Brain dynamics of mild cognitive impairment during face encoding

Brain oscillations are closely correlated with human information processing and fundamental aspects of cognition. Previous literature shows that due to the relation between brain oscillations and memory processes, spectral dynamics during such tasks are good candidates to study and characterize memory related pathologies. Mild cognitive impairment (MCI), defined as a clinical condition characterized by memory impairment and/ or deterioration of additional cognitive domains, is considered a preliminary stage in the dementia process. In consequence, the study of its brain patterns could help to achieve an early diagnosis of Alzheimer Disease.

Network-based biomarkers in Alzheimer's disease: review and future directions

By 2050 it is estimated that the number of worldwide Alzheimer?s disease (AD) patients will quadruple from the current number of 36 million people. To date, no single test, prior to postmortem examination, can confirm that a person suffers from AD. Therefore, there is a strong need for accurate and sensitive tools for the early diagnoses of AD. The complex etiology and multiple pathogenesis of AD call for a system-level understanding of the currently available biomarkers and the study of new biomarkers via network-based modeling of heterogeneous data types. In this review, we summarize recent research on the study of AD as a connectivity syndrome. We argue that a network-based approach in biomarker discovery will provide key insights to fully understand the network degeneration hypothesis (disease starts in specific network areas and progressively spreads to connected areas of the initial loci-networks) with a potential impact for early diagnosis and disease-modifying treatments. We introduce a new framework for the quantitative study of biomarkers that can help shorten the transition between academic research and clinical diagnosis in AD.

QuantiDOPA: A Quantification Software for Dopaminergic Neurotransmission SPECT

Quantification of neurotransmission Single-Photon Emission Computed Tomography (SPECT) studies of the dopaminergic system can be used to track, stage and facilitate early diagnosis of the disease. The aim of this study was to implement QuantiDOPA, a semi-automatic quantification software of application in clinical routine to reconstruct and quantify neurotransmission SPECT studies using radioligands which bind the dopamine transporter (DAT). To this end, a workflow oriented framework for the biomedical imaging (GIMIAS) was employed. QuantiDOPA allows the user to perform a semiautomatic quantification of striatal uptake by following three stages: reconstruction, normalization and quantification. QuantiDOPA is a useful tool for semi-automatic quantification inDAT SPECT imaging and it has revealed simple and flexible

Guiding functional connectivity estimation by structural connectivity in MEG: an application to discrimination of mild cognitive impaired conditions

Whole brain resting state connectivity is a promising biomarker that might help to obtain an early diagnosis in many neurological diseases, such as dementia. Inferring resting-state connectivity is often based on correlations, which are sensitive to indirect connections, leading to an inaccurate representation of the real backbone of the network. The precision matrix is a better representation for whole brain connectivity, as it considers only direct connections. The network structure can be estimated using the graphical lasso (GL), which achieves sparsity through l1-regularization on the precision matrix. In this paper, we propose a structural connectivity adaptive version of the GL, where weaker anatomical connections are represented as stronger penalties on the corre- sponding functional connections. We applied beamformer source reconstruction to the resting state MEG record- ings of 81 subjects, where 29 were healthy controls, 22 were single-domain amnestic Mild Cognitive Impaired (MCI), and 30 were multiple-domain amnestic MCI. An atlas-based anatomical parcellation of 66 regions was ob- tained for each subject, and time series were assigned to each of the regions. The fiber densities between the re- gions, obtained with deterministic tractography from diffusion-weighted MRI, were used to define the anatomical connectivity. Precision matrices were obtained with the region specific time series in five different frequency bands. We compared our method with the traditional GL and a functional adaptive version of the GL, in terms of log-likelihood and classification accuracies between the three groups. We conclude that introduc- ing an anatomical prior improves the expressivity of the model and, in most cases, leads to a better classification between groups.

