​As a step towards creating an evidence-based fundament for transformational psychotherapy, ​RECONNECT Foundation is currently supporting a multi-year clinical research programme at the University Hospital of Psychiatry in Zurich.

The goal is to investigate the therapeutic potential of plant-based psychoactive compounds to reverse maladaptive neurobehavioral patterns in stress-related mood disorders and to enhance psychotherapeutic learning capabilities.

The vision for this research programme is to translate basic research results into innovative evidence-based clinical practice, promising high and positive societal impact.

​Iterative research and development program towards process-oriented transformational psychotherapy

​​We intend to provide alternatives to widely used symptom- and substitution-oriented treatments in psychiatry, which have shown only limited efficacy. This novel process-oriented treatment paradigm is less focused on specific symptoms and their substitution by means of psychotropic drugs, but rather aims at directly resolving underlying maladaptive processes. Transformation-based psychotherapy is therefore hypothesized to provide more sustainable therapeutic outcomes by empowering the innate dynamic capacity of the patient to access more adaptive states of consciousness.

​Multimodal brain imaging study of ayahuasca-analogue psychointegrative compounds in healthy subjects

Within this project, ​we are developing a standardized and quality-controlled botanical extract analogue to traditional ayahuasca preparations with the goal to assess its safety and tolerability in healthy subjects. Using state-of-the-art multimodal neuroimaging technology we will investigate how pharmacological modulation of glutamate- and serotonin-responsive cerebral circuits predict behavioral change in terms of enhanced adaptive neuroplasticity, emotion regulation, psychological flexibility, and mindfulness-related capabilities.

​Clinical studies about efficacy of ayahuasca-based psychotherapy for stress-related affective disorders

​​While ketamine and psilocybin have been researched extensively in the past decades, the plant medicine ayahuasca currently stimulates a tremendous public and scientific interest due to its antidepressant, anxiolytic and anti-addictive properties ​(Frecska, 2016; ​​Labate, 2013). ​Ayahuasca has been used in the Amazon as traditional indigenous medicine but is currently spreading all over the world with increasing numbers of Westerners seeking treatment in established retreat centers in South America​ ​(​​​Tupper, 2008). ​This rapid dissemination points to 1) an unmet need for increased psychosocial well-being through rapid and sustainable relief from various mental health problems and 2) the observational evidence that ayahuasca provides a pharmacological tool to facilitate transformational processes with beneficial health outcomes. In this context, Reconnect aims to further investigate biomechanisms and clinical efficacy of ayahuasca-assisted psychotherapy for stress-related mood disorders. 

​Developing machine learning methods for characterizing, predicting and optimizing the contextual factors of psychotropic drug effects
(in collaboration with the Global Ayahuasca Project)

In contrast to conventional antidepressant drugs, the neuroplasticity-inducing properties of ayahuasca highlight the importance and modulatory role of contextual factors for beneficial effects on mental health. This project aims at analyzing epidemiological data from the Global Ayahuasca Survey providing information about ayahuasca consumption, beneficial health effects and side effects, as well as a broad range of bio-psycho-social context variables. The translational application of machine learning algorithms developed in this project will: 1) promote improvements in safety and tolerability of pharmacologically-assisted therapy concepts, 2) derive empirically testable hypotheses for clinical studies, and 3) translate epidemiological findings towards effective personalized medicine. 

Related ​peer-review publications



​Affective mood disorders

​Neuronal underpinnings