English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  A hierarchical method for whole-brain connectivity-based parcellation

Moreno-Dominguez, D., Anwander, A., & Knösche, T. R. (2014). A hierarchical method for whole-brain connectivity-based parcellation. Human Brain Mapping, 35(10), 5000-5025. doi:10.1002/hbm.22528.

Item is

Basic

show hide
Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0019-0C87-3 Version Permalink: http://hdl.handle.net/21.11116/0000-0003-1501-4
Genre: Journal Article

Files

show Files
hide Files
:
Moreno-Dominguez_HumBrainMapp_preprint_2014.pdf (Preprint), 24MB
Name:
Moreno-Dominguez_HumBrainMapp_preprint_2014.pdf
Description:
-
Visibility:
Public
MIME-Type / Checksum:
application/pdf / [MD5]
Technical Metadata:
Copyright Date:
-
Copyright Info:
-
License:
-

Locators

show

Creators

show
hide
 Creators:
Moreno-Dominguez, David1, Author              
Anwander, Alfred2, Author              
Knösche, Thomas R.1, Author              
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, ou_634551              

Content

show
hide
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.

Details

show
hide
Language(s): eng - English
 Dates: 2014-04-032014-04-172014-10
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.1002/hbm.22528
PMID: 24740833
Other: Epub 2014
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: Human Brain Mapping
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: 35 (10) Sequence Number: - Start / End Page: 5000 - 5025 Identifier: ISSN: 1065-9471
CoNE: https://pure.mpg.de/cone/journals/resource/954925601686