Please use this identifier to cite or link to this item: https://ahro.austin.org.au/austinjspui/handle/1/16034
Full metadata record
DC FieldValueLanguage
dc.contributor.authorYeh, Chun-Hung-
dc.contributor.authorSmith, Robert E-
dc.contributor.authorLiang, Xiaoyun-
dc.contributor.authorCalamante, Fernando-
dc.contributor.authorConnelly, Alan-
dc.date2016-05-19-
dc.date.accessioned2016-06-01T07:32:37Z-
dc.date.accessioned2016-06-01T06:40:24Z-
dc.date.accessioned2016-06-01T07:32:36Z-
dc.date.available2016-06-01T06:40:24Z-
dc.date.available2016-06-01T07:32:37Z-
dc.date.available2016-06-01T07:32:36Z-
dc.date.issued2016-11-15-
dc.identifier.citationNeuroimage 2016; 142: 150-162en_US
dc.identifier.urihttp://ahro.austin.org.au/austinjspui/handle/1/16034-
dc.description.abstractDiffusion MRI streamlines tractography has become a major technique for inferring structural networks through reconstruction of brain connectome. However, quantification of structural connectivity based on the number of streamlines interconnecting brain grey matter regions is known to be problematic in a number of aspects, such as the ill-posed nature of streamlines terminations and the non-quantitative nature of streamline counts. This study investigates the effects of state-of-the-art connectome construction methods on the subsequent analyses of structural brain networks using graph theoretical approaches. Our results demonstrate that the characteristics of structural connectivity, including connectome variability, global network metrics, small-world attributes and network hubs, alter significantly following the improvement in biological accuracy of streamlines tractograms provided by anatomically-constrained tractography (ACT) and spherical-deconvolution informed filtering of tractograms (SIFT). Importantly, the commonly-used correction for connection density based on scaling the contribution of each streamline to the connectome by its inverse length is shown to provide incomplete correction, highlighting the necessity for the use of advanced tractogram reconstruction techniques in structural connectomics research.en_US
dc.subjectDiffusion MRIen_US
dc.subjectFibre-trackingen_US
dc.subjectTractographyen_US
dc.subjectStructural connectomeen_US
dc.titleCorrection for diffusion MRI fibre tracking biases: The consequences for structural connectomic metrics.en_US
dc.typeJournal Articleen_US
dc.identifier.journaltitleNeuroimageen_US
dc.identifier.affiliationDepartment of Medicine, Northern Health, University of Melbourne, Melbourne, Victoria, Australia-
dc.identifier.affiliationThe Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australia-
dc.identifier.affiliationDepartment of Medicine, Austin Health, The University of Melbourne, Heidelberg, Victoria, Australia -
dc.identifier.pubmedurihttps://pubmed.ncbi.nlm.nih.gov/27211472en_US
dc.identifier.doi10.1016/j.neuroimage.2016.05.047en_US
dc.type.contentTexten_US
dc.type.austinJournal Articleen_US
item.cerifentitytypePublications-
item.grantfulltextnone-
item.openairetypeJournal Article-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
Appears in Collections:Journal articles
Show simple item record

Page view(s)

4
checked on Feb 3, 2023

Google ScholarTM

Check


Items in AHRO are protected by copyright, with all rights reserved, unless otherwise indicated.