Setting, acute reaction and mental health history shape ayahuasca's longer-term psychological effects

Some acute post-ayahuasca “adverse effects” like visual distortions were associated with better reported mental health at a later date, while other adverse effects like feeling isolated or energetically attacked were associated with worse mental health

PLOS

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Mounting evidence supports ayahuasca’s potential to improve mental health, but its long-term effects are shaped by both individual mental health history and the context in which the psychedelic is used, according to a study published on April 30, 2025 in the open-access journal PLOS Mental Health by Óscar Andión from Research Sherpas, Spain; José Carlos Bouso from the International Centre for Ethnobotanical Education, Research, and Services (ICEERS) and the University of Rovira i Virgili, Spain; Daniel Perkins from the University of Melbourne and Swinburne University; and colleagues.

Ayahuasca, a psychedelic medicine traditionally used by Indigenous communities in South America, has received increasing interest from Western researchers and clinicians for its potential mental health benefits, but its potential risks and adverse effects remain understudied. In a previous review of adverse effects reported in a global survey of ayahuasca ceremony participants, José Carlos Bouso, Andión, and colleagues found that over half reported adverse mental states after ayahuasca use, with greater adverse experiences associated with a history of mental illness and using the drug in non-traditional settings. Importantly, potential adverse effects reported ranged from visual distortions or hallucinations to “feeling down, depressed, or hopeless”, “feeling disconnected or alone”, and “feeling energetically attacked”. 

In their new analysis, the authors applied machine learning and classical statistical approaches to the same dataset to better understand the mediating factors shaping the relationship between adverse events and mental health outcomes in ayahuasca users. The survey included 10,836 participants, of whom 5,400 with complete data were included in the final analysis. Among these, 14.2% had a prior anxiety disorder and 19.7% a prior depressive disorder.. Although the Global Ayahuasca Survey reflects a large, diverse population of users, it was voluntary and administered potentially years after an individual’s ayahuasca experience, introducing self-selection and recall biases. 

The researchers found that participants with a history of anxiety or depression, as well as those using ayahuasca in non-traditional settings, were more likely to report adverse mental states after use. Some “adverse effects” like visual distortions, however, were associated with significantly better mental health outcomes reported in the present. Adverse effects like “feeling down”, “feeling disconnected”, and “feeling energetically attacked” however, were associated with poorer mental health in participants in the longer term. The authors suggest that the context in which ayahuasca is used, as well as factors like age and mental health history, influence whether an individual experiences psychological benefits following an ayahuasca experience, and note that “adverse” effects of ayahuasca may be subjective. 

Their findings appear to indicate that it would be more beneficial to use ayahuasca under the supervision of experienced users who can provide additional support to those with a history of depression, who may otherwise face a higher risk of negative outcomes. They propose that, while psychedelics are becoming increasingly medicalized, ayahuasca is most often consumed in group or community settings. Therefore, future studies should examine the effects of ayahuasca use in these real-life communal contexts. 

Dr. José Carlos Bouso notes: "What stood out most to us was the significant difference in mental health outcomes between users who had supportive environments [during their use] and those who didn’t. This emphasizes the importance of a responsible and well-prepared setting for those seeking healing through ayahuasca."

 The authors add: "Our study reveals that the post-ayahuasca mental states, traditionally seen as adverse, can contribute to improved mental health, especially in individuals with previous anxiety and depressive disorders. This suggests the need for a more nuanced understanding of these states as potentially beneficial experiences.”

Additional quotes:

On the Research Process:
"The insights gathered from the Global Ayahuasca Survey (GAS) provided a deeper understanding of the complex relationship between ayahuasca use and mental health outcomes. It was particularly interesting to see how the setting, preparation, and integration practices play a pivotal role in shaping the overall experience" (Dr. José Carlos Bouso).

 On the use of ayahuasca:
"Ayahuasca use, when experienced in safe, supportive environments, may offer therapeutic benefits, particularly for individuals with a history of mood disorders, highlighting the importance of the ceremony's setting and the role of facilitators."

 On the role of spirituality:
"Our research also highlights that the spiritual significance of ayahuasca ceremonies plays a protective role, reducing adverse emotional states like anxiety, depression, and disconnection, thus contributing to overall mental health improvement.

