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

• Published in: Human 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
• Language: English
• 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)

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...