258 research outputs found

    Compilación de la historia del programa académico de Bibliotecología en la Pontificia Universidad Javeriana 1973 - 2010

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    Este documento es el resultado de una investigación relacionada con la recopilación y análisis histórico de diferentes fuentes de información que dan cuenta de los hechos que dieron paso a la creación de la Carrera Ciencia de la Información - Bibliotecología en la Pontificia Universidad Javeriana, así como también de su devenir hasta la actualidad. Las diversas fuentes de información consultadas, tanto primarias como secundarias corresponden a lo escrito en el periodo comprendido desde el año 1973 hasta el año 2010. La primera fecha por ser el año de inicio de creación del Programa Académico de Ciencia de la Información en la Pontificia Universidad Javeriana y la segunda para dar un alcance y límite temporal a la investigación. Todo ello con el propósito de recopilar los soportes que muestran la trayectoria de la formación bibliotecaria en Colombia y específicamente en la Pontificia Universidad Javeriana.Profesional en Ciencia de la Información - Bibliotecólogo (a)Pregrad

    Analysis of the Usefulness of a Serious Game to Raise Awareness about Mental Health Problems in a Sample of High School and University Students: Relationship with Familiarity and Time Spent Playing Video Games

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    Background: One of the main challenges in the field of mental health today is the stigma towards individuals who have psychological disorders. Aims: This study aims to analyse the usefulness of applying a serious game developed for the purpose of raising awareness among students about mental health problems and analyse whether its usefulness can be influenced by the type of video games or the time that students usually devote to playing with this type of entertainment. Method: The serious game introduces four characters who display the symptoms of different psychological disorders. A total of 530 students participated in the study, 412 of whom comprised the experimental group and 118 the control group, 291 came from secondary school classes and 239 were university students. Results: The findings show that this serious game significantly reduced total stigma among students. Variables like time habitually spent playing video games or video game preference had no bearing on the results. Conclusion: Our findings suggest that the serious game is an appropriated tool to reduce stigma, both in high school and university students, independently of the type of video games that young people usually play, or time spent playing video games

    Structural and functional, empirical and modeled connectivity in the cerebral cortex of the rat

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    [EN] Connectomics data from animal models provide an invaluable opportunity to reveal the complex interplay between structure and function in the mammalian brain. In this work, we investigate the relationship between structural and functional connectivity in the rat brain cortex using a directed anatomical network generated from a carefully curated meta-analysis of published tracing data, along with resting-state functional MRI data obtained from a group of 14 anesthetized Wistar rats. We found a high correspondence between the strength of functional connections, measured as blood oxygen level dependent (BOLD) signal correlations between cortical regions, and the weight of the corresponding anatomical links in the connectome graph (maximum Spearman rank-order correlation rho = 0.48). At the network-level, regions belonging to the same functionally defined community tend to form more mutual weighted connections between each other compared to regions located in different communities. We further found that functional communities in resting-state networks are enriched in densely connected anatomical motifs. Importantly, these higher-order structural subgraphs cannot be explained by lower-order topological properties, suggesting that dense structural patterns support functional associations in the resting brain. Simulations of brain-wide resting-state activity based on neural mass models implemented on the empirical rat anatomical connectome demonstrated high correlation between the simulated and the measured functional connectivity (maximum Pearson correlation rho = 0: 53), further suggesting that the topology of structural connections plays an important role in shaping functional cortical networks.This work was supported in part by the Spanish Ministerio de Economia y Competitividad (MINECO) and FEDER funds under grants BFU2015-64380-C2-1-R (S.C) and BFU2015-64380-C2-2-R (D.M.) and EU Horizon 2020 Program 668863-SyBil-AA grant (S.C.). S.C. acknowledges financial support from the Spanish State Research Agency, through the "Severo Ochoa" Programme for Centres of Excellence in R&D (ref. SEV-2013-0317). A. D.-P., was supported by grant FPU13/01475 from the Spanish Ministerio de Educacion, Cultura y Deporte (MECD). O.S. acknowledges support by the J.S. McDonnell Foundation (#220020387) and the National Institutes of Health (NIH R01 AT009036-01). We are also grateful to Andrea Avena-Koenigsberger and Begona Fernandez for their technical support.Díaz-Parra, A.; Osborn, Z.; Canals Gamoneda, S.; Moratal, D.; Sporns, O. (2017). Structural and functional, empirical and modeled connectivity in the cerebral cortex of the rat. NeuroImage. 159:170-184. https://doi.org/10.1016/j.neuroimage.2017.07.046S17018415

    Cambio estructural y desempleo en Castilla y León.

