Advances in Spectral Learning with Applications to Text Analysis and Brain Imaging

Spectral learning algorithms are becoming increasingly popular in data-rich domains, driven in part by recent advances in large scale randomized SVD, and in spectral estimation of Hidden Markov Models. Extensions of these methods lead to statistical estimation algorithms which are not only fast, scalable, and useful on real data sets, but are also provably correct. Following this line of research, we make two contributions. First, we propose a set of spectral algorithms for text analysis and natural language processing. In particular, we propose fast and scalable spectral algorithms for learning word embeddings -- low dimensional real vectors (called Eigenwords) that capture the “meaning” of words from their context. Second, we show how similar spectral methods can be applied to analyzing brain images. State-of-the-art approaches to learning word embeddings are slow to train or lack theoretical grounding; We propose three spectral algorithms that overcome these limitations. All three algorithms harness the multi-view nature of text data i.e. the left and right context of each word, and share three characteristics: 1). They are fast to train and are scalable. 2). They have strong theoretical properties. 3). They can induce context-specific embeddings i.e. different embedding for “river bank” or “Bank of America”. \end{enumerate} They also have lower sample complexity and hence higher statistical power for rare words. We provide theory which establishes relationships between these algorithms and optimality criteria for the estimates they provide. We also perform thorough qualitative and quantitative evaluation of Eigenwords and demonstrate their superior performance over state-of-the-art approaches. Next, we turn to the task of using spectral learning methods for brain imaging data. Methods like Sparse Principal Component Analysis (SPCA), Non-negative Matrix Factorization (NMF) and Independent Component Analysis (ICA) have been used to obtain state-of-the-art accuracies in a variety of problems in machine learning. However, their usage in brain imaging, though increasing, is limited by the fact that they are used as out-of-the-box techniques and are seldom tailored to the domain specific constraints and knowledge pertaining to medical imaging, which leads to difficulties in interpretation of results. In order to address the above shortcomings, we propose Eigenanatomy (EANAT), a general framework for sparse matrix factorization. Its goal is to statistically learn the boundaries of and connections between brain regions by weighing both the data and prior neuroanatomical knowledge. Although EANAT incorporates some neuroanatomical prior knowledge in the form of connectedness and smoothness constraints, it can still be difficult for clinicians to interpret the results in specific domains where network-specific hypotheses exist. We thus extend EANAT and present a novel framework for prior-constrained sparse decomposition of matrices derived from brain imaging data, called Prior Based Eigenanatomy (p-Eigen). We formulate our solution in terms of a prior-constrained l1 penalized (sparse) principal component analysis. Experimental evaluation confirms that p-Eigen extracts biologically-relevant, patient-specific functional parcels and that it significantly aids classification of Mild Cognitive Impairment when compared to state-of-the-art competing approaches

Integrated Structural And Functional Biomarkers For Neurodegeneration

Alzheimer\u27s Disease consists of a complex cascade of pathological processes, leading to the death of cortical neurons and development of dementia. Because it is impossible to regenerate neurons that have already died, a thorough understanding of the earlier stages of the disease, before significant neuronal death has occurred, is critical for developing disease-modifying therapies. The various components of Alzheimer\u27s Disease pathophysiology necessitate a variety of measurement techniques. Image-based measurements known as biomarkers can be used to assess cortical thinning and cerebral blood flow, but non-imaging characteristics such as performance on cognitive tests and age are also important determinants of risk of Alzheimer\u27s Disease. Incorporating the various imaging and non-imaging sources of information into a scientifically interpretable and statistically sound model is challenging. In this thesis, I present a method to include imaging data in standard regression analyses in a data-driven and anatomically interpretable manner. I also introduce a technique for disentangling the effect of cortical structure from blood flow, enabling a clearer picture of the signal carried by cerebral blood flow beyond the confounding effects of anatomical structure. In addition to these technical developments in multi-modal image analysis, I show the results of two clinically-oriented studies focusing on the relative importance of various biomarkers for predicting presence of Alzheimer\u27s Disease pathology in the earliest stages of disease. In the first, I present evidence that white matter hyperintensities, a marker of small vessel disease, are more highly associated with Alzheimer\u27s Disease pathology than current mainstream imaging biomarkers in elderly control patients. In the second, I show that once Alzheimer\u27s Disease has progressed to the point of noticeable cognitive decline, cognitive tests are as predictive of presence of Alzheimer\u27s pathology as standard imaging biomarkers. Taken together, these studies demonstrate that the relative importance of biomarkers and imaging modalities changes over the course of disease progression, and sophisticated data-driven methods for combining a variety of modalities is likely to lead to greater biological insight into the disease process than a single modality

