Neural representation of current and intended task sets during sequential judgements on human faces

Engaging in a demanding activity while holding in mind another task to be performed in the near future requires the maintenance of information about both the currently-active task set and the intended one. However, little is known about how the human brain implements such action plans. While some previous studies have examined the neural representation of current task sets and others have investigated delayed intentions, to date none has examined the representation of current and intended task sets within a single experimental paradigm. In this fMRI study, we examined the neural representation of current and intended task sets, employing sequential classification tasks on human faces. Multivariate decoding analyses showed that current task sets were represented in the orbitofrontal cortex (OFC) and fusiform gyrus (FG), while intended tasks could be decoded from lateral prefrontal cortex (lPFC). Importantly, a ventromedial region in PFC/OFC contained information about both current and delayed tasks, although cross-classification between the two types of information was not possible. These results help delineate the neural representations of current and intended task sets, and highlight the importance of ventromedial PFC/OFC for maintaining task-relevant information regardless of when it is needed.This work was supported by the Spanish Ministry of Science and Innovation (PSI2016-78236-P to M.R.) and the Spanish Ministry of Education, Culture and Sports (FPU2014/04272 to P.D.G.)

Enhancing multimodal patterns in neuroimaging by siamese neural networks with self-attention mechanism

The combination of different sources of information is currently one of the most relevant aspects in the diagnostic process of several diseases. In the field of neurological disorders, different imaging modalities providing structural and functional information are frequently available. Those modalities are usually analyzed separately, although a joint of the features extracted from both sources can improve the classification performance of Computer-Aided Diagnosis (CAD) tools. Previous studies have computed independent models from each individual modality and combined them in a subsequent stage, which is not an optimum solution. In this work, we propose a method based on the principles of siamese neural networks to fuse information from Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET). This framework quantifies the similarities between both modalities and relates them with the diagnostic label during the training process. The resulting latent space at the output of this network is then entered into an attention module in order to evaluate the relevance of each brain region at different stages of the development of Alzheimer's disease. The excellent results obtained and the high flexibility of the method proposed allow fusing more than two modalities, leading to a scalable methodology that can be used in a wide range of contexts.Projects PGC2018- 098813-B-C32 and RTI2018-098913-B100 (Spanish “Ministerio de Ciencia, Innovación y Universidades”)UMA20-FEDERJA-086, A-TIC-080- UGR18 and P20 00525 (Consejería de economía y conocimiento, Junta de Andalucía)European Regional Development Funds (ERDF)Spanish “Ministerio de Universidades” through Margarita-Salas gran

Atlas-based classification algorithms for identification of informative brain regions in fMRI data

Multi-voxel pattern analysis (MVPA) has been successfully applied to neuroimaging data due to its larger sensitivity compared to univariate traditional techniques. Although a Searchlight strategy that locally sweeps all voxels in the brain is the most extended approach to assign functional value to different regions of the brain, this method does not offer information about the directionality of the results and it does not allow studying the combined patterns of more distant voxels. In the current study, we examined two different alternatives to searchlight. First, an atlas- based local averaging (ABLA, Schrouff et al., 2013a) method, which computes the relevance of each region of an atlas from the weights obtained by a whole-brain analysis. Second, a Multiple-Kernel Learning (MKL, Rakotomamonjy et al., 2008) approach, which combines different brain regions from an atlas to build a classification model. We evaluated their performance in two different scenarios where differential neural activity between conditions was large vs. small, and employed nine different atlases to assess the influence of diverse brain parcellations. Results show that all methods are able to localize informative regions when differences were large, demonstrating stability in the identification of regions across atlases. Moreover, the sign of the weights reported by these methods provides the sensitivity of multivariate approaches and the directionality of univariate methods. However, in the second context only ABLA localizes informative regions, which indicates that MKL leads to a lower performance when differences between conditions are small. Future studies could improve their results by employing machine learning algorithms to compute individual atlases fit to the brain organization of each participant.Spanish Ministry of Science and Innovation through grant PSI2016-78236-PSpanish Ministry of Economy and Competitiveness through grant BES-2014-06960

Short-term Prediction of MCI to AD conversion based on Longitudinal MRI analysis and neuropsychological tests