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    En este artículo se analiza el desempleo bajo un enfoque macroeconómico, se apoya en la teoría del crecimiento de Passinetti, que permite establecer la condición de equilibrio en el mercado de trabajo en base a un análisis a nivel desagregado de producto y de integración vertical de los coeficientes técnicos. Establecida ésta se pueden estudiar los factores generadores de desempleo, como son los aumentos de las productividades de las ramas de actividad, las variaciones en la tasa de actividad y los desajustes introducidos por las variaciones en la composición de la demanda. El análisis del desempleo en Castilla y León y en España se realiza a través de dicha descomposición y utilizando los datos de las tablas input-output (TIO) de Castilla y León (1985 y 1990), y de España (1980, 1985 y 1990), una vez homogeneizadas y convértidas a precios constantes

    Evaluating Functional Connectivity Alterations in Autism Spectrum Disorder Using Network-Based Statistics

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    [EN] The study of resting-state functional brain networks is a powerful tool to understand the neurological bases of a variety of disorders such as Autism Spectrum Disorder (ASD). In this work, we have studied the differences in functional brain connectivity between a group of 74 ASD subjects and a group of 82 typical-development (TD) subjects using functional magnetic resonance imaging (fMRI). We have used a network approach whereby the brain is divided into discrete regions or nodes that interact with each other through connections or edges. Functional brain networks were estimated using the Pearson's correlation coefficient and compared by means of the Network-Based Statistic (NBS) method. The obtained results reveal a combination of both overconnectivity and underconnectivity, with the presence of networks in which the connectivity levels differ significantly between ASD and TD groups. The alterations mainly affect the temporal and frontal lobe, as well as the limbic system, especially those regions related with social interaction and emotion management functions. These results are concordant with the clinical profile of the disorder and can contribute to the elucidation of its neurological basis, encouraging the development of new clinical approaches.A.D.-P. was supported by grant FPU13/01475 from the Spanish Ministerio de Educacion, Cultura y Deporte (MECD). This work was supported in part by the Spanish Ministerio de Economia y Competitividad (MINECO) and FEDER funds under grant BFU2015-64380-C2-2-R.Pascual-Belda, A.; Díaz-Parra, A.; Moratal, D. (2018). Evaluating Functional Connectivity Alterations in Autism Spectrum Disorder Using Network-Based Statistics. Diagnostics. 8(3). https://doi.org/10.3390/diagnostics8030051S8

    Automatic segmentation of the spine by means of a probabilistic atlas with a special focus on ribs suppression

