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You searched for subject:(Redes cerebrales). Showing records 1 – 3 of 3 total matches.

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Universitat Pompeu Fabra

1. Saenger Amoore, Victor Manuel. Uncovering the large-scale effects and mechanisms of brain disease through whole-brain computational modeling.

Degree: Departament de Tecnologies de la Informació i les Comunicacions, 2018, Universitat Pompeu Fabra

La actividad cerebral en reposo revela una arquitectura funcional compleja. Sin embargo, los mecanismos subyacentes que generan dicha arquitectura no han sido esclarecidos del todo. Los modelos computacionales cerebrales a gran escala se han convertido en herramientas fundamentales para explorar estos mecanismos, asi como para aclarar la relación entre la estructura y función, tanto en el cerebro sano como en cerebros con algún tipo de desorden nuerológico. Los estudios y resultados presentados en esta tesis tienen como objetivo principal el mostrar que estos modelos pueden ser usados como marcos teóricos que permiten un mayor entendimiento del origen de los desórdenes. Estos, pueden ser usados para alterar las dinámicas cerebrales de manera artifical ya sea por estimulación o lesiones artificiales sin ningún tipo de intervención clínica. Observaciones empíricas corroboran estos resultados, lo cual esclarece los posibles mecanismos generadores de los desórdenes cerebrales. Advisors/Committee Members: [email protected] (authoremail), true (authoremailshow), Deco, Gustavo (director), true (authorsendemail).

Subjects/Keywords: Whole-brain modeling; Brain disease; Brain networks; Complexity; Criticality; Modelos computacionales cerebrales a gran escala; Enfermedades cerebrales; Redes cerebrales; Complejidad; Criticalidad; 62

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Saenger Amoore, V. M. (2018). Uncovering the large-scale effects and mechanisms of brain disease through whole-brain computational modeling. (Thesis). Universitat Pompeu Fabra. Retrieved from http://hdl.handle.net/10803/565910

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Saenger Amoore, Victor Manuel. “Uncovering the large-scale effects and mechanisms of brain disease through whole-brain computational modeling.” 2018. Thesis, Universitat Pompeu Fabra. Accessed October 27, 2020. http://hdl.handle.net/10803/565910.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Saenger Amoore, Victor Manuel. “Uncovering the large-scale effects and mechanisms of brain disease through whole-brain computational modeling.” 2018. Web. 27 Oct 2020.

Vancouver:

Saenger Amoore VM. Uncovering the large-scale effects and mechanisms of brain disease through whole-brain computational modeling. [Internet] [Thesis]. Universitat Pompeu Fabra; 2018. [cited 2020 Oct 27]. Available from: http://hdl.handle.net/10803/565910.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Saenger Amoore VM. Uncovering the large-scale effects and mechanisms of brain disease through whole-brain computational modeling. [Thesis]. Universitat Pompeu Fabra; 2018. Available from: http://hdl.handle.net/10803/565910

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Universitat de Girona

2. Bonmatí Coll, Ester. Study of brain complexity using information theory tools.

Degree: Departament d'Informàtica, Matemàtica Aplicada i Estadística (2013-), 2016, Universitat de Girona

El cervell humà és una xarxa complexa que comparteix i processa la informació mitjançant els camins estructurals per tal de realitzar una funció. El connectoma és una representació del cervell en forma de graf, on els nodes corresponen a regions del cervell i les arestes a connexions estructurals o funcionals. En aquesta tesi, s'investiga i es proporcionen nous mètodes per estudiar la complexitat del cervell i millorar la comprensió del seu funcionament mitjançant l'ús de la teoria de la informació. En primer lloc, ens centrem en mètodes de parcel.lació del cervell, el qual és un pas clau per realitzar estudis de complexitat ja que determina les regions a analitzar. En segon lloc, ens centrem en la definició de mesures per a caracteritzar la complexitat de les xarxes cerebrals. Finalment, la consistència dels resultats entre els subjectes sans a partir de dades de connectivitat funcional o estructural, demostra la flexibilitat i robustesa dels mètodes proposats Advisors/Committee Members: [email protected] (authoremail), false (authoremailshow), Boada, Imma (director), Bardera i Reig, Antoni (director), true (authorsendemail).

