There is a growing and renewed interest in the use of hallucinogenic drugs in the treatment of psychiatric disorders, especially since the FDA approval of ketamine treatment for resistant depression. The response to hallucinogenic psychedelic substances (ayahuasca, psilocybin, LSD, ketamine) in the treatment of depression and addictions calls for a theoretical explanatory model.

Provide a descriptive / explanatory psychopathological model of the response to treatment with hallucinogenic drugs based on the descriptions of the subjects and the comparison with other extreme life experiences.

Relevant published literature on subjective experiences in treatment with hallucinogenic drugs for depression and addictions is reviewed. It is compared with subjective experiences in life changing experiences.

Intense emotional states, mystical-type experiences including feelings of oneness, transcendence, ineffability, and the complex emotion of awe seem to be consistently presented as psychic elements related to the efficacy of these treatments. The genetic and cultural (memetic) evolutionary value of these emotions in the cohesiveness of human groups and the genesis of affective symptoms, and in the recalibration of cognitions and emotions, is discussed.

The efficacy of hallucinogenic drugs used in the treatment of depression and addictions is accompanied by complex and varied emotions but with common psychopathological elements that could mediate their action.

No significant relationships.

Some Internet interventions are regarded as effective treatments for adult depression, but less is known about who responds to this form of treatment.

An elastic net and random forest were trained to predict depression symptoms and related disability after an 8-week course of an Internet intervention, Deprexis, involving adults (N = 283) from across the USA. Candidate predictors included psychopathology, demographics, treatment expectancies, treatment usage, and environmental context obtained from population databases. Model performance was evaluated using predictive R2$\lpar R_{{\rm pred}}^2\rpar\comma $ the expected variance explained in a new sample, estimated by 10 repetitions of 10-fold cross-validation.

An ensemble model was created by averaging the predictions of the elastic net and random forest. Model performance was compared with a benchmark linear autoregressive model that predicted each outcome using only its baseline. The ensemble predicted more variance in post-treatment depression (8.0% gain, 95% CI 0.8–15; total $R_{{\rm pred}}^2 \; $= 0.25), disability (5.0% gain, 95% CI −0.3 to 10; total $R_{{\rm pred}}^2 \; $= 0.25), and well-being (11.6% gain, 95% CI 4.9–19; total $R_{{\rm pred}}^2 \; $= 0.29) than the benchmark model. Important predictors included comorbid psychopathology, particularly total psychopathology and dysthymia, low symptom-related disability, treatment credibility, lower access to therapists, and time spent using certain Deprexis modules.

A number of variables predict symptom improvement following an Internet intervention, but each of these variables makes relatively small contributions. Machine learning ensembles may be a promising statistical approach for identifying the cumulative contribution of many weak predictors to psychosocial depression treatment response.

Studies have established that many depressed adolescent patients do not receive optimal mental health care. Specifically, depression in primary-care settings is underrecognized, undertreated, and stigmatized. Although the seriousness and prevalence of adolescent depression is well known to primary-care physicians, its assessment, diagnosis, and treatment remains a significant problem in general and in rural communities in particular.

In this article, the author accomplishes three aims: (1) summarizes the most current evidence-based guidelines for depression care for adolescents in primary-care settings, (2) reviews the empirical literature on how key patient demographic variables (race, gender, and age) may be correlated with and predictive of variations in evidence-based depression care (assessment, diagnosis, and treatment) for adolescent patients, particularly in rural areas, and (3) provides implications for translating empirical research findings to evidence-based depression care in rural primary-care settings.