Identifying canonical and replicable multi-scale intrinsic connectivity networks in 100k+ resting-state fMRI datasets

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dc.contributor.authorIraji, A.
dc.contributor.authorFu, Z.
dc.contributor.authorFaghiri, A.
dc.contributor.authorDuda, M.
dc.contributor.authorChen, J.
dc.contributor.authorRachakonda, S.
dc.contributor.authorDeRamus, T.
dc.contributor.authorKochunov, P.
dc.contributor.authorAdhikari, B. M.
dc.contributor.authorBelger, A.
dc.contributor.authorFord, J. M.
dc.contributor.authorMathalon, D. H.
dc.contributor.authorPearlson, G. D.
dc.contributor.authorPotkin, S. G.
dc.contributor.authorPreda, A.
dc.contributor.authorTurner, J. A.
dc.contributor.authorvan Erp, T. G. M.
dc.contributor.authorBustillo, J. R.
dc.contributor.authorYang, K.
dc.contributor.authorIshizuka, K.
dc.contributor.authorFaria, A.
dc.contributor.authorSawa, A.
dc.contributor.authorHutchison, K.
dc.contributor.authorOsuch, E. A.
dc.contributor.authorTheberge, J.
dc.contributor.authorAbbott, C.
dc.contributor.authorMueller, B. A.
dc.contributor.authorZhi, D.
dc.contributor.authorZhuo, C.
dc.contributor.authorLiu, S.
dc.contributor.authorXu, Y.
dc.contributor.authorSalman, M.
dc.contributor.authorLiu, J.
dc.contributor.authorDu, Y.
dc.contributor.authorSui, J.
dc.contributor.authorAdali, Tulay
dc.contributor.authorCalhoun, V. D.
dc.date.accessioned2024-03-27T13:26:09Z
dc.date.available2024-03-27T13:26:09Z
dc.date.issued2023-10-03
dc.description.abstractDespite the known benefits of data-driven approaches, the lack of approaches for identifying functional neuroimaging patterns that capture both individual variations and inter-subject correspondence limits the clinical utility of rsfMRI and its application to single-subject analyses. Here, using rsfMRI data from over 100k individuals across private and public datasets, we identify replicable multi-spatial-scale canonical intrinsic connectivity network (ICN) templates via the use of multi-model-order independent component analysis (ICA). We also study the feasibility of estimating subject-specific ICNs via spatially constrained ICA. The results show that the subject-level ICN estimations vary as a function of the ICN itself, the data length, and the spatial resolution. In general, large-scale ICNs require less data to achieve specific levels of (within- and between-subject) spatial similarity with their templates. Importantly, increasing data length can reduce an ICN's subject-level specificity, suggesting longer scans may not always be desirable. We also find a positive linear relationship between data length and spatial smoothness (possibly due to averaging over intrinsic dynamics), suggesting studies examining optimized data length should consider spatial smoothness. Finally, consistency in spatial similarity between ICNs estimated using the full data and subsets across different data lengths suggests lower within-subject spatial similarity in shorter data is not wholly defined by lower reliability in ICN estimates, but may be an indication of meaningful brain dynamics which average out as data length increases.
dc.description.sponsorshipThis work was supported by grants from the National Institutes ofHealth grant numbers 1U24RR021992, 1U24RR025736, R01EB020407, R01MH118695, R01MH123610, and R01EB006841 and theNational Science Foundation grant number 2112455 to Dr. VinceD. Calhoun; the National Institutes of Health grant numberR01MH117107 to Dr. Jing Sui; the National Science Foundation grantnumber 1631838 to Dr. Tulay Adali; and the Lawson Health ResearchInstitute, grant number LHR D1374, Pfizer Independent InvestigatorAward, grant number WS2249136, and CIHR grant FRN 153359 toDr. Elizabeth A. Osuch. We would also like to acknowledge theGeorgia State University RISE Award, which significantly helped toaccomplish this work. Data were provided in part by the AdolescentBrain Cognitive Development SM (ABCD) Study (Jernigan &Brown,2018), held in the NIMH Data Archive (NDA). This is a multi-site, longitudinal study designed to recruit more than 10,000 childrenaged 9–10 and follow them over 10 years into early adulthood. TheABCD Study®is supported by the National Institutes of Health (NIH)and additional federal partners under award numbers U01DA041048,U01DA050989, U01DA051016, U01DA041022, U01DA051018,U01DA051037, U01DA050987, U01DA041174, U01DA041106,U01DA041117, U01DA041028, U01DA041134, U01DA050988,U01DA051039, U01DA041156, U01DA041025, U01DA041120,U01DA051038, U01DA041148, U01DA041093, U01DA041089,U24DA041123, U24DA041147; by the Autism Brain Imaging DataExchange (ABIDE) (Di Martino et al.,2014,2017) support for the workby Adriana Di Martino provided by the (NIMH K23MH087770) andthe Leon Levy Foundation and primary support for the work byMichael P. Milham and the INDI team was provided by gifts fromJoseph P. Healy and the Stavros Niarchos Foundation to the ChildMind Institute, as well as by an NIMH award to MPM (NIMHR03MH096321); by the Attention Deficit Hyperactivity Disorder-200(ADHD200) Consortium (HD-200 Consortium,2012). Consortiumsteering committee includes Jan Buitelaar, MD, F. Xavier Castellanos,MD, PhD, Daniel Dickstein, PhD, Damien Fair, P.A.