Literatura académica sobre el tema "Computational neuroimaging"
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Artículos de revistas sobre el tema "Computational neuroimaging"
Stephan, Klaas E., Sandra Iglesias, Jakob Heinzle y Andreea O. Diaconescu. "Translational Perspectives for Computational Neuroimaging". Neuron 87, n.º 4 (agosto de 2015): 716–32. http://dx.doi.org/10.1016/j.neuron.2015.07.008.
Texto completoPotter, Aneirin. "044 At what resolution does the brain perform computations?" Journal of Neurology, Neurosurgery & Psychiatry 93, n.º 9 (12 de agosto de 2022): e2.239. http://dx.doi.org/10.1136/jnnp-2022-abn2.88.
Texto completoWandell, Brian A. "COMPUTATIONAL NEUROIMAGING OF HUMAN VISUAL CORTEX". Annual Review of Neuroscience 22, n.º 1 (marzo de 1999): 145–73. http://dx.doi.org/10.1146/annurev.neuro.22.1.145.
Texto completoWandell, Brian A. y Jonathan Winawer. "Computational neuroimaging and population receptive fields". Trends in Cognitive Sciences 19, n.º 6 (junio de 2015): 349–57. http://dx.doi.org/10.1016/j.tics.2015.03.009.
Texto completoFriston, Karl J. y Raymond J. Dolan. "Computational and dynamic models in neuroimaging". NeuroImage 52, n.º 3 (septiembre de 2010): 752–65. http://dx.doi.org/10.1016/j.neuroimage.2009.12.068.
Texto completoStephan, K. E., F. Schlagenhauf, Q. J. M. Huys, S. Raman, E. A. Aponte, K. H. Brodersen, L. Rigoux et al. "Computational neuroimaging strategies for single patient predictions". NeuroImage 145 (enero de 2017): 180–99. http://dx.doi.org/10.1016/j.neuroimage.2016.06.038.
Texto completoDi Ieva, Antonio, Mounir Boukadoum, Salim Lahmiri y Michael D. Cusimano. "Computational Analyses of Arteriovenous Malformations in Neuroimaging". Journal of Neuroimaging 25, n.º 3 (17 de diciembre de 2014): 354–60. http://dx.doi.org/10.1111/jon.12200.
Texto completoPoldrack, Russell A., Krzysztof J. Gorgolewski y Gaël Varoquaux. "Computational and Informatic Advances for Reproducible Data Analysis in Neuroimaging". Annual Review of Biomedical Data Science 2, n.º 1 (20 de julio de 2019): 119–38. http://dx.doi.org/10.1146/annurev-biodatasci-072018-021237.
Texto completoRitter, Petra, Michael Schirner, Anthony R. McIntosh y Viktor K. Jirsa. "The Virtual Brain Integrates Computational Modeling and Multimodal Neuroimaging". Brain Connectivity 3, n.º 2 (abril de 2013): 121–45. http://dx.doi.org/10.1089/brain.2012.0120.
Texto completoGoldstein-Piekarski, Andrea N., Bailey Holt-Gosselin, Kathleen O’Hora y Leanne M. Williams. "Integrating sleep, neuroimaging, and computational approaches for precision psychiatry". Neuropsychopharmacology 45, n.º 1 (19 de agosto de 2019): 192–204. http://dx.doi.org/10.1038/s41386-019-0483-8.
Texto completoTesis sobre el tema "Computational neuroimaging"
Macoveanu, Julian. "Neural mechanisms underlying working memory : computational and neuroimaging studies /". Stockholm, 2006. http://diss.kib.ki.se/2006/91-7140-901-7/.
Texto completoWhalley, Matthew G. "Autobiographical memory in depression : neuroimaging and computational linguistic investigation". Thesis, University of London, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.542382.
Texto completoCattinelli, I. "INVESTIGATIONS ON COGNITIVE COMPUTATION AND COMPUTATIONAL COGNITION". Doctoral thesis, Università degli Studi di Milano, 2011. http://hdl.handle.net/2434/155482.
Texto completoGradin, Iade Victoria B. "Major depression and schizophrenia : investigation of neural mechanisms using neuroimaging and computational modeling of brain function". Thesis, University of Aberdeen, 2011. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=184011.
