Prediction of neurodegenerative diseases from functional brain imaging data

Neurodegenerative diseases are a challenge, especially in thedeveloped society where life expectancy is high. Since these diseasesprogress slowly, they are not easy to diagnose at an earlystage. Moreover, they portray similar disease features, which makesthem hard to differentiate. In this thesis, the objective was todevise techniques to extract biomarkers from brain data for theprediction and classification of neurodegenerative diseases, inparticular parkinsonian syndromes. We used principal componentanalysis in combination with the scaled subprofile model to extractfeatures from the brain data to classify these disorders. Thereafter,the features were provided to several classifiers, i.e., decisiontrees, generalized matrix learning vector quantization, and supportvector machine to classify the parkinsonian syndromes. A validationof the classifiers was performed.The decision tree method was compared to the stepwise regressionmethod which aims at linearly combining a few good principalcomponents. The stepwise regression method performed better than thedecision tree method in the classification of the parkinsoniansyndromes. Combining the two methods is feasible. The decision treeshelped us to visualize the classification results, hence providing aninsight into the distribution of features. Both generalized matrixlearning vector quantization and support vector machine are betterthan the decision tree method in the classification of early-stageparkinsonian syndromes.All the classification methods used in this thesis performed well withlater disease stage data. We conclude that generalized matrix learningvector quantization and decision tree methods can be recommended forfurther research on neurodegenerative disease classification andprediction

Prediction of neurodegenerative diseases from functional brain imaging data

Neurodegenerative diseases are a challenge, especially in the developed society where life expectancy is high. Since these diseases progress slowly, they are not easy to diagnose at an early stage. Moreover, they portray similar disease features, which makes them hard to differentiate. In this thesis, the objective was to devise techniques to extract biomarkers from brain data for the prediction and classification of neurodegenerative diseases, in particular parkinsonian syndromes. We used principal component analysis in combination with the scaled subprofile model to extract features from the brain data to classify these disorders. Thereafter, the features were provided to several classifiers, i.e., decision trees, generalized matrix learning vector quantization, and support vector machine to classify the parkinsonian syndromes. A validation of the classifiers was performed. The decision tree method was compared to the stepwise regression method which aims at linearly combining a few good principal components. The stepwise regression method performed better than the decision tree method in the classification of the parkinsonian syndromes. Combining the two methods is feasible. The decision trees helped us to visualize the classification results, hence providing an insight into the distribution of features. Both generalized matrix learning vector quantization and support vector machine are better than the decision tree method in the classification of early-stage parkinsonian syndromes. All the classification methods used in this thesis performed well with later disease stage data. We conclude that generalized matrix learning vector quantization and decision tree methods can be recommended for further research on neurodegenerative disease classification and prediction

Machine Learning Based Analysis of FDG-PET Image Data for the Diagnosis of Neurodegenerative Diseases

Alzheimer's disease (AD) and Parkinson's disease (PD) are two common, progressive neurodegenerative brain disorders. Their diagnosis is very challenging at an early disease stage, if based on clinical symptoms only. Brain imaging techniques such as [18F]-fluoro-deoxyglucose positron emission tomography (FDG-PET) can provide important additional information with respect to changes in the cerebral glucose metabolism. In this study, we use machine learning techniques to perform an automated classification of FDG-PET data. The approach is based on the extraction of features by applying the scaled subprofile model with principal component analysis (SSM/PCA) in order to extract characteristics patterns of glucose metabolism. These features are then used for discriminating healthy controls, PD and AD patients by means of two machine learning frameworks: Generalized Matrix Learning Vector Quantization (GMLVQ) with local and global relevance matrices, and Support Vector Machines (SVMs) with a linear kernel. Datasets from different neuroimaging centers are considered. Results obtained for the individual centers, show that reliable classification is possible. We demonstrate, however, that cross-center classification can be problematic due to potential center-specific characteristics of the available FDG-PET data

Comparison of decision tree and stepwise regression methods in classification of FDG-PET brain data using SSM/PCA features

Objective: To compare the stepwise regression (SR) method and the decision tree (DT) method for classification of parkinsonian syndromes. Method: We applied the scaled subprofile model/principal component analysis (SSM/PCA) method to FDG-PET brain image data to obtain covariance patterns and the corresponding subject scores. The subject scores formed the input to the C4.5 decision tree algorithm to classify the subject brain images. For the SR method, scatter plots and receiver operating characteristic (ROC) curves indicate the subject classifications. We then compare the decision tree classifier results with those of the SR method. Results: We found out that the SR method performs slightly better than the DT method. We attribute this to the fact that the SR method uses a linear combination of the best features to form one robust feature, unlike the DT method. However, when the same robust feature is used as the input for the DT classifier, the performance is as high as that of the SR method. Conclusion: Even though the SR method performs better than the DT method, including the SR procedure in the DT classification yields a better performance. Additionally, the decision tree approach is more suitable for human interpretation and exploration than the SR method

Visualization of Decision Tree State for the Classification of Parkinson's Disease

Decision trees have been shown to be effective at classifying subjects with Parkinson's disease when provided with features (subject scores) derived from FDG-PET data. Such subject scores have strong discriminative power but are not intuitive to understand. We therefore augment each decision node with thumbnails of the principal component (PC) images from which the subject scores are computed, and also provide labeled scatter plots of the distribution of scores. These plots allow the progress of individual subjects to be traced through the tree and enable the user to focus on complex or unexpected classifications. In addition, we present a visual representation of a typical brain activity pattern arriving at each leaf node, and show how this can be compared to a known reference to validate the behaviour of the tree

Validation of Parkinsonian Disease-Related Metabolic Brain Patterns

Background: The objective of this study was to validate disease-related metabolic brain patterns for Parkinson’s disease, multiple system atrophy, and progressive supranuclear palsy. Methods: The study included 20 patients with Parkinson’s disease, 21 with multiple system atrophy, and 17 with progressive supranuclear palsy, all of whom had undergone a clinically motivated [18F]-fluoro-deoxyglucose positron emission tomography scan at an early stage of their disease. At a follow-up time after the scan of 2–4 years, a clinical diagnosis was made according to established clinical research criteria. Patient groups were compared with 18 healthy controls using a multivariate covariance image analysis technique called scaled subprofile model/principal component analysis. Results: Disease-related metabolic brain patterns for these parkinsonian disorders were identified. Validation showed that these patterns were highly discriminative of the 3 disorders. Conclusions: Early diagnosis of parkinsonian disorders is feasible when the expression of disease-related metabolic brain patterns is quantified at a single-subject level

Validation of Parkinsonian Disease-Related Metabolic Brain Patterns

Background: The objective of this study was to validate disease-related metabolic brain patterns for Parkinson’s disease, multiple system atrophy, and progressive supranuclear palsy. Methods: The study included 20 patients with Parkinson’s disease, 21 with multiple system atrophy, and 17 with progressive supranuclear palsy, all of whom had undergone a clinically motivated [18F]-fluoro-deoxyglucose positron emission tomography scan at an early stage of their disease. At a follow-up time after the scan of 2–4 years, a clinical diagnosis was made according to established clinical research criteria. Patient groups were compared with 18 healthy controls using a multivariate covariance image analysis technique called scaled subprofile model/principal component analysis. Results: Disease-related metabolic brain patterns for these parkinsonian disorders were identified. Validation showed that these patterns were highly discriminative of the 3 disorders. Conclusions: Early diagnosis of parkinsonian disorders is feasible when the expression of disease-related metabolic brain patterns is quantified at a single-subject level.