Nowadays, 35 million people worldwide su↵er from some form of dementia. Given the increase in life expectancy it is estimated that in 2035 this number will grow to 115 million. Alzheimer’s disease is the most common cause of dementia and it is of great importance diagnose it at an early stage. This is the main goal of this work, the de- velopment of a new automatic method to predict the mild cognitive im- pairment (MCI) patients who will develop Alzheimer’s disease within one year or, conversely, its impairment will remain stable. This technique will analyze data from both magnetic resonance imaging and neuropsycholog- ical tests by utilizing a t-test for feature selection, maximum-uncertainty linear discriminant analysis (MLDA) for classification and leave-one-out cross validation (LOOCV) for evaluating the performance of the meth- ods, which achieved a classification accuracy of 73.95%, with a sensitivity of 72.14% and a specificity of 73.77%.MICINN under the TEC2012-34306 projectConsejería de Innovación, Ciencia y Empresa (Junta de Andalucía, Spain) under the Excellence Project P11-TIC-710

Data fusion based on Searchlight analysis for the prediction of Alzheimer's disease

This work was supported by the MINECO/FEDER, Spain under the RTI2018-098913-B-I00 project, the General Secretariat of Universities, Research and Technology, Junta de Andalucia, Spain under the Excellence FEDER Project A-TIC-117-UGR18, and University of Granada, Spain through grant "Contratos puente'' to J.E.A. Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative, United States (ADNI; National Institutes of Health Grant U01 AG024904). ADNI is funded by the National Institute on Aging, United States, the National Institute of Biomedical Imaging and Bioengineering, United States, and through generous contributions from the following: Abbott, AstraZeneca AB, Bayer Schering Pharma AG, Bristol-Myers Squibb, Eisai Global Clinical Development, Elan Corporation, Genentech, GE Healthcare, GlaxoSmithKline, Innogenetics, Johnson and Johnson, Eli Lilly and Co., Medpace, Inc., Merck and Co., Inc., Novartis AG, Pfizer Inc., F. Hoffman-La Roche, Schering-Plough, Synarc, Inc., as well as non-profit partners the Alzheimer's Association and Alzheimer's Drug Discovery Foundation, with participation from the U.S. Food and Drug Administration. Private sector contributions to ADNI are facilitated by the Foundation for the National Institutes of Health (www.fnih.org).The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro-Imaging at the University of California, Los Angeles. This research was also supported by NIH, Spain grants P30 AG010129, K01 AG030514, and the Dana Foundation, United States.Conceptualization, Methodology, Software, Investigation, Writing – original draft, Writing – review & editing. Javier Ramírez: Conceptualization, Methodology, Investigation, Writing – original draft, Writing – review & editing. Juan M. Górriz: Conceptualization, Methodology, Investigation, Writing – original draft, Writing – review & editing. María Ruz: Conceptualization, Validation, Supervision, Investigation, Writing – original draft, Writing – review & editing.In recent years, several computer-aided diagnosis (CAD) systems have been proposed for an early identification of dementia. Although these approaches have mostly used the transformation of data into a different feature space, more precise information can be gained from a Searchlight strategy. The current study presents a data fusion classification system that employs magnetic resonance imaging (MRI) and neuropsychological tests to distinguish between Mild-Cognitive Impairment (MCI) patients that convert to Alzheimer's disease (AD) and those that remain stable. Specifically, this method uses a nested cross-validation procedure to compute the optimum contribution of each data modality in the final decision. The model employs Support-Vector Machine (SVM) classifiers for both data modalities and is combined with Searchlight when applied to neuroimaging. We compared the performance of our system with an alternative based on Principal Component Analysis (PCA) for dimensionality reduction. Results show that Searchlight outperformed PCA both for uni/multimodal classification, obtaining a maximum accuracy of 80.9% when combining data from six and twelve months before patients converted to AD. Moreover, Searchlight allowed the identification of the most informative regions at different stages of the longitudinal study, which can be crucial for a better understanding of the development of AD. Additionally, results do not depend on the parcellations provided by a specific brain atlas, which manifests the robustness and the spatial precision of the method proposed.MINECO/FEDER, Spain RTI2018-098913-B-I00General Secretariat of Universities, Research and TechnologyJunta de Andalucia A-TIC-117-UGR18University of Granada, Spain through grant "Contratos puente''Alzheimer's Disease Neuroimaging Initiative, United States (ADNI; National Institutes of Health) U01 AG024904National Institute on Aging, United StatesNational Institute of Biomedical Imaging and Bioengineering, United StatesAbbott LaboratoriesAstraZenecaBayer AGBristol-Myers SquibbEisai Co LtdElan CorporationRoche Holding GenentechGeneral Electric GE HealthcareGlaxoSmithKlineInnogeneticsJohnson & Johnson Johnson & Johnson USAEli LillyMedpace, Inc.Merck & CompanyNovartis AGPfizerF. Hoffman-La RocheMerck & Company Schering Plough CorporationSynarc, Inc.United States Department of Health & Human Services National Institutes of Health (NIH) - USANorthern California Institute for Research and EducationUnited States Department of Health & Human Services National Institutes of Health (NIH) - USA P30 AG010129Dana Foundation, United State

