Major depressive disorder (MDD) is a heterogeneous condition; multiple underlying neurobiological substrates could be associated with treatment response variability. Understanding the sources of this variability and predicting outcomes has been elusive. Machine learning has shown promise in predicting treatment response in MDD, but one limitation has been the lack of clinical interpretability of machine learning models. We analyzed data from six clinical trials of pharmacological treatment for depression (total n = 5438) using the Differential Prototypes Neural Network (DPNN), a neural network model that derives patient prototypes which can be used to derive treatment-relevant patient clusters while learning to generate probabilities for differential treatment response. A model classifying remission and outputting individual remission probabilities for five first-line monotherapies and three combination treatments was trained using clinical and demographic data. Model validity and clinical utility were measured based on area under the curve (AUC) and expected improvement in sample remission rate with model-guided treatment, respectively. Post-hoc analyses yielded clusters (subgroups) based on patient prototypes learned during training. Prototypes were evaluated for interpretability by assessing differences in feature distributions and treatment-specific outcomes. A 3-prototype model achieved an AUC of 0.66 and an expected absolute improvement in population remission rate compared to the sample remission rate. We identified three treatment-relevant patient clusters which were clinically interpretable. It is possible to produce novel treatment-relevant patient profiles using machine learning models; doing so may improve precision medicine for depression. Note: This model is not currently the subject of any active clinical trials and is not intended for clinical use.

Summary: Combining neuroimaging and artificial intelligence, researchers identified novel brain signatures unique to the response of each antidepressant. Research led by UT Southwestern has identified MRI brain imaging biomarkers that bring new levels of precision for prescribing the most effective antidepressants. The outcome predictive models were developed in part using data from a large multi-center National Institute of Mental Health-funded study and published in the journal Biological Psychiatry. The findings provide strong evidence that the current trial-and-error approach used in clinical practice for the selection of the right antidepressant can be replaced with this new precision medicine approach. "This is a significant advance. It can be and should be used immediately," said Madhukar Trivedi, M.D., Professor of Clinical Psychiatry, and Director of the Center for Depression Research and Clinical Care, one of the pillars of the Peter O'Donnell Jr. Brain Institute.

For patients seeking relief from depression, it can take months to pin down an effective treatment. But brain wave patterns could potentially help to predict how individual patients would respond to an antidepressant before treatment even begins, according to a new study published Feb. 10 in the journal Nature Biotechnology. The study addresses one of psychiatry's fundamental challenges: a lack of tests that can help doctors decide the best treatment options for patients with depression, said study co-author Dr. Madhukar Trivedi, a psychiatry professor at UT Southwestern Medical Center in Dallas. Instead, Trivedi said, providers rely on a trial-and-error process in which patients try out medications on six- to eight-week cycles. This imprecise method contributes to a general perception that antidepressants are ineffective, added Dr. Amit Etkin, study co-author and a professor of psychiatry at Stanford University.

An AI can predict from people's brainwaves whether an antidepressant is likely to help them. The technique may offer a new approach to prescribing medicines for mental illnesses. "We have a central problem in psychiatry because we characterise diseases by their end point, such as what behaviours they cause," says Amit Etkin at Stanford University in California. "You tell me you're depressed, and I don't know any more than that. I don't really know what's going on in the brain and we prescribe medication on very little information."

An AI can predict from people's brainwaves whether an antidepressant is likely to help them. The technique may offer a new approach to prescribing medicines for mental illnesses. "We have a central problem in psychiatry because we characterise diseases by their end point, such as what behaviours they cause," says Amit Etkin at Stanford University in California. "You tell me you're depressed, and I don't know any more than that. I don't really know what's going on in the brain and we prescribe medication on very little information."