Evaluating a web-based clinical decision support system for language disorders screening in a nursery school

Background: Early and effective identification of developmental disorders during childhood remains a critical task for the international community. The second highest prevalence of common developmental disorders in children are language delays, which are frequently the first symptoms of a possible disorder. Objective: This paper evaluates a Web-based Clinical Decision Support System (CDSS) whose aim is to enhance the screening of language disorders at a nursery school. The common lack of early diagnosis of language disorders led us to deploy an easy-to-use CDSS in order to evaluate its accuracy in early detection of language pathologies. This CDSS can be used by pediatricians to support the screening of language disorders in primary care. Methods: This paper details the evaluation results of the ?Gades? CDSS at a nursery school with 146 children, 12 educators, and 1 language therapist. The methodology embraces two consecutive phases. The first stage involves the observation of each child?s language abilities, carried out by the educators, to facilitate the evaluation of language acquisition level performed by a language therapist. Next, the same language therapist evaluates the reliability of the observed results. Results: The Gades CDSS was integrated to provide the language therapist with the required clinical information. The validation process showed a global 83.6% (122/146) success rate in language evaluation and a 7% (7/94) rate of non-accepted system decisions within the range of children from 0 to 3 years old. The system helped language therapists to identify new children with potential disorders who required further evaluation. This process will revalidate the CDSS output and allow the enhancement of early detection of language disorders in children. The system does need minor refinement, since the therapists disagreed with some questions from the CDSS knowledge base (KB) and suggested adding a few questions about speech production and pragmatic abilities. The refinement of the KB will address these issues and include the requested improvements, with the support of the experts who took part in the original KB development. Conclusions: This research demonstrated the benefit of a Web-based CDSS to monitor children?s neurodevelopment via the early detection of language delays at a nursery school. Current next steps focus on the design of a model that includes pseudo auto-learning capacity, supervised by experts.

Sistemas de ayuda al diagnóstico y reconocimiento de microcalcificaciones en mamografía mediante descriptores de escala y redes jerárquicas

La presente Tesis investiga el campo del reconocimiento automático de imágenes mediante ordenador aplicado al análisis de imágenes médicas en mamografía digital. Hay un interés por desarrollar sistemas de aprendizaje que asistan a los radiólogos en el reconocimiento de las microcalcificaciones para apoyarles en los programas de cribado y prevención del cáncer de mama. Para ello el análisis de las microcalcificaciones se ha revelado como técnica clave de diagnóstico precoz, pero sin embargo el diseño de sistemas automáticos para reconocerlas es complejo por la variabilidad y condiciones de las imágenes mamográficas. En este trabajo se analizan los planteamientos teóricos de diseño de sistemas de reconocimiento de imágenes, con énfasis en los problemas específicos de detección y clasificación de microcalcificaciones. Se ha realizado un estudio que incluye desde las técnicas de operadores morfológicos, redes neuronales, máquinas de vectores soporte, hasta las más recientes de aprendizaje profundo mediante redes neuronales convolucionales, contemplando la importancia de los conceptos de escala y jerarquía a la hora del diseño y sus implicaciones en la búsqueda de la arquitectura de conexiones y capas de la red. Con estos fundamentos teóricos y elementos de diseño procedentes de otros trabajos en este área realizados por el autor, se implementan tres sistemas de reconocimiento de mamografías que reflejan una evolución tecnológica, culminando en un sistema basado en Redes Neuronales Convolucionales (CNN) cuya arquitectura se diseña gracias al análisis teórico anterior y a los resultados prácticos de análisis de escalas llevados a cabo en nuestra base de datos de imágenes. Los tres sistemas se entrenan y validan con la base de datos de mamografías DDSM, con un total de 100 muestras de entrenamiento y 100 de prueba escogidas para evitar sesgos y reflejar fielmente un programa de cribado. La validez de las CNN para el problema que nos ocupa queda demostrada y se propone un camino de investigación para el diseño de su arquitectura. ABSTRACT This Dissertation investigates the field of computer image recognition applied to medical imaging in mammography. There is an interest in developing learning systems to assist radiologists in recognition of microcalcifications to help them in screening programs for prevention of breast cancer. Analysis of microcalcifications has emerged as a key technique for early diagnosis of breast cancer, but the design of automatic systems to recognize them is complicated by the variability and conditions of mammographic images. In this Thesis the theoretical approaches to design image recognition systems are discussed, with emphasis on the specific problems of detection and classification of microcalcifications. Our study includes techniques ranging from morphological operators, neural networks and support vector machines, to the most recent deep convolutional neural networks. We deal with learning theory by analyzing the importance of the concepts of scale and hierarchy at the design stage and its implications in the search for the architecture of connections and network layers. With these theoretical facts and design elements coming from other works in this area done by the author, three mammogram recognition systems which reflect technological developments are implemented, culminating in a system based on Convolutional Neural Networks (CNN), whose architecture is designed thanks to the previously mentioned theoretical study and practical results of analysis conducted on scales in our image database. All three systems are trained and validated against the DDSM mammographic database, with a total of 100 training samples and 100 test samples chosen to avoid bias and stand for a real screening program. The validity of the CNN approach to the problem is demonstrated and a research way to help in designing the architecture of these networks is proposed.