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    [EN] Purpose: The development of automatic and reliable algorithms for the detection and segmentation of the vertebrae are of great importance prior to any diagnostic task. However, an important problem found to accurately segment the vertebrae is the presence of the ribs in the thoracic region. To overcome this problem, a probabilistic atlas of the spine has been developed dealing with the proximity of other structures, with a special focus on ribs suppression. Methods: The data sets used consist of Computed Tomography images corresponding to 21 patients suffering from spinal metastases. Two methods have been combined to obtain the final result: firstly, an initial segmentation is performed using a fully automatic level-set method; secondly, to refine the initial segmentation, a 3D volume indicating the probability of each voxel of belonging to the spine has been developed. In this way, a probability map is generated and deformed to be adapted to each testing case. Results: To validate the improvement obtained after applying the atlas, the Dice coefficient (DSC), the Hausdorff distance (HD), and the mean surface-to-surface distance (MSD) were used. The results showed up an average of 10 mm of improvement accuracy in terms of HD, obtaining an overall final average of 15.51 2.74 mm. Also, a global value of 91.01 3.18% in terms of DSC and a MSD of 0.66 0.25 mm were obtained. The major improvement using the atlas was achieved in the thoracic region, as ribs were almost perfectly suppressed. Conclusion: The study demonstrated that the atlas is able to detect and appropriately eliminate the ribs while improving the segmentation accuracy.The authors thank the financial support of the Spanish Ministerio de Economia y Competitividad (MINECO) and FEDER funds under Grants TEC2012-33778 and BFU2015-64380-C2-2-R (D.M.) and DPI2013-4572-R (J.D., E.D.)Ruiz-España, S.; Domingo, J.; Díaz-Parra, A.; Dura, E.; D'ocon-Alcaniz, V.; Arana, E.; Moratal, D. (2017). Automatic segmentation of the spine by means of a probabilistic atlas with a special focus on ribs suppression. Medical Physics. 44(9):4695-4707. https://doi.org/10.1002/mp.12431S46954707449Harris, R. I., & Macnab, I. (1954). STRUCTURAL CHANGES IN THE LUMBAR INTERVERTEBRAL DISCS. The Journal of Bone and Joint Surgery. British volume, 36-B(2), 304-322. doi:10.1302/0301-620x.36b2.304Oliveira, M. F. de, Rotta, J. M., & Botelho, R. V. (2015). Survival analysis in patients with metastatic spinal disease: the influence of surgery, histology, clinical and neurologic status. Arquivos de Neuro-Psiquiatria, 73(4), 330-335. doi:10.1590/0004-282x20150003Chou, R. (2011). Diagnostic Imaging for Low Back Pain: Advice for High-Value Health Care From the American College of Physicians. Annals of Internal Medicine, 154(3), 181. doi:10.7326/0003-4819-154-3-201102010-00008Brayda-Bruno, M., Tibiletti, M., Ito, K., Fairbank, J., Galbusera, F., Zerbi, A., … Sivan, S. S. (2013). Advances in the diagnosis of degenerated lumbar discs and their possible clinical application. European Spine Journal, 23(S3), 315-323. doi:10.1007/s00586-013-2960-9Quattrocchi, C. C., Santini, D., Dell’Aia, P., Piciucchi, S., Leoncini, E., Vincenzi, B., … Zobel, B. B. (2007). A prospective analysis of CT density measurements of bone metastases after treatment with zoledronic acid. Skeletal Radiology, 36(12), 1121-1127. doi:10.1007/s00256-007-0388-1Doi, K. (2007). Computer-aided diagnosis in medical imaging: Historical review, current status and future potential. Computerized Medical Imaging and Graphics, 31(4-5), 198-211. doi:10.1016/j.compmedimag.2007.02.002Ruiz-España, S., Arana, E., & Moratal, D. (2015). Semiautomatic computer-aided classification of degenerative lumbar spine disease in magnetic resonance imaging. Computers in Biology and Medicine, 62, 196-205. doi:10.1016/j.compbiomed.2015.04.028Alomari, R. S., Ghosh, S., Koh, J., & Chaudhary, V. (2014). Vertebral Column Localization, Labeling, and Segmentation. Lecture Notes in Computational Vision and Biomechanics, 193-229. doi:10.1007/978-3-319-12508-4_7Hamarneh, G., & Li, X. (2009). Watershed segmentation using prior shape and appearance knowledge. Image and Vision Computing, 27(1-2), 59-68. doi:10.1016/j.imavis.2006.10.009Ghebreab, S., & Smeulders, A. W. (2004). Combining Strings and Necklaces for Interactive Three-Dimensional Segmentation of Spinal Images Using an Integral Deformable Spine Model. IEEE Transactions on Biomedical Engineering, 51(10), 1821-1829. doi:10.1109/tbme.2004.831540Mastmeyer, A., Engelke, K., Fuchs, C., & Kalender, W. A. (2006). A