Subjects/Keywords: Connectome; Connectoma; Conectoma; Brain complexity; Complexitat del cervell; Complejidad del cerebro; Information theory; Teoria de la informació; Teoría de la información; Brain networks; Xarxes cerebrals; Redes cerebrales; 004; 616.8

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Bonmatí Coll, E. (2016). Study of brain complexity using information theory tools. (Thesis). Universitat de Girona. Retrieved from http://hdl.handle.net/10803/404384

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Bonmatí Coll, Ester. “Study of brain complexity using information theory tools.” 2016. Thesis, Universitat de Girona. Accessed October 27, 2020. http://hdl.handle.net/10803/404384.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Bonmatí Coll, Ester. “Study of brain complexity using information theory tools.” 2016. Web. 27 Oct 2020.

Vancouver:

Bonmatí Coll E. Study of brain complexity using information theory tools. [Internet] [Thesis]. Universitat de Girona; 2016. [cited 2020 Oct 27]. Available from: http://hdl.handle.net/10803/404384.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Bonmatí Coll E. Study of brain complexity using information theory tools. [Thesis]. Universitat de Girona; 2016. Available from: http://hdl.handle.net/10803/404384

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Universitat Pompeu Fabra

3. Pallarés Picazo, Vicente. Individual traits versus invariances of cognitive functions: a model-based study of brain connectivity.

Degree: Departament de Tecnologies de la Informació i les Comunicacions, 2019, Universitat Pompeu Fabra

There is consistent evidence in the neuroimaging literature that functional brain networks reflect personal traits. Individual specificity may interfere with the characterization of cognition, in terms of coordination of brain networks to perform a task, such as sustained attention, memory retrieval or visual information processing. How individual traits coexist with invariant mechanisms is, therefore, a key question in brain connectivity research. This work aims to examine the relationship between subject- and task-specific connectivity signatures. It focuses on two different timescales: day-to-day variability and faster fluctuations exhibited within a scanning session. We adopt a machine learning approach to quantitatively disentangle the contribution of subject information and cognitive state to the connectivity patterns. The proposed methodology allows us to extract the specific brain networks that support each of the two dimensions, as well as to delve into their changes over time, suggesting the relevant timescales for cognition. Advisors/Committee Members: [email protected] (authoremail), true (authoremailshow), Gilson, Matthieu (director), Deco, Gustavo (director), Ramírez, Rafael,1966- (director).

Subjects/Keywords: Neurociencia computacional; fMRI; Conectividad cerebral; Modelos cerebrales; Aprendizaje automático; Selección de features; Conectividad dinámica; Redes cerebrales; Análisis multivariado; Conectividad funcional; Correlación; Integración; Segregación; Neurociència computacional; Connectivitat cerebral; Models cerebrals; Aprenentatge automàtic; Selecció de trets; Connectivitat dinàmica; Xarxes cerebrals; Anàlisi multivariat; Connectivitat funcional; Correlació; Integració; Segregació; Computational neuroscience; Brain connectivity; Whole-brain modelling; Machine learning; Feature selection; Dynamic connectivity; Brain networks; Multivariate analysis; Functional connectome; Correlation; Integration; Segregation; 62

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Pallarés Picazo, V. (2019). Individual traits versus invariances of cognitive functions: a model-based study of brain connectivity. (Thesis). Universitat Pompeu Fabra. Retrieved from http://hdl.handle.net/10803/666806

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Pallarés Picazo, Vicente. “Individual traits versus invariances of cognitive functions: a model-based study of brain connectivity.” 2019. Thesis, Universitat Pompeu Fabra. Accessed October 27, 2020. http://hdl.handle.net/10803/666806.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Pallarés Picazo, Vicente. “Individual traits versus invariances of cognitive functions: a model-based study of brain connectivity.” 2019. Web. 27 Oct 2020.

Vancouver:

Pallarés Picazo V. Individual traits versus invariances of cognitive functions: a model-based study of brain connectivity. [Internet] [Thesis]. Universitat Pompeu Fabra; 2019. [cited 2020 Oct 27]. Available from: http://hdl.handle.net/10803/666806.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Pallarés Picazo V. Individual traits versus invariances of cognitive functions: a model-based study of brain connectivity. [Thesis]. Universitat Pompeu Fabra; 2019. Available from: http://hdl.handle.net/10803/666806

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

.