Researchers Discover Second Type of Schizophrenia

Penn Medicine researchers are the first to discover two distinct neuroanatomical subtypes of schizophrenia after analyzing the brain scans of over 300 patients. The first type showed lower widespread volumes of gray matter when compare to healthy controls, while the second type had volumes largely similar to normal brains. The findings, published in the journal Brain, suggest that, in the future, accounting for these differences could inform more personalized treatment options.

“Numerous other studies have shown that people with schizophrenia have significantly smaller volumes of brain tissue than healthy controls. However, for at least a third of patients we looked at, this was not the case at all — their brains were almost completely normal,” said principal investigator Christos Davatzikos, PhD, the Wallace T. Miller Professor of Radiology in the Perelman School of Medicine at the University of Pennsylvania. “In the future, we’re not going to be saying, ‘This patient has schizophrenia,’ We’re going to be saying, ‘This patient has this subtype’ or ‘this abnormal pattern,’ rather than having a wide umbrella under which everyone is categorized.”

Schizophrenia is a poorly understood mental disorder that typically presents with hallucinations, delusions, and other cognitive issues — though symptoms and responses to treatment vary widely from patient to patient. Up until now, attempts to study the disease, by comparing healthy to diseased brains, has neglected to account for this heterogeneity, which Davatzikos says has muddled research findings and undermined clinical care.

To better characterize the distinct brain differences within the schizophrenia patient population, Davatzikos established a research consortium that spanned three continents — the United States, China, and Germany. The international cohort of study participants included 307 schizophrenia patients and 364 healthy controls, all of whom were 45-years-old or younger.

Davatzikos and engineering colleagues then analyzed the brain scans using a machine learning method developed at Penn called HYDRA (Heterogeneity Through Discriminative Analysis). The approach helps to identify “true disease subtypes” by limiting the influence of confounding variables, such as age, sex, imaging protocols, and other factors, according to the study authors.

“This method enabled us to sub-categorize patients and find how they differed from the controls, while allowing us, at the same time, to dissect this heterogeneity and tease out multiple pathologies, rather than trying to find a dominant pattern,” Davatzikos said.

After applying this machine learning method to the brain images, the researchers found that 115 patients with schizophrenia, or nearly 40 percent, did not have the typical pattern of reduced gray matter volume that has been historically linked to the disorder. In fact, their brains showed increases of brain volume in the middle of the brain, in an area called the striatum, which plays a role in voluntary movement. When controlling for differences in medication, age, and other demographics, the researchers could not find any clear explanation for the variation.

“The subtype 2 patients are very interesting, because they have similar demographic and clinical measures with subtype 1, and the only differences were their brain structures,” said Ganesh Chand, PhD, a lead author and postdoctoral researcher in the radiology department at Penn.

There are a variety of antipsychotic medications available to manage the symptoms of schizophrenia, but how they will affect a particular patient — both positively or negatively — is often a shot in the dark, according to study co-senior author Daniel Wolf, MD, PhD, an associate professor of Psychiatry at Penn.

“The treatments for schizophrenia work really well in a minority of people, pretty well in most people, and hardly at all in a minority of people. We mostly can’t predict that outcome, so it becomes a matter of trial and error,” Wolf said. “Now that we are starting to understand the biology behind this disorder, then we will hopefully one day have more informed, personalized approaches to treatment.”

As to why an entire subset of patients with schizophrenia have brains that resemble healthy people, Davatzikos is not willing to speculate.

“This is where we are puzzled right now,” Davatzikos said. “We don’t know. What we do know is that studies that are putting all schizophrenia patients in one group, when seeking associations with response to treatment or clinical measures, might not be using the best approach.”

Future research, he said, will provide a more detailed picture of these subtypes in relation to other aspects of brain structure and function, clinical symptoms, disease progression, and etiology.

Imperfect Education

Making observations of life is the most fundamental discovery process of biology. Today’s imaging technology shows scientists more detail than ever before, with vast and complex image sets produced in labs around the world every day. Analysing these data takes specialist skills and lots of time, and is subject to the individual analyst’s subjective perspective. Automating the process would speed it up and remove inconsistencies. Machine algorithms must be trained by examples, however, so it’s possible that they might replicate the subjectivity they aim to eradicate if trained by one expert’s examples. To investigate, researchers compared algorithms trained on a single expert’s work to those trained on many experts’ analyses (pictured, with different experts’ marks on a mouse brain image in different colours). Training the algorithm with many people’s input reduced subjectivity and improved outcomes, showing that many teachers are better than one.

Written by Anthony Lewis

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Blob Opera 

David Li and Google Arts and Culture made this little machine-learning experiment to create opera-singing blobs.

The music is created with a machine learning model that learned about opera by listening to Tenor, Christian Joel, bass Frederick Tong, mezzo‑soprano Joanna Gamble and soprano Olivia Doutney (plus Ingunn Gyda Hrafnkelsdottir and John Holland-Avery). It’s not playing their audio--rather, it’s playing what the model thinks opera music sounds like based on analyzing 16 hours of their singing.

I like how the eyes watch the cursor - that’s one of those little procedural animation touches that’s simple but adds a lot of life.

Learning Data Science where to start?

I often come across youngsters, working people and IT pros who ask me how to pursue data science as career?

Start learning statistics and meaning of statistical algorithms and their usage. Buy book to start reading book – "Statistics For Dummies (For Dummies (Lifestyle))" by Deborah J. Rumsey.
Learn python to start with and visit w3schools for simple easy to understand format and language.
Don't start technical.. It wouldn't help and after sometime you will be frustrated and may loose motivation to advance yourself in this field
Don't believe in courses that try to convince you that you will get 5 figure salary once you completed certification with them.
Superhard materials are in high demand in industry, from energy production to aerospace, but finding suitable new materials has largely been a matter of trial and error based on classical materials such as diamonds. Until now.
Researchers from the University of Houston and Manhattan College have reported a machine learning model that can accurately predict the hardness of new materials, allowing scientists to more readily find compounds suitable for use in a variety of applications. The work was reported in Advanced Materials.
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“That makes identifying new materials challenging,” said Jakoah Brgoch, associate professor of chemistry at the University of Houston and corresponding author for the paper. “That is why materials like synthetic diamond are still used even though they are challenging and expensive to make.”