High sensitivity biosensors based on shear mode AIN resonators for in liquid operation

El diagnóstico y detección temprana de enfermedades son clave para reducir la tasa de mortalidad, las hospitalizaciones de larga duración y el desaprovechamiento de recursos. En los últimos años se ha impulsado, mediante un aumento de la financiación, el desarrollo de nuevos biosensores de bajo coste capaces de detectar y cuantificar cantidades muy pequeñas de especies biológicas de una forma barata y sencilla. El trabajo presentado en esta Tesis Doctoral describe la investigación llevada a cabo en el desarrollo de sensores gravimétricos basados en resonadores de ondas acústicas de volumen (BAW) de estructura maciza (SMR). Los dispositivos emplean películas delgadas de A1N como material piezoeléctrico y operan en modo de cizalladura, para así poder detectar especies biológicas en medio líquido. El principio de funcionamiento de estos sensores se basa en la variación que experimenta la frecuencia de resonancia al quedar una pequeña masa adherida a su superficie. Necesitan operar en modo de cizalladura para que su resonancia no se atenúe al trabajar en medio líquido, así como ofrecer una superficie capaz de ser funcionalizada específicamente para la especie biológica a detectar. El reto planteado en esta tesis requiere un acercamiento pluridisciplinar al problema que incluye el estudio de los diferentes materiales que constituyen la estructura multicapa que forma un SMR, el diseño y fabricación del dispositivo y del sistema de fluídica, la funcionalización bioquímica de la superficie del sensor, la demostración de la capacidad de detección de especies biológicas y finalmente el diseño y fabricación de la electrónica asociada para la detección de la señal eléctrica. Todas esas tareas han sido abordadas en las distintas etapas del desarrollo de esta tesis y las contribuciones más relevantes se describen en el documento. En el campo de desarrollo de los materiales, se propone un proceso en dos etapas para la pulverización reactiva de capas de A1N que contengan microcristales inclinados capaces de excitar el modo de cizalladura. Se caracteriza la velocidad acústica del modo de cizalladura en todos los materiales que componen la estructura, con el fin de poder obtener un diseño más adecuado del reflector acústico. Se propone un nuevo tipo de material aislante de alta impedancia acústica consistente en capas de W03 pulverizadas que presenta ciertas ventajas tecnológicas frente a las capas convencionales de Ta205. Respecto del diseño del transductor, se estudia la influencia que tienen los con tactos eléctricos extendidos del resonador necesarios para poder adaptar el sistema de fluídica a la estructura. Los resultados indican que es necesario trabajar sobre sustratos aislantes (tanto el soporte como el espejo acústico) para evitar efectos parásitos asociados al uso de capas metálicas bajo los electrodos del resonador que dañan su resonancia. Se analiza la influencia de las diferentes capas del dispositivo en el coeficiente de temperatura de la frecuencia (TCF) del resonador llegando a la conclusión de que las dos últimas capas del reflector acústico afectan significativamente al TCF del SMR, pudiendo reducirse ajusfando adecuadamente sus espesores. De acuerdo con los resultados de estos estudios, se han diseñado finalmente resonadores SMR operando a f .3 GHz en modo de cizalladura, con un área activa de 65000 /xm2, contactos eléctricos que se extienden f .7 mm y factores de calidad en líquido de f 50. Las extensiones eléctricas permiten adaptar el resonador a un sistema de fluídica de metacrilato. Para la detección de especies biológicas se realiza un montaje experimental que permite circular 800 ¡A por la superficie del sensor a través de un circuito cerrado que trabaja a volumen constante. La circulación de soluciones iónicas sobre el sensor descubierto pone de manifiesto que las altas frecuencias de operación previenen los cortocircuitos y por tanto el aislamiento de los electrodos es prescindible. Se desarrolla un protocolo ad-hoc de funcionalización basado en el proceso estándar APTESGlutaraldehído. Se proponen dos alternativas novedosas para la funcionalización de las áreas activas del sensor basadas en el uso de capas de oxidación de Ir02 y su activación a través de un plasma de oxígeno que no daña al dispositivo. Ambos procesos contribuyen a simplificar notablemente la funcionalización de los sensores gravimétricos. Se utilizan anticuerpos y aptámeros como receptores para detectar trombina, anticuerpo monoclonal IgG de ratón y bacteria sonicadas. Una calibración preliminar del sensor con depósitos de capas finas de Si02 