Representational organization of novel task sets during proactive encoding

Recent multivariate analyses of brain data have boosted our understanding of the organizational principles that shape neural coding. However, most of this progress has focused on perceptual visual regions (Connolly et al., 2012), whereas far less is known about the organization of more abstract, action-oriented representations. In this study, we focused on humans{\textquoteright} remarkable ability to turn novel instructions into actions. While previous research shows that instruction encoding is tightly linked to proactive activations in fronto-parietal brain regions, little is known about the structure that orchestrates such anticipatory representation. We collected fMRI data while participants (both males and females) followed novel complex verbal rules that varied across control-related variables (integrating within/across stimuli dimensions, response complexity, target category) and reward expectations. Using Representational Similarity Analysis (Kriegeskorte et al., 2008) we explored where in the brain these variables explained the organization of novel task encoding, and whether motivation modulated these representational spaces. Instruction representations in the lateral prefrontal cortex were structured by the three control-related variables, while intraparietal sulcus encoded response complexity and the fusiform gyrus and precuneus organized its activity according to the relevant stimulus category. Reward exerted a general effect, increasing the representational similarity among different instructions, which was robustly correlated with behavioral improvements. Overall, our results highlight the flexibility of proactive task encoding, governed by distinct representational organizations in specific brain regions. They also stress the variability of motivation-control interactions, which appear to be highly dependent on task attributes such as complexity or novelty.SIGNIFICANCE STATEMENTIn comparison with other primates, humans display a remarkable success in novel task contexts thanks to our ability to transform instructions into effective actions. This skill is associated with proactive task-set reconfigurations in fronto-parietal cortices. It remains yet unknown, however, how the brain encodes in anticipation the flexible, rich repertoire of novel tasks that we can achieve. Here we explored cognitive control and motivation-related variables that might orchestrate the representational space for novel instructions. Our results showed that different dimensions become relevant for task prospective encoding depending on the brain region, and that the lateral prefrontal cortex simultaneously organized task representations following different control-related variables. Motivation exerted a general modulation upon this process, diminishing rather than increasing distances among instruction representations

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 then 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 and modality at different stages of the development of Alzheimer’s disease. The excellent results obtained and the high flexibility of the method proposed allows fusing more than two modalities, leading to a scalable methodology that can be used in a wide range of contexts.This work was supported by 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) and by European Regional Development Funds (ERDF); and by Spanish “Ministerio de Universidades” through Margarita-Salas grant to J.E. Arco

Probabilistic combination of eigenlungs-based classifiers for COVID-19 diagnosis in chest CT images

The outbreak of the COVID-19 (Coronavirus disease 2019) pandemic has changed the world. According to the World Health Organization (WHO), there have been more than 100 million confirmed cases of COVID-19, including more than 2.4 million deaths. It is extremely important the early detection of the disease, and the use of medical imaging such as chest X-ray (CXR) and chest Computed Tomography (CCT) have proved to be an excellent solution. However, this process requires clinicians to do it within a manual and time-consuming task, which is not ideal when trying to speed up the diagnosis. In this work, we propose an ensemble classifier based on probabilistic Support Vector Machine (SVM) in order to identify pneumonia patterns while providing information about the reliability of the classification. Specifically, each CCT scan is divided into cubic patches and features contained in each one of them are extracted by applying kernel PCA. The use of base classifiers within an ensemble allows our system to identify the pneumonia patterns regardless of their size or location. Decisions of each individual patch are then combined into a global one according to the reliability of each individual classification: the lower the uncertainty, the higher the contribution. Performance is evaluated in a real scenario, yielding an accuracy of 97.86%. The large performance obtained and the simplicity of the system (use of deep learning in CCT images would result in a huge computational cost) evidence the applicability of our proposal in a real-world environment.Comment: 15 pages, 9 figure

Uncertainty-Aware Semi-supervised Method using Large Unlabelled and Limited Labeled COVID-19 Data

