Differentiating the neural and cognitive processes that convey risk for cannabis use is not merely pedantic

The same neural resources supporting top-down regulation of emotion are also used to support non-emotional forms of “top-down cognitive control ”. These non-emotional control processes are also implicated in cannabis use . EA who can engage top-down control systems are able to make more active choices around cannabis use, rather than simply “defaulting to use ”or “following peer norms ”. Yet, emotional and cognitive control are of- ten treated as independent risk factors, ignoring the dynamic interplay between them. In fact, under the widely-accepted assumption of a finite resource pool, when attention is directed to emotional aspects of a stimulus or situation, it is necessarily directed away from other aspects of top- down control . Thus, individual differences in both reactive and regulatory responses to emotional stimuli may directly limit the resources available for top-down cognitive control and impair decision- making in emotion-laden contexts . Understanding how each of these processes is differentially related to cannabis use trajectories is a critical step in clarifying mechanisms of risk.

The bottom-up and top-down systems sub-serving emotional decision-making do not develop linearly throughout childhood, adolescence, EA, and adulthood. These processes rely on maturation of the prefrontal cortex and amygdala-striatal emotional and reward processing regions that undergird perception and valuation of emotions . EA reflects a period of natural, but consequential, imbalances between these systems, when limited top-down control resources may be easily disrupted in emotional situations , inherently leading to potentially more hazardous decisions in and around cannabis use contexts . Existing cannabis grow lights interventions that focus separately on modifying attention biases  or building top-down control  have thus far, only shown modest efficacy in this age group. Clarifying dynamic relationships among the processes that support emotion regulation and their relationships to development of negative emotion and cannabis use over time will be critical to identifying intervention targets and increasing effectiveness of novel interventions for EA en- gaged in cannabis use.Emotion regulation may be a key risk factor that interacts with social inequality to increase hazardous cannabis use in EA.

A growing body of work confirms that reactive attention capture, top-down regulation of emotion, and non-emotional top-down control processes may all be affected by the experience of social inequality . Much of this work has focused on objective social inequality, and particularly SES, with consistent evidence that individuals from lower-SES backgrounds show increased reactive attention capture by negatively-valenced stimuli, more top-down re- sources directed to regulate emotion , as well as differences in the amount of neural resources that are engaged by non-affective top-down control tasks . What emerges is a picture in which EA who experience more objective social inequality tend to direct more neural resources to responding to emotional stimuli than their peers, cannabis grow tent while at the same time requiring more neural resources to effectively make non-emotional decisions.

We note that some of these differences likely reflect adaptive response to environments that consistently stress emotional response systems , but may nonetheless increase risk in other contexts. While very little work has considered subjective social inequality, there is emerging evidence that experiences such as perceived discrimination are related to differences in both central  and peripheral  nervous system measures of emotional regulation as well.Further, the implications of these differences in emotion regulation may vary depending on social inequality context. In lab-based studies, differences in neural response are often found in the absence of performance differences on low-stakes tasks.however, there may be more significant effects on higher-stakes, real-world outcomes. For example, disengagement from negative emotion predicts greater negative affect over time, and this association is amplified in youth from low SES back grounds.

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