Deutsch
 
Hilfe Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

 
 
DownloadE-Mail
  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

Basisdaten

einblenden: ausblenden:
Genre: Zeitschriftenartikel

Dateien

einblenden: Dateien
ausblenden: Dateien
:
Moreno-Dominguez_HumBrainMapp_preprint_2014.pdf (Preprint), 24MB
Name:
Moreno-Dominguez_HumBrainMapp_preprint_2014.pdf
Beschreibung:
-
OA-Status:
Sichtbarkeit:
Öffentlich
MIME-Typ / Prüfsumme:
application/pdf / [MD5]
Technische Metadaten:
Copyright Datum:
-
Copyright Info:
-
Lizenz:
-

Externe Referenzen

einblenden:

Urheber

einblenden:
ausblenden:
 Urheber:
Moreno-Dominguez, David1, Autor           
Anwander, Alfred2, Autor           
Knösche, Thomas R.1, Autor           
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              

Inhalt

einblenden:
ausblenden:
Schlagwörter: Diffusion MRI; Connectome; Whole-brain parcellation; Hierarchical clustering; Tractography; Dendrogram
 Zusammenfassung: 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

einblenden:
ausblenden:
Sprache(n): eng - English
 Datum: 2014-04-032014-04-172014-10
 Publikationsstatus: Erschienen
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: DOI: 10.1002/hbm.22528
PMID: 24740833
Anderer: Epub 2014
 Art des Abschluß: -

Veranstaltung

einblenden:

Entscheidung

einblenden:

Projektinformation

einblenden:

Quelle 1

einblenden:
ausblenden:
Titel: Human Brain Mapping
Genre der Quelle: Zeitschrift
 Urheber:
Affiliations:
Ort, Verlag, Ausgabe: -
Seiten: - Band / Heft: 35 (10) Artikelnummer: - Start- / Endseite: 5000 - 5025 Identifikator: ISSN: 1065-9471
CoNE: https://pure.mpg.de/cone/journals/resource/954925601686