hierarchical 3D segmentation method and the definition of vertebral body coordinate systems for QCT of the lumbar spine. Medical Image Analysis, 10(4), 560-577. doi:10.1016/j.media.2006.05.005Rasoulian, A., Rohling, R., & Abolmaesumi, P. (2013). Lumbar Spine Segmentation Using a Statistical Multi-Vertebrae Anatomical Shape+Pose Model. IEEE Transactions on Medical Imaging, 32(10), 1890-1900. doi:10.1109/tmi.2013.2268424Ma, J., & Lu, L. (2013). Hierarchical segmentation and identification of thoracic vertebra using learning-based edge detection and coarse-to-fine deformable model. Computer Vision and Image Understanding, 117(9), 1072-1083. doi:10.1016/j.cviu.2012.11.016Kim, Y., & Kim, D. (2009). A fully automatic vertebra segmentation method using 3D deformable fences. Computerized Medical Imaging and Graphics, 33(5), 343-352. doi:10.1016/j.compmedimag.2009.02.006Klinder, T., Ostermann, J., Ehm, M., Franz, A., Kneser, R., & Lorenz, C. (2009). Automated model-based vertebra detection, identification, and segmentation in CT images. Medical Image Analysis, 13(3), 471-482. doi:10.1016/j.media.2009.02.004Štern, D., Likar, B., Pernuš, F., & Vrtovec, T. (2011). Parametric modelling and segmentation of vertebral bodies in 3D CT and MR spine images. Physics in Medicine and Biology, 56(23), 7505-7522. doi:10.1088/0031-9155/56/23/011Korez, R., Ibragimov, B., Likar, B., Pernus, F., & Vrtovec, T. (2015). A Framework for Automated Spine and Vertebrae Interpolation-Based Detection and Model-Based Segmentation. IEEE Transactions on Medical Imaging, 34(8), 1649-1662. doi:10.1109/tmi.2015.2389334Castro-Mateos, I., Pozo, J. M., Pereanez, M., Lekadir, K., Lazary, A., & Frangi, A. F. (2015). Statistical Interspace Models (SIMs): Application to Robust 3D Spine Segmentation. IEEE Transactions on Medical Imaging, 34(8), 1663-1675. doi:10.1109/tmi.2015.2443912Pereanez, M., Lekadir, K., Castro-Mateos, I., Pozo, J. M., Lazary, A., & Frangi, A. F. (2015). Accurate Segmentation of Vertebral Bodies and Processes Using Statistical Shape Decomposition and Conditional Models. IEEE Transactions on Medical Imaging, 34(8), 1627-1639. doi:10.1109/tmi.2015.2396774Michael Kelm, B., Wels, M., Kevin Zhou, S., Seifert, S., Suehling, M., Zheng, Y., & Comaniciu, D. (2013). Spine detection in CT and MR using iterated marginal space learning. Medical Image Analysis, 17(8), 1283-1292. doi:10.1016/j.media.2012.09.007Yan Kang, Engelke, K., & Kalender, W. A. (2003). A new accurate and precise 3-D segmentation method for skeletal structures in volumetric CT data. IEEE Transactions on Medical Imaging, 22(5), 586-598. doi:10.1109/tmi.2003.812265Huang, J., Jian, F., Wu, H., & Li, H. (2013). An improved level set method for vertebra CT image segmentation. BioMedical Engineering OnLine, 12(1), 48. doi:10.1186/1475-925x-12-48Lim, P. H., Bagci, U., & Bai, L. (2013). Introducing Willmore Flow Into Level Set Segmentation of Spinal Vertebrae. IEEE Transactions on Biomedical Engineering, 60(1), 115-122. doi:10.1109/tbme.2012.2225833Forsberg, D., Lundström, C., Andersson, M., & Knutsson, H. (2013). Model-based registration for assessment of spinal deformities in idiopathic scoliosis. Physics in Medicine and Biology, 59(2), 311-326. doi:10.1088/0031-9155/59/2/311Yao, J., Burns, J. E., Forsberg, D., Seitel, A., Rasoulian, A., Abolmaesumi, P., … Li, S. (2016). A multi-center milestone study of clinical vertebral CT segmentation. Computerized Medical Imaging and Graphics, 49, 16-28. doi:10.1016/j.compmedimag.2015.12.006Shi, C., Wang, J., & Cheng, Y. (2015). Sparse Representation-Based Deformation Model for Atlas-Based Segmentation of Liver CT Images. Image and Graphics, 410-419. doi:10.1007/978-3-319-21969-1_36Domingo, J., Dura, E., Ayala, G., & Ruiz-España, S. (2015). Means of 2D and 3D Shapes and Their Application in Anatomical Atlas Building. Lecture Notes in Computer Science, 522-533. doi:10.1007/978-3-319-23192-1_44Hyunjin Park, Bland, P. H., & Meyer, C. R. (2003). Construction of an abdominal probabilistic atlas and its application in segmentation. IEEE Transactions on Medical Imaging, 22(4), 483-492. doi:10.1109/tmi.2003.809139Cabezas, M., Oliver, A., Lladó, X., Freixenet, J., & Bach Cuadra, M. (2011). A review of atlas-based segmentation for magnetic resonance brain images. Computer Methods and Programs in Biomedicine, 104(3), e158-e177. doi:10.1016/j.cmpb.2011.07.015Fortunati, V., Verhaart, R. F., van der Lijn, F., Niessen, W. J., Veenland, J. F., Paulides, M. M., & van Walsum, T. (2013). Tissue segmentation of head and neck CT images for treatment planning: A multiatlas approach combined with