Genetic algorithm supported by graphical processing unit improves the exploration of effective connectivity in functional brain imaging

published_or_final_versio

Genetic algorithm supported by graphical processing unit improves the exploration of effective connectivity in functional brain imaging

published_or_final_versio

Probing resting-state functional connectivity in the infant brain: methods and potentiality

Early brain development is characterized by rapid growth and perpetual reconfiguration, driven by a dynamic milieu of heterogeneous processes. Moreover, potent postnatal brain plasticity engenders increased vulnerability to environmental stimuli. However, little is known regarding the ontogeny and temporal manifestations of inter- and intra-regional functional connectivity that comprise functional brain networks. Recently, resting-state functional magnetic resonance imaging (fMRI) emerged as a promising non-invasive neuroinvestigative tool, measuring spontaneous fluctuations in blood oxygen level dependent (BOLD) signal at rest that reflect baseline neuronal activity. Its application has expanded to infant populations in the past decade, providing unprecedented insight into functional organization of the developing brain, as well as early biomarkers of abnormal/ disease states. However, rapid extension of the resting-state technique to infant populations leaves many methodological issues need to be resolved prior to standardization of the technique. The purpose of this thesis is to describe a protocol for intrinsic functional connectivity analysis, and extraction of resting-state networks in infants <12 months of age using the data-driven approach independent component analysis (ICA). To begin, we review the evolution of resting-state fMRI application in infant populations, including the biological premise for neural networks. Next, we present a protocol designed such that investigators without previous knowledge in the field can implement the analysis and reliably obtain viable results consistent with previous literature. Presented protocol provides detailed, albeit basic framework for RSN analysis, with interwoven discussion of basic theory behind each technique, as well as the rationale behind selecting parameters. The overarching goal is to catalyze efforts towards development of robust, infant-specific acquisition and preprocessing pipelines, as well as promote greater transparency by researchers regarding methods used. Finally, we review the literature, current methodological challenges and potential future directions for the field of infant resting-state fMRI

The Connectome Viewer Toolkit: An Open Source Framework to Manage, Analyze, and Visualize Connectomes

Advanced neuroinformatics tools are required for methods of connectome mapping, analysis, and visualization. The inherent multi-modality of connectome datasets poses new challenges for data organization, integration, and sharing. We have designed and implemented the Connectome Viewer Toolkit – a set of free and extensible open source neuroimaging tools written in Python. The key components of the toolkit are as follows: (1) The Connectome File Format is an XML-based container format to standardize multi-modal data integration and structured metadata annotation. (2) The Connectome File Format Library enables management and sharing of connectome files. (3) The Connectome Viewer is an integrated research and development environment for visualization and analysis of multi-modal connectome data. The Connectome Viewer's plugin architecture supports extensions with network analysis packages and an interactive scripting shell, to enable easy development and community contributions. Integration with tools from the scientific Python community allows the leveraging of numerous existing libraries for powerful connectome data mining, exploration, and comparison. We demonstrate the applicability of the Connectome Viewer Toolkit using Diffusion MRI datasets processed by the Connectome Mapper. The Connectome Viewer Toolkit is available from http://www.cmtk.org

Bayesian Methods in Brain Connectivity Change Point Detection with EEG Data and Genetic Algorithm

Human brain is processing a great amount of information everyday, and our brain regions are organized optimally for this information processing. There have been increasing number of studies focusing on functional or effective connectivity in human brain regions in the last decade. In this dissertation, Bayesian methods in Brain connectivity change point detection are discussed. First, a review of state-of-the-art Bayesian-inference-based methods applied to functional magnetic resonance imaging (fMRI) data is carried out, three methods are reviewed and compared. Second, the Bayesian connectivity change point model is extended to change point analysis in electroencephalogram (EEG) data, and the ability of EEG measures of frontal and temporo-parietal activity during mindfulness therapy to track response to dysfunctional anxiety patients\u27 treatment is tested successfully. Then an optimized method for Bayesian connectivity change point model with genetic algorithm (GA) is proposed and proved to be more efficient in change point detection. And due to the good parallel performance of GA, the change point detection method can be parallelized in GPU or multi-processor computers as a future work. Furthermore, a more advanced Bayesian bi-cluster connectivity change point model is developed to simultaneously detect change point of each subject within a group, and cluster subjects into different groups according to their change point distribution and connectivity dynamics. The method is also validated on experimental datasets. After discussing brain change point detection, a review of Bayesian analysis of complex mutations in HBV HCV and HIV studies is also included as part of my Ph.D. work. Finally, conclusions are drawn and future work is discussed

