Release date: 2017-08-01 Recently, IBM scientists and the University of Alberta, Edmonton, Canada, released new data in the journal Nature Journal of Schizophrenia, demonstrating that AI and machine learning algorithms are accurate at 74%. Rate helps predict schizophrenia cases. This retrospective analysis also showed that the technique was able to predict the severity of specific symptoms in patients with schizophrenia from a high correlation based on the association between activities observed in different regions of the brain. This groundbreaking study can also help scientists identify more reliable and objective Neuroimaging Biomarkers for predicting schizophrenia and its severity. Schizophrenia is a chronic debilitating neurological disorder that affects 7 to 8 people per 1,000 people. People with schizophrenia may have hallucinations, delusions, or mental disorders, as well as cognitive impairments such as inability to concentrate and physical impairments, such as movement disorders. Dr. Serdar Dursun, professor of psychiatry and neuroscience at the University of Alberta, said: "This unique and innovative multidisciplinary approach deepens our understanding of the neurobiological principles of schizophrenia and can help improve the treatment and treatment of this disease. management. We have found many important anomalous connections in the brain, and future studies can explore these connections, and the models created by AI allow us to take a step further from discovering objective models based on neuroimaging, which can be used as a diagnosis of schizophrenia. Prognostic indicators. †In the paper, the researchers analyzed de-identified brain functional magnetic resonance imaging (fMRI) data from the Open Dataset Biomedical Informatics Functional Research Network (fBIRN), which included both schizophrenia and schizoaffective disorders. The patient also included a healthy experimental control group. fMRI measures brain activity through changes in blood flow in specific areas of the brain. Specifically, the fBIRN dataset reflects the study of brain network execution at different levels of clarity based on data collected during a normal auditory test of the survey participants. By examining scanned images from 95 participants, the researchers used machine learning techniques to develop a model of schizophrenia that identifies the closest connections in the brain to the disease. From the above figure, we can see that some brain regions show statistically significant differences between patients with schizophrenia and those without the disease. For example, arrow 1 represents the central front (Precentral Gyrus) and arrow 5 represents the precunels (Precuneus) involved in processing visual information. Quantitative study of mental illness Research by IBM and the University of Alberta shows that machine learning algorithms use activity associations between different brain regions even on more challenging neuroimaging data collected from multiple sites (different machines, across different subject groups, etc.) It can also distinguish schizophrenia patients from experimental control groups with an accuracy of 74%. In addition, studies have shown that functional network connections can also help determine the severity of multiple symptoms exhibited by patients, including attention deficits, behavioral disorders, and thought-form disorders, as well as aphasia (poor speech) and lack of motivation. By predicting the severity of symptoms, a more quantitative, measurement-based schizophrenia can be obtained. The disease can be determined in a range, not just a binary label (diagnostic or non-diagnostic). This objective, data-driven, severe-level analysis method can ultimately help clinicians tailor treatment options for patients. Ajay Royyuru, vice president of medical and life sciences at IBM Research, said: "The ultimate goal of this research effort is to identify and develop objective, data-driven measurements to characterize mental states and apply them to psychiatry and neurological disorders. It also hopes to provide new insights into how AI and machine learning can be used to analyze mental and neurological disorders and to help psychiatrists assess and treat patients." The NIMH (National Institute of Mental Health) Research Area Standards (RDoC) initiative emphasizes the importance of objective measures in psychiatry. This area, often referred to as Computational Psychiatry, aims to improve evidence-based medical decisions in psychiatry using modern technology and data-driven methods, often relying on subjective assessment methods. As part of this ongoing collaboration, researchers will continue to investigate areas and connections in the brain that are importantly linked to schizophrenia. We will continue to work to improve these algorithms, perform machine learning analysis on larger data sets, and explore ways to extend these technologies to other mental illnesses such as depression or post-traumatic stress disorder. Source: Arterial network (micro signal: vcbeat) Air pollution is getting more and more serious. Sudden virus outbreaks can harm the human body in many ways. However, if you do not protect yourself in time, you will be the next victim. Therefore, it is urgent to protect your health. Protective masks, breathe freely, I will protect your health. 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