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

• Type of Material: Article
• Authors: A. Iraji, Emory University; Z. Fu, Emory University; A. Faghiri, Emory University; m. Duda, Emory University; J. Chen, Emory University; S. Rachakonda, Emory University; T. Deramus, Emory University; P. Kochunov, University of Maryland, Baltimore; B.M. Adhikari, University of Maryland, Baltimore; A. Belger, University of North Carolina, Chapel Hill; J.M. Ford, University of California, San Francisco; D.H. Mathalon, University of California, San Francisco; G.D. Pearlson, Yale University; S.G. Potkin, University of California, Irvine; A. Preda, University of California, Irvine; J.A. Turner, Ohio State University; T.G.M. van Erp, University of California, Irvine; J.R. Bustillo, University of New Mexico, Albuquerque; K. yang, Johns Hopkins University; K. Ishizuka, Johns Hopkins University; A. Faria, Johns Hopkins University; A. Sawa, Johns Hopkins University; K. Hutchison, University of Colorado, Boulder; E.A. Osuch, Schulich School of Medicine and Dentistry; J. Theberge, Schulich School of Medicine and Dentistry; C. Abbott, University of New Mexico, Albuquerque; B.A. Mueller, University of Minnesota, Minneapolis; D. Zhi, Beijing Normal University; C. Zhuo, Nankai University; S. Liu, Shanxi Medical University; Y. Xu, Shanxi Medical University; M. Salman, Emory University; J. Liu, Emory University; Y. Du, Emory University; J. Sui, Emory University; T. Adali, University of Maryland, Baltimore; Vince D. Calhoun, Emory University
• Language: English
• Date: 2023-10-03
• Publisher: John Wiley and Sons
• Publication Version: Final Published Version
• Copyright Statement: © 2023 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.
• Final Published Version (URL): https://doi.org/10.1002/hbm.26472
• Title of Journal or Parent Work: Human Brain Mapping
• Volume: 44
• Issue: 17
• Start Page: 5729
• End Page: 5748
• Grant/Funding Information: This work was supported by grants from the National Institutes of Health grant numbers 1U24RR021992, 1U24RR025736, R01EB020407, R01MH118695, R01MH123610, and R01EB006841 and the National Science Foundation grant number 2112455 to Dr. Vince D. Calhoun; the National Institutes of Health grant number R01MH117107 to Dr. Jing Sui; the National Science Foundation grant number 1631838 to Dr. Tulay Adali; and the Lawson Health Research Institute, grant number LHR D1374, Pfizer Independent Investigator Award, grant number WS2249136, and CIHR grant FRN 153359 to Dr. Elizabeth A. Osuch. We would also like to acknowledge the Georgia State University RISE Award, which significantly helped to accomplish this work. Data were provided in part by the Adolescent Brain Cognitive Development SM (ABCD) Study (Jernigan & Brown, 2018), held in the NIMH Data Archive (NDA). This is a multisite, longitudinal study designed to recruit more than 10,000 children aged 9–10 and follow them over 10 years into early adulthood. The ABCD 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 Data Exchange (ABIDE) (Di Martino et al., 2014, 2017) support for the work by Adriana Di Martino provided by the (NIMH K23MH087770) and the Leon Levy Foundation and primary support for the work by Michael P. Milham and the INDI team was provided by gifts from Joseph P. Healy and the Stavros Niarchos Foundation to the Child Mind Institute, as well as by an NIMH award to MPM (NIMH R03MH096321); by the Attention Deficit Hyperactivity Disorder‐200 (ADHD200) Consortium (HD‐200 Consortium, 2012). Consortium steering committee includes Jan Buitelaar, MD, F. Xavier Castellanos, MD, PhD, Daniel Dickstein, PhD, Damien Fair, P.A.‐C., PhD, David Kennedy, PhD, Beatric Luna, PhD, Michael P. Milham (Project Coordinator), MD, PhD, Stewart Mostofsky, MD, Joel Nigg, PhD, Julie B. Schweitzer, PhD, Katerina Velanova, PhD, Yu‐Feng Wang, MD, PhD, Yu‐Feng Zang, MD; by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (Jack Jr. et al., 2008) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH‐12‐2‐0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer's Association; Alzheimer's Drug Discovery 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 Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health. The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California; by the Bipolar & Schizophrenia Consortium for Parsing Intermediate Phenotypes (B‐SNIP) study (Tamminga et al., 2013); by the Brain Genomics Superstruct Project (GSP) (Holmes et al., 2015) of Harvard University and the Massachusetts General Hospital, (Principal Investigators: Randy Buckner, Joshua Roffman, and Jordan Smoller), with support from the Center for Brain Science Neuroinformatics Research Group, the Athinoula A. Martinos Center for Biomedical Imaging, and the Center for Human Genetic Research. 20 individual investigators at Harvard 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 (van Essen et al., 2013), WU‐Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University; by the OASIS (LaMontagne et al., 2019) Longitudinal Multimodal Neuroimaging: Principal Investigators: T. Benzinger, D. Marcus, J. Morris; NIH P50 AG00561, P30 NS09857781, P01 AG026276, P01 AG003991, R01 AG043434, UL1 TR000448, R01 EB009352. AV‐45 doses were provided by Avid Radiopharmaceuticals, a wholly owned subsidiary of Eli Lilly; and using the UK Biobank (Littlejohns et al., 2020) Resource under Application Number 49636.
• Keywords: intrinsic connectivity networks (ICNs); functional templates; independent component analysis (ICA); functional connectivity (FC)
• Research Categories: Biology, Biostatistics; Biology, Neuroscience

Abstract

Despite 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.