There's a problem here: If you were teaching an algorithm to tell the difference between pictures of cars and planes, you would give it pictures of cars and planes to learn from, telling it which ones are cars and which are planes, so it can be trained. Similarly, if we want to develop an algorithm to detect people with schizophrenia, we need lots of people with schizophrenia to collect data from to tell the algorithm how to spot it.But those people with schizophrenia got their diagnoses using the DSM. When we compare them to controls, even if it leads to the development of a 100 percent accurate algorithm that can differentiate between the two groups, the algorithm is essentially just replicating the DSM categories, since that’s what it was trained on.Andrea Mechelli, a Professor of Early Intervention in Mental Health at King's College London, calls this a circularity problem. “We don’t know exactly what we’re looking for,” agreed Vince Calhoun, an engineer and neuroscientist at Georgia State University. “We don’t necessarily know if the answer is the right answer.”This problem reveals how just training an algorithm and collecting brain data isn't enough—algorithms need to be applied in ways that are actually useful. Mechelli thinks the algorithms should be dedicated to what clinicians can’t do already, but desperately need: not just determining what disorder people have, but predicting what will happen to patients in the future, how their disease will progress, and what medication they'll thrive on.
It may be years or decades before we fully understand the biological underpinnings of mental illness, or find a true biomarker for schizophrenia or bipolar disorder.
In a 2016 paper in The Lancet, and in a follow-up paper in JAMA in 2017, Chekroud and his colleagues found that their approach could help people feel better in eight weeks, compared to those who didn’t.Chekroud thinks that while the brain imaging data is important to research for basic science, self-assessments are more accessible in clinical practice, and can help people right now. Brain scans are expensive, and can be hard for people in rural areas to access. It may be years or decades before we fully understand the biological underpinnings of mental illness, or find a true biomarker for schizophrenia or bipolar disorder. Perhaps the underlying biological isn't distinct enough to help with diagnostics, the brain imaging tools we have aren't powerful enough yet, or we don't have enough data.“The more academic crowd has continued to pursue brain and genetic markers, mostly because it's like there's a biological fact,” Chekroud said. “It's way cooler and way sexier if they can figure it out like a brain biomarker. The reality is that the signal is just not there yet. I think the clinical data is particularly attractive because it seems like it has the most value. It's like the closest thing to the symptoms.”Schnyer agreed that in the short term he doesn’t think that machine learning is going to be very helpful to reveal the underlying mechanisms of mental illnesses. Even if an algorithm detects that a depressed person’s brain is different in certain areas than a healthy person’s, does that mean that difference caused the depression, or the depression caused those areas to be different?
A brain scanner would never fully replace a human person interviewing and talking with a patient.