Дисертації з теми "Computational neuroimaging"
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Macoveanu, Julian. "Neural mechanisms underlying working memory : computational and neuroimaging studies /." Stockholm, 2006. http://diss.kib.ki.se/2006/91-7140-901-7/.
Повний текст джерелаWhalley, 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.
Повний текст джерелаCattinelli, I. "INVESTIGATIONS ON COGNITIVE COMPUTATION AND COMPUTATIONAL COGNITION." Doctoral thesis, Università degli Studi di Milano, 2011. http://hdl.handle.net/2434/155482.
Повний текст джерелаGradin, 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.
Повний текст джерелаSalimi-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.
Повний текст джерелаD'ANGELO, LAURA. "Bayesian modeling of calcium imaging data." Doctoral thesis, Università degli Studi di Padova, 2022. https://hdl.handle.net/10281/399067.
Повний текст джерелаPetitet, 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.
Повний текст джерелаWeiler, Florian [Verfasser], Horst [Akademischer Betreuer] Hahn, Horst [Gutachter] Hahn, Lars [Gutachter] Linsen, Bernhard [Gutachter] Preim, and 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.
Повний текст джерелаIyappan, 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.
Повний текст джерелаGloaguen, 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.
Повний текст джерелаA 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
Mitchell, Brittany L. "Statistical genetic analyses of neuropsychological traits." Thesis, Queensland University of Technology, 2022. https://eprints.qut.edu.au/227852/14/Brittany%20Mitchell%20Thesis.pdf.
Повний текст джерелаGanjgahi, Habib. "Computationally efficient mixed effect model for genetic analysis of high dimensional neuroimaging data." Thesis, University of Warwick, 2016. http://wrap.warwick.ac.uk/91328/.
Повний текст джерелаHassall, Cameron Dale. "Learning in Non-Stationary Environments." 2013. http://hdl.handle.net/10222/36240.
Повний текст джерелаBerteau, Stefan André. "Modeling biophysical and neural circuit bases for core cognitive abilities evident in neuroimaging patterns: hippocampal mismatch, mismatch negativity, repetition positivity, and alpha suppression of distractors." Thesis, 2018. https://hdl.handle.net/2144/27671.
Повний текст джерелаSreenivasan, Varsha. "Structural connectivity correlates of human cognition explored with diffusion MRI and tractography." Thesis, 2021. https://etd.iisc.ac.in/handle/2005/5228.
Повний текст джерела(10514360), Uttara Vinay Tipnis. "Data Science Approaches on Brain Connectivity: Communication Dynamics and Fingerprint Gradients." Thesis, 2021.
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