Erik B. Erhardt

TL;DR: In this article, the authors describe an approach to assess whole-brain functional connectivity dynamics based on spatial independent component analysis, sliding time window correlation, and k-means clustering of windowed correlation matrices.

TL;DR: A multivariate analytic approach that optimizes sensitivity and reduces unnecessary testing is introduced and is demonstrated by identifying the effects of age and gender on the resting-state networks of 603 healthy adolescents and adults.

TL;DR: Comparisons of subject‐specific, spatial concatenation, and group data mean subject‐level reduction strategies using PCA and probabilistic PCA (PPCA) show that computationally intensive PPCA is equivalent to PCA, and that subject‐ specific and group Data mean subject-level PCA are preferred because of well‐estimated TCs and SMs.

TL;DR: Simulated functional magnetic resonance imaging data is used to determine the capabilities and limitations of group independent component analysis under conditions of spatial, temporal, and amplitude variability and makes a number of recommendations regarding analytic choices for application to functional imaging data.

TL;DR: A new framework for accessing dynamic graph properties of time-varying functional brain connectivity in resting-state fMRI data is developed and applied to healthy controls and patients with schizophrenia, indicating that SZs show decreased variance in the dynamic graph metrics.