000199551 001__ 199551
000199551 005__ 20190316235924.0
000199551 0247_ $$2doi$$a10.5075/epfl-thesis-6274
000199551 02470 $$2urn$$aurn:nbn:ch:bel-epfl-thesis6274-6
000199551 02471 $$2nebis$$a10162924
000199551 037__ $$aTHESIS
000199551 041__ $$aeng
000199551 088__ $$a6274
000199551 245__ $$aDynamic brain networks explored by structure-revealing methods
000199551 269__ $$a2014
000199551 260__ $$aLausanne$$bEPFL$$c2014
000199551 336__ $$aTheses
000199551 502__ $$aProf. N. Stergiopulos (président) ; Prof. D. Van De Ville (directeur) ; Dr S. Achard,  Prof. M. Greicius,  Prof. J.D.R. Millán Ruiz (rapporteurs)
000199551 520__ $$aThe human brain is a complex system able to continuously adapt. How and where brain activity is modulated by behavior can be studied with functional magnetic resonance imaging (fMRI), a non-invasive neuroimaging technique with excellent spatial resolution and whole-brain coverage. FMRI scans of healthy adults completing a variety of behavioral tasks have greatly contributed to our understanding of the functional role of individual brain regions. However, by statistically analyzing each region independently, these studies ignore that brain regions act in concert rather than in unison. Thus, many studies since have instead examined how brain regions interact. Surprisingly, structured interactions between distinct brain regions not only occur during behavioral tasks but also while a subject rests quietly in the MRI scanner. Multiple groups of regions interact very strongly with each other and not only do these groups bear a striking resemblance to the sets of regions co-activated in tasks, but many of these interactions are also progressively disrupted in neurological diseases. This suggests that spontaneous fluctuations in activity can provide novel insights into fundamental organizing principles of the human brain in health and disease. Many techniques to date have segregated regions into spatially distinct networks, which ignores that any brain region can take part in multiple networks across time. A more natural view is to estimate dynamic brain networks that allow flexible functional interactions (or connectivity) over time. The estimation and analysis of such dynamic functional interactions is the subject of this dissertation. We take the perspective that dynamic brain networks evolve in a low-dimensional space and can be described by a small number of characteristic spatiotemporal patterns. Our proposed approaches are based on well-established statistical methods, such as principal component analysis (PCA), sparse matrix decompositions, temporal clustering, as well as a multiscale analysis by novel graph wavelet designs. We adapt and extend these methods to the analysis of dynamic brain networks. We show that PCA and its higher-order equivalent can identify co-varying functional interactions, which reveal disturbed dynamic properties in multiple sclerosis and which are related to the timing of stimuli for task studies, respectively. Further we show that sparse matrix decompositions provide a valid alternative approach to PCA and improve interpretability of the identified patterns. Finally, assuming an even simpler low-dimensional space and the exclusive temporal expression of individual patterns, we show that specific transient interactions of the medial prefrontal cortex are disturbed in aging and relate to impaired memory.
000199551 6531_ $$abrain imaging
000199551 6531_ $$afMRI
000199551 6531_ $$aresting state
000199551 6531_ $$afunctional connectivity
000199551 6531_ $$adynamic networks
000199551 6531_ $$agraph wavelets
000199551 6531_ $$amatrix factorization
000199551 6531_ $$atensor decomposition
000199551 6531_ $$asparsity
000199551 6531_ $$amultiple sclerosis
000199551 6531_ $$aaging
000199551 700__ $$0244753$$aLeonardi, Nora$$g159752
000199551 720_2 $$0240173$$aVan De Ville, Dimitri$$edir.$$g152027
000199551 8564_ $$s21807407$$uhttps://infoscience.epfl.ch/record/199551/files/EPFL_TH6274.pdf$$yn/a$$zn/a
000199551 909CO $$ooai:infoscience.tind.io:199551$$pthesis$$pthesis-bn2018$$pDOI$$qDOI2$$qGLOBAL_SET
000199551 917Z8 $$x108898
000199551 917Z8 $$x108898
000199551 917Z8 $$x108898
000199551 917Z8 $$x108898
000199551 918__ $$aSTI$$cIBI-STI$$dEDBB
000199551 919__ $$aGR-VDV
000199551 920__ $$a2014-6-27$$b2014
000199551 970__ $$a6274/THESES
000199551 973__ $$aEPFL$$sPUBLISHED
000199551 980__ $$aTHESIS