de densidad y espesor conocidos permite obtener una sensibilidad de 1800 kHz/pg-cm2 y un límite de detección of 4.2 pg. Finalmente se propone el prototipo de un circuito electrónico de excitación y lectura de bajo coste diseñado empleando teoría de circuitos de microondas. Aunque su diseño y funcionamiento admite mejoras, constituye la última etapa de un sistema completo de bajo coste para el diagnóstico de especies biológicas basado en resonadores SMR de A1N. ABSTRACT Early diagnosis and detection of diseases are essential for reducing mortality rate and preventing long-term hospitalization and waste of resources. These requirements have boosted the efforts and funding on the research of accurate and reliable means for detection and quantification of biological species, also known as biosensors. The work presented in this thesis describes the development and fabrication of gravimetric biosensors based on piezoelectric AlN bulk acoustic wave (BAW) solidly mounted resonators (SMRs) for detection of biological species in liquid media. These type of devices base their sensing principles in the variation that their resonant frequency suffers when a mass is attached to their surface. They need to operate in the shear mode to maintain a strong resonance in liquid and an adequate functionalisation of their sensing area to guarantee that only the targeted molecules cause the shift. The challenges that need to be overcome to achieve piezoelectric BAW resonators for high sensitivity detection in fluids require a multidisciplinary approach, that include the study of the materials involved, the design of the device and the fluidic system, the biochemical functionalisation of the active area, the experimental proof-of-concept with different target species and the design of an electronic readout circuit. All this tasks have been tackled at different stages of the thesis and the relevant contributions are described in the document. In the field of materials, a two-stage sputtering deposition process has been developed to obtain good-quality AlN films with uniformly tilted grains required to excite the shear mode. The shear acoustic velocities of the materials composing the acoustic reflector have been accurately studied to ensure an optimum design of the reflector stack. WO3 sputtered films have been proposed as high acoustic impedance material for insulating acoustic reflectors. They display several technological advantages for the processing of the resonators. Regarding the design, a study of the influence of the electrical extensions necessary to fit a fluidic system on the performance of the devices has been performed. The results indicate that high resistivity substrates and insulating reflectors are necessary to avoid the hindering of the resonance due to the parasitic effects induced by the extensions. The influence of the different layers of the stack on the resultant TCF of the SMRs has also been investigated. The two layers of the reflector closer to the piezoelectric layer have a significant influence on the TCF, which can be reduced by modifying their thicknesses accordingly. The data provided by these studies has led to the final design of the devices, which operate at 1.3 GHz in the shear mode and display an active area of 65000 /xm2 and electrical extensions of 1.7 mm while keeping a Qahear=150 in liquid. The extensions enable to fit a custom-made fluidic system made of methacrylate. To perform the biosensing experiments, an experimental setup with a liquid closed circuit operating at constant flow has been developed. Buffers of ionic characteristics have been tested on non-isolated devices, revealing that high operation frequencies prevent the risk of short circuit. An ad-hoc functionalisation protocol based on the standard APTES - Glutaraldehyde process has been developed. It includes two new processes that simplify the fabrication of the transducers: the use of IrO2 as oxidation layer and its functionalisation through an O2 plasma treatment that does not damage the resonators. Both antibodies and aptamers are used as receptors. In liquid sensing proof-of-concept experiments with thrombin, IgG mouse monoclonal antibody and sonicated bacteria have been displayed. A preliminary calibration of the devices using SiO2 layers reveals a sensitivity of 1800 kHz/pg-cm2 and a limit of detection of 4.2 pg. Finally, a first prototype of a low-cost electronic readout circuit designed using a standard microwave approach has been developed. Although its performance can be significantly improved, it is an effective first approach to the final stage of a portable low-cost diagnostic system based on shear mode AlN SMRs.

