Latent representation for the characterisation of mental diseases

Mención Internacional en el título de doctorMachine learning (ML) techniques are becoming crucial in the field of health and, in particular, in the analysis of mental diseases. These are usually studied with neuroimaging, which is characterised by a large number of input variables compared to the number of samples available. The main objective of this PhD thesis is to propose different ML techniques to analyse mental diseases from neuroimaging data including different extensions of these models in order to adapt them to the neuroscience scenario. In particular, this thesis focuses on using brainimaging latent representations, since they allow us to endow the problem with a reduced low dimensional representation while obtaining a better insight on the internal relations between the disease and the available data. This way, the main objective of this PhD thesis is to provide interpretable results that are competent with the state-of-the-art in the analysis of mental diseases. This thesis starts proposing a model based on classic latent representation formulations, which relies on a bagging process to obtain the relevance of each brainimaging voxel, Regularised Bagged Canonical Correlation Analysis (RB-CCA). The learnt relevance is combined with a statistical test to obtain a selection of features. What’s more, the proposal obtains a class-wise selection which, in turn, further improves the analysis of the effect of each brain area on the stages of the mental disease. In addition, RB-CCA uses the relevance measure to guide the feature extraction process by using it to penalise the least informative voxels for obtaining the low-dimensional representation. Results obtained on two databases for the characterisation of Alzheimer’s disease and Attention Deficit Hyperactivity Disorder show that the model is able to perform as well as or better than the baselines while providing interpretable solutions. Subsequently, this thesis continues with a second model that uses Bayesian approximations to obtain a latent representation. Specifically, this model focuses on providing different functionalities to build a common representation from different data sources and particularities. For this purpose, the proposed generative model, Sparse Semi-supervised Heterogeneous Interbattery Bayesian Factor Analysis (SSHIBA), can learn the feature relevance to perform feature selection, as well as automatically select the number of latent factors. In addition, it can also model heterogeneous data (real, multi-label and categorical), work with kernels and use a semi-supervised formulation, which naturally imputes missing values by sampling from the learnt distributions. Results using this model demonstrate the versatility of the formulation, which allows these extensions to be combined interchangeably, expanding the scenarios in which the model can be applied and improving the interpretability of the results. Finally, this thesis includes a comparison of the proposed models on the Alzheimer’s disease dataset, where both provide similar results in terms of performance; however, RB-CCA provides a more robust analysis of mental diseases that is more easily interpretable. On the other hand, while RB-CCA is more limited to specific scenarios, the SSHIBA formulation allows a wider variety of data to be combined and is easily adapted to more complex real-life scenarios.Las técnicas de aprendizaje automático (ML) están siendo cruciales en el campo de la salud y, en particular, en el análisis de las enfermedades mentales. Estas se estudian habitualmente con neuroimagen, que se caracteriza por un gran número de variables de entrada en comparación con el número de muestras disponibles. El objetivo principal de esta tesis doctoral es proponer diferentes técnicas de ML para el análisis de enfermedades mentales a partir de datos de neuroimagen incluyendo diferentes extensiones de estos modelos para adaptarlos al escenario de la neurociencia. En particular, esta tesis se centra en el uso de representaciones latentes de imagen cerebral, ya que permiten dotar al problema de una representación reducida de baja dimensión a la vez que obtienen una mejor visión de las relaciones internas entre la enfermedad mental y los datos disponibles. De este modo, el objetivo principal de esta tesis doctoral es proporcionar resultados interpretables y competentes con el estado del arte en el análisis de las enfermedades mentales. Esta tesis comienza proponiendo un modelo basado en formulaciones clásicas de representación latente, que se apoya en un proceso de bagging para obtener la relevancia de cada voxel de imagen cerebral, el Análisis de Correlación Canónica Regularizada con Bagging (RBCCA). La relevancia aprendida se combina con un test estadístico para obtener una selección de características. Además, la propuesta obtiene una selección por clases que, a su vez, mejora el análisis del efecto de cada área cerebral en los estadios de la enfermedad mental. Por otro lado, RB-CCA utiliza la medida de relevancia para guiar el proceso de extracción de características, utilizándola para penalizar los vóxeles menos relevantes para obtener la representación de baja dimensión. Los resultados obtenidos en dos bases de datos para la caracterización de la enfermedad de Alzheimer y el Trastorno por Déficit de Atención e Hiperactividad demuestran que el modelo es capaz de rendir igual o mejor que los baselines a la vez que proporciona soluciones interpretables. Posteriormente, esta tesis continúa con un segundo modelo que utiliza aproximaciones Bayesianas para obtener una representación latente. En concreto, este modelo se centra en proporcionar diferentes funcionalidades para construir una representación común a partir de diferentes fuentes de datos y particularidades. Para ello, el modelo generativo propuesto, Sparse Semisupervised Heterogeneous Interbattery Bayesian Factor Analysis (SSHIBA), puede aprender la relevancia de las características para realizar la selección de las mismas, así como seleccionar automáticamente el número de factores latentes. Además, también puede modelar datos heterogéneos (reales, multietiqueta y categóricos), trabajar con kernels y utilizar una formulación semisupervisada, que imputa naturalmente los valores perdidos mediante el muestreo de las distribuciones aprendidas. Los resultados obtenidos con este modelo demuestran la versatilidad de la formulación, que permite combinar indistintamente estas extensiones, ampliando los escenarios en los que se puede aplicar el modelo y mejorando la interpretabilidad de los resultados. Finalmente, esta tesis incluye una comparación de los modelos propuestos en el conjunto de datos de la enfermedad de Alzheimer, donde ambos proporcionan resultados similares en términos de rendimiento; sin embargo, RB-CCA proporciona un análisis más robusto de las enfermedades mentales que es más fácilmente interpretable. Por otro lado, mientras que RB-CCA está más limitado a escenarios específicos, la formulación SSHIBA permite combinar una mayor variedad de datos y se adapta fácilmente a escenarios más complejos de la vida real.Programa de Doctorado en Multimedia y Comunicaciones por la Universidad Carlos III de Madrid y la Universidad Rey Juan CarlosPresidente: Manuel Martínez Ramón.- Secretario: Emilio Parrado Hernández.- Vocal: Sancho Salcedo San