This work was partly supported by the MINECO/ FEDER under the RTI2018-098913-B100, CV20-45250 and A-TIC-080-UGR18 projects.The new coronavirus has caused more than 1 million deaths and continues to spread rapidly. This virus targets the lungs, causing respiratory distress which can be mild or severe. The X-ray or computed tomography (CT) images of lungs can reveal whether the patient is infected with COVID-19 or not. Many researchers are trying to improve COVID-19 detection using artificial intelligence. In this paper, relying on Generative Adversarial Networks (GAN), we propose a Semi-supervised Classification using Limited Labelled Data (SCLLD) for automated COVID-19 detection. Our motivation is to develop learning method which can cope with scenarios that preparing labelled data is time consuming or expensive. We further improved the detection accuracy of the proposed method by applying Sobel edge detection. The GAN discriminator output is a probability value which is used for classification in this work. The proposed system is trained using 10,000 CT scans collected from Omid hospital. Also, we validate our system using the public dataset. The proposed method is compared with other state of the art supervised methods such as Gaussian processes. To the best of our knowledge, this is the first time a COVID-19 semi-supervised detection method is presented. Our method is capable of learning from a mixture of limited labelled and unlabelled data where supervised learners fail due to lack of sufficient amount of labelled data. Our semi-supervised training method significantly outperforms the supervised training of Convolutional Neural Network (CNN) in case labelled training data is scarce. Our method has achieved an accuracy of 99.60%, sensitivity of 99.39%, and specificity of 99.80% where CNN (trained supervised) has achieved an accuracy of 69.87%, sensitivity of 94%, and specificity of 46.40%.Spanish Government RTI2018-098913-B100 CV20-45250 A-TIC-080UGR1

Using XAI in the Clock Drawing Test to reveal the cognitive impairment pattern.

he prevalence of dementia is currently increasing worldwide. This syndrome produces a deteriorationin cognitive function that cannot be reverted. However, an early diagnosis can be crucial for slowing itsprogress. The Clock Drawing Test (CDT) is a widely used paper-and-pencil test for cognitive assessmentin which an individual has to manually draw a clock on a paper. There are a lot of scoring systems forthis test and most of them depend on the subjective assessment of the expert. This study proposes acomputer-aided diagnosis (CAD) system based on artificial intelligence (AI) methods to analyze the CDTand obtain an automatic diagnosis of cognitive impairment (CI). This system employs a preprocessingpipeline 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 informativepatterns 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 byclinical experts in a balanced sample size of 3282 drawings. The proposed method provides an accuracyof 75.65% in the binary case-control classification task, with an AUC of 0.83. These results are indeedrelevant considering the use of the classic version of the CDT. The large size of the sample suggests thatthe method proposed has a high reliability to be used in clinical contexts and demonstrates the suitabilityof CAD systems in the CDT assessment process. Explainable artificial intelligence (XAI) methods areapplied to identify the most relevant regions during classification. Finding these patterns is extremelyhelpful to understand the brain damage caused by CI. A validation method using resubstitution withupper bound correction in a machine learning approach is also discusseThis 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 An765 dalucia) and FEDER under CV20-45250, A-TIC080-UGR18, B-TIC-586-UGR20 and P20-00525 projects, and by the Ministerio de Universidades under the FPU18/04902 grant given to C. JimenezMesa and the Margarita-Salas grant to J.E. Arco