intensity modeling. Medical Physics, 40(7), 071905. doi:10.1118/1.4810971Zhuang, X., Bai, W., Song, J., Zhan, S., Qian, X., Shi, W., … Rueckert, D. (2015). Multiatlas whole heart segmentation of CT data using conditional entropy for atlas ranking and selection. Medical Physics, 42(7), 3822-3833. doi:10.1118/1.4921366Zhou, J., Yan, Z., Lasio, G., Huang, J., Zhang, B., Sharma, N., … D’Souza, W. (2015). Automated compromised right lung segmentation method using a robust atlas-based active volume model with sparse shape composition prior in CT. Computerized Medical Imaging and Graphics, 46, 47-55. doi:10.1016/j.compmedimag.2015.07.003Linguraru, M. G., Sandberg, J. K., Li, Z., Shah, F., & Summers, R. M. (2010). Automated segmentation and quantification of liver and spleen from CT images using normalized probabilistic atlases and enhancement estimation. Medical Physics, 37(2), 771-783. doi:10.1118/1.3284530Xu, Y., Xu, C., Kuang, X., Wang, H., Chang, E. I.-C., Huang, W., & Fan, Y. (2016). 3D-SIFT-Flow for atlas-based CT liver image segmentation. Medical Physics, 43(5), 2229-2241. doi:10.1118/1.4945021Michopoulou, S. K., Costaridou, L., Panagiotopoulos, E., Speller, R., Panayiotakis, G., & Todd-Pokropek, A. (2009). Atlas-Based Segmentation of Degenerated Lumbar Intervertebral Discs From MR Images of the Spine. IEEE Transactions on Biomedical Engineering, 56(9), 2225-2231. doi:10.1109/tbme.2009.2019765Taso, M., Le Troter, A., Sdika, M., Ranjeva, J.-P., Guye, M., Bernard, M., & Callot, V. (2013). Construction of an in vivo human spinal cord atlas based on high-resolution MR images at cervical and thoracic levels: preliminary results. Magnetic Resonance Materials in Physics, Biology and Medicine, 27(3), 257-267. doi:10.1007/s10334-013-0403-6Lévy, S., Benhamou, M., Naaman, C., Rainville, P., Callot, V., & Cohen-Adad, J. (2015). White matter atlas of the human spinal cord with estimation of partial volume effect. NeuroImage, 119, 262-271. doi:10.1016/j.neuroimage.2015.06.040Hardisty, M., Gordon, L., Agarwal, P., Skrinskas, T., & Whyne, C. (2007). Quantitative characterization of metastatic disease in the spine. Part I. Semiautomated segmentation using atlas-based deformable registration and the level set method. Medical Physics, 34(8), 3127-3134. doi:10.1118/1.2746498Forsberg, D. (2015). Atlas-Based Registration for Accurate Segmentation of Thoracic and Lumbar Vertebrae in CT Data. Lecture Notes in Computational Vision and Biomechanics, 49-59. doi:10.1007/978-3-319-14148-0_5Ibañez MV Schroeder W Cates L Insight software Consortium. The ITK Software Guide 2016 http://www.itk.org/ItkSoftwareGuide.pdfLoader C R package: Local regression, likelihood and density estimation. CRAN repository 2013 2016 http://cran.r-project.org/web/packages/locfitPARK, H., HERO, A., BLAND, P., KESSLER, M., SEO, J., & MEYER, C. (2010). Construction of Abdominal Probabilistic Atlases and Their Value in Segmentation of Normal Organs in Abdominal CT Scans. IEICE Transactions on Information and Systems, E93-D(8), 2291-2301. doi:10.1587/transinf.e93.d.2291Pohl, K. M., Fisher, J., Bouix, S., Shenton, M., McCarley, R. W., Grimson, W. E. L., … Wells, W. M. (2007). Using the logarithm of odds to define a vector space on probabilistic atlases. Medical Image Analysis, 11(5), 465-477. doi:10.1016/j.media.2007.06.003Baddeley, A., & Molchanov, I. (1998). Journal of Mathematical Imaging and Vision, 8(1), 79-92. doi:10.1023/a:1008214317492De Bruijne, M., van Ginneken, B., Viergever, M. A., & Niessen, W. J. (2003). Adapting Active Shape Models for 3D Segmentation of Tubular Structures in Medical Images. Information Processing in Medical Imaging, 136-147. doi:10.1007/978-3-540-45087-0_12Zhang, K., Zhang, L., Song, H., & Zhou, W. (2010). Active contours with selective local or global segmentation: A new formulation and level set method. Image and Vision Computing, 28(4), 668-676. doi:10.1016/j.imavis.2009.10.009Kalpathy-Cramer, J., Awan, M., Bedrick, S., Rasch, C. R. N., Rosenthal, D. I., & Fuller, C. D. (2013). Development of a Software for Quantitative Evaluation Radiotherapy Target and Organ-at-Risk Segmentation Comparison. Journal of Digital Imaging, 27(1), 108-119. doi:10.1007/s10278-013-9633-4Huttenlocher, D. P., Klanderman, G. A., & Rucklidge, W. J. (1993). Comparing images using the Hausdorff distance. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(9), 850-863. doi:10.1109/34.232073Aspert, N., Santa-Cruz, D., & Ebrahimi, T. (s. f.). MESH: measuring errors between surfaces using the Hausdorff distance. Proceedings. IEEE International Conference on Multimedia and Expo. doi:10.1109/icme.2002.103587

