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Researchers in the United States, Chile, and the United Kingdom have leveraged machine learning to hone in on the parts of the brain responsible for psychosis. Their findings help to illuminate a common yet elusive experience and could contribute to the development of novel treatments for psychosis and the conditions that cause it.

Around 3 in every 100 people will experience at least one psychotic episode in their lifetimes. Commonly misunderstood, these episodes are characterized by hallucinations (a false perception involving the senses) or delusions (false beliefs not rooted in reality). Many people who experience psychosis have a condition like schizophrenia or bipolar disorder; others have a history of substance abuse, and still others have no particular condition at all.

Regardless of its cause, psychosis can be debilitating for those who experience it, leading some people to seek out antipsychotic medication aimed at staving off future episodes. Though antipsychotic medications are often a godsend for the people who take them, they've historically disrupted neurological psychosis research. During brain scans, it's difficult to know whether specific brain activity can be attributed to the person's condition or to the drugs they're taking. This means medical professionals and pharmaceutical companies work with a fairly limited understanding of psychosis as they help patients manage their episodes.

Researchers at Stanford University, the University of California Los Angeles, Universidad del Desarrollo, and the University of Oxford relied on two strategies to circumvent this issue. To start, they gathered study participants from a wide range of ages and conditions in the hope of uncovering an overarching theme. The group of nearly 900 participants included people ages 6 to 39, some of whom had a history of psychosis or schizophrenia and some of whom had never experienced either. Just over 100 participants had 22q11.2 deletion syndrome, meaning they're missing part of one of their copies of chromosome 22a condition known to carry a 30% risk of experiencing psychosis, schizophrenia, or both. Another 120 participants experienced psychosis but had not been diagnosed with any particular hallucination- or delusion-causing condition.

Credit: Supekar et al, Molecular Psychiatry/DOI 10.1038/s41380-024-02495-8

The team also used machine learning to spot the minute distinctions between the brain activity of those who experience psychosis and the brain activity of those who don't. To map out the participants' neurological activity, the team used functional magnetic resonance imaging (fMRI). This technique allows medical professionals and researchers to track the tiny fluctuations in blood flow triggered by brain changes.

With a custom spatiotemporal deep neural network (stDNN), the researchers compared the functional brain signatures of all participants and found among those with 22q11.2 deletion syndrome. Regardless of demographic, these participants experienced what appeared to be "malfunctions" in the anterior insula and the ventral striatum. These two parts of the brain are involved in humans' cognitive filters and reward predictors, respectively. The stDNN continued to find clear discrepancies between the anterior insulae and ventral striata of those who experienced psychosis and those who did not, further indicating that these two regions of the brain played a vital role in hallucinations and delusions.

These findings, shared Friday in a paper for Molecular Psychiatry, support a standing theory regarding the reliance of psychosis on malfunctioning cognitive filters. Scientists have long wondered whether, during a psychotic episode, the brain struggles to distinguish what's true from what isn't. This is a key function of the brain's salience network, which detects and assigns importance to incoming stimuli. When the salience network cannot work correctly, the brain might incorrectly assign importance and attention to the wrong stimuli, resulting in a hallucination or delusion.

Our discoveries underscore the importance of approaching people with psychosis with compassion, said Stanford neuroscientist and senior study author Dr. Vinod Menon in a statement. Menon and his colleague, psychiatrist Kaustubh Supekar, hope their findings will assist in the development of antipsychotic treatments, especially for those with schizophrenia.

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Machine Learning Helps Scientists Locate the Neurological Origin of Psychosis - ExtremeTech