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Brain Scan Study Discovers a New Type of Schizophrenia
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by Rich Haridy for New Atlas

February 27, 2020

Compelling new research led by scientists from the University of Pennsylvania has used a novel machine learning method to analyze hundreds of brain scans from patients with schizophrenia. The results reveal significant differences in gray matter volumes, distinguishing two distinct types of schizophrenia and busting the previously held notion that all schizophrenia patients brains are the same. 

In 2013, the American Psychiatric Association (APA) eliminated a number of different diagnostic classifications for schizophrenia, and rolled them all into a single category. The APA at the time noted these different subtypes of schizophrenia had “limited diagnostic stability, low reliability, and poor validity.” These former subtypes were not based on any objective physiological biomarkers, but instead relied on observations of behavior and symptoms.

A number of impressive research breakthroughs over the past few years have offered new insights into the origins of schizophrenia, from links with gut bacteria and vitamin D deficiencies, to novel diagnostic methods using hair samples and eye scans. However, those diagnosed with the disease have generally been gathered under the same umbrella of ‘schizophrenia.' This is despite the extraordinarily heterogeneous nature of the disease with notable variations in symptoms and treatment responses from patient to patient.

This new research set out to apply a machine learning method called HYDRA (Heterogeneity Through Discriminate Analysis) to over 300 MRI brain scans from schizophrenia patients spanning three continents. The results are challenging the prevailing notion that a general neuroanatomical feature of schizophrenia is lower volumes of gray matter in several brain regions.

Read the full article at this link.


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