A variance components model for statistical inference on functional connectivity networks

• Published in: NeuroImage (Orlando, Fla.) Vol. 149; pp. 256 - 266
• Main Authors: Fiecas, Mark, Cribben, Ivor, Bahktiari, Reyhaneh, Cummine, Jacqueline
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.04.2017; Elsevier Limited
• Subjects: Accounting; Adults; Alzheimer's disease; Brain - physiology; Brain Mapping - methods; Data processing; Dyslexia; Economic models; Equality; Estimates; Female; Functional connectivity networks; Functional magnetic resonance imaging; Humans; Image Processing, Computer-Assisted - methods; Language; Magnetic Resonance Imaging - methods; Male; Mathematical models; Medical imaging; Models, Neurological; Neural Pathways - physiology; Neurological disorders; Reading; Resting-state fMRI; Statistical analysis; Statistical inference; Statistics; Subject heterogeneity; Temporal autocorrelation; Time series; Young Adult; Young adults; Subject heterogeneity; Resting-state fMRI; Temporal autocorrelation; Dyslexia; Functional connectivity networks
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2017.01.051

We propose a variance components linear modeling framework to conduct statistical inference on functional connectivity networks that directly accounts for the temporal autocorrelation inherent in functional magnetic resonance imaging (fMRI) time series data and for the heterogeneity across subjects in the study. The novel method estimates the autocorrelation structure in a nonparametric and subject-specific manner, and estimates the variance due to the heterogeneity using iterative least squares. We apply the new model to a resting-state fMRI study to compare the functional connectivity networks in both typical and reading impaired young adults in order to characterize the resting state networks that are related to reading processes. We also compare the performance of our model to other methods of statistical inference on functional connectivity networks that do not account for the temporal autocorrelation or heterogeneity across the subjects using simulated data, and show that by accounting for these sources of variation and covariation results in more powerful tests for statistical inference. •A new linear model for testing entire functional connectivity networks.•The model accounts for autocorrelation and for variability both within and between subjects.•The test controls for false positives while maintaining high power.