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Substance use disorders are prevalent, causing extensive morbidity and mortality worldwide. Evidence-based treatments are of low to moderate effect size. Growth in the neurobiological understanding of addiction (e.g., craving) along with technological advancements in neuromodulation have enabled an evaluation of neurosurgical treatments for substance use disorders. Deep brain stimulation (DBS) involves surgical implantation of leads into brain targets and subcutaneous tunneling to connect the leads to a programmable implanted pulse generator (IPG) under the skin of the chest. DBS allows direct testing of neurobiologically-guided hypotheses regarding the etiology of substance use disorders in service of developing more effective treatments. Early studies, although with multiple limitations, have been promising. Still the authors express caution regarding implementation of DBS studies in this population and emphasize the importance of safeguards to ensure patient safety and meaningful study results. In this perspectives article, we review lessons learned through the years of planning an ongoing trial of DBS for methamphetamine use disorder.

Identification of the majority of organisms present in human-associated microbial communities is feasible with the advent of high throughput sequencing technology. As substantial variability in microbiota communities is seen across subjects, the use of longitudinal study designs is important to better understand variation of the microbiome within individual subjects. Complex study designs with longitudinal sample collection require analytic approaches to account for this additional source of variability. A common approach to assessing community changes is to evaluate the change in alpha diversity (the variety and abundance of organisms in a community) over time. However, there are several commonly used alpha diversity measures and the use of different measures can result in different estimates of magnitude of change and different inferences. It has recently been proposed that diversity profile curves are useful for clarifying these differences, and may provide a more complete picture of the community structure. However, it is unclear how to utilize these curves when interest is in evaluating changes in community structure over time. We propose the use of a bi-exponential function in a longitudinal model that accounts for repeated measures on each subject to compare diversity profiles over time. Furthermore, it is possible that no change in alpha diversity (single community/sample) may be observed despite the presence of a highly divergent community composition. Thus, it is also important to use a beta diversity measure (similarity between multiple communities/samples) that captures changes in community composition. Ecological methods developed to evaluate temporal turnover have currently only been applied to investigate changes of a single community over time. We illustrate the extension of this approach to multiple communities of interest (i.e., subjects) by modeling the beta diversity measure over time. With this approach, a rate of change in community composition is estimated. There is a need for the extension and development of analytic methods for longitudinal microbiota studies. In this paper, we discuss different approaches to model alpha and beta diversity indices in longitudinal microbiota studies and provide both a review of current approaches and a proposal for new methods.