A Longitudinal Model for Functional Connectivity Using Resting-State fMRINeuroImage (2018)
Many neuroimaging studies collect functional magnetic resonance imaging (fMRI) data in a longitudinal manner. Repeated measures data calls for a longitudinal model which properly accounts for the natural correlation present in the data. In this work, we build a longitudinal functional connectivity 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 the within-subject variance component shared across the population, the connectivity network, and the connectivity network's longitudinal trend. Our novel method 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 data, while restricting the number of parameters in order to make the method computationally feasible and stable. We utilize a permutation testing procedure to draw valid inference on group differences in baseline connectivity and change in connectivity over 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.
Software available here: https://github.com/mfiecas/longitudinalFC
- functional connectivity,
- temporal autocorrelation
Citation InformationHart, B., Cribben, I., Fiecas, M., & Alzheimer's Disease Neuroimaging Initiative. (2018). A longitudinal model for functional connectivity networks using resting-state fMRI. NeuroImage, 178, 687-701. doi.org/10.1016/j.neuroimage.2018.05.071