Harmonizing functional connectivity reduces scanner effects in community detection

Community detection on graphs constructed from functional magnetic resonance imaging (fMRI) data has led to important insights into brain functional organization. Large studies of brain community structure often include images acquired on multiple scanners across different studies. Differences in sc...

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Vydané v:	NeuroImage (Orlando, Fla.) Ročník 256; s. 119198
Hlavní autori:	Chen, Andrew A., Srinivasan, Dhivya, Pomponio, Raymond, Fan, Yong, Nasrallah, Ilya M., Resnick, Susan M., Beason-Held, Lori L., Davatzikos, Christos, Satterthwaite, Theodore D., Bassett, Dani S., Shinohara, Russell T., Shou, Haochang
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States Elsevier Inc 01.08.2022 Elsevier Limited Elsevier
Predmet:	Algorithms Alzheimer's disease Benchmarking Brain - diagnostic imaging Brain architecture Brain mapping Brain Mapping - methods Brain networks Community detection Community structure Functional connectivity Functional magnetic resonance imaging Functional morphology Gene expression Harmonization Humans Magnetic Resonance Imaging - methods Methods Network analyses Neural networks Neuroimaging Reproducibility of Results Scanners Site effects Time series Functional connectivity Community detection Site effects Network analyses Brain networks Harmonization
ISSN:	1053-8119, 1095-9572, 1095-9572
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Popis
Shrnutí:	Community detection on graphs constructed from functional magnetic resonance imaging (fMRI) data has led to important insights into brain functional organization. Large studies of brain community structure often include images acquired on multiple scanners across different studies. Differences in scanner can introduce variability into the downstream results, and these differences are often referred to as scanner effects. Such effects have been previously shown to significantly impact common network metrics. In this study, we identify scanner effects in data-driven community detection results and related network metrics. We assess a commonly employed harmonization method and propose new methodology for harmonizing functional connectivity that leverage existing knowledge about network structure as well as patterns of covariance in the data. Finally, we demonstrate that our new methods reduce scanner effects in community structure and network metrics. Our results highlight scanner effects in studies of brain functional organization and provide additional tools to address these unwanted effects. These findings and methods can be incorporated into future functional connectivity studies, potentially preventing spurious findings and improving reliability of results.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Equal contribution
ISSN:	1053-8119 1095-9572 1095-9572
DOI:	10.1016/j.neuroimage.2022.119198