Probabilistic Combination of Non-Linear Eigenprojections For Ensemble Classification

The emergence of new technologies has changed the way clinicians perform diagnosis. Medical imaging play a crucial role in this process, given the amount of information that they usually provide as non-invasive techniques. Despite the high quality offered by these images and the expertise of clinicians, the diagnostic process is not a straightforward task since different pathologies can have similar signs and symptoms. For this reason, it is extremely useful to assist this process with the inclusion of an automatic tool that reduces the bias when analyzing this kind of images. In this work, we propose an ensemble classifier based on probabilistic Support Vector Machine (SVM) in order to identify relevant patterns while providing information about the reliability of the classification. Specifically, each image is divided into patches and features contained in each one of them are extracted by applying kernel principal component analysis (PCA). The use of base classifiers within an ensemble allows our system to identify the informative patterns regardless of their size or location. Decisions of each individual patch are then combined according to the reliability of each individual classification: the lower the uncertainty, the higher the contribution. Performance is evaluated in a real scenario where distinguishing between pneumonia patients and controls from chest Computed Tomography (CCT) images, yielding an accuracy of 97.86%. The large performance obtained and the simplicity of the system (use of deep learning in CCT images would highly increase the computational cost) evidence the applicability of our proposal in a real-world environment.Projects PGC2018-098813-B-C32 and RTI2018-098913-B100 (Spanish “Ministerio de Ciencia, Innovación y Universidades”)UMA20-FEDERJA-086, A-TIC-080-UGR18 and P20 00525 (Consejer´ıa de econom´ıa y conocimiento, Junta de Andaluc´ıa)European Regional Development Funds (ERDF)Spanish ”Ministerio de Universidades” through Margarita-Salas gran

Supervised-learning methods for pattern recognition in fMRI data for the identification of informative brain regions in psychological contexts

In the last years, there has been an exponential increase in the use of multivariate analysis in neuroimaging data. This has led to a new perspective in the study of brain function, which lets identify the brain areas involved in a cognitive function and the characterization of the patterns of information associated with them. This kind of analyses are based on a classification framework that increases the sensitivity of classic univariate approaches and detects subtle changes in neural activity of different experimental conditions. This thesis focuses on three main aspects of the classification pipeline. First, an optimal estimation of the activation patterns to isolate the contribution of each experimental condition to the hemodynamic response. We have employed a Least-Squares Separate method, an approach that iteratively fits a new model for each trial in the experiment. Each model has two regressors: one for the target trial and another one for the rest. We show for the first time that this method can effectively identify the activity associated with different events even though they belong to different cognitive processes with a substantial difference in their duration. Second, a classification algorithm based on Multiple Kernel Learning (MKL). This approach combines different brain regions from an atlas to build the classification function and identifies the relevance of each region in a specific psychological context. We propose a modification of this algorithm and employ an L2-regularization to avoid the sparsity that L1-regularization entails. This approach yields simultaneously the high sensitivity of multivariate methods and the directionality of univariate approaches. Third, we employed a non-parametric approach based on permutation testing that computes more accurately the significance thresholds for each voxel in the brain. Hence, it takes into account the differences in maximum decoding accuracy that different brain regions have, enhancing sensitivity and detecting true informative that otherwise would not be marked as significant. Our results show that differences between parametric and non-parametric methods can be much larger when trying to detect subtle changes in neural activity. We have shown that information derived from just above-chance accuracies should not be underestimated if these accuracies are significant. We should expect high values of accuracy when contrasting stimuli with large perceptual differences. If these differences are minimal, the accuracy will be small. Future research should continue the development of machine learning methods especially optimized for Cognitive Neuroscience, where obtaining large accuracies is not of first interest but to provide a clear interpretation of the mathematics behind these algorithms.Tesis Univ. Granada

Uncertainty-driven ensembles of multi-scale deep architectures for image classification

