The connectivity domain: Analyzing resting state fMRI data using feature-based data-driven and model-based methods

• Published in: NeuroImage (Orlando, Fla.) Vol. 134; pp. 494 - 507
• Main Authors: Iraji, Armin, Calhoun, Vince D., Wiseman, Natalie M., Davoodi-Bojd, Esmaeil, Avanaki, Mohammad R.N., Haacke, E. Mark, Kou, Zhifeng
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.07.2016; Elsevier Limited
• Subjects: Adult; Brain - anatomy & histology; Brain - physiology; Brain mapping; Connectivity domain; Connectome - methods; Data Interpretation, Statistical; Data processing; Feature-based analysis; Functional magnetic resonance imaging; General linear model (GLM); Grants; Humans; Independent component analysis (ICA); Magnetic Resonance Imaging - methods; Methods; Middle Aged; Model-based method; Multivariate Analysis; Neural networks; Neural Pathways - anatomy & histology; Neural Pathways - physiology; Reproducibility of Results; Resting state functional MRI (rsfMRI); Seeds; Signal Processing, Computer-Assisted; General linear model (GLM); Feature-based analysis; Model-based method; Independent component analysis (ICA); Resting state functional MRI (rsfMRI); Connectivity domain
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2016.04.006

Spontaneous fluctuations of resting state functional MRI (rsfMRI) have been widely used to understand the macro-connectome of the human brain. However, these fluctuations are not synchronized among subjects, which leads to limitations and makes utilization of first-level model-based methods challenging. Considering this limitation of rsfMRI data in the time domain, we propose to transfer the spatiotemporal information of the rsfMRI data to another domain, the connectivity domain, in which each value represents the same effect across subjects. Using a set of seed networks and a connectivity index to calculate the functional connectivity for each seed network, we transform data into the connectivity domain by generating connectivity weights for each subject. Comparison of the two domains using a data-driven method suggests several advantages in analyzing data using data-driven methods in the connectivity domain over the time domain. We also demonstrate the feasibility of applying model-based methods in the connectivity domain, which offers a new pathway for the use of first-level model-based methods on rsfMRI data. The connectivity domain, furthermore, demonstrates a unique opportunity to perform first-level feature-based data-driven and model-based analyses. The connectivity domain can be constructed from any technique that identifies sets of features that are similar across subjects and can greatly help researchers in the study of macro-connectome brain function by enabling us to perform a wide range of model-based and data-driven approaches on rsfMRI data, decreasing susceptibility of analysis techniques to parameters that are not related to brain connectivity information, and evaluating both static and dynamic functional connectivity of the brain from a new perspective.