A COMPUTATIONAL PIPELINE FOR MCI DETECTION FROM HETEROGENEOUS BRAIN IMAGES

The aging population has increased the importance of identifying and understanding mild cognitive impairment (MCI), particularly given that 6 - 15 % of MCI cases convert to Alzheimer\u27s disease (AD) each year. The early identification of MCI has the potential for timely therapeutic interventions that would limit the advancement of MCI to AD. However, it is difficult to identify MCI-related pathology based on visual inspection because these changes in brain morphology are subtle and spatially distributed. Therefore, reliable and automated methods to identify subtle changes in morphological characteristics of MCI would aid in the identification and understanding of MCI. Meanwhile, usability becomes a major limitation in the development of clinically applicable classifiers. Furthermore, subject privacy is an additional issue in the usage of human brain images. To address the critical need, a complete computer aided diagnosis (CAD) system for automated detection of MCI from heterogeneous brain images is developed. This system provides functions for image processing, classification of MCI subjects from control, visualization of affected regions of interest (ROIs), data sharing among different research sites, and knowledge sharing through image annotation

Deep learning of brain asymmetry digital biomarkers to support early diagnosis of cognitive decline and dementia

Early identification of degenerative processes in the human brain is essential for proper care and treatment. This may involve different instrumental diagnostic methods, including the most popular computer tomography (CT), magnetic resonance imaging (MRI) and positron emission tomography (PET) scans. These technologies provide detailed information about the shape, size, and function of the human brain. Structural and functional cerebral changes can be detected by computational algorithms and used to diagnose dementia and its stages (amnestic early mild cognitive impairment - EMCI, Alzheimer’s Disease - AD). They can help monitor the progress of the disease. Transformation shifts in the degree of asymmetry between the left and right hemispheres illustrate the initialization or development of a pathological process in the brain. In this vein, this study proposes a new digital biomarker for the diagnosis of early dementia based on the detection of image asymmetries and crosssectional comparison of NC (normal cognitively), EMCI and AD subjects. Features of brain asymmetries extracted from MRI of the ADNI and OASIS databases are used to analyze structural brain changes and machine learning classification of the pathology. The experimental part of the study includes results of supervised machine learning algorithms and transfer learning architectures of convolutional neural networks for distinguishing between cognitively normal subjects and patients with early or progressive dementia. The proposed pipeline offers a low-cost imaging biomarker for the classification of dementia. It can be potentially helpful to other brain degenerative disorders accompanied by changes in brain asymmetries

Contribution of FDG-PET and MRI to improve Understanding, Detection and Differentiation of Dementia

Progression and pattern of changes in different biomarkers of Alzheimer’s disease (AD) and frontotemporal lobar degeneration (FTLD) like [18F]fluorodeoxyglucose positron emission tomography (FDG-PET) and magnetic resonance imaging (MRI) have been carefully investigated over the past decades. However, there have been substantially less studies investigating the potential of combining these imaging modalities to make use of multimodal information to further improve understanding, detection and differentiation of various dementia syndromes. Further the role of preprocessing has been rarely addressed in previous research although different preprocessing algorithms have been shown to substantially affect diagnostic accuracy of dementia. In the present work common preprocessing procedures used to scale FDG-PET data were compared to each other. Further, FDG-PET and MRI information were jointly analyzed using univariate and multivariate techniques. The results suggest a highly differential effect of different scaling procedures of FDG-PET data onto detection and differentiation of various dementia syndromes. Additionally, it has been shown that combining multimodal information does further improve automatic detection and differentiation of AD and FTLD

Quantitative MRI Brain Studies in Mild Cognitive Impairment and Alzheimer's disease: A Methodological Review

Classifying and predicting Alzheimer's disease (AD) in individuals with memory disorders through clinical and psychometric assessment is challenging especially in Mild Cognitive Impairment (MCI) subjects. Quantitative structural Magnetic Resonance Imaging (MRI) acquisition methods in combination with Computer-Aided Diagnosis (CAD) are currently being used for the assessment AD. These acquisitions methods include: i) Voxel-based Morphometry (VBM), ii) volumetric measurements in specific Regions of Interest (ROIs), iii) cortical thickness measurements, iv) shape analysis and v) texture analysis. This review evaluates the aforementioned methods in the classification of cases into one of the following 3 groups: Normal Controls (NC), MCI and AD subjects. Furthermore, the performance of the methods is assessed on the prediction of conversion from MCI to AD. In parallel, it is also assessed which ROIs are preferred in both classification and prognosis through the different states of the disease. Structural changes in the early stages of the disease are more pronounced in the Medial Temporal Lobe (MTL) especially in the entorhinal cortex, whereas with disease progression both entorhinal cortex and hippocampus offer similar discriminative power. However, for the conversion from MCI subjects to AD, entorhinal cortex provides better predictive accuracies rather than other structures, such as the hippocampus

