A New, Quick Approach to Predict Future Course of Psychosis

A New, Quick Approach to Predict Future Course of Psychosis

Posted: January 19, 2012

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From The Quarterly, Winter 2012

Paola Dazzan, M.D., who leads early psychosis research at the Institute of Psychiatry, King’s College London, has pursued the goal of applying neuroimaging to the study of psychosis with support from three NARSAD Grants. In this most recent work, she, and King’s College and University College London colleague Dr. Janaina Mourao-Miranda, led a multi-institutional collaboration in the UK.

Dr. Dazzan has been seeking to find a reliable way to predict how psychotic illness will develop after a first psychotic break. What currently happens can range from recovery with minimal symptoms to the opposite extreme of persistent psychosis with serious cognitive and functional loss. Delusions and hallucinations are the hallmark symptoms of schizophrenia and can occur in some other mental illnesses as well, but there has been no way of knowing what course a psychotic illness will take in a particular individual. This inability to predict a patient’s future illness has made it difficult to know what treatment to prescribe.

In recent years, the introduction of magnetic resonance imaging (MRI) has made it possible to visualize brain structure, but the neuroanatomical changes that occur in early psycho-sis are too subtle for standard MRI to be useful diagnostically. Now, a study conducted by Dr. Dazzan and colleagues has provided preliminary evidence that an innovative technique to evaluate MRI, called support vector machine (SVM) MRI, can be used to predict the course of illness. Reporting in the Nov. 7 online edition of the journal Psychological Medicine, the scientists state that if validated in larger trials, their finding “could enable targeted clinical decisions based on imaging data.” [Published in print: May 2012 Edition of the journal of Psychological Medicine.]

“This is the first step towards being able to use brain imaging to provide tangible benefit to patients affected by psychosis," says Dr. Dazzan. “This could offer a fast and reliable way of predicting the outcome for an individual patient, allowing us to optimize treatments for those most in need, while avoiding long-term exposure to antipsychotic medications in those with mild forms.”

The researchers began by making MRI scans of 100 patients at the time of their first psychotic episode and of 91 healthy controls. The patients were then re-examined six years later and classified as having experienced a continuous, episodic or intermediate course of illness. The team then applied SVM to these MRI scans to study changes in brain structure, creating computer algorithms that could identify, at the time of this first scan, patients who would then experience long periods of remission and those who would remain continuously unwell. Algorithms that quantify risk of future episodes of disease are common in other medical areas, such as cardiovascular medicine and oncology, but have not been available for mental illnesses.

According to Dr. Dazzan, the technique she and her group have refined is easy to apply—a ten-minute test that could be incorporated into routine clinical investigations. “The information this provides,” she states, “could help inform the treatment options available to each patient and help us better manage their illness."

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