Acquisition of uropygial gland microbiome by hoopoe nestlings

Mutualistic symbioses between animals and bacteria depend on acquisition of appropriate symbionts while avoiding exploitation by non-beneficial microbes. The mode of acquisition of symbionts would determine, not only the probability of encountering but also evolutionary outcomes of mutualistic counterparts. The microbiome inhabiting the uropygial gland of the European hoopoe (Upupa epops) includes a variety of bacterial strains, some of them providing antimicrobial benefits. Here, the mode of acquisition and stability of this microbiome is analyzed by means of Automated rRNA Intergenic Spacer Analysis and two different experiments. The first experiment impeded mothers’ access to their glands, thus avoiding direct transmission of microorganisms from female to offspring secretions. The second experiment explored the stability of the microbiomes by inoculating glands with secretions from alien nests. The first experiment provoked a reduction in similarity of microbiomes of mother and nestlings. Interestingly, some bacterial strains were more often detected when females had not access to their glands, suggesting antagonistic effects among bacteria from different sources. The second experiment caused an increase in richness of the microbiome of receivers in terms of prevalence of Operational Taxonomic Units (OTUs) that reduced differences in microbiomes of donors and receivers. That occurred because OTUs that were present in donors but not in receivers incorporated to the microbiome of the latter, which provoked that cross-inoculated nestlings got similar final microbiomes that included the most prevalent OTUs. The results are therefore consistent with a central role of vertical transmission in bacterial acquisition by nestling hoopoes and support the idea that the typical composition of the hoopoe gland microbiome is reached by the incorporation of some bacteria during the nestling period. This scenario suggests the existence of a coevolved core microbiome composed by a mix of specialized vertically transmitted strains and facultative symbionts able to coexist with them. The implications of this mixed mode of transmission for the evolution of the mutualism are discussedMinisterio de Ciencia e Innovación (España)Junta de Andalucí

A922 Sequential measurement of 1 hour creatinine clearance (1-CRCL) in critically ill patients at risk of acute kidney injury (AKI)

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Tiled Sparse Coding in Eigenspaces for Image Classification.

The automation in the diagnosis of medical images is currently a challenging task. The use of Computer Aided Diagnosis (CAD) systems can be a powerful tool for clinicians, especially in situations when hospitals are overflowed. These tools are usually based on artificial intelligence (AI), a field that has been recently revolutionized by deep learning approaches. These alternatives usually obtain a large performance based on complex solutions, leading to a high computational cost and the need of having large databases. In this work, we propose a classification framework based on sparse coding. Images are first partitioned into different tiles, and a dictionary is built after applying PCA to these tiles. The original signals are then transformed as a linear combination of the elements of the dictionary. Then, they are reconstructed by iteratively deactivating the elements associated with each component. Classification is finally performed employing as features the subsequent reconstruction errors. Performance is evaluated in a real context where distinguishing between four different pathologies: control versus bacterial pneumonia versus viral pneumonia versus COVID-19. Our system differentiates between pneumonia patients and controls with an accuracy of 97.74%, whereas in the 4-class context the accuracy is 86.73%. The excellent results and the pioneering use of sparse coding in this scenario evidence that our proposal can assist clinicians when their workload is high.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 Consejer´ıa de Econom´ıa, Innovaci´on, Ciencia y Empleo (Junta de Andaluc´ıa) and FEDER under CV20-45250, A TIC-080-UGR18, B-TIC-586-UGR20 and P20-00525 projects

Transient and sustained control mechanisms supporting novel instructed behavior

The success of humans in novel environments is partially supported by our ability to implement new task procedures via instructions. This complex skill has been associated with the activity of control-related brain areas. Current models link fronto-parietal and cingulo-opercular networks with transient and sustained modes of cognitive control, based on observations during repetitive task settings or rest. The current study extends this dual model to novel instructed tasks. We employed a mixed design and an instruction-following task to extract phasic and tonic brain signals associated with the encoding and implementation of novel verbal rules. We also performed a representation similarity analysis to capture consistency in task-set encoding within trial epochs. Our findings show that both networks are involved while following novel instructions: transiently, during the implementation of the instruction, and in a sustained fashion, across novel trials blocks. Moreover, the multivariate results showed that task representations in the cingulo-opercular network were more stable than in the fronto-parietal one. Our data extend the dual model of cognitive control to novel demanding situations, highlighting the high flexibility of control-related regions in adopting different temporal profiles