Relation of Childhood Home Environment to Cortical Thickness in Late Adolescence: Specificity of Experience and Timing

What are the long-term effects of childhood experience on brain development? Research with animals shows that the quality of environmental stimulation and parental nurturance both play important roles in shaping lifelong brain structure and function. Human research has so far been limited to the effects of abnormal experience and pathological development. Using a unique longitudinal dataset of in-home measures of childhood experience at ages 4 and 8 and MRI acquired in late adolescence, we were able to relate normal variation in childhood experience to later life cortical thickness. Environmental stimulation at age 4 predicted cortical thickness in a set of automatically derived regions in temporal and prefrontal cortex. In contrast, age 8 experience was not predictive. Parental nurturance was not predictive at either age. This work reveals an association between childhood experience and later brain structure that is specific relative to aspects of experience, regions of brain, and timing

Neuroconductor: an R platform for medical imaging analysis

Neuroconductor (https://neuroconductor.org) is an open-source platform for rapid testing and dissemination of reproducible computational imaging software. The goals of the project are to: (i) provide a centralized repository of R software dedicated to image analysis, (ii) disseminate software updates quickly, (iii) train a large, diverse community of scientists using detailed tutorials and short courses, (iv) increase software quality via automatic and manual quality controls, and (v) promote reproducibility of image data analysis. Based on the programming language R (https://www.r-project.org/), Neuroconductor starts with 51 inter-operable packages that cover multiple areas of imaging including visualization, data processing and storage, and statistical inference. Neuroconductor accepts new R package submissions, which are subject to a formal review and continuous automated testing. We provide a description of the purpose of Neuroconductor and the user and developer experience

Inferring individual-level variations in the functional parcellation of the cerebral cortex

Objective: Functional parcellation of the cerebral cortex is variable across different subjects or between cognitive states. Ignoring individual - or state - dependent variations in the functional parcellation may lead to inaccurate representations of individual functional connectivity, limiting the precision of interpretations of differences in individual connectivity profiles. However, it is difficult to infer the individual-level variations due to the relatively low robustness of methods for parcellation of individual subjects. Methods: We propose a method called “joint K-means” to robustly parcellate the cerebral cortex using fMRI data for contrasts between two states or subjects that intended to characterize variance in individual functional parcellations. The key idea of the proposed method is to jointly infer parcellations in contrasted datasets by iterative descent, while constraining the similarity of the two pathways in searches for local minima to reduce spurious variations. Results: Parcellations of resting-state fMRI datasets from the Human Connectome Project show that the similarity of parcellations for an individual subject studied on two sessions is greater than that between different subjects. Differences in parcellations between subjects are non-uniformly distributed across the cerebral cortex, with clusters of higher variance in the prefrontal, lateral temporal and occipito-parietal cortices. This pattern is reproducible across sessions, between groups and using different numbers of parcels. Conclusion: The individual-level variations inferred by the proposed method are plausible and consistent with the previously reported functional connectivity variability. Significance: The proposed method is a promising tool for investigating relationships between the cerebral functional organization and behavioral differences

Neuroconductor: an R platform for medical imaging analysis

Neuroconductor (https://neuroconductor.org) is an open-source platform for rapid testing and dissemination of reproducible computational imaging software. The goals of the project are to: (i) provide a centralized repository of R software dedicated to image analysis, (ii) disseminate software updates quickly, (iii) train a large, diverse community of scientists using detailed tutorials and short courses, (iv) increase software quality via automatic and manual quality controls, and (v) promote reproducibility of image data analysis. Based on the programming language R (https://www.r-project.org/), Neuroconductor starts with 51 inter-operable packages that cover multiple areas of imaging including visualization, data processing and storage, and statistical inference. Neuroconductor accepts new R package submissions, which are subject to a formal review and continuous automated testing. We provide a description of the purpose of Neuroconductor and the user and developer experience

Application of Deep Learning to Brain Connectivity Classification in Large MRI Datasets

