Connectome-based predictive modeling of attention: Comparingdifferent functional connectivity features and prediction methods acrossdatasets

摘要

Connectome-based predictive modeling (CPM; ; href="#R53" rid="R53" class=" bibr popnode tag_hotlink tag_tooltip" id="__tag_672739168">Shen et al., 2017) was recently developed to predict individual differences in traits and behaviors, including fluid intelligence () and sustained attention (), from functional brain connectivity (FC) measured with fMRI. Here, using the CPM framework, we compared the predictive power of three different measures of FC (Pearson’s correlation, accordance, and discordance) and two different prediction algorithms (linear and partial least square [PLS] regression) for attention function. Accordance and discordance are recently proposed FC measures that respectively track in-phase synchronization and out-of-phase anti-correlation (href="#R41" rid="R41" class=" bibr popnode tag_hotlink tag_tooltip" id="__tag_672739207">Meskaldji et al., 2016). We defined connectome-based models using task-based or resting-state FC data, and tested the effects of (1) functional connectivity measure and (2) feature-selection/prediction algorithm on individualized attention predictions. Models were internally validated in a training dataset using leave-one-subject-out cross-validation, and externally validated with three independent datasets. The training dataset included fMRI data collected while participants performed a sustained attention task and rested (N=25; ). The validationdatasets included: 1) data collected during performance of a stop-signal taskand at rest (N=83, including 19 participants who were administeredmethylphenidate prior to scanning; href="#R50" rid="R50" class=" bibr popnode tag_hotlink tag_tooltip" id="__tag_678063291">Rosenberg etal., 2016b; f al., 2014a), 2) data collected during Attention NetworkTask performance and rest (N=41, Rosenberg et al., in press), and 3)resting-state data and ADHD symptom severity from the ADHD-200 Consortium(N=113; ). Modelsdefined using all combinations of functional connectivity measure(Pearson’s correlation, accordance, and discordance) and predictionalgorithm (linear and PLS regression) predicted attentional abilities, withcorrelations between predicted and observed measures of attention as high as 0.9for internal validation, and 0.6 for external validation (all p’s< 0.05). Models trained on task data outperformed models trained on restdata. Pearson’s correlation and accordance features generally showed asmall numerical advantage over discordance features, while PLS regression modelswere usually better than linear regression models. Overall, in addition tocorrelation features combined with linear models (), it is useful to consideraccordance features and PLS regression for CPM.