Rehabilitating psychedelic drugs: Another key to treating severe mental health disorders
Lori Tipton's life was a cascade of trauma that even a soap opera would not dare inflict upon a character: a mentally unstable family; a brother who died of a drug overdose; the shocking discovery of the bodies of two persons her mother had killed before turning the gun on herself; the devastation of Hurricane Katrina that savaged her hometown of New Orleans; being raped by someone she trusted; and having an abortion. She suffered from severe PTSD.
“My life was filled with anxiety and hypervigilance,” she says. “I was constantly afraid and had mood swings, panic attacks, insomnia, intrusive thoughts and suicidal ideation. I tried to take my life more than once.” She was fortunate to be able to access multiple mental health services, “And while at times some of these modalities would relieve the symptoms, nothing really lasted and nothing really address the core trauma.”
Then in 2018 Tipton enrolled in a clinical trial that combined intense sessions of psychotherapy with limited use of Methylenedioxymethamphetamine, or MDMA, a drug classified as a psychedelic and commonly known as ecstasy or Molly. The regimen was arduous; 1-2 hour preparation sessions, three sessions where MDMA was used, which lasted 6-8 hours, and lengthy sessions afterward to process and integrate the experiences. Two therapists were with her every moment of the three-month program that totaled more than 40 hours.
“It was clear to me that [the therapists] weren't going to heal me, that I was going to have to do the work for myself, but that they were there to completely support my process,” she says. “But the effects of MDMA were really undeniable for me. I felt embodied in a way that I hadn't in years. PTSD had robbed me of the ability to feel safe in my own body.”
Tipton doesn’t think the therapy completely cured her PTSD. “But when I completed the trial in 2018, I no longer qualified for the diagnosis, and I still don't qualify for the diagnosis today,” she told an April workshop on psychedelics as mental health treatment by the National Academies of Sciences, Engineering and Medicine, or NASEM.
A Champion
Rick Doblin has been a catalyst behind much of the contemporary research into psychedelics. Prior to the DEA clamp down, the Boston psychotherapist had seen that MDMA and other psychedelics could benefit some of his patients where other measures had failed. He immediately organized efforts to question the drug rescheduling but to little avail. In 1986, he created the nonprofit Multidisciplinary Association for Psychedelic Studies (MAPS), which slowly laid the scientific foundation for clinical trials, including the one that Tipton joined, using psychedelics to treat mental health conditions.
Now, only slowly, have researchers been able to explore the power of these drugs to treat a broad spectrum of severely debilitating mental health conditions, including trauma, depression, and PTSD, where other available treatments proved inadequate.
“Psychedelic psychotherapy is an attempt to go after the root causes of the problems with just a relatively few administrations, as contrasted to most of the psychiatric drugs used today that are mostly just reducing symptoms and are meant to be taken on a daily basis,” Doblin said in a 2019 TED Talk. Most of these drugs can have broad effect but “some are probably more effective than others for certain conditions,” he added in a recent interview with Leaps.org. Comparative head-to-head studies of psychedelic therapies simply have not been conducted.
Their mechanisms of action are poorly understood and can vary between drugs, but it is generally believed that psychedelics change the activity of neurons so that the brain processes information differently, says Katrin Preller, a neuropsychologist at the University of Zurich. A recent important study in Nature Medicine by Richard Daws and colleagues used functional magnetic resonance imaging (fMRI) of the brain and found that “functional networks became more functionally interconnected and flexible after psilocybin treatment…implying that psilocybin's antidepressant action may depend on a global increase in brain network integration.”
Rosalind Watts, a clinical investigator at the Imperial College in London, believes there is “an overestimation of the importance of the drug and an underestimation of the importance of the [therapeutic] context” in psychedelic research. “It is unethical to provide the drug without the other,” she says. Doblin notes that “psychotherapy outcomes research demonstrates that the therapeutic alliance between the therapist and the patients is the single most predictive factor of outcomes. [It is] trust and the sense of safety, the willingness to go into difficult spaces” that makes clinical breakthroughs possible with the drug.
Excitement and Challenges
Recurrent themes expressed at the NASEM workshop were exciting glimpses of the potential for psychedelics to treat mental health conditions combined with the challenges of realizing those potentials. A recent review paper found evidence that using psychedelics can help with treating a variety of common mental illnesses, but the paper could identify only 14 clinical trials of classic psychedelics published since 1991. Much of the reason is that the drugs are not patentable and so the pharmaceutical industry has no interest in investing in expensive clinical trials to bring them to market. MAPS has raised about $135 million over its 36-year history to conduct such research, says Doblin, the vast majority of it from individual donors and none from foundations.
