The results showed that spatiotemporally focused TMS may enhance extinction learning and/or consolidation of extinction memory and suggested novel cortical areas and large-scale networks for targeting in future studies.

Increased synchrony within neuroanatomical networks is often observed in neurophysiologic studies of human brain disease. Most often, this phenomenon is ascribed to a compensatory process in the face of injury, though evidence supporting such accounts is limited. Given the known dependence of resting-state functional connectivity (rsFC) on underlying structural connectivity (SC), we examine an alternative hypothesis: that topographical changes in SC, specifically particular patterns of disconnection, contribute to increased network rsFC. We obtain measures of rsFC using fMRI and SC using probabilistic tractography in 50 healthy and 28 multiple sclerosis subjects. Using a computational model of neuronal dynamics, we simulate BOLD using healthy subject SC to couple regions. We find that altering the model by introducing structural disconnection patterns observed in those multiple sclerosis subjects with high network rsFC generates simulations with high rsFC as well, suggesting that disconnection itself plays a role in producing high network functional connectivity. We then examine SC data in individuals. In multiple sclerosis subjects with high network rsFC, we find a preferential disconnection between the relevant network and wider system. We examine the significance of such network isolation by introducing random disconnection into the model. As observed empirically, simulated network rsFC increases with removal of connections bridging a community with the remainder of the brain. We thus show that structural disconnection known to occur in multiple sclerosis contributes to network rsFC changes in multiple sclerosis and further that community isolation is responsible for elevated network functional connectivity.

Learning associations between sensory stimuli and outcomes, and generalizing these associations to novel stimuli, are a fundamental feature of adaptive behavior. Given a noisy olfactory world, stimulus generalization holds unique relevance for the olfactory system. Recent studies suggest that aversive outcomes induce wider generalization curves by modulating discrimination thresholds, but evidence for similar processes in olfaction does not exist. Here, we use a novel olfactory discrimination learning paradigm to address the question of how outcome valence impacts associative learning and generalization in humans. Subjects underwent discrimination learning, where they learned to associate odor mixtures with either aversive (shock) or neutral (air puff) outcomes. We find better olfactory learning for odors associated with aversive compared to neutral outcomes. We further show that generalization gradients are also modulated by outcome valence, with the shock group exhibiting a steeper gradient. Computational modeling revealed that differences in generalization are driven by a narrower excitatory gradient in the shock group, indicating more discriminatory responses. These findings provide novel evidence that olfactory learning and generalization are strongly affected by the valence of outcomes. This adaptive mechanism allows for behavioral flexibility in novel situations with related stimuli and with outcomes of different valences. Because odor stimuli differ considerably from one encounter to the next, adaptive generalization may be especially important in the olfactory system.