Contributions to the study of Austism Spectrum Brain conectivity

164 p.Autism Spectrum Disorder (ASD) is a largely prevalent neurodevelopmental condition with a big social and economical impact affecting the entire life of families. There is an intense search for biomarkers that can be assessed as early as possible in order to initiate treatment and preparation of the family to deal with the challenges imposed by the condition. Brain imaging biomarkers have special interest. Specifically, functional connectivity data extracted from resting state functional magnetic resonance imaging (rs-fMRI) should allow to detect brain connectivity alterations. Machine learning pipelines encompass the estimation of the functional connectivity matrix from brain parcellations, feature extraction and building classification models for ASD prediction. The works reported in the literature are very heterogeneous from the computational and methodological point of view. In this Thesis we carry out a comprehensive computational exploration of the impact of the choices involved while building these machine learning pipelines

Computational and Imaging Methods for Studying Neuronal Populations during Behavior

One of the central questions in neuroscience is how the nervous system generates and regulates behavior. To understand the neural code for any behavior, an ideal experiment would entail (i) quantitatively defining that behavior, (ii) recording neuronal activity in relevant brain regions to identify the underlying neuronal circuits and eventually (iii) manipulating them to test their function. Novel methods in neuroscience have greatly advanced our abilities to conduct such experiments but are still insufficient. In this thesis, I developed methods for these three goals. In Chapter 2, I describe an automatic behavior identification and classification method for the cnidarian Hydra vulgaris using machine learning. In Chapter 3, I describe a fast volumetric two-photon microscope with dual-color laser excitation that can image in 3D the activity of populations of neurons from visual cortex of awake mice. In Chapter 4, I present a machine learning method that identifies cortical ensembles and pattern completion neurons in mouse visual cortex, using two-photon calcium imaging data. These methods advance current technologies, providing opportunities for new discoveries

Implementation of a 3D CNN for COPD classification

Segons les prediccions de la Organització Mundial de la Salut (OMS) pels voltants del 2030 la Malaltia Pulmonar Obstructiva Crònica (MPOC) es convertirá en la tercera causa de mort en tot el món. L’MPOC és una patologia que afecta a les vies respiratòries i als pulmons. Avui en dia esdevé crónica i incurable però, és una malaltia tractable i prevenible. Fins ara les proves de diagnòstic usades per a detectar l’MPOC es basen en l’espirometria, aquesta prova, tot i indicar el grau d’obstrucció al pas de l’aire que es produeix en els pulmons, sovint no és molt fiable. És per aquest motiu que s’estan començant a usar tècniques basades en algorismes de Deep Learning per a la classificaió més acurada d’aquesta patologia, basant-se en imatges tomogràfiques de pacients malalts d’MPOC. Les xarxes neuronals convolucionals en tres dimensions (3D-CNN) en són un exemple. A partir de les dades i les imatges obtingudes en l’estudi observacional d’ECLIPSE proporcionades per l’equip de recerca de BRGE de ISGlobal, s’implementa una 3D-CNN per a la classificació de pacients amb risc d’MPOC. Aquest treball té com a objectiu desenvolupar una recerca extensa sobre la recerca actual en aquest àmbit i proposa millores per a l’optimització i reducció del cost computacional d’una 3D-CNN per aquest cas d’estudi concret.Según las predicciones de la Organización Mundial de la Salud (OMS), para alrededor del 2030, la Enfermedad Pulmonar Obstructiva Crónica (EPOC) se convertirá en la tercera causa de muerte en todo el mundo. La EPOC es una enfermedad que afecta las vías respiratorias y los pulmones. En la actualidad, se considera crónica e incurable, pero es una enfermedad tratable y prevenible. Hasta ahora, las pruebas de diagnóstico utilizadas para detectar la EPOC se basan en la espirometría. Esta prueba, a pesar de indicar el grado de obstrucción en el flujo de aire que ocurre en los pulmones, a menudo no es muy confiable. Es por esta razón que se están empezando a utilizar técnicas basadas en algoritmos de Deep Learning para una clasificación más precisa de esta patología, utilizando imágenes tomográficas de pacientes enfermos de EPOC. Las redes neuronales convolucionales en tres dimensiones (3D-CNN) son un ejemplo de esto. A partir de los datos y las imágenes obtenidas en el estudio observacional ECLIPSE proporcionado por el equipo de investigación de BRGE de ISGlobal, se implementa una 3D-CNN para la clasificación de pacientes con riesgo de EPOC. Este trabajo tiene como objetivo desarrollar una investigación exhaustiva sobre la investigación actual en este campo y propone mejoras para la optimización y reducción del costo computacional de una 3D-CNN para este caso de estudio concreto.According to predictions by the World Health Organization (WHO), by around 2030, Chronic Obstructive Pulmonary Disease (COPD) will become the third leading cause of death worldwide. COPD is a condition that affects the respiratory tract and lungs. Currently, it is considered chronic and incurable, but it is a treatable and preventable disease. Up to now, diagnostic tests used to detect COPD have been based on spirometry. Despite indicating the degree of airflow obstruction in the lungs, this test is often not very reliable. That is why techniques based on Deep Learning algorithms are being increasingly used for more accurate classification of this pathology, based on tomographic images of COPD patients. Three-dimensional Convolutional Neural Networks (3D-CNN) are an example of such techniques. Based on the data and images obtained in the observational study called ECLIPSE, provided by the research team at BRGE of ISGlobal, a 3D-CNN is implemented for the classification of patients at risk of COPD. This work aims to conduct extensive research on the current state of research in this field and proposes improvements for the optimization and reduction of the computational cost of a 3D-CNN for this specific case study