Texto completoSalimi-Khorshidi, Gholamreza. "Statistical models for neuroimaging meta-analytic inference". Thesis, University of Oxford, 2011. http://ora.ox.ac.uk/objects/uuid:40a10327-7f36-42e7-8120-ae04bd8be1d4.
Texto completoD'ANGELO, LAURA. "Bayesian modeling of calcium imaging data". Doctoral thesis, Università degli Studi di Padova, 2022. https://hdl.handle.net/10281/399067.
Texto completoPetitet, Pierre. "Sensorimotor adaptation : mechanisms, modulation and rehabilitation potential". Thesis, University of Oxford, 2018. http://ora.ox.ac.uk/objects/uuid:5935d96d-625a-4778-b42d-bb56c96d96cc.
Texto completoWeiler, Florian [Verfasser], Horst [Akademischer Betreuer] Hahn, Horst [Gutachter] Hahn, Lars [Gutachter] Linsen, Bernhard [Gutachter] Preim y Jan [Gutachter] Klein. "Computational tools for objective assessment in Neuroimaging / Florian Weiler ; Gutachter: Horst Hahn, Lars Linsen, Bernhard Preim, Jan Klein ; Betreuer: Horst Hahn". Bremen : IRC-Library, Information Resource Center der Jacobs University Bremen, 2020. http://d-nb.info/1203875983/34.
Texto completoIyappan, Anandhi [Verfasser]. "Conceptualization of computational modeling approaches and interpretation of the role of neuroimaging indices in pathomechanisms for pre-clinical detection of Alzheimer Disease / Anandhi Iyappan". Bonn : Universitäts- und Landesbibliothek Bonn, 2018. http://d-nb.info/1173789685/34.
Texto completoGloaguen, Arnaud. "A statistical and computational framework for multiblock and multiway data analysis". Electronic Thesis or Diss., université Paris-Saclay, 2020. http://www.theses.fr/2020UPASG016.
Texto completoA challenging problem in multivariate statistics is to study relationships between several sets of variables measured on the same set of individuals. In the literature, this paradigm can be stated under several names as “learning from multimodal data”, “data integration”, “data fusion” or “multiblock data analysis”. Typical examples are found in a large variety of fields such as biology, chemistry, sensory analysis, marketing, food research, where the common general objective is to identify variables of each block that are active in the relationships with other blocks. Moreover, each block can be composed of a high number of measurements (~1M), which involves the computation of billion(s) of associations. A successful investigation of such a dataset requires developing a computational and statistical framework that fits both the peculiar structure of the data as well as its heterogeneous nature.The development of multivariate statistical methods constitutes the core of this work. All these developments find their foundations on Regularized Generalized Canonical Correlation Analysis (RGCCA), a flexible framework for multiblock data analysis that grasps in a single optimization problem many well known multiblock methods. The RGCCA algorithm consists in a single yet very simple update repeated until convergence. If this update is gifted with certain conditions, the global convergence of the procedure is guaranteed. Throughout this work, the optimization framework of RGCCA has been extended in several directions:(i) From sequential to global. We extend RGCCA from a sequential procedure to a global one by extracting all the block components simultaneously with a single optimization problem.(ii) From matrix to higher order tensors. Multiway Generalized Canonical Correlation Analysis (MGCCA) has been proposed as an extension of RGCCA to higher order tensors. Sequential and global strategies have been designed for extracting several components per block. The different variants of the MGCCA algorithm are globally convergent under mild conditions.(iii) From sparsity to structured sparsity. The core of the Sparse Generalized Canonical Correlation Analysis (SGCCA) algorithm has been improved. It provides a much faster globally convergent algorithm. SGCCA has been extended to handle structured sparse penalties.In the second part, the versatility and usefulness of the proposed methods have been investigated on various studies: (i) two imaging-genetic studies, (ii) two Electroencephalography studies and (iii) one Raman Microscopy study. For these analyses, the focus is made on the interpretation of the results eased by considering explicitly the multiblock, tensor and sparse structures
Libros sobre el tema "Computational neuroimaging"
Li, Ping y Hua Shu. Language and the brain: computational and neuroimaging evidence from Chinese. Oxford University Press, 2010. http://dx.doi.org/10.1093/oxfordhb/9780199541850.013.0007.