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In your coverage please use this URL to provide access to the freely available article in PLOS Mental Health: https://plos.io/3YRG4DD

Citation: Andión Ó, Bouso JC, Sarris JJ, Tófoli LF, Opaleye ES, Perkins D (2025) A new insight into ayahuasca’s adverse effects: Reanalysis and perspectives on its mediating role in mental health from the Global Ayahuasca Survey (GAS). PLOS Ment Health 2(4): e0000097. https://doi.org/10.1371/journal.pmen.0000097

Author Countries: Australia, Brazil, Spain

Funding: The authors received no specific funding for this work.

Competing Interests:  I have read the journal's policy and the authors of this manuscript have the following competing interests: DP and JJS hold equity in a commercial entity, Psychae Therapeutics, which is undertaking research with psychedelic compounds and are co-CEOs of the same organisation.

 

 

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Journal

PLOS Mental Health

DOI

10.1371/journal.pmen.0000097 

Article Publication Date

30-Apr-2025

COI Statement

Competing Interests: I have read the journal's policy and the authors of this manuscript have the following competing interests: DP and JJS hold equity in a commercial entity, Psychae Therapeutics, which is undertaking research with psychedelic compounds and are co-CEOs of the same organisation.

 

Machine learning brings new insights to cell’s role in addiction, relapse

University of Cincinnati, University of Houston collaborate on research published in Science Advances

University of Cincinnati

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Object recognition software is used by law enforcement to help identify suspects, by self-driving cars to navigate roadways and by many consumers to unlock their cell phones or pay for their morning coffee.

Now, researchers led by the University of Cincinnati’s Anna Kruyer and the University of Houston’s Demetrio Labate have applied object recognition technology to track changes in brain cell structure and provide new insights into how the brain responds to heroin use, withdrawal and relapse. The research was published April 30 in the journal Science Advances.

Study background

Kruyer’s lab focuses on relapse to heroin use, as many overdose deaths occur when people overestimate their capacity for drug use during relapse. The team has developed an animal model of relapse over the past seven years, studying interactions between brain cells and the reward center of the brain that orchestrates the relapse process.

“We want to understand the neurons that are involved and all of the different cells and molecules that can shape that activity,” said Kruyer, PhD, assistant professor in the Department of Pharmaceutical Sciences in UC’s James L. Winkle College of Pharmacy. “The idea would be if you can interfere with relapse, you can help someone stay clean.”

While neurons are a more commonly studied brain cell, Kruyer has focused on another cell called an astrocyte. Astrocytes have many functions, including metabolic support for neurons, providing molecules that neurons turn into neurotransmitters, and shielding or uncovering different receptors during synaptic activity.

“Astrocytes are a kind of protective cell that can restore synaptic homeostasis,” Kruyer said. “They are super dynamic relative to the synapse, and they’re moving toward and away from the synapse in real time in a way that can impact drug seeking. So if you prevent this reassociation with synapses during relapse, you can increase and prolong relapse."

Labate is an applied mathematician with expertise in harmonic analysis and machine learning.

“A central focus of my research is the development and application of mathematical techniques to uncover meaningful patterns in non-Euclidean data, such as the analysis of complex shapes,” said Labate, PhD, professor in the University of Houston Department of Mathematics.  “The study of astrocytes provides an ideal setting for this type of investigation: these cells are highly heterogeneous, varying widely in size and shape, and are capable of dynamically remodeling their morphology in response to external stimuli.” 

A new approach with machine learning

While animal model studies have produced results, Kruyer and her colleagues faced a barrier in that the techniques used could not be translated for human subjects. To work around this issue, they focused on an astrocyte protein that essentially acts as the cell’s skeleton.

“We thought if we could figure out a way to translate what we’re seeing at the synaptic level to changes in the cytoskeleton, maybe we could see if astrocytes are doing something critical during relapse in humans,” Kruyer said.

A team of mathematicians led by Labate trained object recognition machine learning models on hundreds of astrocyte cells until the technology could accurately detect an astrocyte within an image, similar to how object recognition software works.

“Machine learning techniques have been widely applied in the literature to image classification tasks, where the objective is to assign each cell to a specific category,” Labate explained. “In such contexts, machine learning is particularly powerful for identifying image-based cellular features that are difficult to capture using traditional geometric descriptors, yet serve as effective discriminators between classes.” 