The use of machine learning for whole-brain classification of magnetic resonance imaging (MRI) data is of clear interest, both for understanding phenotypic differences in brain structure and function and for diagnostic applications. Developments of deep learning models in the past decade have revolutionized photographic image and speech recognition, bringing promise to do the same to other fields of science. However, there are many practical and theoretical challenges in the translation of such methods to the unique context of MRIs of the brain. This thesis presents a theoretical underpinning for whole-brain classification of extremely large datasets of multi-site MRIs, including machine learning model architecture, dataset curation methods, machine learning visualization methods, encoding of MRI data, and feature extraction. To replicate large sample sizes typically applied to deep learning models, a dataset of over 50,000 functional and structural MRIs was amassed from nine different databases, and the undertaken analyses were conducted on three covariates commonly found across these collections: sex, resting state/task, and autism spectrum disorder. I find that deep learning is not only a method that has promise for clinical application in the future, but also a powerful statistical tool for analyzing complex, nonlinear relationships in brain data where conventional statistics may fail. However, results are also dependent on factors such as dataset imbalances, confounding factors such as motion and head size, selected methods of encoding MRI data, variability of machine learning models and selected methods of visualizing the machine learning results. In this thesis, I present the following methodological innovations: (1) a method of balancing datasets as a means of regressing out measurable confounding factors; (2) a means of removing spatial biases from deep learning visualization methods; (3) methods of encoding functional and structural datasets as connectivity matrices; (4) the use of ensemble models and convolutional neural network architectures to improve classification accuracy and consistency; (5) adaptation of deep learning visualization methods to study brain connections utilized in the classification process. Additionally, I discuss interpretations, limitations, and future directions of this research.Gates Cambridge Scholarshi

Organización topológica de la corteza cerebral en el envejecimiento normal y en la enfermedad de Alzheimer

Programa de Doctorado en NeurocienciasLa corteza cerebral representa el más avanzado sistema de computación biológica que existe en la naturaleza. Su sofisticada organización anatómica y funcional facilita el procesamiento de la información en paralelo, de forma distribuida y jerarquizada; siendo precisamente su nivel de eficiencia uno de los aspectos que nos diferencia de otros mamíferos superiores. Estas propiedades emergentes pueden estudiarse in vivo mediante la aplicación de la Teoría de Grafos a los mapas cerebrales obtenidos con diferentes técnicas de neuroimagen. En este contexto, estudios previos han mostrado que las propiedades topológicas del cerebro humano varían en función del número de regiones incluidas en el grafo, añadiendo un grado de incertidumbre a los resultados obtenidos con esta aproximación analítica. Además, el envejecimiento se caracteriza por una serie de cambios anátomo-funcionales en la corteza cerebral que alteran su organización topológica, lo cual podría afectar a la relación entre los patrones de conectividad estructural y funcional, aspectos que podrían sufrir un deterioro adicional durante el proceso de neurodegeneración que acompaña a la enfermedad de Alzheimer (EA). El presente trabajo de Tesis trata de arrojar luz sobre estas cuestiones. Para ello, se han adquirido imágenes cerebrales de resonancia magnética estructural (RM) y de tomografía por emisión de positrones (PET) con el radiotrazador 18F-fluorodeoxiglucosa (FDG) en 29 personas mayores cognitivamente sanas, 29 personas mayores con deterioro cognitivo leve tipo amnésico (DCLa) y 29 pacientes con EA leve. A partir de estas imágenes, se han construido redes estructurales y funcionales de la corteza cerebral para posteriormente determinar si los cambios observados en su organización topológica y en las propiedades de sus nodos permiten caracterizar la transición entre el envejecimiento normal y el patológico. Nuestros resultados confirman, en primer lugar, que la organización topológica de la red cortical estructural cambia significativamente con la escala de parcelación, demostrándose que las parcelaciones óptimas son aquellas compuestas por regiones corticales de aproximadamente 250 mm2 de superficie. En segundo lugar, los resultados indican que la conectividad local es un rasgo predominante en el envejecimiento normal, siendo las áreas sensoriomotoras y visuales las que presentan un mayor nivel de segregación. Por otra parte, las conexiones de larga distancia y la capacidad de integración cortical se restringen a regiones de asociación heteromodales, que mostraron una mayor vulnerabilidad ante ataques simulados de la red. Tal como cabría esperar, este patrón de conectividad estructural guarda una estrecha relación con el patrón de conectividad funcional de personas mayores sanas. Por el contrario, los individuos con DCLa y los pacientes con EA mostraron una organización topológica de la corteza cerebral menos eficiente, caracterizada por la presencia de elementos más aislados a nivel global, hemisférico y lobular, lo cual además de favorecer la segregación reduce la capacidad de integración del sistema cortical. Este deterioro de la conectividad estructural y funcional produce además una disminución del acoplamiento local y un aumento del acoplamiento global entre ambas redes. En conjunto, nuestros resultados muestran cómo la topología de la red cortical y los atributos de sus nodos se alteran progresivamente desde el envejecimiento normal hasta llegar a la EA. En la era de la medicina personalizada y en una sociedad marcadamente envejecida, se espera que en un futuro no tan lejano estas aproximaciones contribuyan a mejorar el diagnóstico temprano de la EA así como de otras patologías neurodegenerativas asociadas al envejecimiento.Universidad Pablo de Olavide. Departamento de Fisiología, Anatomía y Biología Celula