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Using Machine Learning to Diagnose ASD and Schizophrenia

By: Dinesh Patel

In recent years, evidence supporting a link between inflammation and neuropsychiatric disorders has been mounting. The diagnosis of ASD and schizophrenia relies solely on clinical symptoms, and to date, there is no clinically useful biomarker to diagnose or monitor the course of such illnesses. In a new study, researchers started analyzing how machine learning can help us understand the similarities and differences in the brains of patients with schizophrenia and autism spectrum disorder.


Schizophrenia is a severe mental illness with positive symptoms such as hallucinations. People with schizophrenia also suffer from disorganization and negative symptoms. Patients may find it hard to socialize and find employment. Schizophrenia is considered one of the most burdensome illnesses in the world. For some people, it can be a lifelong condition.


Subsequently, understanding the disease processes of complex neurodevelopmental disorders, such as Autism Spectrum Disorder (ASD), has been a focus of research for many years. An ability to organize and semantically integrate subject data concerning phenotypic manifestations as well as genetic and environmental risk factors among cohorts of ASD subjects could yield important knowledge regarding commonalities and differences that characterize subtypes of ASD, and also help elucidate the processes underlying the development of the disorder, whose mechanisms are still unknown.


Most of the modern medicine has physical tests or objective techniques to define much of what ails us. Yet, there is currently no blood or genetic test, or impartial procedure that can definitively diagnose a mental illness, and certainly none to distinguish between different psychiatric disorders with similar symptoms. Experts at the University of Tokyo are combining machine learning with brain imaging tools to redefine the standard for diagnosing mental illnesses.


Other researchers have designed machine learning algorithms to distinguish between those with a mental health condition and nonpatients who volunteer as controls for such experiments.


The UTokyo research team says 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 to be somehow linked.


Autism spectrum disorder patients have a 10-times higher risk of schizophrenia than the general population. 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.


A multidisciplinary team of medical and machine learning experts trained their computer algorithm using MRI (magnetic resonance imaging) brain scans of 206 Japanese adults, a combination of patients already diagnosed with autism spectrum disorder or schizophrenia, individuals considered high risk for schizophrenia, and those who experienced their first instance of psychosis, as well as neurotypical people with no mental health concerns. All of the volunteers with autism were men, but there was a roughly equal number of male and female volunteers in the other groups.


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 this study learned to associate different psychiatric diagnoses with variations in the thickness, surface area, or volume of areas of the brain in MRI images. 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. The machine’s diagnosis matched the psychiatrists’ assessments with high reliability and up to 85 percent accuracy.


Importantly, the machine learning algorithm could distinguish between nonpatients, patients with an autism spectrum disorder, and patients with either schizophrenia or schizophrenia risk factors. The research team notes that the success of distinguishing between the brains of nonpatients and individuals at risk for schizophrenia may 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 5 centimeters of the brain, was the most useful feature for correctly distinguishing between typical individuals and individuals with autism spectrum disorder and schizophrenia. This unravels 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.


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


If you take a photo with an iPhone or Android camera phone, the images will be slightly different. MRI machines are also like this – each MRI takes slightly different images, so when designing new machine learning protocols, the team used the same MRI machine and the same MRI procedure.


Using data from MRI brain scan images, machine learning was 85% accurate at providing a diagnosis of psychiatric disorders that matched psychiatrists’ assessments. The algorithm could also distinguish between patients with ASD, schizophrenia, risk factors for psychosis, and those with no history of mental health problems.


Now that their machine learning algorithm has proven its value, the researchers plan to begin using larger datasets and hopefully coordinate multisite studies to train the program to work regardless of the MRI differences.

References:

1. Addington J, Epstein I, Liu L, French P, Boydell KM, Zipursky RB. A randomized controlled trial of cognitive-behavioral therapy for individuals at clinical high risk of psychosis. Schizophrenia Research. 2011.

2. Mugzach O, Peleg M, Bagley SC, Guter SJ, Cook EH, Altman RB. An ontology for autism spectrum disorder (ASD) to infer ASD phenotypes from autism diagnostic interview-revised data. Journal of Biomedical Informatics. 2015.

3. Shinsuke Koike, Walid Yassin, Hironori Nakatani, Yinghan Zhu, Masaki Kojima, Keiho Owada, Hitoshi Kuwabara. Machine learning classification using neuroimaging data in schizophrenia, autism, ultra-high risk, and first-episode psychosis. Translational Psychiatry. 2020.

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