The insula plays a critical role in maintaining nicotine dependence and reactivity to smoking cues. More broadly, the insula and the dorsal anterior cingulate cortex (dACC) are key nodes of the salience network (SN), which integrates internal and extrapersonal information to guide behavior. Thus, insula-dACC interactions may be integral in processing salient information such as smoking cues that facilitate continued nicotine use. We evaluated functional magnetic resonance imaging (fMRI) data from nicotine-dependent participants during rest, and again when they viewed smoking-related images. Greater insula-dACC coupling at rest was significantly correlated with enhanced smoking cue-reactivity in brain areas associated with attention and motor preparation, including the visual cortex, right ventral lateral prefrontal cortex, and the dorsal striatum. In an independent cohort, we found that insula-dACC connectivity was stable over 1-h delay and was not influenced by changes in subjective craving or expired carbon monoxide, suggesting that connectivity strength between these regions may be a trait associated with heightened cue-reactivity. Finally, we also showed that insula reactivity to smoking cues correlates with a rise in cue-reactivity throughout the entire SN, indicating that the insula's role in smoking cue-reactivity is not functionally independent, and may actually represent the engagement of the entire SN. Collectively, these data provide a more network-level understanding of the insula's role in nicotine dependence and shows a relationship between inherent brain organization and smoking cue-reactivity.

There is mounting literature that examines brain activation during tasks of working memory in individuals with neurological disorders such as traumatic brain injury. These studies represent a foundation for understanding the functional brain changes that occur after moderate and severe traumatic brain injury, but the focus on topographical brain-'activation' differences ignores potential alterations in how nodes communicate within a distributed neural network. The present study makes use of the most recently developed connectivity modelling (extended-unified structural equation model) to examine performance during a well-established working-memory task (the n-back) in individuals sustaining moderate and severe traumatic brain injury. The goal is to use the findings observed in topographical activation analysis as the basis for second-level effective connectivity modelling. Findings reveal important between-group differences in within-hemisphere connectivity during task acquisition, with the control sample demonstrating rapid within-left hemisphere connectivity increases and the traumatic brain injury sample demonstrating consistently elevated within-right hemisphere connectivity. These findings also point to important maturational effects from 'early' to 'late' during task performance, including diminished right prefrontal cortex involvement and an anterior to posterior shift in connectivity with increased task exposure. We anticipate that this approach to functional imaging data analysis represents an important future direction for understanding how neural plasticity is expressed in brain disorders.

Resting-state analyses evaluating large-scale brain networks have largely focused on static correlations in brain activity over extended time periods, however emerging approaches capture time-varying or dynamic patterns of transient functional networks. In light of these new approaches, there is a need to classify common transient network states (TNS) in terms of their spatial and dynamic properties. To fill this gap, two independent resting state scans collected in 462 healthy adults from the Human Connectome Project were evaluated using coactivation pattern analysis to identify (eight) TNS that recurred across participants and over time. These TNS spatially overlapped with prototypical resting state networks, but also diverged in notable ways. In particular, analyses revealed three TNS that shared cortical midline overlap with the default mode network (DMN), but these "complex" DMN states also encompassed distinct regions that fall beyond the prototypical DMN, suggesting that the DMN defined using static methods may represent the average of distinct complex-DMN states. Of note, dwell time was higher in "complex" DMN states, challenging the idea that the prototypical DMN, as a single unit, is the dominant resting-state network as typically defined by static resting state methods. In comparing the two resting state scans, we also found high reliability in the spatial organization and dynamic activities of network states involving DMN or sensorimotor regions. Future work will determine whether these TNS defined by coactivation patterns are in other samples, and are linked to fundamental cognitive properties.