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Title: Connectome Spatial Smoothing (CSS): concepts, methods, and evaluation.
Austin Authors: Mansour L, Sina;Seguin, Caio;Smith, Robert E;Zalesky, Andrew
Affiliation: The Florey Institute of Neuroscience and Mental Health
The University of Sydney, School of Biomedical Engineering, Sydney, Australia
Florey Department of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, Australia
Department of Biomedical Engineering, The University of Melbourne, Parkville, Victoria, Australia
Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Parkville, Victoria, Australia
Issue Date: 15-Apr-2022
Date: 2022-01-22
Publication information: NeuroImage 2022; 250: 118930
Abstract: Structural 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.
DOI: 10.1016/j.neuroimage.2022.118930
ORCID: 0000-0003-3636-4642
Journal: NeuroImage
PubMed URL: 35077853
PubMed URL:
Type: Journal Article
Subjects: Connectome smoothing
High-resolution connectomics
Structural connectivity
Appears in Collections:Journal articles

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