Table 4:

Key papers for various supervised learning application domains

Brain development and aging
Prediction of individual brain maturity using fMRI (Dosenbach et al.,2010)[120] Method: SVM, Target: Age, Contribution: Early influential work demonstrating the feasibility of using RSFC features for predicting brain maturation.
Neurological and Psychiatric Disorders
Classification of Alzheimer disease, mild cognitive impairment, and normal cognitive status with large-scale network analysis based on resting-state functional MR imaging. (Chen et al.,2011)[24] Method: Fisher LDA, Target: Alzheimer/MCI/controls, Contribution: Early work highlighting the potential of RSFC to diagnose neurological disorders
Deriving reproducible biomarkers from multi-site resting-state data: An Autism-based example(Abraham et al.,2017)[66] Method: Multiple, Target: ASD/controls, Contribution: Extensively evaluated the impact of ROI choice, connectivity metric and classifier on prediction performance in intra-site and inter-site settings
Altered resting state complexity in schizophrenia (Bassett et al.,2012)[132] Method: SVM, Target: Schizophrenia/controls, Contribution: Demonstrated the utility of resting-state network complexity measures in distinguishing patients with schizophrenia
Cognitive abilities and personality traits
Functional connectome fingerprinting: Identifying individuals using patterns of brain connectivity (Finn et al.,2015)[46] Method: Linear regression, Target: Fluid intelligence, Contribution: Demonstrated that RSFC can uniquely identify individuals and reliably predict fluid intelligence
Disruptions of network connectivity predict impairment in multiple behavioral domains after stroke (Siegel et al., 2016) [142] Method: Ridge regression, Target: Multiple cognitive measures , Contribution: Demonstrated the ability of ML coupled with RSFC to predict cognitive deficits in clinical populations
Vigilance fluctuations and sleep studies
Automatic sleep staging using fMRI functional connectivity data (Tagliazucchi et al.,2012) [147] Decoding wakefulness levels from typical fMRI resting-state data reveals reliable drifts between wakefulness and sleep (Tagliazucchi et al.,2014) [148] Method: SVM, Target: NREM sleep stages/wakefulness, Contribution: Demonstrated the ability of ML to detect sleep stages in resting-state
Heritability
Heritability of the human connectome: A connectotyping study (Miranda-Dominguez et al.,2018) [153] Method: SVM, Target: Twins/sibling/unrelated, Contribution: Provided evidence for relationship between genetics and RSFC through predictive modelling
Other neuroimaging modalities
Task-free MRI predicts individual differences in brain activity during task performance (Tavor et al.,2016) [154] Method: Multiple regression models, Target: Task-activation map, Contribution: Demonstrated that resting-state can capture the rich repertoire of cognitive states expressed during different behavioral tasks