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Computer identifies differences in brains of patients with psychiatric disorders

Image credit: Chernetskaya/Dreamstime

Researchers in Japan have developed a machine-learning algorithm that distinguishes between different psychiatric disorders with similar symptoms, such as schizophrenia or autism.

Most of modern medicine has physical tests or objective techniques to define much of what afflicts our minds. But there are currently no blood or genetic tests, or impartial procedures that can definitively diagnose a mental illness. There are also none, as of late, that distinguish between different psychiatric disorders with similar symptoms. To tackle this issue, a study conducted by researchers at the University of Tokyo have combined machine learning with brain imaging tools to “redefine” this standard for diagnosing mental illnesses.

“Psychiatrists, including me, often talk about symptoms and behaviours with patients and their teachers, friends, and parents. We only meet patients in the hospital or clinic, not out in their daily lives. We have to make medical conclusions using subjective, secondhand information,” explained Dr Shinsuke Koike, an associate professor at the university. “Frankly, we need objective measures.”

Other researchers have previously designed machine-learning algorithms to distinguish between those with a mental health condition and non-patients who volunteer as “controls” for such experiments. “It’s easy to tell who is a patient and who is a control, but it is not so easy to tell the difference between different types of patients,” said Koike.

The UTokyo research team said theirs is the first study to differentiate between multiple psychiatric diagnoses, including autism spectrum disorder and schizophrenia. Although depicted very differently in popular culture, scientists have long suspected autism and schizophrenia are somehow linked.

“Autism spectrum disorder patients have a 10-times higher risk of schizophrenia than the general population,” Koike explained. “Social support is needed for autism, but generally the psychosis of schizophrenia requires medication, so distinguishing between the two conditions or knowing when they co-occur is very important.”

As part of the study, the multidisciplinary team of medical and machine-learning experts trained their computer algorithm using MRI (magnetic resonance imaging) brain scans of 206 Japanese adults. Among the participants was a combination of patients already diagnosed with autism spectrum disorder or schizophrenia, those who experienced their first instance of psychosis, as well as neurotypical people with no mental health concerns.

MRI images like this one were screened by a machine learning computer algorithm designed by the research team. The algorithm learned to identify the brains of nonpatients, patients diagnosed with autism, and patients diagnosed with schizophrenia based on subtle but statistically important differences in the thickness, volume, or surface area of certain regions of the brain.

MRI images like this one were screened by a machine learning computer algorithm designed by the research team. The algorithm learned to identify the brains of non-patients, patients diagnosed with autism, and patients diagnosed with schizophrenia based on subtle but statistically important differences in the thickness, volume, or surface area of certain regions of the brain.

Image credit: Shinsuke Koike, CC-BY, University of Tokyo

Machine learning uses statistics to find patterns in large amounts of data. These programs find similarities within groups and differences between groups that occur too often to be easily dismissed as coincidence. This study used six different algorithms to distinguish between the different MRI images of the patient groups.

The algorithm used in the study learned to associate different psychiatric diagnoses with variations in the thickness, surface area or volume of areas of the brain in MRI images. The team stressed that it is not yet known why any physical difference in the brain is often found with a specific mental health condition.

After the training period, the algorithm was tested with brain scans from 43 additional patients. Here, the machine’s diagnosis matched the psychiatrists’ assessments with high reliability and up to 85 per cent accuracy, according to the researchers.

The researchers said that the success of distinguishing between the brains of non-patients and individuals at risk for schizophrenia could reveal that the physical differences in the brain that cause schizophrenia is present even before symptoms arise and then remain consistent over time.

The research team also noted that the thickness of the cerebral cortex, the top 1.5 to 5cm of the brain, was the most useful feature for correctly distinguishing between individuals with autism spectrum disorder, schizophrenia, and typical individuals. This could unravel an important aspect of the role thickness of the cortex plays in distinguishing between different psychiatric disorders and may direct future studies to understand the causes of mental illness.

Furthermore, although the research team trained their machine learning algorithm using brain scans from approximately 200 individuals, all of the data was between 2010 to 2013 on one MRI machine. This ensured the images were consistent.

“If you take a photo with an iPhone or Android camera phone, the images will be slightly different,” Koike explained. “MRI machines are also like this – each MRI takes slightly different images, so when designing new machine learning protocols like ours, we use the same MRI machine and the exact same MRI procedure.”

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