With his group at the Max Planck Institute of Psychiatry in Munich, Germany, he studies how mathematics and statistics can prove to be a useful tool to determine the most effective treatment in psychiatric disorders.
Differences among individuals are critical when issuing a diagnosis or deciding a course of treatment. Discernment of certain psychiatric diseases such as major depression, PTSD, and schizophrenia is mostly based on symptoms weighted by the physician, but there are other variables that may influence the decision on a course of treatment or even a differential diagnosis.
Bertram Müller-Myhsok leads the Statistical Genetics group at the Max Planck Institute of Psychiatry in Munich, Germany, and in his visit at IBioBA, he explained that the common theme of his investigations is individualized medicine.
“There are people that may have medical problems and their treatment often is decided via a fairly standard scheme, ‘we use this diagnosis, this medication, this treatment’, and this does not credit how different people are. And there might be underlying differences among patients, groups of symptoms, or diagnosis hidden under a common heading”, he explains.
For that, they analyze different sets of data such as genetics, transcriptomics, metabolomics, and imaging, as well as shared and differential symptoms, to discriminate and individualize each condition.
For instance, in major depression, the prediction of treatment effects is an area of intense study, as well as whether stable, latent treatment subtypes can be detected and predicted.
“In the psychiatric field there is actually a big movement now to redefine diagnostic categories because they have found that they do not really correlate so well with treatment responses, so there’s a lot of development back and forth to redefine them in a data-driven matter”, says Müller-Myhsok.
However, the diagnosis of psychiatric disorders historically relies on subjective variables estimated by the physician and one of the most defying tasks of this research team is to include them in the data analysis.
“We may not be able to translate them into numbers directly, but we can still deal with categories and the general concept to add them to the equation. From a medical perspective, we want to be able to fine-tune the treatment, basically down to the patient level, and for each patient as much as we can”, he concludes.