Dimensionality Reduction, Classification and Reconstruction Problems in Statistical Learning Approaches

Statistical learning theory explores ways of estimating functional dependency from a given collection of data. The specific sub-area of supervised statistical learning covers important models like Perceptron, Support Vector Machines (SVM) and Linear Discriminant Analysis (LDA). In this paper we review the theory of such models and compare their separating hypersurfaces for extracting group-differences between samples. Classification and reconstruction are the main goals of this comparison. We show recent advances in this topic of research illustrating their application on face and medical image databases.Statistical learning theory explores ways of estimating functional dependency from a given collection of data. The specific sub-area of supervised statistical learning covers important models like Perceptron, Support Vector Machines (SVM) and Linear Discriminant Analysis (LDA). In this paper we review the theory of such models and compare their separating hypersurfaces for extracting group-differences between samples. Classification and reconstruction are the main goals of this comparison. We show recent advances in this topic of research illustrating their application on face and medical image databases

Generative Embedding for Model-Based Classification of fMRI Data

Decoding models, such as those underlying multivariate classification algorithms, have been increasingly used to infer cognitive or clinical brain states from measures of brain activity obtained by functional magnetic resonance imaging (fMRI). The practicality of current classifiers, however, is restricted by two major challenges. First, due to the high data dimensionality and low sample size, algorithms struggle to separate informative from uninformative features, resulting in poor generalization performance. Second, popular discriminative methods such as support vector machines (SVMs) rarely afford mechanistic interpretability. In this paper, we address these issues by proposing a novel generative-embedding approach that incorporates neurobiologically interpretable generative models into discriminative classifiers. Our approach extends previous work on trial-by-trial classification for electrophysiological recordings to subject-by-subject classification for fMRI and offers two key advantages over conventional methods: it may provide more accurate predictions by exploiting discriminative information encoded in ‘hidden’ physiological quantities such as synaptic connection strengths; and it affords mechanistic interpretability of clinical classifications. Here, we introduce generative embedding for fMRI using a combination of dynamic causal models (DCMs) and SVMs. We propose a general procedure of DCM-based generative embedding for subject-wise classification, provide a concrete implementation, and suggest good-practice guidelines for unbiased application of generative embedding in the context of fMRI. We illustrate the utility of our approach by a clinical example in which we classify moderately aphasic patients and healthy controls using a DCM of thalamo-temporal regions during speech processing. Generative embedding achieves a near-perfect balanced classification accuracy of 98% and significantly outperforms conventional activation-based and correlation-based methods. This example demonstrates how disease states can be detected with very high accuracy and, at the same time, be interpreted mechanistically in terms of abnormalities in connectivity. We envisage that future applications of generative embedding may provide crucial advances in dissecting spectrum disorders into physiologically more well-defined subgroups

Métodos para la Clasificación Automática de Imágenes de Resonancia Magnética del Cerebro

Las imágenes digitales adquiridas como resultado de la Resonancia Magnética son ampliamente utilizadas para diagnosticar, estudiar y pronosticar la evolución y respuesta al tratamiento de una gran variedad de patologías. La interpretación adecuada de estas imágenes requiere un extenso y complejo análisis asociado que involucra numerosas técnicas informáticas. En la práctica, las principales dificultades para clasificar las Imágenes de Resonancia Magnética (IRM) utilizando métodos de Aprendizaje Automático son el extenso volumen de información asociada a este formato, el ruido intrínseco y la normalmente escasa cantidad de sujetos presentes en las investigaciones, lo que dificulta notablemente su procesamiento. Este trabajo analiza el comportamiento en situaciones reales de las técnicas más utilizadas en el estado del arte para la clasificación automática de IRM cerebrales como Discriminante Lineal, Redes Neuronales y Maquina de Vectores de Soporte, así como la influencia de distintos pre-procesamientos de la imagen (alineamiento, recorte de la imagen, extracción de ROIs) en el resultado de la clasificación. Además, se investigan adicionalmente otros factores importantes, como el uso de diferentes tipos de imágenes de Resonancia Magnética (T2 y Difusión) y la incorporación adicional de sujetos de control al entrenamiento. Con este fin se han utilizado las bases de datos de activación cerebral por apetito y cáncer proporcionadas por el Laboratorio de Imagen y Espectroscopia por Resonancia Magnética del Instituto de Investigaciones Biomédicas Alberto Sols (IIB) CSIC/UAM, en Madrid, España. El objetivo general de este estudio es detectar qué tipo de pre-procesamiento y qué algoritmos de clasificación proporcionan mejor clasificación automática. Los resultados obtenidos muestran como mejor secuencia de procesamiento; el alineamiento con recorte y la reducción dimensional, previo a la clasificación utilizando SVM (Maquina de Vectores de Soporte, Support Vector Machine).Digital images acquired in Magnetic Resonance Imaging (MRI) are widely used to diagnose, study and predict the evolution and response to treatment of a variety of important pathologies. Adequate interpretation of these images requires an extensive and complex associated analysis involving numerous computer techniques. In practice, the main difficulties to classify MRI using machine learning methods are the large volume of information associated to this format, the intrinsic noise and the reduced number of subjects normally present in real research conditions, two circumstances resulting in remarkably difficult data processing. In this work we systematically investigate the performance in real conditions of the most widely used techniques in automatic classification of brain MRI scans, as Discriminant Analysis (DA), Artificial Neural Networks (ANN) and Support Vector Machine (SVM), as well as the influence of different pre-processing methods of the image (alignment, image cropping , removing ROIs) in the results of the classification. In addition, we further investigate other important factors such as the use of different types of magnetic resonance images (T2w and Diffusion) or the incorporation of additional control subjects when training the classifier. We used two different databases of MRI (cerebral activation by appetite, and response of brain tumours to treatment), both provided by the Laboratory of Imaging and Spectroscopy by Magnetic Resonance Spectroscopy at the Institute of Biomedical Research Alberto Sols (IIB) CSIC / UAM, Madrid, Spain. The main goal of the study is to identify which pre-processing strategies and classification algorithms provide better automatic classification results. Results show as best processing sequence, alignment with clipping and dimensional reduction prior to classification using SVM (Support Vector Machine)

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

Statistical analysis for longitudinal MR imaging of dementia

Serial Magnetic Resonance (MR) Imaging can reveal structural atrophy in the brains of subjects with neurodegenerative diseases such as Alzheimer’s Disease (AD). Methods of computational neuroanatomy allow the detection of statistically significant patterns of brain change over time and/or over multiple subjects. The focus of this thesis is the development and application of statistical and supporting methodology for the analysis of three-dimensional brain imaging data. There is a particular emphasis on longitudinal data, though much of the statistical methodology is more general. New methods of voxel-based morphometry (VBM) are developed for serial MR data, employing combinations of tissue segmentation and longitudinal non-rigid registration. The methods are evaluated using novel quantitative metrics based on simulated data. Contributions to general aspects of VBM are also made, and include a publication concerning guidelines for reporting VBM studies, and another examining an issue in the selection of which voxels to include in the statistical analysis mask for VBM of atrophic conditions. Research is carried out into the statistical theory of permutation testing for application to multivariate general linear models, and is then used to build software for the analysis of multivariate deformation- and tensor-based morphometry data, efficiently correcting for the multiple comparison problem inherent in voxel-wise analysis of images. Monte Carlo simulation studies extend results available in the literature regarding the different strategies available for permutation testing in the presence of confounds. Theoretical aspects of longitudinal deformation- and tensor-based morphometry are explored, such as the options for combining within- and between-subject deformation fields. Practical investigation of several different methods and variants is performed for a longitudinal AD study