In addition to reward- and craving-related processes, habitual mechanisms play an important role in addiction. While the dorsal striatum has been proposed to code for the motivational state of habitual drug-seeking actions, the neural underpinnings of the corresponding drug-taking skills and action knowledge remain poorly understood. We used functional magnetic resonance imaging (fMRI) and a behavioral orientation affordance paradigm to investigate the neural and behavioral correlates of automatized drug-taking actions in nicotine dependence. Smokers exhibited higher fMRI activations than nonsmokers when viewing smoking-related but not when viewing control images. These group differences in fMRI activations were located not only in brain regions associated with craving and habitual learning (left ventral and dorsal striatum, dorsolateral prefrontal cortex, insula, uncus, medial frontal gyrus, right subcallosal gyrus, and bilateral parahippocampal gyrus), but also in a network of brain regions which has been strongly implicated in the encoding of action knowledge and tool use skills (bilateral premotor cortex, left superior parietal lobule, and right lateral cerebellum). A behavioral affordance reaction-time task indicated that smokers, but not nonsmokers, showed an automatized responsiveness to smoking paraphernalia similar to everyday objects. Moreover, smokers showed strong intercorrelations between fMRI activations in tool use-related brain regions, behavioral responsiveness to smoking-related cues, and severity of nicotine dependence. Apparently smoking-related action representations in smokers are stored in brain regions typically representing tool use skills and action knowledge. Most importantly, cortical and behavioral correlates of the respective drug-taking skills vary with the individual degree of nicotine dependence.

Background and Objectives To increase the validity of biomarker measures in multiple sclerosis (MS), factors affecting their concentration need to be identified. Here, we test whether the volume of distribution approximated by the patients9 estimated blood volume (BV) and body mass index (BMI) affect the serum concentrations of glial fibrillary acidic protein (GFAP). As a control, we also determine the relationship between BV/BMI and GFAP concentrations in CSF. To confirm earlier findings, we test the same hypotheses for neurofilament light chain (NfL). Methods NfL and GFAP concentrations were measured in serum and CSF (sNFL/sGFAP and cNFL/cGFAP) in 157 patients (n = 106 with MS phenotype and n = 51 with other neurologic/somatoform diseases). Using multivariate linear regressions, BV was tested in the MS cohort as a predictor for each of the biomarkers while controlling for age, sex, MS phenotype, Expanded Disability Status Scale score, gadolinium-enhancing lesions, and acute relapse. In addition, overweight/obese patients (BMI ≥25 kg/m²) were compared with patients with BMI <25 kg/m² using the general linear model. The analyses were repeated including the neurologic/somatoform controls. Results In the MS cohort, BV predicted sGFAP (ß = −0.301, p = 0.014). Overweight/obese patients with MS had lower sGFAP concentrations compared with patients with MS and BMI <25 kg/m² (F = 4.732, p = 0.032). Repeating the analysis after adding patients with other neurologic/somatoform diseases did not change these findings (ß = −0.276, p = 0.009; F = 7.631, p = 0.006). Although sNfL was inversely correlated with BV (r = −0.275, p = 0.006) and body weight (r = −0.258, p = 0.010), those results did not remain significant after adjusting for covariates. BV and BMI were not associated with cGFAP or cNfL concentrations. Discussion These findings support the notion that the volume of distribution of sGFAP approximated by BV and BMI is a relevant variable and should therefore be controlled for when measuring sGFAP in MS, while this might not be necessary when measuring cGFAP concentrations.