    Structural connectivity centrality changes mark the path towards Alzheimer's disease

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    [EN] Introduction: The pathophysiological process of Alzheimer's disease is thought to begin years before clinical decline, with evidence suggesting prion-like spreading processes of neurofibrillary tangles and amyloid plaques. Methods: Using diffusion magnetic resonance imaging data from the Alzheimer's Disease Neuroimaging Initiative database, we first identified relevant features for dementia diagnosis. We then created dynamic models with the Nathan Kline Institute-Rockland Sample database to estimate the earliest detectable stage associated with dementia in the simulated disease progression. Results: A classifier based on centrality measures provides informative predictions. Strength and closeness centralities are the most discriminative features, which are associated with the medial temporal lobe and subcortical regions, together with posterior and occipital brain regions. Our model simulations suggest that changes associated with dementia begin to manifest structurally at early stages. Discussion: Our analyses suggest that diffusion magnetic resonance imaging-based centrality measures can offer a tool for early disease detection before clinical dementia onset.The authors would like to thank Peter N. Taylor and Yujiang Wang for their stimulating feedback and suggestions. Funding: A.D.-P. was supported by grant FPU13/01475 from the Spanish Ministerio de Educacion, Cultura y Deporte (MECD). This work was supported in part by the Spanish Ministerio de Economıa y Competitividad (MINECO) and FEDER funds under grant BFU2015- 64380-C2-2-R. L.R.P. and J.-P.T. were supported by the NIHR Newcastle Biomedical Research Center awarded to the Newcastle upon Tyne Hospitals NHS Foundation Trust and Newcastle University. M.K. and R.B. were supported by the Engineering and Physical Sciences Research Council of the United Kingdom (EP/K026992/1). R.B. was also supported by (EP/S001433/1) and the Medical Research Council of the United Kingdom (MR/N015037/1). 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 generous contributions from the following organizations: AbbVie, Alzheimer s Association; Alzheimer s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. 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 Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.Peraza, LR.; Díaz-Parra, A.; Kennion, O.; Moratal, D.; Taylor, J.; Kaiser, M.; Bauer, R. (2019). Structural connectivity centrality changes mark the path towards Alzheimer's disease. Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring. 11:98-107. https://doi.org/10.1016/j.dadm.2018.12.004S9810711(2016). 2016 Alzheimer’s disease facts and figures. Alzheimer’s & Dementia, 12(4), 459-509. doi:10.1016/j.jalz.2016.03.001Sperling, R. A., Aisen, P. S., Beckett, L. A., Bennett, D. A., Craft, S., Fagan, A. M., … Phelps, C. H. (2011). Toward defining the preclinical stages of Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s & Dementia, 7(3), 280-292. doi:10.1016/j.jalz.2011.03.003Jack, C. R., Knopman, D. S., Jagust, W. J., Petersen, R. C., Weiner, M. W., Aisen, P. S., … Trojanowski, J. Q. (2013). Tracking pathophysiological processes in Alzheimer’s disease: an updated hypothetical model of dynamic biomarkers. The Lancet Neurology, 12(2), 207-216. doi:10.1016/s1474-4422(12)70291-0Villemagne, V. L., Burnham, S., Bourgeat, P., Brown, B., Ellis, K. A., Salvado, O., … Masters, C. L. (2013). Amyloid β deposition, neurodegeneration, and cognitive decline in sporadic Alzheimer’s disease: a prospective cohort study. The Lancet Neurology, 12(4), 357-367. doi:10.1016/s1474-4422(13)70044-9Jack, C. R., & Holtzman, D. M. (2013). Biomarker Modeling of Alzheimer’s Disease. Neuron, 80(6), 1347-1358. doi:10.1016/j.neuron.2013.12.003Jucker, M., & Walker, L. C. (2011). Pathogenic protein seeding in alzheimer disease and other neurodegenerative disorders. Annals of Neurology, 70(4), 532-540. doi:10.1002/ana.22615Brettschneider, J., Tredici, K. D., Lee, V. M.-Y., & Trojanowski, J. Q. (2015). Spreading of pathology in neurodegenerative diseases: a focus on human studies. Nature Reviews Neuroscience, 16(2), 109-120. doi:10.1038/nrn3887Jucker, M., & Walker, L. C. (2013). Self-propagation of pathogenic protein aggregates in neurodegenerative diseases. Nature, 501(7465), 45-51. doi:10.1038/nature12481Frost, B., & Diamond, M. I. (2009). Prion-like mechanisms in neurodegenerative diseases. Nature Reviews Neuroscience, 11(3), 155-159. doi:10.1038/nrn2786Warren, J. D., Rohrer, J. D., Schott, J. M., Fox, N. C., Hardy, J., & Rossor, M. N. (2013). Molecular nexopathies: a new paradigm of neurodegenerative disease. Trends in Neurosciences, 36(10), 561-569. doi:10.1016/j.tins.2013.06.007Fornito, A., Zalesky, A., & Breakspear, M. (2015). The connectomics of brain disorders. Nature Reviews Neuroscience, 16(3), 159-172. doi:10.1038/nrn3901Zhou, J., Gennatas, E. D., Kramer, J. H., Miller, B. L., & Seeley, W. W. (2012). Predicting Regional Neurodegeneration from the Healthy Brain Functional Connectome. Neuron, 73(6), 1216-1227. doi:10.1016/j.neuron.2012.03.004Brier, M. R., Thomas, J. B., & Ances, B. M. (2014). Network Dysfunction in Alzheimer’s Disease: Refining the Disconnection Hypothesis. Brain Connectivity, 4(5), 299-311. doi:10.1089/brain.2014.0236Delbeuck, X. (2003). Neuropsychology Review, 13(2), 79-92. doi:10.1023/a:1023832305702Tijms, B. M., Wink, A. M., de Haan, W., van der Flier, W. M., Stam, C. J., Scheltens, P., & Barkhof, F. (2013). Alzheimer’s disease: connecting findings from graph theoretical studies of brain networks. Neurobiology of Aging, 34(8), 2023-2036. doi:10.1016/j.neurobiolaging.2013.02.020Stam, C. J. (2014). Modern network science of neurological disorders. Nature Reviews Neuroscience, 15(10), 683-695. doi:10.1038/nrn3801Petersen, R. C., Aisen, P. S., Beckett, L. A., Donohue, M. C., Gamst, A. C., Harvey, D. J., … Weiner, M. W. (2009). Alzheimer’s Disease Neuroimaging Initiative (ADNI): Clinical characterization. Neurology, 74(3), 201-209. doi:10.1212/wnl.0b013e3181cb3e25Nooner, K. B., Colcombe, S. J., Tobe, R. H., Mennes, M., Benedict, M. M., Moreno, A. L., … Milham, M. P. (2012). The