Increased hippocampal shape asymmetry and volumetric ventricular asymmetry in autism spectrum disorder

Autism spectrum disorder (ASD) is a prevalent and fast-growing pervasive neurodevelopmental disorder worldwide. Despite the increasing prevalence of ASD and the breadth of research conducted on the disorder, a conclusive etiology has yet to be established and controversy still exists surrounding the anatomical abnormalities in ASD. In particular, structural asymmetries have seldom been investigated in ASD, especially in subcortical regions. Additionally, the majority of studies for identifying structural biomarkers associated with ASD have focused on small sample sizes. Therefore, the present study utilizes a large-scale, multi-site database to investigate asymmetries in the amygdala, hippocampus, and lateral ventricles, given the potential involvement of these regions in ASD. Contrary to prior work, we are not only computing volumetric asymmetries, but also shape asymmetries, using a new measure of asymmetry based on spectral shape descriptors. This measure represents the magnitude of the asymmetry and therefore captures both directional and undirectional asymmetry. The asymmetry analysis is conducted on 437 individuals with ASD and 511 healthy controls using T1-weighted MRI scans from the Autism Brain Imaging Data Exchange (ABIDE) database. Results reveal significant asymmetries in the hippocampus and the ventricles, but not in the amygdala, in individuals with ASD. We observe a significant increase in shape asymmetry in the hippocampus, as well as increased volumetric asymmetry in the lateral ventricles in individuals with ASD. Asymmetries in these regions have not previously been reported, likely due to the different characterization of neuroanatomical asymmetry and smaller sample sizes used in previous studies. Given that these results were demonstrated in a large cohort, such asymmetries may be worthy of consideration in the development of neurodiagnostic classification tools for ASD

ADNet : diagnóstico assistido por computador para doença de Alzheimer usando rede neural convolucional 3D com cérebro inteiro

