Grey Matter Volume Can Be Used To Predict Mental Health Treatment Outcomes, Study Reveals

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Mental Health News

A research team at the University of Birmingham revealed how grey matter volume in brains can be used to predict treatment outcomes in mental disorders like psychosis and depression. The study is published in Biological Psychiatry.

The Study

The researchers examined data from 300 patients with recent onsets of psychosis and/or depression. They assessed structural MRI scans of the participants’ brains and used a machine-learning algorithm to sort the data into two groups or “clusters”. Each cluster revealed distinct markers related to the causes of illness and patient outcomes. The results were then cross-checked with similar studies conducted in the US and Germany.

The Findings

The results showed that patients with lower volumes of grey matter experienced higher levels of cognitive impairments (like inflammation and poorer concentration) associated with depression and schizophrenia. They also had poorer treatment outcomes. On the other hand, patients with a higher grey matter volume showed a greater likelihood of recovery from their illnesses.

One of the lead researchers, Paris Alexandros Lalousis, elaborated: “We found that the longer the duration of illness, the more likely it was that a patient would fit into the first cluster with lower grey matter volume.”

Drawing Inferences

The study is the first of its kind to use cluster-based biological information—rather than traditional diagnostic measures like patient history, symptoms, clinical observations, etc.—to predict patient outcomes. The researchers are enthusiastic that such a diagnostic system can offer more accurate predictions of treatment outcomes in the early stages of illness. It can also help formulate better treatment plans and prognostic tools for mental health disorders.

To Know More You May Refer To

Lalousis, P. A., Schmaal, L., Wood, S. J., Reniers, R. L., Barnes, N. M., Chisholm, K., Griffiths, S. L., Stainton, A., Wen, J., Hwang, G., Davatzikos, C., Wenzel, J., Kambeitz-Ilankovic, L., Andreou, C., Bonivento, C., Dannlowski, U., Ferro, A., Liechtenstein, T., Riecher-Rössler, A., … Upthegrove, R. (2022). Neurobiologically based stratification of recent onset depression and psychosis: Identification of two distinct transdiagnostic phenotypes. Biological Psychiatry.

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