Once the program could identify astrocytes, the team trained it to analyze specific structures based on 15 different criteria, including astrocyte cytoskeletal density (similar to bone density), size, length versus sphericalness and number of smaller branches coming off of the main branch.

“You can think about this like if you gave a computer a bunch of images of street scenes, it would commonly see pedestrians, cars and buildings,” Kruyer said. “If you give a computer 1,000 images of astrocytes, there are things it would commonly see. This is the segmentation process whereby a computer can now start to make measurements of the different features of the astrocyte.”

Using all 15 measurements weighted by their importance in the computer’s precision to detect astrocytes, researchers developed a single metric to quantify the characteristics of each astrocyte.

“In previous work, I have utilized machine learning for both cell classification and segmentation problems,” Labate said. “In this paper, however, we address a more nuanced question: are there specific subpopulations of astroglia that exhibit more pronounced morphological changes compared to the rest? To investigate this, we introduced the concept of distance to compare the shape characteristics of individual astrocyte cells while accounting for the inherent heterogeneity within the population.”

Applying the model

After developing the machine learning model to identify astrocytes and report the new metric, the team looked at astrocytes specifically within an area of the brain called the nucleus accumbens (NAc) that is active during drug relapse.

The model was able to predict exactly where in the NAc an astrocyte came from based on its structure with 80% accuracy.

“This tells us that astrocyte structure varies by anatomy,” Kruyer said. “Astrocytes have been considered to be this homogenous type of cell, but this indicates to us that astrocyte structure varies significantly by location — perhaps the shape and the size have something to do with their function.”

Using animal models and the new knowledge gained from the computer models, the team found that astrocytes appear to shrink and become less malleable after exposure to heroin.

“These data suggest that heroin is doing something molecularly that makes astrocytes less able to respond to synaptic activity and maintain homeostasis,” Kruyer said.

“This paper exemplifies the strength of interdisciplinary collaboration, where innovative quantitative tools are developed or adapted to tackle complex biological questions,” added Labate. “The success of this research lies in the effective communication between disciplines and in our willingness to push the boundaries of traditional machine learning to address biologically meaningful and timely challenges.”

Next steps

Kruyer said she is most excited about the application of machine learning to a biological question, which eliminates human error and biases and makes the research more easily translatable from animal models to humans.

“We’re asking open-ended questions, and it’s giving us all of these really fine-grained detailed answers, and then what we do with that is up to us,” she said. “Human astrocytes are much larger, much more complex and way more abundant than in the animal models, so applying a tool like this is really cool to carry forward in humans.” 

Moving forward, the team wants to learn more about the specific mechanisms of astrocytes in each region within the NAc and train new models using human tissue samples. Long term, the knowledge gained could help develop new treatments for addiction focused on restoring or replacing astrocytes to their functions prior to being exposed to heroin.

Additionally, the machine learning method Labate’s team developed can be adapted and applied to other types of cells with intricate structures.

“By enabling precise quantification and comparison of single-cell morphological features, this approach opens the door to the development of novel techniques for identifying cellular or molecular biomarkers that reflect biological processes, disease states or responses to therapeutic interventions,” he said. “More broadly, our work introduces a new quantitative framework for uncovering and validating fundamental mechanistic models underlying complex brain conditions, such as addiction to drugs of abuse.”

Other coauthors include Michaela Marini, Yabo Niu, Heng Zhao, Anish Mohan and Nathan Koorndyk. This work was supported by grants from the Simons Foundation (MPS-TSM783 00002738 to Labate) and the National Institutes of Health (DA054339 to Kruyer and NS007453 to Koorndyk). All authors declare no competing interests related to this manuscript.

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Journal

Science Advances

DOI

10.1126/sciadv.ads6841 

Method of Research

Experimental study

Subject of Research

Animals

Article Title

Supervised and Unsupervised Learning Reveals Heroin-Induced Impairments in Astrocyte Structural Plasticity

Article Publication Date

30-Apr-2025

 

The Duke mouse brain atlas will accelerate studies of neurological disorders

Duke University

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Duke Mouse Brain Atlas Will Accelerate Studies of Neurological Disorders

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A new “atlas” developed by researchers at Duke University School of Medicine, University of Tennessee Health Science Center, and the University of Pittsburgh will increase precision in measuring changes in brain structure and make it easier to share results for scientists working to understand neurological diseases such as Alzheimer's disease.  