Sistemas de ayuda al diagnóstico y a la terapia funcional en enfermedades neurodegenerativas

El incremento de la esperanza de vida en los países desarrollados (más de 80 años en 2013), está suponiendo un crecimiento considerable en la incidencia y prevalencia de enfermedades discapacitantes, que si bien pueden aparecer a edades tempranas, son más frecuentes en la tercera edad, o en sus inmediaciones. Enfermedades neuro-degenerativas que suponen un gran hándicap funcional, pues algunas de ellas están asociadas a movimientos involuntarios de determinadas partes del cuerpo, sobre todo de las extremidades. Tareas cotidianas como la ingesta de alimento, vestirse, escribir, interactuar con el ordenador, etc… pueden llegar a ser grandes retos para las personas que las padecen. El diagnóstico precoz y certero resulta fundamental para la prescripción de la terapia o tratamiento óptimo. Teniendo en cuenta incluso que en muchos casos, por desgracia la mayoría, sólo se puede actuar para mitigar los síntomas, y no para sanarlos, al menos de momento. Aun así, acertar de manera temprana en el diagnóstico supone proporcionar al enfermo una mayor calidad de vida durante mucho más tiempo, por lo cual el esfuerzo merece, y mucho, la pena. Los enfermos de Párkinson y de temblor esencial suponen un porcentaje importante de la casuística clínica en los trastornos del movimiento que impiden llevar una vida normal, que producen una discapacidad física y una no menos importante exclusión social. Las vías de tratamiento son dispares de ahí que sea crítico acertar en el diagnóstico lo antes posible. Hasta la actualidad, los profesionales y expertos en medicina, utilizan unas escalas cualitativas para diferenciar la patología y su grado de afectación. Dichas escalas también se utilizan para efectuar un seguimiento clínico y registrar la historia del paciente. En esta tesis se propone una serie de métodos de análisis y de identificación/clasificación de los tipos de temblor asociados a la enfermedad de Párkinson y el temblor esencial. Empleando técnicas de inteligencia artificial basadas en clasificadores inteligentes: redes neuronales (MLP y LVQ) y máquinas de soporte vectorial (SVM), a partir del desarrollo e implantación de un sistema para la medida y análisis objetiva del temblor: DIMETER. Dicho sistema además de ser una herramienta eficaz para la ayuda al diagnóstico, presenta también las capacidades necesarias para proporcionar un seguimiento riguroso y fiable de la evolución de cada paciente. ABSTRACT The increase in life expectancy in developed countries in more than 80 years (data belongs to 2013), is assuming considerable growth in the incidence and prevalence of disabling diseases. Although they may appear at an early age, they are more common in the elderly ages or in its vicinity. Nuero-degenerative diseases that are a major functional handicap, as some of them are associated with involuntary movements of certain body parts, especially of the limbs. Everyday tasks such as food intake, dressing, writing, interact with the computer, etc ... can become large debris for people who suffer. Early and accurate diagnosis is crucial for prescribing optimal therapy or treatment. Even taking into account that in many cases, unfortunately the majority, can only act to mitigate the symptoms, not to cure them, at least for now. Nevertheless, early diagnosis may provide the patient a better quality of life for much longer time, so the effort is worth, and much, grief. Sufferers of Parkinson's and essential tremor represent a significant percentage of clinical casuistry in movement disorders that prevent a normal life, leading to physical disability and not least social exclusion. There are various treatment methods, which makes it necessary the immediate diagnosis. Up to date, professionals and medical experts, use a qualitative scale to differentiate the disease and degree of involvement. Therefore, those scales are used in clinical follow-up. In this thesis, several methods of analysis and identification / classification of types of tremor associated with Parkinson's disease and essential tremor are proposed. Using artificial intelligence techniques based on intelligent classification: neural networks (MLP and LVQ) and support vector machines (SVM), starting from the development and implementation of a system for measuring and objective analysis of the tremor: DIMETER. This system besides being an effective tool to aid diagnosis, it also has the necessary capabilities to provide a rigorous and reliable monitoring of the evolution of each patient.