The workshop participants’ views also were colored by the history of drug crackdowns and a fear that research might easily be shut down in the future. There was great concern that use of psychedelics should be confined to clinical trials with high safety and ethical standards, instead of doctors and patients experimenting on their own. “We need to get it right this time,” says Charles Grob, a psychiatrist at the UCLA School of Medicine. But restricting access to psychedelics will become even more difficult now that Oregon and several cities have acted to decriminalize possession and use of many of these drugs.
The experience with ketamine also troubled Grob. He is hoping to “mitigate the rush of rapid commercialization” that occurred with that drug. Ketamine technically is not a psychedelic though it does share some of their potentially euphoric properties. In 2019, soon after the FDA approved a form of ketamine with a limited label indication to treat depression, for profit clinics sprang up promoting off label use of the drug for psychiatric conditions where there was little clinical evidence of efficacy. He fears the same thing will happen when true psychedelics are made available.
If these therapies are approved, access to them is likely to be a problem. The drugs themselves are cheap but the accompanying therapy is not, and there is a shortage of trained psychotherapists. Mental health services often are not adequately covered by health insurance, while the poor and people of color suffer additional burdens of inadequate access. Doblin is committed to health care equity by training additional providers and by investigating whether some of the preparatory and integration sessions might be handled in a group setting. He says it is important that the legal aspects of psychedelics also be addressed so that patients “don't have to go underground” in order to receive this care.
Researchers probe extreme gene therapy for severe alcoholism
Story by Freethink
A single shot — a gene therapy injected into the brain — dramatically reduced alcohol consumption in monkeys that previously drank heavily. If the therapy is safe and effective in people, it might one day be a permanent treatment for alcoholism for people with no other options.
The challenge: Alcohol use disorder (AUD) means a person has trouble controlling their alcohol consumption, even when it is negatively affecting their life, job, or health.
In the U.S., more than 10 percent of people over the age of 12 are estimated to have AUD, and while medications, counseling, or sheer willpower can help some stop drinking, staying sober can be a huge struggle — an estimated 40-60 percent of people relapse at least once.
A team of U.S. researchers suspected that an in-development gene therapy for Parkinson’s disease might work as a dopamine-replenishing treatment for alcoholism, too.
According to the CDC, more than 140,000 Americans are dying each year from alcohol-related causes, and the rate of deaths has been rising for years, especially during the pandemic.
The idea: For occasional drinkers, alcohol causes the brain to release more dopamine, a chemical that makes you feel good. Chronic alcohol use, however, causes the brain to produce, and process, less dopamine, and this persistent dopamine deficit has been linked to alcohol relapse.
There is currently no way to reverse the changes in the brain brought about by AUD, but a team of U.S. researchers suspected that an in-development gene therapy for Parkinson’s disease might work as a dopamine-replenishing treatment for alcoholism, too.
To find out, they tested it in heavy-drinking monkeys — and the animals’ alcohol consumption dropped by 90% over the course of a year.
How it works: The treatment centers on the protein GDNF (“glial cell line-derived neurotrophic factor”), which supports the survival of certain neurons, including ones linked to dopamine.
For the new study, a harmless virus was used to deliver the gene that codes for GDNF into the brains of four monkeys that, when they had the option, drank heavily — the amount of ethanol-infused water they consumed would be equivalent to a person having nine drinks per day.
“We targeted the cell bodies that produce dopamine with this gene to increase dopamine synthesis, thereby replenishing or restoring what chronic drinking has taken away,” said co-lead researcher Kathleen Grant.
To serve as controls, another four heavy-drinking monkeys underwent the same procedure, but with a saline solution delivered instead of the gene therapy.
The results: All of the monkeys had their access to alcohol removed for two months following the surgery. When it was then reintroduced for four weeks, the heavy drinkers consumed 50 percent less compared to the control group.
When the researchers examined the monkeys’ brains at the end of the study, they were able to confirm that dopamine levels had been replenished in the treated animals, but remained low in the controls.
The researchers then took the alcohol away for another four weeks, before giving it back for four. They repeated this cycle for a year, and by the end of it, the treated monkeys’ consumption had fallen by more than 90 percent compared to the controls.
“Drinking went down to almost zero,” said Grant. “For months on end, these animals would choose to drink water and just avoid drinking alcohol altogether. They decreased their drinking to the point that it was so low we didn’t record a blood-alcohol level.”
