Skip to Content

Brain Scans Predict Treatment Outcome in Depression Patients

A biomarker could cut the trial-and-error of finding a patient’s best therapy.

A brain scan could one day help doctors prescribe the best treatment to patients with major depressive disorder. In JAMA Psychiatry on Wednesday, researchers describe how a PET scan can reveal whether a patient will respond better to drugs or cognitive behavior therapy. This could have a “significant health and economic impact” the researchers note:  most patients of “this highly prevalent, disabling and costly illness” do not get the treatment best-suited to them at first.

By studying newly treated patients with major depressive disorder, the investigators found that the activity of brain cells in one particular brain region aligned with the outcomes for patients: if that region was more metabolically active than the rest of the brain, then patients tended to respond better to an antidepressant than to cognitive behavioral therapy. The opposite treatment results bore out if that region was less active than the rest of the brain.

The results come from a rather small study of only 38 patients, but the group, headed by Helen Mayberg, a neurologist at Emory University in Atlanta, plans to continue testing the potential of this brain scan biomarker for depression treatment response, reports Nature. Nature’s coveage also notes that although such a biomarker would be an exciting first for depression research, an ideal biomarker would be one that is easier to query:

Psychiatrists would prefer a biomarker that could be determined by a simple method such as a blood test rather than a PET scan, which can be inconvenient for doctors without easy access to the technology. And, costing up to US$2,000 a time, the scans are expensive. Mayberg says that it could be possible eventually to find a surrogate measure of insula activity that does not involve a brain scan. But she argues that even if PET scans remain the only option, then they would still be worthwhile to save a distressed patient from months of ineffective therapy.

Keep Reading

Most Popular

Large language models can do jaw-dropping things. But nobody knows exactly why.

And that's a problem. Figuring it out is one of the biggest scientific puzzles of our time and a crucial step towards controlling more powerful future models.

How scientists traced a mysterious covid case back to six toilets

When wastewater surveillance turns into a hunt for a single infected individual, the ethics get tricky.

The problem with plug-in hybrids? Their drivers.

Plug-in hybrids are often sold as a transition to EVs, but new data from Europe shows we’re still underestimating the emissions they produce.

It’s time to retire the term “user”

The proliferation of AI means we need a new word.

Stay connected

Illustration by Rose Wong

Get the latest updates from
MIT Technology Review

Discover special offers, top stories, upcoming events, and more.

Thank you for submitting your email!

Explore more newsletters

It looks like something went wrong.

We’re having trouble saving your preferences. Try refreshing this page and updating them one more time. If you continue to get this message, reach out to us at customer-service@technologyreview.com with a list of newsletters you’d like to receive.