Many neuroimaging studies collect functional magnetic resonance imaging (fMRI) data in a longitudinal manner. However, the current fMRI literature lacks a general framework for analyzing functional connectivity (FC) networks in fMRI data obtained from a longitudinal study. In this work, we build a novel longitudinal FC model using a variance components approach. First, for all subjects’ visits, we account for the autocorrelation inherent in the fMRI time series data using a non-parametric technique. Second, we use a generalized least squares approach to estimate 1) the within-subject variance component shared across the population, 2) the baseline FC strength, and 3) the FC's longitudinal trend. Our novel method for longitudinal FC networks seeks to account for the within-subject dependence across multiple visits, the variability due to the subjects being sampled from a population, and the autocorrelation present in fMRI time series data, while restricting the number of parameters in order to make the method computationally feasible and stable. We develop a permutation testing procedure to draw valid inference on group differences in the baseline FC network and change in FC over longitudinal time between a set of patients and a comparable set of controls. To examine performance, we run a series of simulations and apply the model to longitudinal fMRI data collected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Overall, we found no difference in the global FC network between Alzheimer's disease patients and healthy controls, but did find differing local aging patterns in the FC between the left hippocampus and the posterior cingulate cortex.
Bibliographical noteFunding Information:
Ivor Cribben was supported by the Natural Sciences and Engineering Research Council (NSERC; grant number 39117-2018 ) of Canada. Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) ( National Institutes of Health Grant U19 AG024904 ) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012 ). ADNI is funded by the National Institute on Aging , the National Institute of Biomedical Imaging and Bioengineering , and through generous contributions from the following: AbbVie , Alzheimer's Association ; Alzheimers Drug Discovery Foundation ; Araclon Biotech ; BioClinica, Inc .; Biogen ; Bristol-Myers Squibb Company ; CereSpir, Inc .; Cogstate ; Eisai Inc .; Elan Pharmaceuticals, Inc .; Eli Lilly and Company ; EuroImmun ; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc .; Fujirebio ; GE Healthcare ; IXICO Ltd .; Janssen Alzheimer Immunotherapy Research & Development, LLC .; Johnson & Johnson Pharmaceutical Research & Development LLC .; Lumosity ; Lundbeck ; Merck & Co., Inc .; Meso Scale Diagnostics, LLC .; NeuroRx Research ; Neurotrack Technologies ; Novartis Pharmaceuticals Corporation ; Pfizer Inc .; Piramal Imaging ; Servier ; Takeda Pharmaceutical Company ; and Transition Therapeutics . The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health ( www.fnih.org ). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimers Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.
- Functional connectivity
- Temporal autocorrelation