We explore various sparse regularization techniques for analyzing fMRI data, such as the ℓ1 norm (often called LASSO in the context of a squared loss function), elastic net, and the recently introduced k-support norm. Employing sparsity regularization allows us to handle the curse of dimensionality, a problem commonly found in fMRI analysis. In this work we consider sparse regularization in both the regression and classification settings.
View Article and Find Full Text PDFDespite the high prevalence and consequences associated with externalizing psychopathologies, little is known about their underlying neurobiological mechanisms. Studying multiple externalizing disorders, each characterized by compromised inhibition, could reveal both common and distinct mechanisms of impairment. The present study therefore compared individuals with intermittent explosive disorder (IED) (N = 11), individuals with cocaine use disorder (CUD) (N = 21), and healthy controls (N = 17) on task performance and functional magnetic resonance imaging (fMRI) activity during an event-related color-word Stroop task; self-reported trait anger expression was also collected in all participants.
View Article and Find Full Text PDFBackground: Gene polymorphisms that affect serotonin signaling modulate reactivity to salient stimuli and risk for emotional disturbances. Here, we hypothesized that these serotonin genes, which have been primarily explored in depressive disorders, could also have important implications for drug addiction, with the potential to reveal important insights into drug symptomatology, severity, and/or possible sequelae such as dysphoria.
Methods: Using an imaging genetics approach, the current study tested in 62 cocaine abusers and 57 healthy controls the separate and combined effects of variations in the serotonin transporter (5-HTTLPR) and monoamine oxidase A (MAOA) genes on processing of aversive information.