we argue that in the context of patch based segmentation, convolutional layers have little advantage over the canonical artificial neural network architecture. This is because a patch is small, and does not need decomposition and thus will not benefit from convolution. Instead, we make use of the canonical architecture in which neurons only compute dot products, but also incorporate modern techniques of deep learning. The resulting classifier is much faster and less memory-hungry than convolution based networks. In a test application to the segmentation of hippocampus in human brain MR images, we significantly outperformed prior art with a median Dice score up to 90.98% at a near real-time speed (<1s). The second study is an investigation into mouse phenotyping, and develops a high-throughput framework to detect morphological abnormality in mouse embryo micro-CT images. Existing work in this line is centred on, either the detection of phenotype-specific features or comparative analytics. The former approach lacks generality and the latter can often fail, for example, when the abnormality is not associated with severe volume variation. Both these approaches often require image segmentation as a pre-requisite, which is very challenging when applied to embryo phenotyping. A new approach to this problem in which non-rigid registration is combined with robust principal component analysis (RPCA), is proposed. The new framework is able to efficiently perform abnormality detection in a batch of images. It is sensitive to both volumetric and non-volumetric variations, and does not require image segmentation. In a validation study, it successfully distinguished the abnormal VSD and polydactyly phenotypes from the normal, respectively, at 85.19% and 88.89% specificities, with 100% sensitivity in both cases. The third study investigates the RPCA technique in more depth. RPCA is an extension of PCA that tolerates certain levels of data distortion during feature extraction, and is able to decompose images into regular and singular components. It has previously been applied to many computer vision problems (e.g. video surveillance), attaining excellent performance. However these applications commonly rest on a critical condition: in the majority of images being processed, there is a background with very little variation. By contrast in biomedical imaging there is significant natural variation across different images, resulting from inter-subject variability and physiological movements. Non-rigid registration can go some way towards reducing this variance, but cannot eliminate it entirely. To address this problem we propose a modified framework (RPCA-P) that is able to incorporate natural variation priors and adjust outlier tolerance locally, so that voxels associated with structures of higher variability are compensated with a higher tolerance in regularity estimation. An experimental study was applied to the same mouse embryo micro-CT data, and notably improved the detection specificity to 94.12% for the VSD and 90.97% for the polydactyly, while maintaining the sensitivity at 100%.Open Acces

Immunocytochemical evaluation of cellular changes in a mouse model of direct cranial blast and advanced chronic traumatic encephalopathy in human postmortem brains

Traumatic brain injury (TBI) is a serious public health concern. Although moderate and severe forms of TBI receive considerable attention, mild TBI accounts for the majority of all injuries. The first two aims of this work used a rodent model of mild blast to simulate primary injury (damage from the blast wave only). The first aim evaluated potential changes in interneurons containing the calcium-binding proteins calretinin or parvalbumin. In addition, morphological changes in astrocytes and microglia were assessed. Brains were analyzed 48 hours and one month following exposure to single or repeated blasts, with a focus on the hippocampus due to its integral role in learning and memory. Results showed significant region-specific alterations in microglia morphology 48 hours following blast. The absence of structural alterations in microglia one month following blast indicated that the regional hippocampal vulnerability may be transient. The second aim compared glial morphologies in the retina and brain (the lateral geniculate nucleus, superior colliculus, and visual cortex) 48 hours or one month following multiple blasts. Fiber degeneration has received considerable attention, however, less is known about the status of glia throughout the visual pathway following mild blasts. Although no structural alterations were detected, it is possible that alterations in glia occurred at a more acute time scale as changes in glia can be rapid and reversible. The final aim of this work focused on the immunocytochemical characterization of tau pathology in the visual cortices of human postmortem brains with advanced chronic traumatic encephalopathy (CTE). CTE is a devastating tauopathy associated with mild, repetitive TBIs. Although visual deficits are reported in CTE, the primary visual cortex is often spared. The main hypothesis under investigation was whether visual association areas would have tau pathology, despite sparing of primary visual cortex. In addition, a sub-class of interneurons containing parvalbumin was used to evaluate a potential cell-specific vulnerability. Results showed increased tau pathology in visual association areas in advanced CTE, that was largely absent from the primary visual cortex. There was no effect on parvalbumin positive interneurons. The results of this work provides valuable insight regarding potential cell-specific resistance to CTE pathology.2018-11-03T00:00:00