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

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 rsf...

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Published inHuman brain mapping Vol. 44; no. 17; pp. 5729 - 5748
Main Authors Iraji, A., Fu, Z., Faghiri, A., Duda, M., Chen, J., Rachakonda, S., DeRamus, T., Kochunov, P., Adhikari, B. M., Belger, A., Ford, J. M., Mathalon, D. H., Pearlson, G. D., Potkin, S. G., Preda, A., Turner, J. A., van Erp, T. G. M., Bustillo, J. R., Yang, K., Ishizuka, K., Faria, A., Sawa, A., Hutchison, K., Osuch, E. A., Theberge, J., Abbott, C., Mueller, B. A., Zhi, D., Zhuo, C., Liu, S., Xu, Y., Salman, M., Liu, J., Du, Y., Sui, J., Adali, T., Calhoun, V. D.
Format Journal Article
LanguageEnglish
Published United States John Wiley & Sons, Inc 01.12.2023
Subjects
Brain - diagnostic imaging
Brain mapping
Brain Mapping - methods
Datasets
Feasibility studies
Functional magnetic resonance imaging
Humans
Independent component analysis
Magnetic Resonance Imaging - methods
Medical imaging
Nerve Net - diagnostic imaging
Neural networks
Neuroimaging
Reproducibility of Results
Similarity
Smoothness
Spatial data
Spatial discrimination
Spatial resolution
functional connectivity (FC)
functional templates
independent component analysis (ICA)
intrinsic connectivity networks (ICNs)
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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.
AbstractList 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.
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.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.
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. Multi‐spatial‐scale intrinsic connectivity networks (ICNs) obtained using rsfMRI data from over 100k individuals across private and public datasets.
Author Bustillo, J. R.
Sawa, A.
Adhikari, B. M.
Ishizuka, K.
Abbott, C.
Kochunov, P.
Belger, A.
Mueller, B. A.
Potkin, S. G.
Fu, Z.
Preda, A.
Mathalon, D. H.
Du, Y.
Faghiri, A.
Sui, J.
Calhoun, V. D.
Liu, J.
Yang, K.
DeRamus, T.
Osuch, E. A.
Pearlson, G. D.
Xu, Y.
Ford, J. M.
Liu, S.
Zhuo, C.
Rachakonda, S.
Adali, T.
Chen, J.
Faria, A.
Theberge, J.
Duda, M.
van Erp, T. G. M.
Iraji, A.
Turner, J. A.
Zhi, D.
Salman, M.
Hutchison, K.
AuthorAffiliation 8 Department of Psychiatry and Human Behavior University of California Irvine Irvine California USA
10 Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior University of California Irvine Irvine California USA
16 Department of Psychiatry, Schulich School of Medicine and Dentistry London Health Sciences Centre, Lawson Health Research Institute London Canada
4 Department of Psychiatry University of North Carolina Chapel Hill North Carolina USA
1 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
7 Departments of Psychiatry and Neuroscience, School of Medicine Yale University New Haven Connecticut USA
23 School of Computer and Information Technology Shanxi University Taiyuan China
5 Department of Psychiatry University of California San Francisco San Francisco California USA
3 Maryland Psychiatric Research Center, Depa
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/37787573$$D View this record in MEDLINE/PubMed
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Keywords functional connectivity (FC)
functional templates
independent component analysis (ICA)
intrinsic connectivity networks (ICNs)
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Snippet Despite the known benefits of data‐driven approaches, the lack of approaches for identifying functional neuroimaging patterns that capture both individual...
Despite the known benefits of data-driven approaches, the lack of approaches for identifying functional neuroimaging patterns that capture both individual...
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SubjectTerms Brain - diagnostic imaging
Brain mapping
Brain Mapping - methods
Datasets
Feasibility studies
Functional magnetic resonance imaging
Humans
Independent component analysis
Magnetic Resonance Imaging - methods
Medical imaging
Nerve Net - diagnostic imaging
Neural networks
Neuroimaging
Reproducibility of Results
Similarity
Smoothness
Spatial data
Spatial discrimination
Spatial resolution
Title Identifying canonical and replicable multi‐scale intrinsic connectivity networks in 100k+ resting‐state fMRI datasets
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