Regularized bagged canonical component analysis for multiclass learning in brain imaging

Alzheimer’s Disease Neuroimaging Initiative (ADNI) is a Group/Institutional Author. Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report.A fundamental problem of supervised learning algorithms for brain imaging applications is that the number of features far exceeds the number of subjects. In this paper, we propose a combined feature selection and extraction approach for multiclass problems. This method starts with a bagging procedure which calculates the sign consistency of the multivariate analysis (MVA) projection matrix feature-wise to determine the relevance of each feature. This relevance measure provides a parsimonious matrix, which is combined with a hypothesis test to automatically determine the number of selected features. Then, a novel MVA regularized with the sign and magnitude consistency of the features is used to generate a reduced set of summary components providing a compact data description. We evaluated the proposed method with two multiclass brain imaging problems: 1) the classification of the elderly subjects in four classes (cognitively normal, stable mild cognitive impairment (MCI), MCI converting to AD in 3 years, and Alzheimer’s disease) based on structural brain imaging data from the ADNI cohort; 2) the classification of children in 3 classes (typically developing, and 2 types of Attention Deficit/Hyperactivity Disorder (ADHD)) based on functional connectivity. Experimental results confirmed that each brain image (defined by 29.852 features in the ADNI database and 61.425 in the ADHD) could be represented with only 30 − 45% of the original features. Furthermore, this information could be redefined into two or three summary components, providing not only a gain of interpretability but also classification rate improvements when compared to state-of-art reference methods.C. Sevilla-Salcedo and V. Gomez-Verdejo's work has been partly funded by the Spanish MINECO grant TEC2014-52289-R and TEC2017-83838-R as well as KERMES, which is a NoE on kernel methods for structured data, funded by the Spanish Ministry of Economy and Competitiveness, TEC2016-81900-REDT ru. Jussi Tohka's work is supported by the Academy of Finland (grant 316258)

Multi-Objective Genetic Algorithm for Multi-View Feature Selection

Multi-view datasets offer diverse forms of data that can enhance prediction models by providing complementary information. However, the use of multi-view data leads to an increase in high-dimensional data, which poses significant challenges for the prediction models that can lead to poor generalization. Therefore, relevant feature selection from multi-view datasets is important as it not only addresses the poor generalization but also enhances the interpretability of the models. Despite the success of traditional feature selection methods, they have limitations in leveraging intrinsic information across modalities, lacking generalizability, and being tailored to specific classification tasks. We propose a novel genetic algorithm strategy to overcome these limitations of traditional feature selection methods for multi-view data. Our proposed approach, called the multi-view multi-objective feature selection genetic algorithm (MMFS-GA), simultaneously selects the optimal subset of features within a view and between views under a unified framework. The MMFS-GA framework demonstrates superior performance and interpretability for feature selection on multi-view datasets in both binary and multiclass classification tasks. The results of our evaluations on three benchmark datasets, including synthetic and real data, show improvement over the best baseline methods. This work provides a promising solution for multi-view feature selection and opens up new possibilities for further research in multi-view datasets

