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1. Gene coexpression patterns predict opiate-induced brain-state transitions

   https://doi.org/10.1073/pnas.2003601117  

   Brynildsen, Julia K.; Mace, Kyla D.; Cornblath, Eli J.; Weidler, Carmen; Pasqualetti, Fabio; Bassett, Danielle S.; Blendy, Julie A. (August 2020, Proceedings of the National Academy of Sciences)

   Opioid addiction is a chronic, relapsing disorder associated with persistent changes in brain plasticity. Reconfiguration of neuronal connectivity may explain heightened abuse liability in individuals with a history of chronic drug exposure. To characterize network-level changes in neuronal activity induced by chronic opiate exposure, we compared FOS expression in mice that are morphine-naïve, morphine-dependent, or have undergone 4 wk of withdrawal from chronic morphine exposure, relative to saline-exposed controls. Pairwise interregional correlations in FOS expression data were used to construct network models that reveal a persistent reduction in connectivity strength following opiate dependence. Further, we demonstrate that basal gene expression patterns are predictive of changes in FOS correlation networks in the morphine-dependent state. Finally, we determine that regions of the hippocampus, striatum, and midbrain are most influential in driving transitions between opiate-naïve and opiate-dependent brain states using a control theoretic approach. This study provides a framework for predicting the influence of specific therapeutic interventions on the state of the opiate-dependent brain. 

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2. Hedonic eating, obesity, and addiction result from increased neuropeptide Y in the nucleus accumbens during human brain evolution

   https://doi.org/10.1073/pnas.2311118120  

   Raghanti, Mary Ann; Miller, Elaine N.; Jones, Danielle N.; Smith, Heather N.; Munger, Emily L.; Edler, Melissa K.; Phillips, Kimberley A.; Hopkins, William D.; Hof, Patrick R.; Sherwood, Chet C.; et al (September 2023, Proceedings of the National Academy of Sciences)

   The nucleus accumbens (NAc) is central to motivation and action, exhibiting one of the highest densities of neuropeptide Y (NPY) in the brain. Within the NAc, NPY plays a role in reward and is involved in emotional behavior and in increasing alcohol and drug addiction and fat intake. Here, we examined NPY innervation and neurons of the NAc in humans and other anthropoid primates in order to determine whether there are differences among these various species that would correspond to behavioral or life history variables. We quantified NPY-immunoreactive axons and neurons in the NAc of 13 primate species, including humans, great apes, and monkeys. Our data show that the human brain is unique among primates in having denser NPY innervation within the NAc, as measured by axon length density to neuron density, even after accounting for brain size. Combined with our previous finding of increased dopaminergic innervation in the same region, our results suggest that the neurochemical profile of the human NAc appears to have rendered our species uniquely susceptible to neurophysiological conditions such as addiction. The increase in NPY specific to the NAc may represent an adaptation that favors fat intake and contributes to an increased vulnerability to eating disorders, obesity, as well as alcohol and drug dependence. Along with our findings for dopamine, these deeply rooted structural attributes of the human brain are likely to have emerged early in the human clade, laying the groundwork for later brain expansion and the development of cognitive and behavioral specializations. 

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3. Identifying and Characterizing Opioid Addiction States Using Social Media Posts

   https://doi.org/10.1109/BIBM52615.2021.9669628  

   Jha, D; La Marca, S; Singh R (December 2021, Proceedings IEEE International Conference on Bioinformatics and Biomedicine Workshops)

   Opioid addiction constitutes a significant contemporary health crisis that is multifarious in its complexity. Modeling the epidemiology of any addiction is challenging in its own right. For opioid addiction, the challenge is exacerbated due to the difficulties in collecting real-time data and the circumscribed nature of information opioid users may disclose owing to stigma associated with prescription misuse. Given this context, identifying the progression of individuals through the stages of (opioid) addiction is one of the more acute problems in epidemiological modeling whose solution is crucial for designing specific interventions at both personal and population levels. We describe a computational approach for determining and characterizing addiction stages of opioid users from their social media posts. The proposed approach combines recurrent neural network learning with information-theoretic analysis of word-associations and context-based word embedding to determine addiction stage-specific language usage. Users who have a high likelihood for relapsing back to drug-use are identified and characterized using propensity score matching and logistic regression. Experimental evaluations indicate that the proposed approach can distinguish between various addiction stages and identify users prone to relapse with high accuracy as evidenced by F1 scores of 0.88 and 0.79 respectively 

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4. Analysis of associations between emotions and activities of drug users and their addiction recovery tendencies from social media posts using structural equation modeling

   https://doi.org/0.1186/s12859-020-03893-9  

   Jha, Deeptanshu; Singh, Rahul (January 2021, BMC bioinformatics)

   null (Ed.)

   Background: Addiction to drugs and alcohol constitutes one of the significant factors underlying the decline in life expectancy in the US. Several context-specific reasons influence drug use and recovery. In particular emotional distress, physical pain, relationships, and self-development efforts are known to be some of the factors associated with addiction recovery. Unfortunately, many of these factors are not directly observable and quantifying, and assessing their impact can be difficult. Based on social media posts of users engaged in substance use and recovery on the forum Reddit, we employed two psycholinguistic tools, Linguistic Inquiry and Word Count and Empath and activities of substance users on various Reddit sub-forums to analyze behavior underlining addiction recovery and relapse. We then employed a statistical analysis technique called structural equation modeling to assess the effects of these latent factors on recovery and relapse. Results: We found that both emotional distress and physical pain significantly influence addiction recovery behavior. Self-development activities and social relationships of the substance users were also found to enable recovery. Furthermore, within the context of self-development activities, those that were related to influencing the mental and physical well-being of substance users were found to be positively associated with addiction recovery. We also determined that lack of social activities and physical exercise can enable a relapse. Moreover, geography, especially life in rural areas, appears to have a greater correlation with addiction relapse. Conclusions: The paper describes how observable variables can be extracted from social media and then be used to model important latent constructs that impact addiction recovery and relapse. We also report factors that impact self-induced addiction recovery and relapse. To the best of our knowledge, this paper represents the first use of structural equation modeling of social media data with the goal of analyzing factors influencing addiction recovery. 

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5. Thinking about Social Determinants of Health through the Relationality of Work and Drug Use

   https://doi.org/10.1111/maq.12690  

   Parsons, Michelle Anne (June 2022, Medical Anthropology Quarterly)

   Public health often frames drug use and addiction as destructive and antithetical to productive citizenship, particularly formal employment. Anthropologists show how drug use emerges in specific institutional, social, and political economic contexts. This attention to context suggests that the relationship between drug use and work may not be as stable as epidemiology models it. There is a multiplicity to the relationality of work and drug use. These results are based on in‐depth interviews conducted in 2018 and 2019 with 16 individuals undergoing addiction treatment at a residential facility in northern Arizona. In some cases, drug and alcohol use led to losing work. In other cases, drug and alcohol use made work more possible. The entanglements between work and drug use fluctuated through time. Social determinants of health are relationally brought into being, part of larger assemblages, and dynamic. 

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