… rsFCS features in our behavioral/cognitive predictions were … step in resting-state functional
connectivity analyses (Fox et al., … pattern of rsFC feature importance in GSDT predictions. The …

… FC combined with a machine learning algorithm could help to predict whether FDRs are …
weight for each ROI was also calculated by summing one half of the consensus feature weights …

… a machine learning model based on the PSCI network at 6 … reliably predict long-term (36
months poststroke) cognitive … did the functional connectivity patterns with the greatest weight in …

… as more recently applied deep learning methods. Moreover, … , and cognition during rest with
dynamic connectivity features, … features and ranks the features according to their importance…

… In the present study, we exploited a connectome-based machine learning … functional
connections in model building, we observed comparable prediction accuracies and weight map …

… region’s contribution to cognitive function, we use the occlusion … network (GCN) based
framework to predict subjects’ … can have an effect on the weight distribution of the connected edges…

… machine learning algorithm (kernel regression) in predicting … The weights are the trainable
parameters in FNN. The … a curated set of 13 HCP cognitive measures averaged across 20 test …

… the individual-shared connectivity component of the pth ROI, and is the weight vector that …
accuracy, we used functional connectivity to predict five behavioral dimensions: i) cognition, ii) …

… six different connectivity … features for prediction, through the use of Gradient Attribution Map
and (iv) to compare the DL model's performance, feature robustness and feature importance …

… performance of a machine learning setup. It has been used for … In addition, our highest
prediction result of 99.29% (p < … However, in terms of ranking importance, functional connectivity …