Machine learning has helped scientists understand how the brain gives rise to complex human characteristics, uncovering patterns of brain activity that are related to behaviors like working memory, traits like impulsivity, and disorders like depression. And with these tools, scientists can create models of these relationships that can then be used, in theory, to make predictions about the behavior and health of individuals.

But that only works if models represent everyone, and previous research has shown that they don’t; for any model, there are some people that the model just doesn’t fit.

In a study published Aug. 24 in Nature, Yale researchers examined who these models tend to fail, why that happens, and what can be done about it.

For models to be maximally useful, they need to apply to any given individual, says Abigail Greene, an M.D.-Ph.D. student at Yale School of Medicine and lead author of the study.

“If we want to move this kind of work into a clinical application, for example, we need to make sure the model applies to the patient sitting in front of us,” she said.

Greene and her colleagues are interested in how models might provide more precise psychiatric characterization, which they think could be achieved in two ways. The first is by better categorizing patient populations. A diagnosis of schizophrenia, for example, encompasses an array of symptoms, and it can look very different from person to person. A deeper understanding of the neural underpinnings of schizophrenia, including its symptoms and subcategories, could allow researchers to group patients in a more nuanced way.

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