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  Whole‐brain cortical parcellation: A hierarchical method based on dMRI tractography

Moreno-Dominguez, D. (2014). Whole‐brain cortical parcellation: A hierarchical method based on dMRI tractography. PhD Thesis, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0019-EB22-D Version Permalink: http://hdl.handle.net/21.11116/0000-0002-9EB7-E
Genre: Thesis

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Submitted_A4_Moreno-Dominguez_PhD_Dissertation.pdf (Preprint), 33MB
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 Creators:
Moreno-Dominguez, David1, Author              
Anwander, Alfred2, Advisor              
Haueisen, Jens3, Advisor
Knösche, Thomas R.1, Advisor              
Affiliations:
1Methods and Development Group MEG and EEG - Cortical Networks and Cognitive Functions, MPI for Human Cognitive and Brain Sciences, Max Planck Society, Leipzig, DE, ou_2205650              
2Department Neuropsychology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, Leipzig, DE, ou_634551              
3Institut für Biomedizinische Technik und Informatik, Fakultät für Informatik und Automatisierung, Technische Universität Ilmenau, Ilmenau, DE, ou_persistent22              

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Free keywords: Diffusion MRI; Connectome; Whole-brain parcellation; Hierarchical clustering; Tractography; Dendrogram
 Abstract: In modern neuroscience there is general agreement that brain function relies on networks and that connectivity is therefore of paramount importance for brain function. Accordingly, the delineation of functional brain areas on the basis of diffusion magnetic resonance imaging (dMRI) and tractography may lead to highly relevant brain maps. Existing methods typically aim to find a predefined number of areas and/or are limited to small regions of grey matter. However, it is in general not likely that a single parcellation dividing the brain into a finite number of areas is an adequate representation of the function‐anatomical organization of the brain. In this work, we propose hierarchical clustering as a solution to overcome these limitations and achieve whole‐brain parcellation. We demonstrate that this method encodes the information of the underlying structure at all granularity levels in a hierarchical tree or dendrogram. We develop an optimal tree building and processing pipeline that reduces the complexity of the tree with minimal information loss. We show how these trees can be used to compare the similarity structure of different subjects or recordings and how to extract parcellations from them. Our novel approach yields a more exhaustive representation of the real underlying structure and successfully tackles the challenge of whole‐brain parcellation.

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Language(s): eng - English
 Dates: 2014-05-192014-06-192014-11-192014-11-19
 Publication Status: Published in print
 Pages: 143
 Publishing info: Leipzig : Max Planck Institute for Human Cognitive and Brain Sciences
 Table of Contents: 1. Introduction
2. Brain analysis based on water diffusion measured by MRI
3. A hierarchical method for whole-brain connectivity-based parcellation
4. A proof-of-principle study on multi-granularity dMRI-based whole-brain characterization
5. Approaches and challenges in validation of tractography-based clustering
6. Discussion
7. Summary and outlook
 Rev. Type: -
 Identifiers: ISBN: 978-3-941504-45-5
URN: urn:nbn:gbv:ilm1-2014000383
 Degree: PhD

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Title: MPI Series in Human Cognitive and Brain Sciences
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Publ. Info: Leipzig : Max Planck Institute for Human Cognitive and Brain Sciences
Pages: - Volume / Issue: 161 Sequence Number: - Start / End Page: - Identifier: -