The tool, the Duke Mouse Brain Atlas, combines microscopic resolution, three-dimensional images from three different techniques to create a detailed map of the entire mouse brain, from large structures down to individual cells and circuits. 

“This is the first truly three-dimensional, stereotaxic atlas of the mouse brain,” said G. Allan Johnson, PhD, Charles E. Putman University Distinguished Professor of Radiology at Duke. He is also professor in the Department of Physics and the Department of Biomedical Engineering.

Stereotaxic roughly means “in life,” or that the atlas accurately represents the brain as it appears in a living mouse, with external landmarks that can guide experimental procedures.

The atlas is needed because different types of imaging have their own pros and cons, Johnson said. Some can provide a high-resolution view of single brain cells, but that view is distorted by the tissue preparation and scanning, making it difficult to compare results to those from others’ work. “The atlas provides a common space to which many different types of data can be registered so that it’s correctly oriented and undistorted,” he said. 

The details are reported April 30, 2025, in the journal Science Advances. 

Other Duke authors of the study are first author Harrison Mansour, a programmer/analyst in the Duke Center for In Vivo Microscopy, and Leonard E. White, associate professor in neurology.

Anyone can download and use the atlas in a range of open-source display packages. “Grade school students can appreciate the beauty of the brain, and neuroscientists can obtain much more accurate measures of brain changes,” Johnson said.  

For instance, researchers are currently using the atlas to follow neurodegeneration in mouse models of Alzheimer's disease, Huntington's disease, and environmental exposure to toxic metals and pesticides. 

To create the atlas, the researchers started with MRI, using diffusion tensor imaging  to capture three-dimensional images of five postmortem mouse brains at the highest resolution ever reported (15 microns), Johnson said. Imaging strategies and hardware developed over the past 40 years at the Duke Center for In Vivo Microscopy allowed the researchers to capture these images at a resolution 2.4 million times higher than clinical MRIs.  

They then merged these images with microCT scans of the mouse skull to pinpoint key “boney landmarks.” Finally, they removed the brains from the skull to allow the use of light sheet microscopy to map cells in the same space.  

“The combination of all three methods, at the highest spatial resolution in the same space, provides one of the most comprehensive maps of the mouse brain ever developed,” Johnson said.  

Duke Mouse Brain Atlas Will Accelerate Studies of Neurological Disorders [VIDEO] 

No full 3D mouse brain atlas existed that showed everything from the overall structure down to individual cells — until now.

Credit

Duke Pratt School of Engineerin

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Journal

Science Advances

DOI

10.1126/sciadv.adq8089 

Method of Research

Imaging analysis

Subject of Research

Animals

Article Title

The Duke Mouse Brain Atlas: MRI and light sheet microscopy stereotaxic atlas of the mouse brain

Article Publication Date

30-Apr-2025

 

In VR school, fish teach robots

Scientists use virtual reality for fish to teach robots how to swarm

University of Konstanz

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From fish to machines: The natural “control law” of fish was embedded in swarms of robotic cars, drones, and boats.

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Credit: Christian Ziegler, Liang Li

Fish are masters of coordinated motion. Schools of fish have no leader, yet individuals manage to stay in formation, avoid collisions, and respond with liquid flexibility to changes in their environment. Reproducing this combination of robustness and flexibility has been a long-standing challenge for human engineered systems like robots. Now, using virtual reality for freely-moving fish, a research team based in Konstanz has taken an important step towards that goal.

“Our work illustrates that solutions evolved by nature over millennia can inspire robust and efficient control laws in engineered systems,” said first author Liang Li from the University of Konstanz. Co-author Máté Nagy from Eötvös University underscores this: “The discovery opens up exciting possibilities for future applications in robotics and autonomous vehicle design.”