Texto completoTang, Xiaoying, Thomas Fletcher y Michael I. Miller, eds. Bayesian Estimation and Inference in Computational Anatomy and Neuroimaging: Methods & Applications. Frontiers Media SA, 2019. http://dx.doi.org/10.3389/978-2-88945-984-1.
Texto completoSensory Nervous System - Computational Neuroimaging Investigations of Topographical Organization in Human Sensory Cortex [Working Title]. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.98172.
Texto completoAdams, Reginald B., Daniel N. Albohn y Kestutis Kveraga. A Social Vision Account of Facial Expression Perception. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780190613501.003.0017.
Texto completoChung, Moo K. Statistical and Computational Methods in Brain Image Analysis. Taylor & Francis Group, 2013.
Buscar texto completoStatistical and Computational Methods in Brain Image Analysis. Taylor & Francis Group, 2013.
Buscar texto completoChung, Moo K. Statistical and Computational Methods in Brain Image Analysis. Taylor & Francis Group, 2013.
Buscar texto completoChung, Moo K. Statistical and Computational Methods in Brain Image Analysis. Taylor & Francis Group, 2013.
Buscar texto completoRoggeman, Chantal, Wim Fias y Tom Verguts. Basic Number Representation and Beyond. Editado por Roi Cohen Kadosh y Ann Dowker. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780199642342.013.68.
Texto completoShaikh, Mohd Faraz. Machine Learning in Detecting Auditory Sequences in Magnetoencephalography Data : Research Project in Computational Modelling and Simulation. Technische Universität Dresden, 2021. http://dx.doi.org/10.25368/2022.411.
Texto completoCapítulos de libros sobre el tema "Computational neuroimaging"
Hanson, Stephen José, Michiro Negishi y Catherine Hanson. "Connectionist Neuroimaging". En Emergent Neural Computational Architectures Based on Neuroscience, 560–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44597-8_40.
Texto completoLahmiri, Salim, Mounir Boukadoum y Antonio Di Ieva. "Fractals in Neuroimaging". En Springer Series in Computational Neuroscience, 295–309. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-3995-4_19.
Texto completoRay, Kimberly y Angela Marie Richmond Laird. "Meta-analysis in Neuroimaging". En Encyclopedia of Computational Neuroscience, 1687–89. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4614-6675-8_542.
Texto completoRay, Kimberly y Angela Laird. "Meta-analysis in Neuroimaging". En Encyclopedia of Computational Neuroscience, 1–3. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-7320-6_542-1.
Texto completoKawato, Mitsuo. "Brain-Machine Interface and Neuroimaging". En Encyclopedia of Computational Neuroscience, 441–43. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4614-6675-8_523.
Texto completoPoline, Jean Baptiste y David Kennedy. "Software for Neuroimaging Data Analysis". En Encyclopedia of Computational Neuroscience, 2733–44. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4614-6675-8_538.
Texto completoOzaki, Tohru. "Statistical Analysis of Neuroimaging Data". En Encyclopedia of Computational Neuroscience, 2868–70. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4614-6675-8_539.
Texto completoBojak, Ingo y Michael Breakspear. "Neuroimaging, Neural Population Models for". En Encyclopedia of Computational Neuroscience, 1919–44. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4614-6675-8_70.
Texto completoKawato, Mitsuo. "Brain Machine Interface and Neuroimaging". En Encyclopedia of Computational Neuroscience, 1–3. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-7320-6_523-1.
Texto completoPoline, Jean Baptiste y David Kennedy. "Software for Neuroimaging Data Analysis". En Encyclopedia of Computational Neuroscience, 1–14. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-7320-6_538-1.
Texto completoActas de conferencias sobre el tema "Computational neuroimaging"
Wandell, Brian A. y Robert F. Dougherty. "Computational neuroimaging: maps and tracks in the human brain". En Electronic Imaging 2006, editado por Bernice E. Rogowitz, Thrasyvoulos N. Pappas y Scott J. Daly. SPIE, 2006. http://dx.doi.org/10.1117/12.674141.
