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Iraji A, Fu Z, Faghiri A, Duda M, Chen J, Rachakonda S, DeRamus T, Kochunov P, Adhikari BM, Belger A, Ford JM, Mathalon DH, Pearlson GD, Potkin SG, Preda A, Turner JA, van Erp TGM, Bustillo JR, Yang K, Ishizuka K, Faria A, Sawa A, Hutchison K, Osuch EA, Theberge J, Abbott C, Mueller BA, Zhi D, Zhuo C, Liu S, Xu Y, Salman M, Liu J, Du Y, Sui J, Adali T, Calhoun VD. Identifying canonical and replicable multi-scale intrinsic connectivity networks in 100k+ resting-state fMRI datasets. Hum Brain Mapp 2023; 44:5729-5748. [PMID: 37787573 PMCID: PMC10619392 DOI: 10.1002/hbm.26472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 04/30/2023] [Accepted: 06/19/2023] [Indexed: 10/04/2023] Open
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.
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Affiliation(s)
- A. Iraji
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State UniversityGeorgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
- Department of Computer ScienceGeorgia State UniversityAtlantaGeorgiaUSA
| | - Z. Fu
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State UniversityGeorgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
| | - A. Faghiri
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State UniversityGeorgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
| | - M. Duda
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State UniversityGeorgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
| | - J. Chen
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State UniversityGeorgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
| | - S. Rachakonda
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State UniversityGeorgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
| | - T. DeRamus
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State UniversityGeorgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
| | - P. Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, School of MedicineUniversity of MarylandBaltimoreMarylandUSA
| | - B. M. Adhikari
- Maryland Psychiatric Research Center, Department of Psychiatry, School of MedicineUniversity of MarylandBaltimoreMarylandUSA
| | - A. Belger
- Department of PsychiatryUniversity of North CarolinaChapel HillNorth CarolinaUSA
| | - J. M. Ford
- Department of PsychiatryUniversity of California San FranciscoSan FranciscoCaliforniaUSA
- San Francisco VA Medical CenterSan FranciscoCaliforniaUSA
| | - D. H. Mathalon
- Department of PsychiatryUniversity of California San FranciscoSan FranciscoCaliforniaUSA
- San Francisco VA Medical CenterSan FranciscoCaliforniaUSA
| | - G. D. Pearlson
- Departments of Psychiatry and Neuroscience, School of MedicineYale UniversityNew HavenConnecticutUSA
| | - S. G. Potkin
- Department of Psychiatry and Human BehaviorUniversity of California IrvineIrvineCaliforniaUSA
| | - A. Preda
- Department of Psychiatry and Human BehaviorUniversity of California IrvineIrvineCaliforniaUSA
| | - J. A. Turner
- Department of Psychiatry and Behavioral HealthOhio State University Medical Center in ColumbusColumbusOhioUSA
| | - T. G. M. van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human BehaviorUniversity of California IrvineIrvineCaliforniaUSA
| | - J. R. Bustillo
- Department of Psychiatry and Behavioral SciencesUniversity of New MexicoAlbuquerqueNew MexicoUSA
| | - K. Yang
- Department of Psychiatry, School of MedicineJohns Hopkins UniversityBaltimoreMarylandUSA
| | - K. Ishizuka
- Department of Psychiatry, School of MedicineJohns Hopkins UniversityBaltimoreMarylandUSA
| | - A. Faria
- Department of Psychiatry, School of MedicineJohns Hopkins UniversityBaltimoreMarylandUSA
| | - A. Sawa
- Departments of Psychiatry, Neuroscience, Biomedical Engineering, Pharmacology, and Genetic MedicineJohns Hopkins University School of MedicineBaltimoreMarylandUSA
- Department of Mental HealthJohns Hopkins University Bloomberg School of Public HealthBaltimoreMarylandUSA
| | - K. Hutchison
- Department of PsychologyUniversity of ColoradoBoulderColoradoUSA
| | - E. A. Osuch
- Department of Psychiatry, Schulich School of Medicine and DentistryLondon Health Sciences Centre, Lawson Health Research InstituteLondonCanada
| | - J. Theberge
- Department of Psychiatry, Schulich School of Medicine and DentistryLondon Health Sciences Centre, Lawson Health Research InstituteLondonCanada
| | - C. Abbott
- Department of Psychiatry (CCA)University of New MexicoAlbuquerqueNew MexicoUSA
| | - B. A. Mueller
- Department of PsychiatryUniversity of MinnesotaMinneapolisMinnesotaUSA
| | - D. Zhi
- The State Key Lab of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijingChina
| | - C. Zhuo
- Tianjin Mental Health CenterNankai University Affiliated Anding HospitalTianjinChina
| | - S. Liu
- The Department of PsychiatryFirst Clinical Medical College/First Hospital of Shanxi Medical UniversityTaiyuanChina
| | - Y. Xu
- The Department of PsychiatryFirst Clinical Medical College/First Hospital of Shanxi Medical UniversityTaiyuanChina
| | - M. Salman
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State UniversityGeorgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
- School of Electrical & Computer EngineeringGeorgia Institute of TechnologyAtlantaGeorgiaUSA
| | - J. Liu
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State UniversityGeorgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
- Department of Computer ScienceGeorgia State UniversityAtlantaGeorgiaUSA
| | - Y. Du
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State UniversityGeorgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
