Brain connectivity analysis: a short survey

This short survey the reviews recent literature on brain connectivity studies. It encompasses all forms of static and dynamic connectivity whether anatomical, functional, or effective. The last decade has seen an ever increasing number of studies devoted to deduce functional or effective connectivity, mostly from functional neuroimaging experiments. Resting state conditions have become a dominant experimental paradigm, and a number of resting state networks, among them the prominent default mode network, have been identified. Graphical models represent a convenient vehicle to formalize experimental findings and to closely and quantitatively characterize the various networks identified. Underlying these abstract concepts are anatomical networks, the so-called connectome, which can be investigated by functional imaging techniques as well. Future studies have to bridge the gap between anatomical neuronal connections and related functional or effective connectivities

A full-scale timbrel cross vault subjected to vertical cyclical displacements in one of its supports

[EN] Up-and-down cyclical displacement of supports-foundations, due for example to the presence of expansive soils, can affect the integrity of a structure and may even lead to its collapse. A recent study carried out at the ICITECH laboratories of the Universitat Politècnica de València analysed the effects of earth settlements on the behaviour of masonry cross vaults. One of the tests involved the construction and testing of a full-scale timbrel cross vault, one of whose supports was subjected to up-and-down vertical displacement cycles. The 4×4 m2 vault was composed of four 3.6 m diameter arches supporting a masonry web. Vertical displacements were applied to one of the supports by means of two synchronised mechanical jacks. The results of the tests provide valuable information to the scientific community, architects and engineers on the behaviour of timbrel cross vaults when one of their supports is subjected to cyclical movements.The authors wish to express their gratitude to the Spanish Ministry of Economy, Industry and Competitiveness for the funding provided through Project BIA 2014-59036-R, and also to LIC-Levantina Ingenieria y Construction and the Grupo Puma for their invaluable assistance. The second author (Elisa Bertolesi) would like to thank the Universitat Politecnica de Valencia for funding received for her postdoctoral grant (PAID-10-17).Torres Górriz, B.; Bertolesi, E.; Calderón García, PA.; Moragues, JJ.; Adam, JM. (2019). A full-scale timbrel cross vault subjected to vertical cyclical displacements in one of its supports. Engineering Structures. 183:791-804. https://doi.org/10.1016/j.engstruct.2019.01.054S79180418

Robust Ensemble Classification Methodology for I123-Ioflupane SPECT Images and Multiple Heterogeneous Biomarkers in the Diagnosis of Parkinson’s Disease

In last years, several approaches to develop an effective Computer-Aided-Diagnosis (CAD) system for Parkinson’s Disease (PD) have been proposed. Most of these methods have focused almost exclusively on brain images through the use of Machine-Learning algorithms suitable to characterize structural or functional patterns. Those patterns provide enough information about the status and/or the progression at intermediate and advanced stages of Parkinson’s Disease. Nevertheless this information could be insufficient at early stages of the pathology. The Parkinson’s ProgressionMarkers Initiative (PPMI) database includes neurological images along with multiple biomedical tests. This information opens up the possibility of comparing different biomarker classification results. As data come from heterogeneous sources, it is expected that we could include some of these biomarkers in order to obtain new information about the pathology. Based on that idea, this work presents an Ensemble Classification model with Performance Weighting. This proposal has been tested comparing Healthy Control subjects (HC) vs. patients with PD (considering both PD and SWEDD labeled subjects as the same class). This model combines several Support-Vector-Machine (SVM) with linear kernel classifiers for different biomedical group of tests—including CerebroSpinal Fluid (CSF), RNA, and Serum tests—and pre-processed neuroimages features (Voxels-As-Features and a list of definedMorphological Features) fromPPMI database subjects. The proposed methodology makes use of all data sources and selects the most discriminant features (mainly from neuroimages). Using this performance-weighted ensemble classification model, classification results up to 96% were obtained.This work was supported by the MINECO/FEDER under the TEC2015-64718-R project and the Ministry of Economy, Innovation, Science and Employment of the Junta de Andalucía under the Excellence Project P11-TIC-7103

Automatic ROI Selection in Structural Brain MRI Using SOM 3D Projection

This paper presents a method for selecting Regions of Interest (ROI) in brain Magnetic Resonance Imaging (MRI) for diagnostic purposes, using statistical learning and vector quantization techniques. The proposed method models the distribution of GM and WM tissues grouping the voxels belonging to each tissue in ROIs associated to a specific neurological disorder. Tissue distribution of normal and abnormal images is modelled by a Self-Organizing map (SOM), generating a set of representative prototypes, and the receptive field (RF) of each SOM prototype defines a ROI. Moreover, the proposed method computes the relative importance of each ROI by means of its discriminative power. The devised method has been assessed using 818 images from the Alzheimer's disease Neuroimaging Initiative (ADNI) which were previously segmented through Statistical Parametric Mapping (SPM). The proposed algorithm was used over these images to parcel ROIs associated to the Alzheimer's Disease (AD). Additionally, this method can be used to extract a reduced set of discriminative features for classification, since it compresses discriminative information contained in the brain. Voxels marked by ROIs which were computed using the proposed method, yield classification results up to 90% of accuracy for controls (CN) and Alzheimer's disease (AD) patients, and 84% of accuracy for Mild Cognitive Impairment (MCI) and AD patients.This work was partly supported by the MICINN under the TEC2012-34306 project and the Consejería de Innovación, Ciencia y Empresa (Junta de Andalucía, Spain) under the Excellence Projects P09-TIC-4530 and P11-TIC-7103. Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Alzheimer's Association; Alzheimer's Drug Discovery Foundation; BioClinica, Inc.; Biogen Idec Inc.; Bristol-Myers Squibb Company; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; GE Healthcare; Innogenetics, N.V.; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Medpace, Inc.; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRxResearch; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Synarc Inc.; and Takeda Pharmaceutical Company. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions 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 Southern California