Learning Graphical Models of Multivariate Functional Data with Applications to Neuroimaging

This dissertation investigates the functional graphical models that infer the functional connectivity based on neuroimaging data, which is noisy, high dimensional and has limited samples. The dissertation provides two recipes to infer the functional graphical model: 1) a fully Bayesian framework 2) an end-to-end deep model. We first propose a fully Bayesian regularization scheme to estimate functional graphical models. We consider a direct Bayesian analog of the functional graphical lasso proposed by Qiao et al. (2019).. We then propose a regularization strategy via the graphical horseshoe. We compare both Bayesian approaches to the frequentist functional graphical lasso, and compare the Bayesian functional graphical lasso to the functional graphical horseshoe. We applied the proposed methods with electroencephalography (EEG) data and diffusion tensor imaging (DTI) data. We find that the Bayesian methods tend to outperform the standard functional graphical lasso, and that the functional graphical horseshoe performs best overall, a procedure for which there is no direct frequentist analog. Then we consider a deep neural network architecture to estimate functional graphical models, by combining two simple off-the-shelf algorithms: adaptive functional principal components analysis (FPCA) Yao et al., 2021a) and convolutional graph estimator (Belilovsky et al., 2016). We train our proposed model with synthetic data which emulate the real world observations and prior knowledge. Based on synthetic data generation process, our model convert an inference problem as a supervised learning problem. Compared with other framework, our proposed deep model which offers a general recipe to infer the functional graphical model based on data-driven approach, take the raw functional dataset as input and avoid deriving sophisticated closed-form. Through simulation studies, we find that our deep functional graph model trained on synthetic data generalizes well and outperform other popular baselines marginally. In addition, we apply deep functional graphical model in the real world EEG data, and our proposed model discover meaningful brain connectivity. Finally, we are interested in estimating casual graph with functional input. In order to process functional covariates in causal estimation, we leverage the similar strategy as our deep functional graphical model. We extend popular deep causal models to infer causal effects with functional confoundings within the potential outcomes framework. Our method is simple yet effective, where we validate our proposed architecture in variety of simulation settings. Our work offers an alternative way to do causal inference with functional data

Point process modeling as a framework to dissociate intrinsic and extrinsic components in neural systems

Understanding the factors shaping neuronal spiking is a central problem in neuroscience. Neurons may have complicated sensitivity and, often, are embedded in dynamic networks whose ongoing activity may influence their likelihood of spiking. One approach to characterizing neuronal spiking is the point process generalized linear model (GLM), which decomposes spike probability into explicit factors. This model represents a higher level of abstraction than biophysical models, such as Hodgkin-Huxley, but benefits from principled approaches for estimation and validation. Here we address how to infer factors affecting neuronal spiking in different types of neural systems. We first extend the point process GLM, most commonly used to analyze single neurons, to model population-level voltage discharges recorded during human seizures. Both GLMs and descriptive measures reveal rhythmic bursting and directional wave propagation. However, we show that GLM estimates account for covariance between these features in a way that pairwise measures do not. Failure to account for this covariance leads to confounded results. We interpret the GLM results to speculate the mechanisms of seizure and suggest new therapies. The second chapter highlights flexibility of the GLM. We use this single framework to analyze enhancement, a statistical phenomenon, in three distinct systems. Here we define the enhancement score, a simple measure of shared information between spike factors in a GLM. We demonstrate how to estimate the score, including confidence intervals, using simulated data. In real data, we find that enhancement occurs prominently during human seizure, while redundancy tends to occur in mouse auditory networks. We discuss implications for physiology, particularly during seizure. In the third part of this thesis, we apply point process modeling to spike trains recorded from single units in vitro under external stimulation. We re-parameterize models in a low-dimensional and physically interpretable way; namely, we represent their effects in principal component space. We show that this approach successfully separates the neurons observed in vitro into different classes consistent with their gene expression profiles. Taken together, this work contributes a statistical framework for analyzing neuronal spike trains and demonstrates how it can be applied to create new insights into clinical and experimental data sets