… We also include suggestions for visualizing the most predictive features (ie, brain … protocol
for developing predictive models of brain–behavior relationships from connectivity data using …

… MCI, but in the current study the discordance-based predictive model did not outperform the
other two connectivity measures in predicting attention. Instead, accordance and Pearson's …

… predictive modeling (CPM) on each task connectome independently, CPM on a general
functional connectivity … information for prediction, we adopted forward feature selection to select …

… predictive modeling to brain connectivity data. These rules explain common issues aimed
at both novice and experienced users of predictive models … feature of predictive modeling—…

… The goal of this study is to evaluate the test-retest reliability of resting-state functional
connectivity feature weights estimated by predictive models of intelligence and cognitive function. …

… We extract the lower triangular part of the connectivity matrix as a set of features for the
prediction task [3]. We consider the Linear Support Vector Classifier (SVC) with l1 and l2 …

… the ability of functional connectivity during frustrative … predictive modeling, a machine
learning approach, with tenfold cross-validation was conducted to identify networks predicting …

… predictive modeling, scholars have begun to characterize patterns of brain activity and
connectivity … The test-retest reliability of predictive model feature weights could be assessed by …

… -based predictive modeling approach to estimating individualized scores from multiple
cognitive domains using structural connectivity (SC) and functional connectivity (FC) features. …

… Therefore, we propose to evaluate three critical properties for well-behaved FC-based
predictive models: (a) stability of feature selection in a test/retest setting, (b) specificity of edge …