A Structural Parametrization of the Brain Using Hidden Markov Models Based Paths in Alzheimer's Disease

The usage of biomedical imaging in the diagnosis of dementia is increasingly widespread. A number of works explore the possibilities of computational techniques and algorithms in what is called Computed Aided Diagnosis. Our work presents an automatic parametrization of the brain structure by means of a path generation algorithm based on Hidden Markov Models. The path is traced using information of intensity and spatial orientation in each node, adapting to the structural changes of the brain. Each path is itself a useful way to extract features from the MRI image, being the intensity levels at each node the most straightforward. However, a further processing consisting of a modification of the Gray Level Co-occurrence Matrix can be used to characterize the textural changes that occur throughout the path, yielding more meaningful values that could be associated to the structural changes in Alzheimer's Disease, as well as providing a significant feature reduction. This methodology achieves high performance, up to 80.3\% of accuracy using a single path in differential diagnosis involving Alzheimer-affected subjects versus controls belonging to the Alzheimer's Disease Neuroimaging Initiative (ADNI).TIC218, MINECO TEC2008-02113 and TEC2012-34306 projects, Consejería de Economía, Innovación, Ciencia y Empleo de la Junta de Andalucía P09-TIC-4530 and P11-TIC-71

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

Assisted Diagnosis of Parkinsonism Based on the Striatal Morphology

Parkinsonism is a clinical syndrome characterized by the progressive loss of striatal dopamine. Its diagnosis is usually corroborated by neuroimaging data such as DaTSCAN neuroimages that allow visualizing the possible dopamine deficiency. During the last decade, a number of computer systems have been proposed to automatically analyze DaTSCAN neuroimages, eliminating the subjectivity inherent to the visual examination of the data. In this work, we propose a computer system based on machine learning to separate Parkinsonian patients and control subjects using the size and shape of the striatal region, modeled from DaTSCAN data. First, an algorithm based on adaptative thresholding is used to parcel the striatum. This region is then divided into two according to the brain hemisphere division and characterized with 152 measures, extracted from the volume and its three possible 2-dimensional projections. Afterwards, the Bhattacharyya distance is used to discard the least discriminative measures and, finally, the neuroimage category is estimated by means of a Support Vector Machine classifier. This method was evaluated using a dataset with 189 DaTSCAN neuroimages, obtaining an accuracy rate over 94%. This rate outperforms those obtained by previous approaches that use the intensity of each striatal voxel as a feature.This work was supported by the MINECO/ FEDER under the TEC2015-64718-R project, the Ministry of Economy, Innovation, Science and Employment of the Junta de Andaluc´ıa under the P11-TIC-7103 Excellence Project and the Vicerectorate of Research and Knowledge Transfer of the University of Granada

POS-255 EFFECT OF DAPAGLIFLOZIN ON BLOOD PRESSURE IN PATIENTS WITH CKD: A PRE-SPECIFIED ANALYSIS FROM DAPA-CKD

Introduction: Hypertension is common in patients with chronic kidney disease (CKD). Sodium-glucose cotransporter 2 inhibitors decrease blood pressure in patients with type 2 diabetes, but the consistency and magnitude of blood pressure lowering with dapagliflozin in patients with CKD is unknown. We performed a pre-specified analysis of the DAPA-CKD trial to investigate the effect of dapagliflozin on systolic blood pressure in patients with CKD, with and without type 2 diabetes. Methods: We randomized 4,304 adults with baseline eGFR 25–75 mL/min/1.73m2and urinary albumin-to-creatinine ratio (UACR) 200–5,000 mg/g to either dapagliflozin 10 mg or placebo once daily; median follow-up was 2.4 years. The primary outcome was a composite of sustained ≥50% eGFR decline, end-stage kidney disease, or death from a kidney or cardiovascular cause. Change in systolic blood pressure was a pre-specified endpoint. Subgroup analyses were performed according to baseline type 2 diabetes status. Results: Baseline mean (SD) systolic blood pressure was 137.1 mmHg (17.4); in participants with and without type 2 diabetes 139.2 mmHg (17.3) and 132.6 mmHg (16.7), respectively. By week 2, dapagliflozin compared to placebo reduced systolic blood pressure by 3.6 mmHg (95%CI 2.8, 4.4; p\u3c0.001), an effect maintained over the duration of the trial, with similar reductions in patients with and without type 2 diabetes (Table). The reduction in systolic blood pressure with dapagliflozin explained 7.6% (95%CI 1.8, 20.9) of the effect on the primary composite outcome, with similar proportions explained in patients with and without type 2 diabetes. Conclusions: In participants with CKD, dapagliflozin lowered systolic blood pressure with a consistent effect in participants with and without type 2 diabetes. The modest reduction in blood pressure explained a small proportion of the benefit of dapagliflozin on the primary outcome. Conflict of interest Potential conflict of interest: HLH received grant funding and honoraria for consultancy as a member of the steering committee of the DAPA-CKD trial from AstraZeneca. Honoraria for steering committee membership paid to his institution from Janssen, Gilead, Bayer, Chinook, CSL Pharma honoraria for consultancy paid to his institution from Abbvie, Boehringer Ingleheim, Retrophin, Novo Nordisk honoraria for advisory board participation paid to his institution from Janssen, Merck, Mitsubishi Tanabe and Munipharma lecture fees received from AstraZeneca and Mitsubishi Tanabe and grant support received from Boehringer Ingelheim