Orientadores: Anderson de Rezende Rocha, Marina WeilerDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Demência por doença de Alzheimer (DA) é uma síndrome clínica caracterizada por múltiplos problemas cognitivos, incluindo dificuldades na memória, funções executivas, linguagem e habilidades visuoespaciais. Sendo a forma mais comum de demência, essa doença mata mais do que câncer de mama e de próstata combinados, além de ser a sexta principal causa de morte nos Estados Unidos. A neuroimagem é uma das áreas de pesquisa mais promissoras para a detecção de biomarcadores estruturais da DA, onde uma técnica não invasiva é usada para capturar uma imagem digital do cérebro, a partir da qual especialistas extraem padrões e características da doença. Nesse contexto, os sistemas de diagnóstico assistido por computador (DAC) são abordagens que visam ajudar médicos e especialistas na interpretação de dados médicos, para fornecer diagnósticos aos pacientes. Em particular, redes neurais convolucionais (RNCs) são um tipo especial de rede neural artificial (RNA), que foram inspiradas em como o sistema visual funciona e, nesse sentido, têm sido cada vez mais utilizadas em tarefas de visão computacional, alcançando resultados impressionantes. Em nossa pesquisa, um dos principais objetivos foi utilizar o que há de mais avançado sobre aprendizagem profunda (por exemplo, RNC) para resolver o difícil problema de identificar biomarcadores estruturais da DA em imagem por ressonância magnética (IRM), considerando três grupos diferentes, ou seja, cognitivamente normal (CN), comprometimento cognitivo leve (CCL) e DA. Adaptamos redes convolucionais com dados fornecidos principalmente pela ADNI e avaliamos no desafio CADDementia, resultando em um cenário mais próximo das condições no mundo real, em que um sistema DAC é usado em um conjunto de dados diferente daquele usado no treinamento. Os principais desafios e contribuições da nossa pesquisa incluem a criação de um sistema de aprendizagem profunda que seja totalmente automático e comparativamente rápido, ao mesmo tempo em que apresenta resultados competitivos, sem usar qualquer conhecimento específico de domínio. Nomeamos nossa melhor arquitetura ADNet (Alzheimer's Disease Network) e nosso melhor método ADNet-DA (ADNet com adaptação de domínio), o qual superou a maioria das submissões no CADDementia, todas utilizando conhecimento prévio da doença, como regiões de interesse específicas do cérebro. A principal razão para não usar qualquer informação da doença em nosso sistema é fazer com que ele aprenda e extraia padrões relevantes de regiões importantes do cérebro automaticamente, que podem ser usados para apoiar os padrões atuais de diagnóstico e podem inclusive auxiliar em novas descobertas para diferentes ou novas doenças. Após explorar uma série de técnicas de visualização para interpretação de modelos, associada à inteligência artificial explicável (XAI), acreditamos que nosso método possa realmente ser empregado na prática médica. Ao diagnosticar pacientes, é possível que especialistas usem a ADNet para gerar uma diversidade de visualizações explicativas para uma determinada imagem, conforme ilustrado em nossa pesquisa, enquanto a ADNet-DA pode ajudar com o diagnóstico. Desta forma, os especialistas podem chegar a uma decisão mais informada e em menos tempoAbstract: Dementia by Alzheimer's disease (AD) is a clinical syndrome characterized by multiple cognitive problems, including difficulties in memory, executive functions, language and visuospatial skills. Being the most common form of dementia, this disease kills more than breast cancer and prostate cancer combined, and it is the sixth leading cause of death in the United States. Neuroimaging is one of the most promising areas of research for early detection of AD structural biomarkers, where a non-invasive technique is used to capture a digital image of the brain, from which specialists extract patterns and features of the disease. In this context, computer-aided diagnosis (CAD) systems are approaches that aim at assisting doctors and specialists in interpretation of medical data to provide diagnoses for patients. In particular, convolutional neural networks (CNNs) are a special kind of artificial neural network (ANN), which were inspired by how the visual system works, and, in this sense, have been increasingly used in computer vision tasks, achieving impressive results. In our research, one of the main goals was bringing to bear what is most advanced in deep learning research (e.g., CNN) to solve the difficult problem of identifying AD structural biomarkers in magnetic resonance imaging (MRI), considering three different groups, namely, cognitively normal (CN), mild cognitive impairment (MCI), and AD. We tailored convolutional networks with data primarily provided by ADNI, and evaluated them on the CADDementia challenge, thus resulting in a scenario very close to the real-world conditions, in which a CAD system is used on a dataset differently from the one used for training. The main challenges and contributions of our research include devising a deep learning system that is both completely automatic and comparatively fast, while also presenting competitive results, without using any domain specific knowledge. We named our best architecture ADNet (Alzheimer's Disease Network), and our best method ADNet-DA (ADNet with domain adaption), which outperformed most of the CADDementia submissions, all of them using prior knowledge from the disease, such as specific regions of interest of the brain. The main reason for not using any information from the disease in our system is to make it automatically learn and extract relevant patterns from important regions of the brain, which can be used to support current diagnosis standards, and may even assist in new discoveries for different or new diseases. After exploring a number of visualization techniques for model interpretability, associated with explainable artificial intelligence (XAI), we believe that our method can be actually employed in medical practice. While diagnosing patients, it is possible for specialists to use ADNet to generate a diversity of explanatory visualizations for a given image, as illustrated in our research, while ADNet-DA can assist with the diagnosis. This way, specialists can come up with a more informed decision and in less timeMestradoCiência da ComputaçãoMestre em Ciência da Computaçã

Computer aided diagnosis in temporal lobe epilepsy and Alzheimer's dementia

Computer aided diagnosis within neuroimaging must rely on advanced image processing techniques to detect and quantify subtle signal changes that may be surrogate indicators of disease state. This thesis proposes two such novel methodologies that are both based on large volumes of interest, are data driven, and use cross-sectional scans: appearance-based classification (ABC) and voxel-based classification (VBC).The concept of appearance in ABC represents the union of intensity and shape information extracted from magnetic resonance images (MRI). The classification method relies on a linear modeling of appearance features via principal components analysis, and comparison of the distribution of projection coordinates for the populations under study within a reference multidimensional appearance eigenspace. Classification is achieved using forward, stepwise linear discriminant analyses, in multiple cross-validated trials. In this work, the ABC methodology is shown to accurately lateralize the seizure focus in temporal lobe epilepsy (TLE), differentiate normal aging individuals from patients with either Alzheimer's dementia (AD) or Mild Cognitive Impairment (MCI), and finally predict the progression of MCI patients to AD. These applications demonstrated that the ABC technique is robust to different signal changes due to two distinct pathologies, to low resolution data and motion artifacts, and to possible differences inherent to multi-site acquisition.The VBC technique relies on voxel-based morphometry to identify regions of grey and white matter concentration differences between co-registered cohorts of individuals, and then on linear modeling of variables extracted from these regions. Classification is achieved using linear discriminant analyses within a multivariate space composed of voxel-based morphometry measures related to grey and white matter concentration, along with clinical variables of interest. VBC is shown to increase the accuracy of prediction of one-year clinical status from three to four out of five TLE patients having undergone selective amygdalo-hippocampectomy. These two techniques are shown to have the necessary potential to solve current problems in neurological research, assist clinical physicians with their decision-making process and influence positively patient management

