A Machine Learning Approach to Reveal the NeuroPhenotypes of Autisms

This work was partly supported by the MINECO Under the TEC2015-64718-R Project, the Salvador de Madariaga Mobility Grants 2017 and the Consejería de Economía, Innovación, Ciencia y Empleo (Junta de Andalucía, Spain) under the Excellence Project P11-TIC-7103. The study was conducted in association with the National Institute for Health Research Collaborations for Leadership in Applied Health Research and Care (NIHR CLAHRC) East of England (EoE). The Project was supported by the UK Medical Research Council (Grant No. GO 400061) and European Autism Interventions — a Multicentre Study for Developing New Medications (EU-AIMS); EU-AIMS has received support from the Innovative Medicines Initiative Joint Undertaking Under Grant Agreement No. 115300, resources of which are composed of financial contribution from the European Union’s Seventh Framework Programme (FP7/2007–2013) and EFPIA companies’ in-kind contribution. During the period of this work, M-CL was supported by the OBrien Scholars Program in the Child and Youth Mental Health Collaborative at the Centre for Addiction and Mental Health (CAMH) and The Hospital for Sick Children, Toronto, the Academic Scholar Award from the Department of Psychiatry, University of Toronto, the Slaight Family Child and Youth Mental Health Innovation Fund, CAMH Foundation, and the Ontario Brain Institute via the Province of Ontario Neurodevelopmental Disorders (POND) Network; MVL was supported by the British Academy, Jesus College Cambridge, Wellcome Trust, and an ERC Starting Grant (ERC-2017-STG; 755816); SB-C was supported by the Autism Research Trust. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health, UK.Although much research has been undertaken, the spatial patterns, developmental course, and sexual dimorphism of brain structure associated with autism remains enigmatic. One of the difficulties in investigating differences between the sexes in autism is the small sample sizes of available imaging datasets with mixed sex. Thus, the majority of the investigations have involved male samples, with females somewhat overlooked. This paper deploys machine learning on partial least squares feature extraction to reveal differences in regional brain structure between individuals with autism and typically developing participants. A four-class classification problem (sex and condition) is specified, with theoretical restrictions based on the evaluation of a novel upper bound in the resubstitution estimate. These conditions were imposed on the classifier complexity and feature space dimension to assure generalizable results from the training set to test samples. Accuracies above 80% on gray and white matter tissues estimated from voxel-based morphometry (VBM) features are obtained in a sample of equal-sized high-functioning male and female adults with and without autism (N=120, n=30/group). The proposed learning machine revealed how autism is modulated by biological sex using a low-dimensional feature space extracted from VBM. In addition, a spatial overlap analysis on reference maps partially corroborated predictions of the “extreme male brain” theory of autism, in sexual dimorphic areas.This work was partly supported by the MINECO Under the TEC2015-64718-R Project, the Salvador de Madariaga Mobility Grants 2017 and the Consejería de Economía, Innovación, Ciencia y Empleo (Junta de Andalucía, Spain) under the Excellence Project P11-TIC-7103The Project was supported by the UK Medical Research Council (Grant No. GO 400061) and European Autism Interventions — a Multicentre Study for Developing New Medications (EU-AIMS)EU-AIMS has received support from the Innovative Medicines Initiative Joint Undertaking Under Grant Agreement No. 115300MVL was supported by the British Academy, Jesus College Cambridge, Wellcome Trust, and an ERC Starting Grant (ERC-2017-STG; 755816

Cortical and subcortical contributions to state- and strength-based perceptual judgments

peer reviewe

A Connection Between Pattern Classification by Machine Learning and Statistical Inference With the General Linear Model

A connection between the general linear model (GLM) with frequentist statistical testing and machine learning (MLE) inference is derived and illustrated. Initially, the estimation of GLM parameters is expressed as a Linear Regression Model (LRM) of an indicator matrix; that is, in terms of the inverse problem of regressing the observations. Both approaches, i.e. GLM and LRM, apply to different domains, the observation and the label domains, and are linked by a normalization value in the least-squares solution. Subsequently, we derive a more refined predictive statistical test: the linear Support Vector Machine (SVM), that maximizes the class margin of separation within a permutation analysis. This MLE-based inference employs a residual score and associated upper bound to compute a better estimation of the actual (real) error. Experimental results demonstrate how parameter estimations derived from each model result in different classification performance in the equivalent inverse problem. Moreover, using real data, the MLE-based inference including model-free estimators demonstrates an efficient trade-off between type I errors and statistical power.Ministerio de Ciencia e Innovacion (Espana)/FEDER RTI2018-098913B100Junta de AndaluciaEuropean Commission CV20-45250 A-TIC-080-UGR18 P20-00525National Health and Medical Research Council (NHMRC) of Australia 18/0490

