Depression affects more than 300 million people worldwide, and while Cognitive Behavioural Therapy (CBT) can be an effective treatment, it does not work for everyone (only 45% of patients).
At present, it is not possible for medical professionals to tell in advance who is going to benefit from CBT and who is not.
But a new study has shown how the brain activity of patients with depression, captured by MRI scans, may help doctors predict who will respond to the therapy.
The research, led by the University of Glasgow and co-authored by the University of Plymouth, shows that brain activity recorded using functional magnetic resonance imaging (fMRI) may help predict response to CBT in depression before the treatment commences. The advances could help patients receive the most appropriate treatment for depression in a timely manner.
“This study involved scanning the patients before CBT and checking their psychological state after CBT in order to find out which brain signals predict their treatment responses – we basically assessed whether they were still depressed or not following CBT.
“We then found that some brain areas in the reward circuitry were differently activated if someone was going to benefit from CBT, showing for the first time a biomarker for potentially predicting the response of clinically depressed patients to CBT.
“CBT is a well-known method of treating depression, but doctors currently have to rely on a trial-and-error approach to see if it will work. This increases the strain on the health care system and prevents some patients, for whom CBT does not work, from getting an alternative course of treatment – so this study is a major step."
The researchers recruited patients with depression, who engaged with self-help internet delivered CBT – the first line of recommended treatment in the UK for mild to moderate depression.
Before starting treatment, participants performed a reinforcement learning task, where on each trial they chose from two options and had to figure out which option was the most rewarding based on feedback information. Brain activity was recorded using fMRI while subjects performed the task inside an MRI scanner. The researchers then fitted mathematical models to the observed choice behaviour and used the best fitting model to analyse fMRI data.
Importantly, rather than focusing only on group differences between CBT responders and non-responders, the authors tested whether the brain activity of individual subjects could predict response to CBT.
Dr Filippo Queirazza, lead author of the paper from the University of Glasgow’s Institute of Neuroscience and Psychology, said:
“So far most fMRI studies that have looked for a brain signature of treatment response in depression have reported on average differences between responders and non-responders and then assumed that these differences generalise to each individual, but this may not be the case. In our study, we show that fMRI activity classifies CBT response at the individual level, with around 80% predictive power."
Dr Marios Philiastides, senior author of the paper, said:
“We explicitly model the mechanisms of treatment response to uncover brain activity that predicts CBT response. While this approach has the potential to enhance the predictive power of imaging biomarkers it can also provide important insights into novel targets for drug development.”
The full study, entitled Neural correlates of weighted reward prediction error during reinforcement learning classify response to cognitive behavioral therapy in depression is now available to view in the journal Science Advances (doi: 10.1126/sciadv.aav4962).