Multi-view hierarchical Variational AutoEncoders with Factor Analysis latent space

Real-world databases are complex, they usually present redundancy and shared correlations between heterogeneous and multiple representations of the same data. Thus, exploiting and disentangling shared information between views is critical. For this purpose, recent studies often fuse all views into a shared nonlinear complex latent space but they lose the interpretability. To overcome this limitation, here we propose a novel method to combine multiple Variational AutoEncoders (VAE) architectures with a Factor Analysis latent space (FA-VAE). Concretely, we use a VAE to learn a private representation of each heterogeneous view in a continuous latent space. Then, we model the shared latent space by projecting every private variable to a low-dimensional latent space using a linear projection matrix. Thus, we create an interpretable hierarchical dependency between private and shared information. This way, the novel model is able to simultaneously: (i) learn from multiple heterogeneous views, (ii) obtain an interpretable hierarchical shared space, and, (iii) perform transfer learning between generative models.Comment: 20 pages main work, 2 pages supplementary, 14 figure

Bayesian learning of feature spaces for multitasks problems

This paper presents a Bayesian framework to construct non-linear, parsimonious, shallow models for multitask regression. The proposed framework relies on the fact that Random Fourier Features (RFFs) enables the approximation of an RBF kernel by an extreme learning machine whose hidden layer is formed by RFFs. The main idea is to combine both dual views of a same model under a single Bayesian formulation that extends the Sparse Bayesian Extreme Learning Machines to multitask problems. From the kernel methods point of view, the proposed formulation facilitates the introduction of prior domain knowledge through the RBF kernel parameter. From the extreme learning machines perspective, the new formulation helps control overfitting and enables a parsimonious overall model (the models that serve each task share a same set of RFFs selected within the joint Bayesian optimisation). The experimental results show that combining advantages from kernel methods and extreme learning machines within the same framework can lead to significant improvements in the performance achieved by each of these two paradigms independently

A Novel Bayesian Linear Regression Model for the Analysis of Neuroimaging Data

In this paper, we propose a novel Machine Learning Model based on Bayesian Linear Regression intended to deal with the low sample-to-variable ratio typically found in neuroimaging studies and focusing on mental disorders. The proposed model combines feature selection capabilities with a formulation in the dual space which, in turn, enables efficient work with neuroimaging data. Thus, we have tested the proposed algorithm with real MRI data from an animal model of schizophrenia. The results show that our proposal efficiently predicts the diagnosis and, at the same time, detects regions which clearly match brain areas well-known to be related to schizophrenia.This paper is part of the project PID2020-115363RB-I00 funded by MCIN/AEI/10.13039/ 501100011033. A.B.-L.’s work is funded by the Community of Madrid through the “Excellence of University Teaching Staff” line of the Multi-year Agreement with UC3M (EPUC3M27), within the framework of the V PRICIT. M.L.S.-M.’s was supported by Ministerio de Ciencia, Innovación y Universidades, Instituto de Salud Carlos III (project number PI17/01766, and grant number BA21/00030), co-financed by European Regional Development Fund (ERDF), “A way to make Europe”, CIBER de Salud Mental (project number CB07/09/0031), Delegación del Gobierno para el Plan Nacional sobre Drogas (project number 2017/085); Fundación Mapfre and Fundación Alicia Koplowitz. M.D.’s work was supported by Ministerio de Ciencia e Innovación (MCIN) and Instituto de Salud Carlos III (ISCIII) (PT20/00044). The CNIC is supported by the ISCIII, the MCIN and the Pro CNIC Foundation, and is a Severo Ochoa Center of Excellence (SEV-2015-0505)