- School of Computer and Information TechnologyShanxi UniversityTaiyuanChina
| | - J. Sui
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State UniversityGeorgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
- The State Key Lab of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijingChina
| | - T. Adali
- Department of CSEEUniversity of Maryland Baltimore CountyBaltimoreMarylandUSA
| | - V. D. Calhoun
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State UniversityGeorgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
- Department of Computer ScienceGeorgia State UniversityAtlantaGeorgiaUSA
- Department of Psychiatry, School of MedicineJohns Hopkins UniversityBaltimoreMarylandUSA
- School of Electrical & Computer EngineeringGeorgia Institute of TechnologyAtlantaGeorgiaUSA
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Duda M, Iraji A, Ford JM, Lim KO, Mathalon DH, Mueller BA, Potkin SG, Preda A, Van Erp TGM, Calhoun VD. Reliability and clinical utility of spatially constrained estimates of intrinsic functional networks from very short fMRI scans. Hum Brain Mapp 2023; 44:2620-2635. [PMID: 36840728 PMCID: PMC10028646 DOI: 10.1002/hbm.26234] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 01/31/2023] [Accepted: 02/01/2023] [Indexed: 02/26/2023] Open
Abstract
Resting-state functional network connectivity (rsFNC) has shown utility for identifying characteristic functional brain patterns in individuals with psychiatric and mood disorders, providing a promising avenue for biomarker development. However, several factors have precluded widespread clinical adoption of rsFNC diagnostics, namely a lack of standardized approaches for capturing comparable and reproducible imaging markers across individuals, as well as the disagreement on the amount of data required to robustly detect intrinsic connectivity networks (ICNs) and diagnostically relevant patterns of rsFNC at the individual subject level. Recently, spatially constrained independent component analysis (scICA) has been proposed as an automated method for extracting ICNs standardized to a chosen network template while still preserving individual variation. Leveraging the scICA methodology, which solves the former challenge of standardized neuroimaging markers, we investigate the latter challenge of identifying a minimally sufficient data length for clinical applications of resting-state fMRI (rsfMRI). Using a dataset containing rsfMRI scans of individuals with schizophrenia and controls (M = 310) as well as simulated rsfMRI, we evaluated the robustness of ICN and rsFNC estimates at both the subject- and group-level, as well as the performance of diagnostic classification, with respect to the length of the rsfMRI time course. We found individual estimates of ICNs and rsFNC from the full-length (5 min) reference time course were sufficiently approximated with just 3-3.5 min of data (r = 0.85, 0.88, respectively), and significant differences in group-average rsFNC could be sufficiently approximated with even less data, just 2 min (r = 0.86). These results from the shorter clinical data were largely consistent with the results from validation experiments using longer time series from both simulated (30 min) and real-world (14 min) datasets, in which estimates of subject-level FNC were reliably estimated with 3-5 min of data. Moreover, in the real-world data we found rsFNC and ICN estimates generated across the full range of data lengths (0.5-14 min) more reliably matched those generated from the first 5 min of scan time than those generated from the last 5 min, suggesting increased influence of "late scan" noise factors such as fatigue or drowsiness may limit the reliability of FNC from data collected after 10+ min of scan time, further supporting the notion of shorter scans. Lastly, a diagnostic classification model trained on just 2 min of data retained 97%-98% classification accuracy relative to that of the full-length reference model. Our results suggest that, when decomposed with scICA, rsfMRI scans of just 2-5 min show good clinical utility without significant loss of individual FNC information of longer scan lengths.
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Affiliation(s)
- Marlena Duda
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA
| | - Armin Iraji
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA
| | - Judith M Ford
- Mental Health Service, San Francisco Veterans Affairs Healthcare System, San Francisco, California, USA
- Department of Psychiatry and Weill Institute for Neurosciences, University of California San Francisco, San Francisco, California, USA
| | - Kelvin O Lim
- Department of Psychiatry, University of Minnesota, Minneapolis, Minnesota, USA
| | - Daniel H Mathalon
- Mental Health Service, San Francisco Veterans Affairs Healthcare System, San Francisco, California, USA
- Department of Psychiatry and Weill Institute for Neurosciences, University of California San Francisco, San Francisco, California, USA
| | - Bryon A Mueller
- Department of Psychiatry, University of Minnesota, Minneapolis, Minnesota, USA
| | - Steven G Potkin
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, California, USA
| | - Adrian Preda
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, California, USA
| | - Theo G M Van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, California, USA
- Center for the Neurobiology of Learning and Memory, University of California Irvine, Irvine, California, USA
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA
- Department of Psychology, Georgia State University, Atlanta, Georgia, USA
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