What big data says about psychedelics

By Danilo Bzdok, MD, Ph.D., Ph.D.

We won’t fully understand the brain anytime soon. It’s just too complicated! But we can put what we do know and put it together in new and better ways to draw new conclusions about how our brains work and how drugs can help people with mental illness. We’re working on that in my lab.

Today, we have unprecedented repositories and capabilities for big data, including human brain data and capabilities we’ve developed while building increasingly complicated systems.

It may sound surprising, but we can use some of the developed approaches to artificial intelligence (AI) to research humans intelligence† Reframing questions in medical science as machine learning problems can help us see these problems in new ways and gain new insights.

For example, research today shows that psychedelics can alleviate the symptoms of mental disorders. But how do they work, and why? This is an important question that is not unique to psychedelics, but is in fact central to much of the treatment of mental health. And it’s one we’re trying to help answer.

The network in standard mode

The brain network default modeDMN) — a system that we have only begun to identify and explore in the 21st century — appears to be extremely relevant to how drugs work. The DMN is the deepest neural processing layer of our brain. When we are not in a task-oriented state of mind (for example, when we are staring out the window instead of reading), this brain network is active.

Why is the DMN so important?

The DMN is the largest consumer of energy of any network in the brain. This is interesting, especially since the brain consumes a lot of energy overall (20% of all energy of the whole body, even though it is only about 2% of body mass). So the DMN is one of the most “expensive” parts of one of the most “expensive” parts of a human being.

In addition, the brain regions involved in the DMN have evolved the most in size compared to other parts of our brain, and compared to the brains of our closest relatives, monkeys.

A system that is always on and consuming a lot of energy, both in everyday life and on an evolutionary time scale, sounds like something very important. Why is the DMN so important?

A answers-supported by empirical evidence of neuroscience experiments – is that it works to model probability. Simply put, the DMN can help people anticipate the future. The DMN helps us see the world from different perspectives, come up with solutions, strategically understand how to value information in our environment and make smart decisions in the future.

A frog cannot plan to hunt an insect in two weeks. It’s just not possible, given the mental capacities made possible by the brain circuits. It can only process and respond to what is in its current sensory environment.

We can do a lot more than that, and that’s a huge evolutionary advantage. The DMN can make this possible and can be central to what defines human thinking, intelligent behavior and consciousness.

What does the DMN have to do with mental health and mental health treatments, including psychedelics?

The DMN—probably the deepest layer of the brain, unique to humans, which expends a tremendous amount of energy and is active by default—looks like the intersection of creativity and of many or most important psychiatric diseases. It’s possible that mental illness is the flip side of this huge evolutionary advantage – and perhaps adapting this system could help such circumstances.

The prevailing understanding of psychedelics is that they have a close relationship with the DMN. To make sense of the world, our brains have to filter out the vast majority of sensory input. One of the ways psychedelics can work is by breaking down those filters that have built up over time that affect how our DMN is processed and interpreted.

How does this AI-based brain research work and what does it show?

Because there is so much data to work with, machine learning tools and approaches help us understand how different brain receptor pathways and neurotransmitters relate to different experiences people have after taking psychedelics.

For example, they have enabled us to combine three different types of information for the first time. First, we have thousands of text stories from people describing experiences with psychedelics. Next, we have data on the known receptor affinities of currently known drug compounds. And finally, we have brain receptor data that shows us where different receptors are and how close they are in the brain.

We trained a machine learning system, using data analysis and natural language processing, to uncover the components of an experience with a psychedelic drug. We can connect a person’s experience with the places in the brain that the drug affects and say that certain parts of the brain, when acted on by specific drugs, produce specific effects.


Science isn’t quite sure yet how drugs affect changes in consciousness. We haven’t even come together on a definition of what consciousness is! But the more we explore the mechanisms of the brain, and the mechanisms of drugs that affect the brain, we may soon be able to better predict which drugs will help which people, researchers, clinical trials and countless patients and those who care about them.

Used with permission.

Danilo Bzdok, MD, Ph.D., Ph.D.

Source: Used with permission.

Danilo Bzdok, MD, Ph.D., Ph.D., is an associate professor, Department of Biomedical Engineering, Faculty of Medicine, McGill University; Canadian Institute for Advanced Research (CIFAR) Chair of Artificial Intelligence at the Mila-Quebec Artificial Intelligence Institute (Montreal). The research center of Dr. Bzdok focuses on reducing knowledge gaps in the brain base of human-determining types of thinking, with a special focus on the higher association cortex in health and disease.

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