Texto completoMayrand, Robin Perry, Christian Yaphet Freytes, Luana Okino Sawada, Micheal Adeyosoye, Rosie E. Curiel Cid, David Lowenstein, Ranjan Duara y Malek Adjouadi. "Computational Analysis of a Light-Weight SUVr Processing Technique for Neuroimaging Alzheimer's Disease". En 2022 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2022. http://dx.doi.org/10.1109/csci58124.2022.00317.
Texto completoSteinkamp, Simon, Iyadh Chaker, Félix Hubert, David Meder y Oliver Hulme. "Computational Parametric Mapping: A Method For Mapping Cognitive Models Onto Neuroimaging Data". En 2022 Conference on Cognitive Computational Neuroscience. San Francisco, California, USA: Cognitive Computational Neuroscience, 2022. http://dx.doi.org/10.32470/ccn.2022.1124-0.
Texto completoBaykova, Reny y Warrick Roseboom. "Effects of Sensory Precision on Behavioral and Neuroimaging Perceptual Biases in Duration Estimation". En 2019 Conference on Cognitive Computational Neuroscience. Brentwood, Tennessee, USA: Cognitive Computational Neuroscience, 2019. http://dx.doi.org/10.32470/ccn.2019.1280-0.
Texto completoObafemi-Ajayi, Tayo, Khalid Al-Jabery, Lauren Salminen, David Laidlaw, Ryan Cabeen, Donald Wunsch y Robert Paul. "Neuroimaging biomarkers of cognitive decline in healthy older adults via unified learning". En 2017 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2017. http://dx.doi.org/10.1109/ssci.2017.8280937.
Texto completoKemtur, Anirudha, Francois Paugam, Basile Pinsard, Pravish sainath, Yann Harel, Maximilien Le clei, Julie Boyle, Karim Jerbi y Pierre Bellec. "AI-based modeling of brain and behavior: Combining neuroimaging, imitation learning and video games". En 2022 Conference on Cognitive Computational Neuroscience. San Francisco, California, USA: Cognitive Computational Neuroscience, 2022. http://dx.doi.org/10.32470/ccn.2022.1303-0.
Texto completoThomas, Armin, Hauke R. Heekeren, Klaus-Robert Müller y Wojciech Samek. "DeepLight: A Structured Framework For The Analysis of Neuroimaging Data Through Recurrent Deep Learning Models". En 2019 Conference on Cognitive Computational Neuroscience. Brentwood, Tennessee, USA: Cognitive Computational Neuroscience, 2019. http://dx.doi.org/10.32470/ccn.2019.1226-0.
Texto completoEnyagina, I., A. Polyakov y D. Kokovin. "SERVICES OF COMPUTATIONAL NEUROBIOLOGY TASKS, BASED ON THE DISTRIBUTED MODULAR PLATFORM «DIGITAL LABORATORY» NRC «KURCHATOV INSTITUTE»". En 9th International Conference "Distributed Computing and Grid Technologies in Science and Education". Crossref, 2021. http://dx.doi.org/10.54546/mlit.2021.13.75.001.
Texto completoNiu, Xin, Hualou Liang y Fengqing Zhang. "Brain age prediction for post-traumatic stress disorder patients with convolutional neural networks: a multi-modal neuroimaging study". En 2018 Conference on Cognitive Computational Neuroscience. Brentwood, Tennessee, USA: Cognitive Computational Neuroscience, 2018. http://dx.doi.org/10.32470/ccn.2018.1121-0.
Texto completoGarimella, Harsha T. y Reuben H. Kraft. "Validation of Embedded Element Method in the Prediction of White Matter Disruption in Concussions". En ASME 2016 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/imece2016-67785.
Texto completoInformes sobre el tema "Computational neuroimaging"
Cohen, Jonathan D. Second Generation Flexible Computing Environment for Computational Modeling of Brain Function and Neuroimaging Data Analysis. Fort Belvoir, VA: Defense Technical Information Center, septiembre de 2010. http://dx.doi.org/10.21236/ada530764.
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