114 research outputs found
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
Estimating Local Function Complexity via Mixture of Gaussian Processes
Real world data often exhibit inhomogeneity, e.g., the noise level, the
sampling distribution or the complexity of the target function may change over
the input space. In this paper, we try to isolate local function complexity in
a practical, robust way. This is achieved by first estimating the locally
optimal kernel bandwidth as a functional relationship. Specifically, we propose
Spatially Adaptive Bandwidth Estimation in Regression (SABER), which employs
the mixture of experts consisting of multinomial kernel logistic regression as
a gate and Gaussian process regression models as experts. Using the locally
optimal kernel bandwidths, we deduce an estimate to the local function
complexity by drawing parallels to the theory of locally linear smoothing. We
demonstrate the usefulness of local function complexity for model
interpretation and active learning in quantum chemistry experiments and fluid
dynamics simulations.Comment: 19 pages, 16 figure
Decoding Brain Activation from Ipsilateral Cortex using ECoG Signals in Humans
Today, learning from the brain is the most challenging issue in many areas. Neural scientists, computer scientists, and engineers are collaborating in this broad research area. With better techniques, we can extract the brain signals by either non-invasive approach such as EEG: electroencephalography), fMRI, or invasive method such as ECoG: electrocorticography), FP: field potential) and signals from single unit. The challenge is, given the brain signals, how can we possibly decipher them? Brain Computer Interfaces, or BCIs, aim at utilizing the brain signals to control prothetic arms or operate devices. Previously almost all the research on BCIs focuses on decoding signals from the contralateral hemisphere to implement BCI systems. However, the loss of functionality in the contralateral cortex often occurs due to strokes, resulting in total failure to motor function of fingers, hands, and limbs contralateral to the damaged hemisphere. Recent studies indicate that the signals from ipsilateral cortex is relevant to the planning phase of motor movements. Therefore, it is critical to find out if human motor movements can be decoded using signals from the ipsilateral cortex. In the thesis, we propose using ECoG signals from the ipsilateral cortex to decode finger movements. To our knowledge, this is the first work that successfully detects finger movements using signals from the ipsilateral cortex. We also investigate the experiment design and decoding directional movements. Our results show high decoding performance. We also show the anatomical feature analysis for ipsilateral cortex in performing motor-associated tasks, and the features are consistent with previous findings. The result reveals promising implications for a stroke relevant BCI
Probabilistic multiple kernel learning
The integration of multiple and possibly heterogeneous information sources for an overall decision-making process has been an open and unresolved research direction in computing science since its very beginning. This thesis attempts to address parts of that direction by proposing probabilistic data integration algorithms for multiclass decisions where an observation of interest is assigned to one of many categories based on a plurality of information channels
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)
A multi-class classification model with parametrized target outputs for randomized-based feedforward neural networks.
Randomized-based Feedforward Neural Networks approach regression and classification (binary and multi-class) problems by minimizing the same optimization problem. Specifically, the model parameters are determined through the ridge regression estimator of the patterns projected in the hidden layer space (randomly generated in its neural network version) for models without direct links and the patterns projected in the hidden layer space along with the original input data for models with direct links. The targets are encoded for the multi-class classification problem according to the 1-of- encoding ( the number of classes), which implies that the model parameters are estimated to project all the patterns belonging to its corresponding class to one and the remaining to zero. This approach has several drawbacks, which motivated us to propose an alternative optimization model for the framework. In the proposed optimization model, model parameters are estimated for each class so that their patterns are projected to a reference point (also optimized during the process), whereas the remaining patterns (not belonging to that class) are projected as far away as possible from the reference point. The final problem is finally presented as a generalized eigenvalue problem. Four models are then presented: the neural network version of the algorithm and its corresponding kernel version for the neural networks models with and without direct links. In addition, the optimization model has also been implemented in randomization-based multi-layer or deep neural networks.Funding for open access charge: Universidad de Málaga / CBU
A multi-class classification model with parametrized target outputs for randomized-based feedforward neural networks
Randomized-based Feedforward Neural Networks approach regression and classification (binary and
multi-class) problems by minimizing the same optimization problem. Specifically, the model parameters are determined through the ridge regression estimator of the patterns projected in the hidden
layer space (randomly generated in its neural network version) for models without direct links and
the patterns projected in the hidden layer space along with the original input data for models with
direct links. The targets are encoded for the multi-class classification problem according to the 1-
of-J encoding (J the number of classes), which implies that the model parameters are estimated to
project all the patterns belonging to its corresponding class to one and the remaining to zero. This
approach has several drawbacks, which motivated us to propose an alternative optimization model
for the framework. In the proposed optimization model, model parameters are estimated for each
class so that their patterns are projected to a reference point (also optimized during the process),
whereas the remaining patterns (not belonging to that class) are projected as far away as possible from
the reference point. The final problem is finally presented as a generalized eigenvalue problem. Four
models are then presented: the neural network version of the algorithm and its corresponding kernel
version for the neural networks models with and without direct links. In addition, the optimization
model has also been implemented in randomization-based multi-layer or deep neural networks. The
empirical results obtained by the proposed models were compared to those reported by state-ofthe-art models in the correct classification rate and a separability index (which measures the degree
of separability in projection terms per class of the patterns belonging to the class of the others).
The proposed methods show very competitive performance in the separability index and prediction
accuracy compared to the neural networks version of the comparison methods (with and without
direct links). Remarkably, the model provides significantly superior performance in deep models with
direct links compared to its deep model counterpart
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