When the researchers examined the monkeys’ brains at the end of the study, they were able to confirm that dopamine levels had been replenished in the treated animals, but remained low in the controls.
Looking ahead: Dopamine is involved in a lot more than addiction, so more research is needed to not only see if the results translate to people but whether the gene therapy leads to any unwanted changes to mood or behavior.
Because the therapy requires invasive brain surgery and is likely irreversible, it’s unlikely to ever become a common treatment for alcoholism — but it could one day be the only thing standing between people with severe AUD and death.
“[The treatment] would be most appropriate for people who have already shown that all our normal therapeutic approaches do not work for them,” said Grant. “They are likely to create severe harm or kill themselves or others due to their drinking.”
This article originally appeared on Freethink, home of the brightest minds and biggest ideas of all time.
Massive benefits of AI come with environmental and human costs. Can AI itself be part of the solution?
The recent explosion of generative artificial intelligence tools like ChatGPT and Dall-E enabled anyone with internet access to harness AI’s power for enhanced productivity, creativity, and problem-solving. With their ever-improving capabilities and expanding user base, these tools proved useful across disciplines, from the creative to the scientific.
But beneath the technological wonders of human-like conversation and creative expression lies a dirty secret—an alarming environmental and human cost. AI has an immense carbon footprint. Systems like ChatGPT take months to train in high-powered data centers, which demand huge amounts of electricity, much of which is still generated with fossil fuels, as well as water for cooling. “One of the reasons why Open AI needs investments [to the tune of] $10 billion from Microsoft is because they need to pay for all of that computation,” says Kentaro Toyama, a computer scientist at the University of Michigan. There’s also an ecological toll from mining rare minerals required for hardware and infrastructure. This environmental exploitation pollutes land, triggers natural disasters and causes large-scale human displacement. Finally, for data labeling needed to train and correct AI algorithms, the Big Data industry employs cheap and exploitative labor, often from the Global South.
Generative AI tools are based on large language models (LLMs), with most well-known being various versions of GPT. LLMs can perform natural language processing, including translating, summarizing and answering questions. They use artificial neural networks, called deep learning or machine learning. Inspired by the human brain, neural networks are made of millions of artificial neurons. “The basic principles of neural networks were known even in the 1950s and 1960s,” Toyama says, “but it’s only now, with the tremendous amount of compute power that we have, as well as huge amounts of data, that it’s become possible to train generative AI models.”
Though there aren’t any official figures about the power consumption or emissions from data centers, experts estimate that they use one percent of global electricity—more than entire countries.
In recent months, much attention has gone to the transformative benefits of these technologies. But it’s important to consider that these remarkable advances may come at a price.
AI’s carbon footprint
In their latest annual report, 2023 Landscape: Confronting Tech Power, the AI Now Institute, an independent policy research entity focusing on the concentration of power in the tech industry, says: “The constant push for scale in artificial intelligence has led Big Tech firms to develop hugely energy-intensive computational models that optimize for ‘accuracy’—through increasingly large datasets and computationally intensive model training—over more efficient and sustainable alternatives.”
Though there aren’t any official figures about the power consumption or emissions from data centers, experts estimate that they use one percent of global electricity—more than entire countries. In 2019, Emma Strubell, then a graduate researcher at the University of Massachusetts Amherst, estimated that training a single LLM resulted in over 280,000 kg in CO2 emissions—an equivalent of driving almost 1.2 million km in a gas-powered car. A couple of years later, David Patterson, a computer scientist from the University of California Berkeley, and colleagues, estimated GPT-3’s carbon footprint at over 550,000 kg of CO2 In 2022, the tech company Hugging Face, estimated the carbon footprint of its own language model, BLOOM, as 25,000 kg in CO2 emissions. (BLOOM’s footprint is lower because Hugging Face uses renewable energy, but it doubled when other life-cycle processes like hardware manufacturing and use were added.)
Luckily, despite the growing size and numbers of data centers, their increasing energy demands and emissions have not kept pace proportionately—thanks to renewable energy sources and energy-efficient hardware.
But emissions don’t tell the full story.
AI’s hidden human cost
“If historical colonialism annexed territories, their resources, and the bodies that worked on them, data colonialism’s power grab is both simpler and deeper: the capture and control of human life itself through appropriating the data that can be extracted from it for profit.” So write Nick Couldry and Ulises Mejias, authors of the book The Costs of Connection.