Connected system for monitoring electrical power transformers using thermal imaging

The stable supply of electricity is essential for the industrial activity and economic development as well as for human welfare. For this reason, electrical system devices are equipped with monitoring systems that facilitate their management and ensure an uninterrupted operation. This is the case of electrical power transformers, which usually have monitoring systems that allow early detection of anomalies in order to prevent potential malfunctions. These monitoring systems typically make use of sensors that are in physical contact with the transformer devices and can therefore be affected by transformer problems. In this work we demonstrate a monitoring system for electrical power transformers based on temperature measurements obtained by means of thermal cameras. Properly positioned, the cameras provide thermal data of the transformer, the incoming and outgoing lines and their surroundings. Subsequently, by appropriate image processing, it is possible to obtain temperature series to monitor the transformer operation. In addition, the system stores and processes thermal data in external equipment (placed in locations other than the transformers) and is equipped with a communications module that allows secure data transmission independent of the power grid. This aspect, along with the fact that there is no need to have physical contact with the transformer, make this approach safer and more reliable than standard approaches based on sensors. The proposed system has been evaluated in 14 stations belonging to the Spanish power grid, obtaining accurate and reliable temperature time seriesConsejería de Economía, Innovación, Ciencia y Empleo (Junta de Andalucía)FEDER under B-TIC-586-UGR20P20-00525 projects and by the University of GranadaEndesa Distribución under the PASTORA (ref. EXP – 00111351/ITC-20181102)RESISTO (ref. 2021/C005/00144188) contract

Análisis de imágenes funcionales cerebrales mediante modelos de mezcla de gaussianas y mínimos cuadrados parciales para el diagnóstico de alteraciones neurológicas

Tesis Univ. Granada. Departamento de Arquitectura y Tecnología de Computadores. Leída el 23 de mayo de 201

Análisis de imágenes funcionales cerebrales mediante modelos de mezcla de gaussianas y mínimos cuadrados parciales para el diagnóstico de alteraciones neurológicas

Tesis Univ. Granada. Departamento de Arquitectura y Tecnología de Computadores. Leída el 23 de mayo de 201

PET imaging analysis using a parcelation approach and multiple kernel classification

peer reviewedPositron Emission Tomography (PET) is a non-invasive medical imaging modality that provides information about physiological processes. Due to its ability to measure the brain metabolism, it is widely used to assist the diagnosis of neurodegenerative disorders such as Alzheimer’s disease (AD) of Parkinsonism. In order to avoid the subjectivity inherent to the visual exploration of the images, several computer systems to analyze PET data were developed during the last years. However, dealing with the huge amount of information provided by PET imaging is still a challenge. In this work we present a novel methodology to analyze PET data that improves the automatic differentiation between controls and AD patients. First the images are divided into small regions or parcels, defined either anatomically, geometrically or randomly. Secondly, the accuray of each single region is estimated using a Support Vector Machine (SVM) classifier and a cross-validation approach. Finally, all the regions are assessed together using multiple kernel SVM with a kernel per region. The classifier is built so that the most discriminative regions have more weight in the final decision. This method was evaluated using a PET dataset that contained images from healthy controls and AD patients. The classification results obtained with the proposed approach outperformed two recently reported computer systems based on Principal Component Analysis and Independent Component Analysis

Classification of MCI and AD patients combining PET data and psychological scores

peer reviewedThis study’s aim was to measure the advantages of using psychological test data in the automatic classification of functional brain images in order to assist the diagnosis of neurodegenerative disorders such as Alzheimer’s disease (AD). Several computer-aided diagnosis systems for AD based on PET images were developed. Some of them used psychological scores beside the image data in the classification step and others did not. The results show the ones that take into account the psychological scores achieve higher accuracy rates

Classification of positron emission tomography images using multiple kernel learning

peer reviewedOver the last years, several approaches to analyze nuclear medicine imaging using computer systems have been proposed with the aim of assisting the diagnosis of neurodegenerative disorders. Probably one of the most complex challenges facing these approaches is to deal with the huge amount of data provided by brain images. In this work, we propose an original approach based on multiple kernel learning. First the images were parcellated (according to the structure of the brain) by means of the automatic anatomical labeling atlas. Then, the importance of each region for the assisted diagnosis was estimated using a classifi- cation procedure. Finally, all the regions were combined in a multiple kernel method in which one kernel per region was computed and all the kernels were weighted according to the importance of the region they represented. For testing purposes, a database with 46 PET images from stable mild cognitive impairment subjects and early Alzheimer’s disease converter patients was used. An accuracy rate of 73.91% was achieved when differentiating between both groups