Table 2: Key papers for application 3.2.

Discovering reproducible patterns of dynamic functional connectivity

Approach a: Decomposition
Principal components of functional connectivity: a new approach to study dynamic brain connectivity during rest (Leonardi et al.,2013)[48] Method:PCA , Contribution: Early work characterizing dFC using latent connectivity patterns and suggesting altered connectivity dynamics in disease
Approach b: Clustering
Tracking whole-brain connectivity dynamics in the resting state, (Allen et al.,2014)[42] Method: K-means, Contribution: Provided evidence for recurring FC states and suggested marked departure of dynamic connectivity patterns from static FC
Dynamic functional connectivity analysis reveals transient states of dysconnectivity in schizophrenia (Damaraju 2014)[81] Method: K-means, Contribution: Revealed strong statistical differences in dwell times of multiple FC states between controls and a disease group
Approach c: Markov models
Unsupervised learning of functional network dynamics in resting state fMRI (Eavani 2013)[44] Method: HMM, Contribution: Earliest application of HMMs to study resting-state functional network dynamics
Brain network dynamics are hierarchically organized in time, (Vidaurre et al.,2017)[43] Method: HMM, Contribution: Demonstrated that transitions between FC states occur in a non-random hierarchically organized fashion and revealed that dwell times of FC states are linked with behavioral traits and heredity.