The energy requirements, hardware manufacture and the cheap human labor behind AI systems disproportionately affect marginalized communities.
Technologies we use daily inexorably gather our data. “Human experience, potentially every layer and aspect of it, is becoming the target of profitable extraction,” Couldry and Meijas say. This feeds data capitalism, the economic model built on the extraction and commodification of data. While we are being dispossessed of our data, Big Tech commodifies it for their own benefit. This results in consolidation of power structures that reinforce existing race, gender, class and other inequalities.
“The political economy around tech and tech companies, and the development in advances in AI contribute to massive displacement and pollution, and significantly changes the built environment,” says technologist and activist Yeshi Milner, who founded Data For Black Lives (D4BL) to create measurable change in Black people’s lives using data. The energy requirements, hardware manufacture and the cheap human labor behind AI systems disproportionately affect marginalized communities.
AI’s recent explosive growth spiked the demand for manual, behind-the-scenes tasks, creating an industry described by Mary Gray and Siddharth Suri as “ghost work” in their book. This invisible human workforce that lies behind the “magic” of AI, is overworked and underpaid, and very often based in the Global South. For example, workers in Kenya who made less than $2 an hour, were the behind the mechanism that trained ChatGPT to properly talk about violence, hate speech and sexual abuse. And, according to an article in Analytics India Magazine, in some cases these workers may not have been paid at all, a case for wage theft. An exposé by the Washington Post describes “digital sweatshops” in the Philippines, where thousands of workers experience low wages, delays in payment, and wage theft by Remotasks, a platform owned by Scale AI, a $7 billion dollar American startup. Rights groups and labor researchers have flagged Scale AI as one company that flouts basic labor standards for workers abroad.
It is possible to draw a parallel with chattel slavery—the most significant economic event that continues to shape the modern world—to see the business structures that allow for the massive exploitation of people, Milner says. Back then, people got chocolate, sugar, cotton; today, they get generative AI tools. “What’s invisible through distance—because [tech companies] also control what we see—is the massive exploitation,” Milner says.
“At Data for Black Lives, we are less concerned with whether AI will become human…[W]e’re more concerned with the growing power of AI to decide who’s human and who’s not,” Milner says. As a decision-making force, AI becomes a “justifying factor for policies, practices, rules that not just reinforce, but are currently turning the clock back generations years on people’s civil and human rights.”
Ironically, AI plays an important role in mitigating its own harms—by plowing through mountains of data about weather changes, extreme weather events and human displacement.
Nuria Oliver, a computer scientist, and co-founder and vice-president of the European Laboratory of Learning and Intelligent Systems (ELLIS), says that instead of focusing on the hypothetical existential risks of today’s AI, we should talk about its real, tangible risks.
“Because AI is a transverse discipline that you can apply to any field [from education, journalism, medicine, to transportation and energy], it has a transformative power…and an exponential impact,” she says.
AI's accountability
“At the core of what we were arguing about data capitalism [is] a call to action to abolish Big Data,” says Milner. “Not to abolish data itself, but the power structures that concentrate [its] power in the hands of very few actors.”
A comprehensive AI Act currently negotiated in the European Parliament aims to rein Big Tech in. It plans to introduce a rating of AI tools based on the harms caused to humans, while being as technology-neutral as possible. That sets standards for safe, transparent, traceable, non-discriminatory, and environmentally friendly AI systems, overseen by people, not automation. The regulations also ask for transparency in the content used to train generative AIs, particularly with copyrighted data, and also disclosing that the content is AI-generated. “This European regulation is setting the example for other regions and countries in the world,” Oliver says. But, she adds, such transparencies are hard to achieve.
Google, for example, recently updated its privacy policy to say that anything on the public internet will be used as training data. “Obviously, technology companies have to respond to their economic interests, so their decisions are not necessarily going to be the best for society and for the environment,” Oliver says. “And that’s why we need strong research institutions and civil society institutions to push for actions.” ELLIS also advocates for data centers to be built in locations where the energy can be produced sustainably.
Ironically, AI plays an important role in mitigating its own harms—by plowing through mountains of data about weather changes, extreme weather events and human displacement. “The only way to make sense of this data is using machine learning methods,” Oliver says.
Milner believes that the best way to expose AI-caused systemic inequalities is through people's stories. “In these last five years, so much of our work [at D4BL] has been creating new datasets, new data tools, bringing the data to life. To show the harms but also to continue to reclaim it as a tool for social change and for political change.” This change, she adds, will depend on whose hands it is in.