NKI-Rockland Sample: A Model for Accelerating the Pace of Discovery Science in Psychiatry. Frontiers in Neuroscience, 6. doi:10.3389/fnins.2012.00152Landau, S. M., Fero, A., Baker, S. L., Koeppe, R., Mintun, M., Chen, K., … Jagust, W. J. (2015). Measurement of Longitudinal  -Amyloid Change with 18F-Florbetapir PET and Standardized Uptake Value Ratios. Journal of Nuclear Medicine, 56(4), 567-574. doi:10.2967/jnumed.114.148981Lim, S., Han, C. E., Uhlhaas, P. J., & Kaiser, M. (2013). Preferential Detachment During Human Brain Development: Age- and Sex-Specific Structural Connectivity in Diffusion Tensor Imaging (DTI) Data. Cerebral Cortex, 25(6), 1477-1489. doi:10.1093/cercor/bht333Pastor-Satorras, R., Castellano, C., Van Mieghem, P., & Vespignani, A. (2015). Epidemic processes in complex networks. Reviews of Modern Physics, 87(3), 925-979. doi:10.1103/revmodphys.87.925Collin, G., & van den Heuvel, M. P. (2013). The Ontogeny of the Human Connectome. The Neuroscientist, 19(6), 616-628. doi:10.1177/1073858413503712Fischi-Gómez, E., Vasung, L., Meskaldji, D.-E., Lazeyras, F., Borradori-Tolsa, C., Hagmann, P., … Hüppi, P. S. (2014). Structural Brain Connectivity in School-Age Preterm Infants Provides Evidence for Impaired Networks Relevant for Higher Order Cognitive Skills and Social Cognition. Cerebral Cortex, 25(9), 2793-2805. doi:10.1093/cercor/bhu073Zhao, T., Cao, M., Niu, H., Zuo, X.-N., Evans, A., He, Y., … Shu, N. (2015). Age-related changes in the topological organization of the white matter structural connectome across the human lifespan. Human Brain Mapping, 36(10), 3777-3792. doi:10.1002/hbm.22877James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning. Springer Texts in Statistics. doi:10.1007/978-1-4614-7138-7Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: Uses and interpretations. NeuroImage, 52(3), 1059-1069. doi:10.1016/j.neuroimage.2009.10.003Batalle, D., Hughes, E. J., Zhang, H., Tournier, J.-D., Tusor, N., Aljabar, P., … Counsell, S. J. (2017). Early development of structural networks and the impact of prematurity on brain connectivity. NeuroImage, 149, 379-392. doi:10.1016/j.neuroimage.2017.01.06510.1162/153244303322753616. (2000). CrossRef Listing of Deleted DOIs, 1. doi:10.1162/153244303322753616Saeys, Y., Inza, I., & Larranaga, P. (2007). A review of feature selection techniques in bioinformatics. Bioinformatics, 23(19), 2507-2517. doi:10.1093/bioinformatics/btm344Lemm, S., Blankertz, B., Dickhaus, T., & Müller, K.-R. (2011). Introduction to machine learning for brain imaging. NeuroImage, 56(2), 387-399. doi:10.1016/j.neuroimage.2010.11.004Pereira, F., Mitchell, T., & Botvinick, M. (2009). Machine learning classifiers and fMRI: A tutorial overview. NeuroImage, 45(1), S199-S209. doi:10.1016/j.neuroimage.2008.11.007Hanley, J. A., & McNeil, B. J. (1982). The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology, 143(1), 29-36. doi:10.1148/radiology.143.1.7063747Yekutieli, D., & Benjamini, Y. (1999). Resampling-based false discovery rate controlling multiple test procedures for correlated test statistics. Journal of Statistical Planning and Inference, 82(1-2), 171-196. doi:10.1016/s0378-3758(99)00041-5Whitwell, J. L., Josephs, K. A., Murray, M. E., Kantarci, K., Przybelski, S. A., Weigand, S. D., … Jack, C. R. (2008). MRI correlates of neurofibrillary tangle pathology at autopsy: A voxel-based morphometry study. Neurology, 71(10), 743-749. doi:10.1212/01.wnl.0000324924.91351.7dBraak, H., Alafuzoff, I., Arzberger, T., Kretzschmar, H., & Del Tredici, K. (2006). Staging of Alzheimer disease-associated neurofibrillary pathology using paraffin sections and immunocytochemistry. Acta Neuropathologica, 112(4), 389-404. doi:10.1007/s00401-006-0127-zBuckner, R. L., Sepulcre, J., Talukdar, T., Krienen, F. M., Liu, H., Hedden, T., … Johnson, K. A. (2009). Cortical Hubs Revealed by Intrinsic Functional Connectivity: Mapping, Assessment of Stability, and Relation to Alzheimer’s Disease. Journal of Neuroscience, 29(6), 1860-1873. doi:10.1523/jneurosci.5062-08.2009Seeley, W. W., Crawford, R. K., Zhou, J., Miller, B. L., & Greicius, M. D. (2009). Neurodegenerative Diseases Target Large-Scale Human Brain Networks. Neuron, 62(1), 42-52. doi:10.1016/j.neuron.2009.03.024Frisoni, G. B., Prestia, A., Rasser, P. E., Bonetti, M., & Thompson, P. M. (2009). In vivo mapping of incremental cortical atrophy from incipient to overt Alzheimer’s disease. Journal of Neurology, 256(6), 916-924. doi:10.1007/s00415-009-5040-7Pini, L., Pievani, M., Bocchetta, M., Altomare, D., Bosco, P., Cavedo, E., … Frisoni, G. B. (2016). Brain atrophy in Alzheimer’s Disease and aging. Ageing Research Reviews, 30, 25-48. doi:10.1016/j.arr.2016.01.002Frisoni, G. B., Fox, N. C., Jack, C. R., Scheltens, P., & Thompson, P. M. (2010). The clinical use of structural MRI in Alzheimer disease. Nature Reviews Neurology, 6(2), 67-77. doi:10.1038/nrneurol.2009.215Mak, E., Gabel, S., Mirette, H., Su, L., Williams, G. B., Waldman, A., … O’Brien, J. (2017). Structural neuroimaging in preclinical dementia: From microstructural deficits and grey matter atrophy to macroscale connectomic changes. Ageing Research Reviews, 35, 250-264. doi:10.1016/j.arr.2016.10.001Miller, K. L., Alfaro-Almagro, F., Bangerter, N. K., Thomas, D. L., Yacoub, E., Xu, J., … Smith, S. M. (2016). Multimodal population brain imaging in the UK Biobank prospective epidemiological study. Nature Neuroscience, 19(11), 1523-1536. doi:10.1038/nn.4393Wirths, O. (2003). α-Synuclein, Aβ and Alzheimer’s disease. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 27(1), 103-108. doi:10.1016/s0278-5846(02)00339-1Saxena, S., & Caroni, P. (2011). Selective Neuronal Vulnerability in Neurodegenerative Diseases: from Stressor Thresholds to Degeneration. Neuron, 71(1), 35-48. doi:10.1016/j.neuron.2011.06.031Fjell, A. M., McEvoy, L., Holland, D., Dale, A. M., & Walhovd, K. B. (2014). What is normal in normal aging? Effects of aging, amyloid and Alzheimer’s disease on the cerebral cortex and the hippocampus. Progress in Neurobiology, 117, 20-40. doi:10.1016/j.pneurobio.2014.02.004Kaiser, M. (2013). The potential of the human connectome as a biomarker of brain disease. Frontiers in Human Neuroscience, 7. doi:10.3389/fnhum.2013.0048