Dynamic semantic ontology generation: a proposal for social robots

[Abstract] During a human-robot interaction by dialogue/voice, the robot cannot extract semantic meaning from the words used, limiting the intervention itself. Semantic knowledge could be a solution by structuring information according to its meaning and its semantic associations. Applied to social robotics, it could lead to a natural and fluid human-robot interaction. Ontologies are useful representations of semantic knowledge, as they capture the relationships between objects and entities. This paper presents new ideas for ontology generation using already generated ontologies as feedback in an iterative way to do it dynamically. This paper also collects and describes the concepts applied in the proposed methodology and discusses the challenges to be overcome.Ministerio de Ciencia, Innovación y Universidades; RTI2018-096338-B-I0

Sparse semi-supervised heterogeneous interbattery bayesian analysis

The Bayesian approach to feature extraction, known as factor analysis (FA), has been widely studied in machine learning to obtain a latent representation of the data. An adequate selection of the probabilities and priors of these bayesian models allows the model to better adapt to the data nature (i.e. heterogeneity, sparsity), obtaining a more representative latent space. The objective of this article is to propose a general FA framework capable of modelling any problem. To do so, we start from the Bayesian Inter-Battery Factor Analysis (BIBFA) model, enhancing it with new functionalities to be able to work with heterogeneous data, to include feature selection, and to handle missing values as well as semi-supervised problems. The performance of the proposed model, Sparse Semi-supervised Heterogeneous Interbattery Bayesian Analysis (SSHIBA), has been tested on different scenarios to evaluate each one of its novelties, showing not only a great versatility and an interpretability gain, but also outperforming most of the state-of-the-art algorithms.The authors wish to thank Irene Santos, for fruitful discussions and help during the earlier stages of our work. The work of Pablo M. Olmos was partly supported by the Spanish government (Ministerio de Ciencia e Innovación) under grants TEC2017-92552-EXP and RTI2018-099655-B-100; the Comunidad de Madrid under grants IND2017/TIC-7618, IND2018/TIC-9649, IND2020/TIC-17372, and Y2018/TCS-4705; the BBVA Foundation under the Domain Alignment and Data Wrangling with Deep Generative Models (Deep-DARWiN) project; and the European Union (European Regional Development Fund and the European Research Council) through the European Union's Horizon 2020 Research and Innovation Program under grant 714161. C. Sevilla-Salcedo and V. Gómez-Verdejo's work has been partly funded by the Spanish MINECO grants TEC2017-83838-R and PID2020-115363RB-I00

Natural language models for social robots

[Resumen] Hoy en día, los robots se están abriendo paso en nuestras vidas en muchos campos. Uno de ellos es el social, en el que encontramos robots capaces de interactuar con las personas y realizar diferentes actividades. El habla de un robot social es un elemento central en la interacción humano-robot; para parecer natural y amigable, debe evitar los textos predefinidos repetitivos. Con esta premisa, junto con el importante crecimiento en el campo de los modelos de generación de lenguaje natural, este artículo explora las capacidades de los modelos de lenguaje natural para conducir a una interacción humano-robot más fluida y abrir un abanico de nuevas oportunidades y aplicaciones. Tras la implementación de las diferentes aplicaciones, se ha observado el potencial de la integración de la generación de lenguaje natural en el campo de la robótica.[Abstract] Nowadays, robots are making their way into our lives in many fields. One of them is social, where we find robots capable of interacting with people and performing different activities. The speech of a social robot is a central element in human-robot interaction; to sound natural and friendly, it must avoid repetitive predefined texts. With this premise, together with the significant growth in the field of natural language generation models, this paper explores the capabilities of natural language models to lead to a more fluent human-robot interaction and open up a range of new opportunities and applications. Following the implementation of different applications, the potential of integrating natural language generation in the field of robotics has been observed.Robots Sociales para Estimulación Física, Cognitiva y Afectiva de Mayores (ROSES), RTI2018-096338-B-I00, financiado por Ministerio de Ciencia, Innovación y Universidades; Robots sociales para mitigar la soledad y el aislamiento en mayores (SOROLI), PID2021-123941OAI00, financiado por Agencia Estatal de Investigación (AEI), Ministerio de Ciencia e Innovación. RoboCity2030-DIH-CM, Madrid Robotics Digital Innovation Hub, S2018/NMT-4331, financiado por Programas de Actividades I+D en la Comunidad de Madrid cofinanciado por los Fondos Sociales Europeos (FSE) de la UE. R&D&Iproject PLEC2021-007819 financiado por MCIN/AEI/10.13039/501100011033 y por European Union NextGenerationEU/PRTRComunidad de Madrid; S2018/NMT-433