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Publication year
2013Number of pages
10 p.
Source
NeuroImage, 66, (2013), pp. 543-552ISSN
Publication type
Article / Letter to editor

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Organization
SW OZ DCC AI
Journal title
NeuroImage
Volume
vol. 66
Languages used
English (eng)
Page start
p. 543
Page end
p. 552
Subject
Cognitive artificial intelligence; DI-BCB_DCC_Theme 4: Brain Networks and Neuronal CommunicationAbstract
Structural brain networks are used to model white-matter connectivity between spatially segregated brain regions. The presence, location and orientation of these white matter tracts can be derived using diffusion-weighted magnetic resonance imaging in combination with probabilistic tractography. Unfortunately, as of yet, none of the existing approaches provide an undisputed way of inferring brain networks from the streamline distributions which tractography produces. State-of-the-art methods rely on an arbitrary threshold or, alternatively, yield weighted results that are difficult to interpret. In this paper, we provide a generative model that explicitly describes how structural brain networks lead to observed streamline distributions. This allows us to draw principled conclusions about brain networks, which we validate using simultaneously acquired resting-state functional MRI data. Inference may be further informed by means of a prior which combines connectivity estimates from multiple subjects. Based on this prior, we obtain networks that significantly improve on the conventional approach.
This item appears in the following Collection(s)
- Academic publications [203608]
- Electronic publications [101974]
- Faculty of Social Sciences [27286]
- Open Access publications [70685]
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