Table 3: Key papers for application 3.3.

Disentangling latent factors of inter-subject RSFC variation

Approach a: Decomposition
Identifying Sparse Connectivity Patterns in the brain using resting-state fMRI (Eavani et al.,2015)[91] Method: Sparse dictionary learning, Contribution: One of the early works explaining inter-subject RSFC variability in terms of sparse connectivity patterns
Approach b: Non-linear embeddings
Discriminative analysis of resting-state functional connectivity patterns of schizophrenia using low dimensional embedding of fMRI (Shen et al.,2010)[94] Method: LLE, Contribution: Proposed an unsupervised learning approach for discriminating Schizophrenia patients from controls with impressive accuracy
Identification of autism spectrum disorder using deep learning and the ABIDE dataset (Heinsfeld et al.,2018)[97] Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia (Kim et al., 2016)[98] Method: Autoencoders, Contribution: More recent works demonstrating the advantages of autoencoder based dimensionality reduction/pre-training for downstream classification
Approach c: Clustering
Unsupervised classification of major depression using functional connectivity MRI (Zeng et al., 2014)[100] Resting-state connectivity biomarkers define neurophysiological subtypes of depression (Drysdale et al., 2017) [101] Method: Maximum margin clustering/HAC, Contribution: Demonstrated the power of clustering approaches for diagnosing depression and identifying its subtypes based on rs-fMRI manifestations