The use of automatic systems for medical image classification has revolutionized the diagnosis of a high number of diseases. These alternatives, which are usually based on artificial intelligence (AI), provide a helpful tool for clinicians, eliminating the inter and intra-observer variability that the diagnostic process entails. Convolutional Neural Network (CNNs) have proved to be an excellent option for this purpose, demonstrating a large performance in a wide range of contexts. However, it is also extremely important to quantify the reliability of the model’s predictions in order to guarantee the confidence in the classification. In this work, we propose a multi-level ensemble classification system based on a Bayesian Deep Learning approach in order to maximize performance while providing the uncertainty of each classification decision. This tool combines the information extracted from different architectures by weighting their results according to the uncertainty of their predictions. Performance is evaluated in a wide range of real scenarios: in the first one, the aim is to differentiate between different pulmonary pathologies: controls vs bacterial pneumonia vs viral pneumonia. A two-level decision tree is employed to divide the 3-class classification into two binary classifications, yielding an accuracy of 98.19%. In the second context, performance is assessed for the diagnosis of Parkinson’s disease, leading to an accuracy of 95.31%. The reduced preprocessing needed for obtaining this high performance, in addition to the information provided about the reliability of the predictions evidence the applicability of the system to be used as an aid for clinicians.MCINFEDER "Una manera de hacer Europa" PGC2018-098813-B-C32 A-TIC-080-UGR18Junta de AndaluciaEuropean Commission B-TIC-586-UGR20 P20-00525Ministerio de UniversidadesUniversidad de Granada/CBUA RTI2018-098913-B100 CV20-4525

Using Explainable Artificial Intelligence in the Clock Drawing Test to Reveal the Cognitive Impairment Pattern

The prevalence of dementia is currently increasing worldwide. This syndrome produces a deterioration in cognitive function that cannot be reverted. However, an early diagnosis can be crucial for slowing its progress. The Clock Drawing Test (CDT) is a widely used paper-and-pencil test for cognitive assessment in which an individual has to manually draw a clock on a paper. There are a lot of scoring systems for this test and most of them depend on the subjective assessment of the expert. This study proposes a computer-aided diagnosis (CAD) system based on artificial intelligence (AI) methods to analyze the CDT and obtain an automatic diagnosis of cognitive impairment (CI). This system employs a preprocessing pipeline in which the clock is detected, centered and binarized to decrease the computational burden. Then, the resulting image is fed into a Convolutional Neural Network (CNN) to identify the informative patterns within the CDT drawings that are relevant for the assessment of the patient’s cognitive status. Performance is evaluated in a real context where patients with CI and controls have been classified by clinical experts in a balanced sample size of 3282 drawings. The proposed method provides an accuracy of 75.65% in this classification task, with an AUC of 0.83. These results overcome previous studies, showing that the method proposed has a high reliability to be used in clinical contexts. The large size of the sample and the performance obtained despite being applied to the classic version of the CDT demonstrate the suitability of CAD systems in the CDT assessment process. Explainable AI (XAI) methods are applied to identify the most relevant regions during classification. Finding these patterns is extremely helpful to understand the brain damage caused by cognitive impairment. A validation method using resubstitution with upper bound correction in a machine learning approach is also discussed.This work was supported by the MCIN/AEI/10.13039/501100011033/ and FEDER “Una manera de hacer Europa” under the RTI2018- 098913-B100 project, by the Consejeria de Economia, Innovacion, Ciencia y Empleo (Junta de Andalucia) and FEDER under CV20-45250, A-TIC-080-UGR18, B-TIC-586-UGR20 and P20-00525 projects, and by the Ministerio de Universidades under the FPU18/04902 grant given to C. Jimenez-Mesa and the Margarita-Salas grant to J.E. Arco

Computational approaches to Explainable Artificial Intelligence: Advances in theory, applications and trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated humanlevel performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9th International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications.CIBERSAM of the Instituto de Salud Carlos III 495-2020UMA18-FEDERJA-084Autonomous Government Andalusia (Spain) RTX A6000 48NVIDIA Corporation 101057746Horizon Europe project PRE-ACTEuropean Commission Horizon Europe Program 22 00058Swiss State Secretariat for Education, Research and Innovation (SERI) 2020-0-01361Institute for Information & Communication Technology Planning & Evaluation (IITP), Republic of Korea Ministry of Science & ICT (MSIT), Republic of KoreaArtificial Intelligence Graduate School Program (Yonsei University)Funding for open access charge: Universidad de Granada / CBU