How AI may help diagnose mental illnesses

Brain information was obtained from fMRI during the resting stage. Researchers divided the whole brain into different regions or parcels. This was done in 14 different ways based on similarities in volume, surface, connectivity etc. From each method of dividing the brain, information was derived on three features based on the region and three features based on connectivity of the brain. These parameters included frequency of brain waves, correlation between brain activity of closely-placed regions, and connectivity between different brain regions. These features were chosen as previous studies show they are altered in a schizophrenic brain.

This helped researchers collate 84 points of data (from 14 brain division schemes, and 6 features extracted from each scheme) from each subject. Using these data points from healthy and schizophrenic patients, the group has built a model that could predict schizophrenia with an accuracy of 87%. The model has been named “EMPaSchiz” or ‘Ensemble algorithm with Multiple Parcellations for Schizophrenia prediction’.

“The classification accuracy our model outperforms earlier machine learning models built for diagnosing schizophrenia using resting state fMRI on large samples,” said Ganesan Venkatasubramanian, a member of the research team, while speaking to India Science Wire.

More research is needed on the model before user-friendly software can be generated, he added. He hoped that such automated and semi-automated diagnostic tools could be developed for detecting other kinds of mental disorders and help predict treatment strategies.

The research team included Rimjhim Agrawal, Venkataram Shivakumar, Janardhanan C. Narayanaswamy, and Ganesan Venkatasubramanian (NIMHANS); Sunil Vasu Kalmady, Matthew R. G. Brown, Andrew J Greenshaw, Serdar M Dursun, Russell Greiner (Alberta Machine Intelligence Institute, University of Alberta). This study has been published in the journal Schizophrenia.

By Dr P Surat

India Science Wire