Deciphering nature’s hidden algorithm
Using a virtual reality (VR) setup that mimics natural schooling, researchers placed individual juvenile zebrafish into networked arenas where each fish could freely interact with “holographic” virtual conspecifics. Each virtual fish was a projection of a real fish from another arena, meaning that fish could swim and interact together in the same virtual world. The fully immersive 3D environment lets researchers precisely manipulate visual stimuli and record how the fish respond. This high level of control allowed the scientists to isolate exactly which cues the fish were using to guide their interactions with other fish. In other words, they could reverse engineer the behaviour of schooling in zebrafish to understand how fish solve the complex problem of coordinating their motion.

The solution they discovered was a simple and robust law based only on the perceived position, not the speed, of their neighbors to regulate their following behaviour.

“We were surprised by how little information the fish need to effectively coordinate movements within a school,” says Iain Couzin, senior author on the study and Director of MPI-AB and Speaker at the Cluster of Excellence Collective Behaviour. “They use local rules that are cognitively minimal, but functionally excellent.”

To see just how realistic the control law was, the team tested it with real fish. They conducted a VR “Turing test”, based on the concept of testing whether people can tell if they are interacting with a real human or with artificial intelligence. In the aquatic Turing test, a real fish would swim with a virtual fish that switched between being real and being controlled by the algorithm they discovered. The real fish could not tell the difference. They behaved the same whether interacting with a real conspecific or the virtual follower governed by the algorithm.

From fish to machines
To test the broader utility of their discovery, the team embedded it in swarms of robotic cars, drones, and boats. The robots were tasked with following a moving target using either parameters from the zebrafish algorithm or from a state-of-the-art method used in autonomous vehicles called Model Predictive Controller (MPC). Across all tests, the natural control law that fish have evolved delivered performance that was nearly indistinguishable from MPC in terms of accuracy and energy consumption – but at a fraction of the complexity.

Oliver Deussen, a co-author on the study and Professor in computer science at the University of Konstanz and Speaker at the Cluster of Excellence Collective Behaviour: “This work highlights the reciprocal relationship between robotics and biology – using robotics to explore biological mechanisms, which in turn can inspire new and effective robotic control strategies.”

 

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Key facts:

• Embargoed: Not for release until Wednesday, 30 April 2025, 14:00 U.S. Eastern Time
• Original publication: Liang Li, Máté Nagy, Guy Amichay, Ruiheng Wu, Wei Wang, Oliver Deussen, Daniela Rus, and Iain D. Couzin, Reverse engineering the control law for schooling in zebrafish using virtual reality, published in Science Robotics, 30. April 2025
• The research was led by scientists at the Cluster of Excellence Collective Behaviour, University of Konstanz, and the Max Planck Institute of Animal Behavior (MPI-AB), Germany, in collaboration with researchers at MIT in the United States and Eötvös University in Hungary.
• The research was funded, among others, by the German Research Foundation (DFG), the European Union’s Horizon 2020 Research and Innovation Programme, the Hungarian Academy of Sciences, and the Messmer Foundation Research Award from the Werner and Erika Messmer Foundation.
• The Centre for the Advanced Study of Collective Behaviour at the University of Konstanz is a global hotspot for the study of collective behaviour across a wide range of species and across scales of organization. It is a Cluster of Excellence within the framework of the German Excellence Strategy of the federal and state governments.

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“The Matrix” for fish: Researchers placed individual zebrafish into networked virtual reality arenas where each fish could freely interact with “holographic” virtual conspecifics.

Credit

Christian Ziegler, Mate Nagy, and Liang Li

Watch our video here:
https://youtu.be/CgZuYNvBHkY

You can download photos here:

1) https://www.uni-konstanz.de/fileadmin/pi/fileserver/2025_extra/virtual_reality_for_fish.jpg
Caption: “The Matrix” for fish: Researchers placed individual zebrafish into networked virtual reality arenas where each fish could freely interact with “holographic” virtual conspecifics.
Copyright: Christian Ziegler, Mate Nagy, and Liang Li

2) https://www.uni-konstanz.de/fileadmin/pi/fileserver/2025_extra/virtual_reality_for_fish_2.jpg
Caption: From fish to machines: The natural “control law” of fish was embedded in swarms of robotic cars, drones, and boats.
Copyright: Christian Ziegler, Liang Li

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Journal

Science Robotics

DOI

10.1126/scirobotics.adq6784 

Article Title

Reverse engineering the control law for schooling in zebrafish using virtual reality

Article Publication Date

30-Apr-2025