Magnetic resonance imaging In Alzheimer’s disease, mild cognitive impairment and normal aging : Multi-template tensor-based morphometry and visual rating

Alzheimer's disease (AD) is the most common neurodegenerative disease preceded by a stage of mild cognitive impairment (MCI). The structural brain changes in AD can be detected more than 20 years before symptoms appear. If we are to reveal early brain changes in AD process, it is important to develop new diagnostic methods. Magnetic resonance imaging (MRI) is an imaging technique used in the diagnosis and monitoring of neurodegenerative diseases. Magnetic resonance imaging can detect the typical signs of brain atrophy of degenerative diseases, but similar changes can also be seen in normal aging. Visual rating methods (VRM) have been developed for visual evaluation of atrophy in dementia. A computer-based tensor-based morphometry (TBM) analysis is capable of assessing the brain volume changes typically encountered in AD. This study compared the VRM and TBM analysis in MCI and AD subjects by cross-sectional and longitudinal examination. The working hypothesis was that TBM analysis would be better than the visual methods in detecting atrophy in the brain. TBM was also used to analyze volume changes in the deep gray matter (DGM). Possible associations between TBM changes and neuropsychological tests performances were examined. This working hypothesis was that the structural DGM changes would be associated with impairments in cognitive functions. In the cross-sectional study, TBM distinguished the MCI from controls more sensitively than VRM, but the methods were equally effective in differentiating AD from MCI and controls. In the longitudinal study, both methods were equally good in the evaluation of atrophy in MCI, if the groups were sufficiently large and the disease progressed to AD. Volume changes were found in DGM structures, and the atrophy of DGM structures was related to cognitive impairment in AD. Based on these results, a TBM analysis is more sensitive in detecting brain changes in early AD as compared to VRM. In addition, the study produced information about the involvement of the deep gray matter in cognitive impairment in AD.Magneettikuvaus Alzheimerin taudissa, lievässä muistihäiriössä ja normaalissa ikääntymisessä: Tensoripohjainen muotoanalyysi ja visuaalinen arviointimenetelmä Alzheimerin tauti (AT) on yleisin dementoiva sairaus, jota edeltää yleensä lievä muistitoimintojen heikentyminen. AT:n aivomuutoksia voidaan todeta yli 20 vuotta ennen sairastumista. Jotta vielä varhaisempia AT:n aivomuutoksia voidaan todeta, on tärkeää kehittää uusia diagnostisia menetelmiä. Magneettikuvausta (MK) käytetään rappeuttavien aivosairauksien diagnostiikassa ja seurannassa. MK:lla voidaan havaita aivorappeumasairauksille tyypillistä kutistumista, mutta samanlaisia muutoksia voi esiintyä myös normaalissa ikääntymisessä. Aivorappeuman arviointiin on kehitetty silmämääräisiä arviointimenetelmiä. Tietokoneperusteinen tensoripohjainen muotoanalyysi (TPM) laskee esimerkiksi AT:lle tyypillisiä aivojen tilavuusmuutoksia. Tämä tutkimus vertaili silmämääräisiä arvioitimenetelmiä ja TPM:ä lievässä muistitoimintojen heikentymisessä ja AT:ssa poikittais- ja pitkittäistutkimuksella. TPM:n oletettiin olevan silmämääräisiä menetelmiä parempi tunnistamaan aivojen kutistumismuutoksia. Lisäksi TPM:llä tutkittiin AT:iin liittyviä aivojen syvän harmaan aiheen muutoksia, joita verrattiin neuropsykologisten testien tuloksiin. Syvän harmaan aineen kutistumisen oletettiin olevan yhteydessä tietojenkäsittelyn heikentymiseen. Tulosten perustella TPM tunnisti AT:iin liittyviä aivomuutoksia silmämääräistä menetelmää paremmin jo lievän muistitoimintojen heikentymisen vaiheessa. AT:iin liittyviä aivomuutoksia löytyi myös aivojen syvästä harmaasta aineesta ja ne olivat osittain yhteydessä neuropsykologisten testien tuloksiin. Tutkimuksen perusteella TPM voi parantaa AT:n varhaisdiagnostiikkaa verrattuna silmämääräisiin arviointimenetelmiin. Tutkimus antoi myös tietoa aivojen syvän harmaan aineen osallisuudesta ihmisen tietojenkäsittelyyn