Wheat allergy in celiac children

Gluten is the main structural protein complex of wheat with equivalent toxic proteins found in other cereals (rye, barley, and oats) which are responsible for different immunologic responses with different clinical expressions of disease. The spectrum of gluten-related disorders has been classified according to pathogenic, clinical, and epidemiological differences in three main forms: (i) wheat allergy (WA), an IgE-mediated disease; (ii) autoimmune disease, including celiac disease (CD), dermatitis herpetiformis, and gluten ataxia; and (iii) possibly immune-mediated, gluten sensitivity [1]. WA is an immunologic Th2 response with typical manifestations which can vary from dermatological, respiratory, and/or intestinal to anaphylactic reactions. In contrast, CD is an autoimmune disorder, a gliadin-specific T-cell response which is enhanced by the action of intestinal tissue transglutaminase (tTG), with a wide clinical spectrum including symptomatic cases with either intestinal (e.g., chronic diarrhea, weight loss) or extraintestinal features (e.g., anemia, osteoporosis, neurologic disturbances) and silent forms that are occasionally discovered as a result of serological screening [1]. We studied wheat allergy in two children with early diagnosis of CD, who developed immediate allergic symptoms after eating small amounts of wheat flour.

EPA-0882 - Prediction of diagnosis of early-onset schizophrenia spectrum disorders using support vector machines

To develop a Support Vector Machine (SVM) algorithm as a predictive tool for diagnostic outcome in patients with FE-EOP, based on clinical and biomedical data at the emergence of the illness.

Self-learning of fault diagnosis identification

A good and early fault detection and isolation system along with efficient alarm management and fine sensor validation systems are very important in today¿s complex process plants, specially in terms of safety enhancement and costs reduction. This paper presents a methodology for fault characterization. This is a self-learning approach developed in two phases. An initial, learning phase, where the simulation of process units, without and with different faults, will let the system (in an automated way) to detect the key variables that characterize the faults. This will be used in a second (on line) phase, where these key variables will be monitored in order to diagnose possible faults. Using this scheme the faults will be diagnosed and isolated in an early stage where the fault still has not turned into a failure.

A supervised cooperative learning system for early detection of language disorders

The Quality of Life of a person may depend on early attention to his neurodevel-opment disorders in childhood. Identification of language disorders under the age of six years old can speed up required diagnosis and/or treatment processes. This paper details the enhancement of a Clinical Decision Support System (CDSS) aimed to assist pediatricians and language therapists at early identification and re-ferral of language disorders. The system helps to fine tune the Knowledge Base of Language Delays (KBLD) that was already developed and validated in clinical routine with 146 children. Medical experts supported the construction of Gades CDSS by getting scientific consensus from literature and fifteen years of regis-tered use cases of children with language disorders. The current research focuses on an innovative cooperative model that allows the evolution of the KBLD of Gades through the supervised evaluation of the CDSS learnings with experts¿ feedback. The deployment of the resulting system is being assessed under a mul-tidisciplinary team of seven experts from the fields of speech therapist, neonatol-ogy, pediatrics, and neurology.