Inter-channel Granger Causality for Estimating EEG Phase Connectivity Patterns in Dyslexia

Methods like Electroencephalography (EEG) and magnetoencephalogram (MEG) record brain oscillations and provide an invaluable insight into healthy and pathological brain function. These signals are helpful to study and achieve an objective and early diagnosis of neural disorders as Developmental Dyslexia (DD). An atypical oscillatory sampling could cause the characteristic phonological difficulties of dyslexia at one or more temporal rates; in this sense, measuring the EEG signal can help to make an early diagnosis of DD. The LEEDUCA study conducted a series of EEG experiments on children listening to amplitude modulated (AM) noise with slow-rhythmic prosodic (0.5–1 Hz) to detect differences in perception of oscillatory ampling that could be associated with dyslexia. The evolution of each EEG channel has been studied in the frequency domain, obtaining the analytical phase using the Hilbert transform. Subsequently, the cause-effect relationships between channels in ach subject have been reflected thanks to Granger causality, obtaining matrices that reflect the interaction between the different parts of the brain. Hence, each subject was classified as belonging or not to the control group or the experimental group. For this purpose, two ensemble classification algorithms were compared, showing that both can reach acceptable classifying erformance in delta band with an accuracy up to 0.77, recall of 0.91 and AUC of 0.97 using Gradient Boosting classifier.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Temporal Phase Synchrony Disruption in Dyslexia: Anomaly Patterns in Auditory Processing

The search for a dyslexia diagnosis based on exclusively objective methods is currently a challenging task. Usually, this disorder is analyzed by means of behavioral tests prone to errors due to their subjective nature; e.g. the subject’s mood while doing the test can affect the results. Understanding the brain processes involved is key to proportionate a correct analysis and avoid these types of problems. It is in this task, biomarkers like electroencephalograms can help to obtain an objective measurement of the brain behavior that can be used to perform several analyses and ultimately making a diagnosis, keeping the human interaction at minimum. In this work, we used recorded electroencephalograms of children with and without dyslexia while a sound stimulus is played. We aim to detect whether there are significant differences in adaptation when the same stimulus is applied at different times. Our results show that following this process, a machine learning pipeline can be built with AUC values up to 0.73.Spanish Government PGC2018-098813-BC32 PGC2018-098813-B-C31Junta de Andalucia UMA20-FEDERJA-086 P18-RT-1624European CommissionBioSiP research group TIC-251MCIN/AEI by "ESF Investing in your future" PRE2019-087350 MICINN "Juan de la Cierva -Incorporacion" FellowshipLeeduca research groupJunta de Andalucia Spanish Governmen

Detecting Phase-Synchrony Connectivity Anomalies in EEG Signals. Application to Dyslexia Diagnosis

Objective Dyslexia diagnosis is a challenging task, since traditional diagnosis methods are not based on biological markers but on behavioural tests. Although dyslexia diagnosis has been addressed by these tests in clinical practice, it is difficult to extract information about the brain processes involved in the different tasks and, then, to go deeper into its biological basis. Thus, the use of biomarkers can contribute not only to the diagnosis but also to a better understanding of specific learning disorders such as dyslexia. In this work, we use Electroencephalography (EEG) signals to discover differences among controls and dyslexic subjects using signal processing and artificial intelligence techniques. Specifically, we measure phase synchronization among channels, to reveal the functional brain network activated during auditory processing. On the other hand, to explore synchronicity patterns risen by low-level auditory processing, we used specific stimuli consisting in band-limited white noise, modulated in amplitude at different frequencies. The differential information contained in the functional (i.e., synchronization) network has been processed by an anomaly detection system that addresses the problem of subjects variability by an outlier-detection method based on vector quantization. The results, obtained for 7 years-old children, show that the proposed method constitutes an useful tool for clinical use, with the area under ROC curve (AUC) values up to 0.95 in differential diagnosis tasks.PGC2018-098813-B-C32 (Spanish “Ministerio de Ciencia, Innovación y Universidades”)UMA20-FEDERJA-086 (Consejería de econnomía y conocimiento, Junta de Andalucía)European Regional Development Funds (ERDF)Grant PRE2019-087350 funded by MCIN/AEI/10.13039/501100011033 by “ESF Investing in your future”

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

Short-term effectiveness of dapagliflozin versus DPP-4 inhibitors in elderly patients with type 2 diabetes: a multicentre retrospective study

Aim To compare effectiveness of dapagliflozin versus DPP-4 inhibitors on individualized HbA1c targets and extra-glycaemic endpoints among elderly patients with type 2 diabetes (T2D).Methods This was a multicentre retrospective study on patients aged 70-80 years with HbA1c above individualized target and starting dapagliflozin or DPP-4 inhibitors in 2015-2017. The primary outcome was the proportion reaching individualized HbA1c targets. Confounding by indication was addressed by inverse probability of treatment weighting (IPTW), multivariable adjustment (MVA), or propensity score matching (PSM).Results Patients initiating dapagliflozin (n = 445) differed from those initiating DPP-4i (n = 977) and balance between groups was achieved with IPTW or PSM. The median follow-up was 7.5 months and baseline HbA1c was 8.3%. A smaller proportion of patients initiating dapagliflozin attained individualized HbA1c target as compared to those initiating DPP-4 inhibitors (RR 0.73, p < 0.0001). IPTW, MVA, and PSM yielded similar results. Between-group difference in the primary outcome was observed among patients with lower eGFR or longer disease duration. Dapagliflozin allowed greater reductions in body weight and blood pressure than DPP-4 inhibitors.Conclusions Elderly patients with T2D initiating dapagliflozin had a lower probability of achieving individualized HbA1c targets than those initiating DPP-4 inhibitors but displayed better improvements in extra-glycaemic endpoints