Please use this identifier to cite or link to this item: https://ahro.austin.org.au/austinjspui/handle/1/28700
Full metadata record
DC FieldValueLanguage
dc.contributor.authorMansour L, Sina-
dc.contributor.authorSeguin, Caio-
dc.contributor.authorSmith, Robert E-
dc.contributor.authorZalesky, Andrew-
dc.date2022-01-22-
dc.date.accessioned2022-02-01T04:44:41Z-
dc.date.available2022-02-01T04:44:41Z-
dc.date.issued2022-04-15-
dc.identifier.citationNeuroImage 2022; 250: 118930en
dc.identifier.urihttps://ahro.austin.org.au/austinjspui/handle/1/28700-
dc.description.abstractStructural connectomes are increasingly mapped at high spatial resolutions comprising many hundreds-if not thousands-of network nodes. However, high-resolution connectomes are particularly susceptible to image registration misalignment, tractography artifacts, and noise, all of which can lead to reductions in connectome accuracy and test-retest reliability. We investigate a network analogue of image smoothing to address these key challenges. Connectome Spatial Smoothing (CSS) involves jointly applying a carefully chosen smoothing kernel to the two endpoints of each tractography streamline, yielding a spatially smoothed connectivity matrix. We develop computationally efficient methods to perform CSS using a matrix congruence transformation and evaluate a range of different smoothing kernel choices on CSS performance. We find that smoothing substantially improves the identifiability, sensitivity, and test-retest reliability of high-resolution connectivity maps, though at a cost of increasing storage burden. For atlas-based connectomes (i.e. low-resolution connectivity maps), we show that CSS marginally improves the statistical power to detect associations between connectivity and cognitive performance, particularly for connectomes mapped using probabilistic tractography. CSS was also found to enable more reliable statistical inference compared to connectomes without any smoothing. We provide recommendations on optimal smoothing kernel parameters for connectomes mapped using both deterministic and probabilistic tractography. We conclude that spatial smoothing is particularly important for the reliability of high-resolution connectomes, but can also provide benefits at lower parcellation resolutions. We hope that our work enables computationally efficient integration of spatial smoothing into established structural connectome mapping pipelines.en
dc.language.isoeng-
dc.subjectConnectome smoothingen
dc.subjectHigh-resolution connectomicsen
dc.subjectStructural connectivityen
dc.subjectTractographyen
dc.titleConnectome Spatial Smoothing (CSS): concepts, methods, and evaluation.en
dc.typeJournal Articleen
dc.identifier.journaltitleNeuroImageen
dc.identifier.affiliationThe Florey Institute of Neuroscience and Mental Healthen
dc.identifier.affiliationThe University of Sydney, School of Biomedical Engineering, Sydney, Australiaen
dc.identifier.affiliationFlorey Department of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, Australiaen
dc.identifier.affiliationDepartment of Biomedical Engineering, The University of Melbourne, Parkville, Victoria, Australiaen
dc.identifier.affiliationMelbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Parkville, Victoria, Australiaen
dc.identifier.pubmedurihttps://pubmed.ncbi.nlm.nih.gov/35077853/en
dc.identifier.doi10.1016/j.neuroimage.2022.118930en
dc.type.contentTexten
dc.identifier.orcid0000-0003-3636-4642en
dc.identifier.pubmedid35077853-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
item.fulltextNo Fulltext-
item.grantfulltextnone-
item.languageiso639-1en-
item.openairetypeJournal Article-
Appears in Collections:Journal articles
Show simple item record

Page view(s)

12
checked on Mar 28, 2024

Google ScholarTM

Check


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