Brazilian Researchers Propose A Quantitative Diagnostic Method For Autism Spectrum Disorder
A new quantitative method for precise autism spectrum disorder diagnosis
Technology
Neurologist ConsultationResearchers in Brazil have developed a new diagnostic method for autism spectrum disorder (ASD) using brain imaging data and machine learning. ASD is a complex neurodevelopmental disorder with varied symptoms and no precise biochemical markers for diagnosis. The study used functional magnetic resonance imaging (fMRI) and electroencephalogram (EEG) data from 500 individuals, half of whom had ASD. The researchers applied machine learning techniques to the data, creating brain maps or cortical networks that illustrated how brain regions were connected. The study demonstrated that this method could accurately identify ASD-associated brain alterations with over 95% accuracy.
This approach is innovative because it considers brain network organization, unlike previous studies that relied on single statistical parameters. The research showed that the cortical networks of ASD patients displayed less connectivity, more segregation, and less information distribution compared to controls. The findings suggest that brain alterations are linked to specific behaviors in ASD patients. Although the methodology is still in development and will require more time to implement, it holds potential for assisting specialists in cases of diagnostic uncertainty.
This research also has broader applications, such as diagnosing other conditions like schizophrenia and Alzheimer's disease using brain maps and artificial intelligence. The researchers hope that understanding the relationships between different mental disorders through brain alterations could lead to more effective treatments and policies in the future. The study involved an interdisciplinary team of physicists, statisticians, physicians, and neuroscientists from Brazil, France, and Germany, reflecting the complexity of the subject and its potential impact on various fields.
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