IBM develops algorithm to diagnose schizophrenia from fMRI scans
IBM has collaborated with the University of Alberta to create a software tool to analyse functional magnetic resonance imaging scans to diagnose schizophrenia. The tool uses algorithms to examine patients’ brain scans and diagnose symptoms with a 74% accuracy rate, as well as determining their severity. Using 95 scans from diagnosed schizophrenia patients, the research team trained a deep learning algorithm to recognise connectivity changes and correlations in brain sectors to determine severity.

A scientific collaboration between researchers at IBM and the University of Alberta in Canada has come up with a software tool that analyzes functional magnetic resonance imaging (fMRI) scans of patient brains and with 74% accuracy diagnoses schizophrenia. Moreover, the software’s algorithms were also able to reasonably estimate how bad the symptoms of the disease were in individual patients.

The team used fMRI data from the Function Biomedical Informatics Research Network that contains a large number and variety of imaging scans of brains of different patients. The investigators took 95 scans from a collection of schizophrenia patients and used them as input to a deep learning algorithm that found correlations in the scans to develop an understanding of what schizophrenia looks like. They then ran the algorithm against a set of scans of patients with the disease and without, demonstrating that the software is able to spot three quarters of the scans showing the disease.

The team published their findings in journal Schizophrenia Research. Here’s a bit from the abstract:

Whole-brain link-weight features achieved 74% accuracy in identifying patients and were more stable than voxel-wise node-degrees. Link-weight features predicted severity of several negative and positive symptom scales, including inattentiveness and bizarre behavior. The most-signiﬁcant, stable and discriminative functional connectivity changes involved increased correlations between thalamus and primary motor/primary sensory cortex, and between precuneus (BA7) and thalamus, putamen, and Brodmann areas BA9 and BA44. Precuneus, along with BA6 and primary sensory cortex, was also involved in predicting severity of several symptoms. Overall, the proposed multi-step methodology may help identify more reliable multivariate patterns allowing for accurate prediction of schizophrenia and its symptoms severity.

Image: Regions of the brain that showed a statistically significant difference between patients with schizophrenia and patients without it. For example, arrow 1 identifies the precentral gyrus, or the motor cortex, and arrow 5 marks the precuneus, which involves processing visual information.

Study in journal Schizophrenia Research: Learning stable and predictive network-based patterns ofschizophrenia and its clinical symptoms…

Via: IBM…