Optical fiber sensors embedded in concrete for measurement of temperature in a real fire test

We present the results of a real fire test using optical fiber sensors embedded in concrete samples. The temperature curve used in this experiment is described in the Spanish/European standard UNE-EN 1363-1 temperature profile for normalized concrete resistance to real fire tests, reaching temperatures of more than 1000◦C inside the fire chamber and up to 600◦C inside the concrete samples. Three types of optical sensors have been embedded in concrete: 1. standard fiber Bragg gratings inscribed in photosensitive germanium-boron co-doped fiber, 2. regenerated fiber Bragg grating (RFGB) inscribed in germanium doped fiber, and 3. RFBG inscribed in germanium-boron co-doped fiber. C 2011 Society of Photo-Optical Instrumentation Engineers (SPIE). [DOI: 10.1117/1.3658760]The authors gratefully acknowledge research funding by the Spanish Ministry of Science and Innovation through Project SOPROMAC P41/08.Bueno Martínez, A.; Torres Górriz, B.; Barrera Vilar, D.; Calderón García, PA.; Lloris, J.; López, M.; Sales Maicas, S. (2011). Optical fiber sensors embedded in concrete for measurement of temperature in a real fire test. Optical Engineering. 50(12):1244041-1244047. https://doi.org/10.1117/1.3658760S124404112440475012Luccioni, B. M., Figueroa, M. I., & Danesi, R. F. (2003). Thermo-mechanic model for concrete exposed to elevated temperatures. Engineering Structures, 25(6), 729-742. doi:10.1016/s0141-0296(02)00209-2Abdel-Fattah, H., & Hamoush, S. A. (1997). Variation of the fracture toughness of concrete with temperature. Construction and Building Materials, 11(2), 105-108. doi:10.1016/s0950-0618(97)00005-6Da Silva, J. C. C., Martelli, C., Kalinowski, H. J., Penner, E., Canning, J., & Groothoff, N. (2007). Dynamic analysis and temperature measurements of concrete cantilever beam using fibre Bragg gratings. Optics and Lasers in Engineering, 45(1), 88-92. doi:10.1016/j.optlaseng.2006.03.003Lin, Y. B., Chern, J. C., Chang, K.-C., Chan, Y.-W., & Wang, L. A. (2004). The utilization of fiber Bragg grating sensors to monitor high performance concrete at elevated temperature. Smart Materials and Structures, 13(4), 784-790. doi:10.1088/0964-1726/13/4/016Lönnermark, A., Hedekvist, P. O., & Ingason, H. (2008). Gas temperature measurements using fibre Bragg grating during fire experiments in a tunnel. Fire Safety Journal, 43(2), 119-126. doi:10.1016/j.firesaf.2007.06.001Kersey, A. D., Davis, M. A., Patrick, H. J., LeBlanc, M., Koo, K. P., Askins, C. G., … Friebele, E. J. (1997). Fiber grating sensors. Journal of Lightwave Technology, 15(8), 1442-1463. doi:10.1109/50.618377Liou, C. L., Wang, L. A., & Shih, M. C. (1997). Characteristics of hydrogenated fiber Bragg gratings. Applied Physics A: Materials Science & Processing, 64(2), 191-197. doi:10.1007/s003390050463Fokine, M. (2004). Underlying mechanisms, applications, and limitations of chemical composition gratings in silica based fibers. Journal of Non-Crystalline Solids, 349, 98-104. doi:10.1016/j.jnoncrysol.2004.08.208Bandyopadhyay, S., Canning, J., Stevenson, M., & Cook, K. (2008). Ultrahigh-temperature regenerated gratings in boron-codoped germanosilicate optical fiber using 193 nm. Optics Letters, 33(16), 1917. doi:10.1364/ol.33.001917Ropelewski, L., & Neufeld, R. D. (1999). Thermal Inertia Properties of Autoclaved Aerated Concrete. Journal of Energy Engineering, 125(2), 59-75. doi:10.1061/(asce)0733-9402(1999)125:2(59