RT Journal Article
SR Electronic
T1 Multi-model Order ICA: A Data-driven Method for Evaluating Brain Functional Network Connectivity Within and Between Multiple Spatial Scales
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.10.24.465635
DO 10.1101/2021.10.24.465635
A1 Xing Meng
A1 Armin Iraji
A1 Zening Fu
A1 Peter Kochunov
A1 Aysenil Belger
A1 Judy M. Ford
A1 Sara McEwen
A1 Daniel H. Mathalon
A1 Bryon A. Mueller
A1 Godfrey Pearlson
A1 Steven G. Potkin
A1 Adrian Preda
A1 Jessica Turner
A1 Theo G.M. van Erp
A1 Jing Sui
A1 Vince D. Calhoun
YR 2021
UL http://biorxiv.org/content/early/2021/10/27/2021.10.24.465635.abstract
AB Background While functional connectivity is widely studied, there has been little work studying functional connectivity at different spatial scales. Likewise, the relationship of functional connectivity between spatial scales is unknown.Methods We proposed an independent component analysis (ICA) - based approach to capture information at multiple model orders (component numbers) and to evaluate functional network connectivity (FNC) both within and between model orders. We evaluated the approach by studying group differences in the context of a study of resting fMRI (rsfMRI) data collected from schizophrenia (SZ) individuals and healthy controls (HC). The predictive ability of FNC at multiple spatial scales was assessed using support vector machine (SVM)-based classification.Results In addition to consistent predictive patterns at both multiple-model orders and single model orders, unique predictive information was seen at multiple-model orders and in the interaction between model orders. We observed that the FNC between model order 25 and 50 maintained the highest predictive information between HC and SZ. Results highlighted the predictive ability of the somatomotor and visual domains both within and between model orders compared to other functional domains. Also, subcortical-somatomotor, temporal-somatomotor, and temporal-subcortical FNCs had relatively high weights in predicting SZ.Conclusions In sum, multi-model order ICA provides a more comprehensive way to study FNC, produces meaningful and interesting results which are applicable to future studies. We shared the spatial templates from this work at different model orders to provide a reference for the community, which can be leveraged in regression-based or fully automated (spatially constrained) ICA approaches.Impact Statement Multi-model order ICA provides a comprehensive way to study brain functional network connectivity within and between multiple spatial scales, highlighting findings that would have been ignored in single model order analysis. This work expands upon and adds to the relatively new literature on resting fMRI-based classification and prediction. Results highlighted the differentiating power of specific intrinsic connectivity networks on classifying brain disorders of schizophrenia patients and healthy participants, at different spatial scales. The spatial templates from this work provide a reference for the community, which can be leveraged in regression-based or fully automated ICA approaches.Competing Interest StatementThe authors have declared no competing interest.