    Prótesis de pirocarbono en fracturas complejas de cabeza de radio.

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    Presentamos los resultados de un estudio observacional retrospectivo sobre 23 casos de fracturas complejas de cabeza de radio tratadas mediante la implantación de una prótesis cabeza radio de pircocarbono (Mo - Pyc). La distribución por sexos fue 10 hombres y 13 mujeres, y la edad media de 54 años. El seguimiento medio fue de 70 meses (48-93 meses). La principal causa fue una fractura de cabeza de radio no reconstruible con inestabilidad asociada de codo. La evaluación clínica se realizó con la Mayo Elbow Performance Score (MEPS). Radiográficamen - te se valoró la congruencia articular, el tamaño de la prótesis, la radiolucencia periprotésica, la osificación heterotópica y la osteoartritis. Al final del seguimiento la media de la escale MEPS fue 82/100, con 84 % resultados de excelentes y buenos. La flexión media fue de 130º, extensión -30º, pronación 76º y supinación 77º. La estabilidad del codo fue buena en todos los casos y no observamos migración proximal del radio. Observamos radiolucencia alrededor del vástago en 5 pacientes, pero sin aparente repercusión clínica. Las complicaciones fueron una paresia del nervio interóseo posterior con recuperación funcional al cabo de 11 semanas, 2 pacientes presentaron "overstuffing" con subluxación posterior asociada que necesitó realizar exéresis de la cabeza y una osificación heterotópica con repercu - sión sobre el balance articular que necesitó 2 cirugías, todos ellos con resultados clínicos aceptables. Los resultados son alentadores.The authors present the results of a retrospective observational study of 23 cases of a complex radial head fractures treated by pyrocarbon radial head prosthesis (MoPyc). This modular radial head prosthesis is compo - sed of a cementless titanium stem and a 15º angulated neck. The gender distribution was 10 men and 13 women, ave - rage age 54 years. The mean follow-up was 70 months (48-93 months). The main etiology was a radial head fracture with elbow instability. Clinical evaluation was performed using the Mayo Elbow Performance Score (MEPS). Was assessed radiographically joint congruity, the size of the prosthesis, periprosthetic radiolucency, heterotopic ossifica - tion and osteoarthritis. At follow-up, the MEPS average was 82/100, with 84% of good and excellent results. Elbow flexion averaged 130º, extension -30º, pronation 76º and supination 77º. Elbow stability was good in all the cases, and no proximal migration of the radius occurred. Asymptomatic bone lucencies were found in five cases around the stem. Complications included paresis of the posterior interosseous nerve with functional recovery after 11 weeks, 2 patients had "overstuffing" associated with posterior subluxation and they need to perform excision of the head and one heterotopic ossification with articular impact on balance that needs two surgeries, all of them with acceptable clinical results. The preliminary results are encouragin

    Active Commuting Behaviours from High School to University in Chile: A Retrospective Study

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    Objective: To compare the differences in the modes and distance of the displacements in high school and university stage in the same sample. Methods: A total of 1288 volunteer university students (614 males and 674 females) participated, with an average age of 22.7 5.8 years, belonging to four private and public universities in Chile where a validated self-report questionnaire was applied to the study, which included the modes, travel time, and distance at school and university. Results: The active commuting decreases from school to university when leaving home (males: 39.6% to 34.0%; p = 0.033 and females: 32.9% to 18.5%, p < 0.001), as well as when returning (males: 44.1% to 33.7%; p < 0.001 and females: 38.6% to 17.6%, p < 0.001). Conversely, non-active modes of transport increase, especially in females (go: 67.1% to 81.4%, return: 61.5% to 82.6%), affected by the increase in the use of public transportation in university. It was also defined that at both school and at university, the active commuting decreases the greater the distance travelled. Conclusion: The active modes of commuting decreased between high school and university and the non-active mode of commuting was the most frequent form of mobility to high school and university, observing that the active trips decreased when the distance from the home to high school or university increased. Public and private intervention policies and strategies are required to maintain or increase the modes of active commuting in the university stage for an active life in adulthood
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