Quantitative texture analysis in MR imaging in the assessment of Alzheimer’s disease

Alzheimer’s disease (AD) is a progressive neurodegenerative disease which is clinically characterized by cognitive impairment and memory loss. Anatomically, AD initially affects specific structures within the Medial Temporal Lobe (MTL), which are essential for declarative memory. A definitive diagnosis of AD relies on post-mortem biopsy therefore, clinical assessment and cognitive tests are currently used. However, these tests are not sensitive to detect AD in an early stage. The aim of this research was to investigate the usefulness of quantitative Magnetic Resonance Imaging (MRI) and specifically of texture features in the assessment of Mild Cognitive Impairment (MCI) which is the pre-dementia stage and AD. Firstly, two types of magnetic fields where investigated in order to examine whether, a stronger MR magnetic field would benefit quantitative imaging analysis derived from texture features. Secondly, texture features were extracted from the entorhinal cortex and evaluated in the diagnosis and prediction of MCI and AD. To the best of our knowledge this is the first research that investigated how the MR field strength affects texture features and used entorhinal cortex texture features on the assessment of AD. The main results of this PhD showed that (1) texture features could provide more sensitive measures when they are extracted from stronger MRI magnetic field, such as 3T, compared to 1.5T. From a disease classification and prediction perspective, (2) entorhinal cortex texture features provide better classification between Normal Controls (NC), MCI and AD subjects, and (3) better prediction of the conversion from MCI to AD. In conclusion, this research has shown for the first time in the literature that entorhinal cortex texture features from MRI could contribute towards the early classification of AD

An Integrated Neuroimaging Approach for the Prediction and Analysis of Alzheimer’s Disease and its Prodromal Stages

This dissertation proposes to combine magnetic resonance imaging (MRI), positron emission tomography (PET) and a neuropsychological test, Mini-Mental State Examination (MMSE), as input to a multidimensional space for the classification of Alzheimer’s disease (AD) and it’s prodromal stages including amnestic MCI (aMCI) and non-amnestic MCI (naMCI). An assessment is provided on the effect of different MRI normalization techniques on the prediction of AD. Statistically significant variables selected for each combination model were used to construct the classification space using support vector machines. To combine MRI and PET, orthogonal partial least squares to latent structures is used as a multivariate analysis to discriminate between AD, early and late MCI (EMCI and LMCI) from cognitively normal (CN)s. In addition, this dissertation proposes a new effective mean indicator (EMI) method for distinguishing stages of AD from CN. EMI utilizes the mean of specific top-ranked measures, determined by incremental error analysis, to achieve optimal separation of AD and CN. For AD vs. CN, the two most discriminative volumetric variables (right hippocampus and left inferior lateral ventricle), when combined with MMSE scores, provided an average accuracy of 92.4% (sensitivity: 84.0%; specificity: 96.1%). MMSE scores were found to improve classification accuracy by 8.2% and 12% for aMCI vs. CN and naMCI vs. CN, respectively. Brain atrophy was almost evenly seen on both sides of the brain for AD subjects, which was different from right side dominance for aMCI and left side dominance for naMCI. Findings suggest that subcortical volume need not be normalized, whereas cortical thickness should be normalized either by intracranial volume or the mean thickness. Furthermore, MRI and PET had comparable predictive power in separating AD from CN. For the EMCI prediction, cortical thickness was found to be the best predictor, even better than using all features together. Validation with an external test set demonstrated that best of feature-selected models for the LMCI group was able to classify 83% of the LMCI subjects. The EMI-based method achieved an accuracy of 92.7% using only MRI features. The performance of the EMI-based method along with its simplicity suggests great potential for its use in clinical trials