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 Diag**ses 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 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 **de-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.
07-22-2017, 11:27 AM
Deep Learning Algorithm Diag**ses Schizophrenia from fMRI Scans
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