-C., PhD, DavidKennedy, PhD, Beatric Luna, PhD, Michael P. Milham (Project Coordi-nator), MD, PhD, Stewart Mostofsky, MD, Joel Nigg, PhD, JulieB. Schweitzer, PhD, Katerina Velanova, PhD, Yu-Feng Wang, MD,PhD, Yu-Feng Zang, MD; by the Alzheimer's Disease NeuroimagingInitiative (ADNI) (Jack Jr. et al.,2008) (National Institutes of HealthGrant U01 AG024904) and DOD ADNI (Department of Defenseaward number W81XWH-12-2-0012). ADNI is funded by theNational Institute on Aging, the National Institute of Biomedical Imag-ing and Bioengineering, and through generous contributions from thefollowing: AbbVie, Alzheimer's Association; Alzheimer's DrugDiscovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen;Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.;Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun;F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.;Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunother-apy Research & Development, LLC.; Johnson & JohnsonPharmaceutical Research & Development LLC.; Lumosity; Lundbeck;Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research;Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfi-zer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company;and Transition Therapeutics. The Canadian Institutes of HealthResearch is providing funds to support ADNI clinical sites in Canada.Private sector contributions are facilitated by the Foundation for theNational Institutes of Health. The grantee organization is the North-ern California Institute for Research and Education, and the study iscoordinated by the Alzheimer's Therapeutic Research Institute at theUniversity of Southern California. ADNI data are disseminated by theLaboratory for Neuro Imaging at the University of Southern California;by the Bipolar & Schizophrenia Consortium for Parsing IntermediatePhenotypes (B-SNIP) study (Tamminga et al.,2013); by the BrainGenomics Superstruct Project (GSP) (Holmes et al.,2015) of HarvardUniversity and the Massachusetts General Hospital, (Principal Investi-gators: Randy Buckner, Joshua Roffman, and Jordan Smoller), withsupport from the Center for Brain Science Neuroinformatics ResearchGroup, the Athinoula A. Martinos Center for Biomedical Imaging, andthe Center for Human Genetic Research. 20 individual investigators atHarvard and MGH generously contributed data to the overall project;by the Human Connectome Project for Early Psychosis study(Lewandowski et al.,2020); the Human Connectome Project (vanEssen et al.,2013), WU-Minn Consortium (Principal Investigators:David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the16 NIH Institutes and Centers that support the NIH Blueprint forNeuroscience Research; and by the McDonnell Center for SystemsNeuroscience at Washington University; by the OASIS (LaMontagneet al.,2019) Longitudinal Multimodal Neuroimaging: Principal Investi-gators: T. Benzinger, D. Marcus, J. Morris; NIH P50 AG00561, P30NS09857781, P01 AG026276, P01 AG003991, R01 AG043434, UL1TR000448, R01 EB009352. AV-45 doses were provided by AvidRadiopharmaceuticals, a wholly owned subsidiary of Eli Lilly; and usingthe UK Biobank (Littlejohns et al.,2020) Resource under ApplicationNumber 49636.
dc.description.urihttps://onlinelibrary.wiley.com/doi/abs/10.1002/hbm.26472
dc.format.extent19 pages
dc.genrejournal articles
dc.identifierdoi:10.13016/m2tdrx-eaub
dc.identifier.citationIraji, A., Z. Fu, A. Faghiri, M. Duda, J. Chen, S. Rachakonda, T. DeRamus, et al. “Identifying Canonical and Replicable Multi-Scale Intrinsic Connectivity Networks in 100k+ Resting-State FMRI Datasets.” Human Brain Mapping 44, no. 17 (2023): 5729–48. https://doi.org/10.1002/hbm.26472.
dc.identifier.urihttps://doi.org/10.1002/hbm.26472
dc.identifier.urihttp://hdl.handle.net/11603/32673
dc.language.isoen_US
dc.publisherWiley
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department
dc.rightsAttribution-NonCommercial-NoDerivs 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectfunctional connectivity (FC)
dc.subjectfunctional templates
dc.subjectindependent component analysis (ICA)
dc.subjectintrinsic connectivity networks (ICNs)
dc.titleIdentifying canonical and replicable multi-scale intrinsic connectivity networks in 100k+ resting-state fMRI datasets
dc.typeText
dcterms.creatorhttps://orcid.org/0000-0003-0594-2796

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