Depression treatment using AI, brain scans

Depression treatment using AI, brain scans

Online version of MBCT for lingering depression symptoms improves access to care To confirm these findings, three more groups of patients were formed to test them. The machine used data from earlier studies to learn what patterns of brain activity were actually linked to the kind of brain activity impairment that improves with antidepressant therapy. This data came from the EMBARC trial for patients with major depression. The trial lasted 16 weeks at four different locations in the US. The aim was to put depression diagnosis and management on a firm foundation. The researchers used brain imaging, DNA testing, blood samples and other relevant tests to devise a biological rather than subjective method to treat these mood disorders. The motivation was provided by a still earlier study, called STAR*D , in which the scientists found that up to a third of patients failed to achieve clinical improvement to an adequate level with the first antidepressant they tried. Therefore, says Trivedi, “We went into this thinking, 'Wouldn't it be better to identify at the beginning of treatment which treatments would be best for which patients?” With EMBARC, older research has assessed the role of magnetic resonance imaging (MRI) of the brain at rest as well as during emotional processing, along with other tests, in predicting the patient’s role. This data is continuing to be processed after these elementary findings. Implications and the future Another researcher, Amit Elkin, says, “This study takes previous research, showing that we can predict who benefits from an antidepressant, and actually brings it to the point of practical utility.” Trivedi says they will probably use the EEG in the majority of patients because it is not only much cheaper but just as or better at risk prediction than other more expensive tests. However, some patients will need to go on to get an MRI scan or a blood test, simply because of the changing expression of depression – the “many signatures of depression”. Explains Trivedi, “Having all these tests available will improve the chances of choosing the right treatment the first time.” The next step is to design an AI interface that is compatible with the broad range of EEG machines used across the USA, and to get approval for the use of the device from the US Food and Drug Administration (FDA). Meanwhile, other large studies are going on to boost the rate of response with antidepressants. One is the D2K study that aims at 2,500 depressed or bipolar individuals, with a follow-up of 20 years. the RAD, on the other hand, has a similar strength of 2,500 people aged 10-24 years. This is designed to pick up risk factors for mood or anxiety disorders. Some of these participants will be used to assess both the EEG and various other test combinations. The aim is to find the best treatment, by eliciting the best fit (biological signature), as Trivedi says, “Our research is showing that they no longer have to endure the painful process of trial and error.” Journal reference: Wu, W., Zhang, Y., Jiang, J. et al. An electroencephalographic signature predicts antidepressant response in major depression. Nat Biotechnol (2020). https://doi.org/10.1038/s41587-019-0397-3



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