Can Big Data Help Psychiatry Unravel the Complexity of Mental Illness?


Psychiatrists are looking to sophisticated computational tools that may be able to disentangle the intricacies of mental illness and improve treatment decisions

By Simon Makin on March 21, 2016

Brain science draws legions of eager students to the field and countless millions in dollars, euros and renminbi to fund research. These endeavors, however, have not yielded major improvements in treating patients who suffer from psychiatric disorders for decades.


The languid pace of translating research into therapies stems from the inherent difficulties in understanding mental illness. “Psychiatry deals with brains interacting with the world and with other brains, so we’re not just considering a brain’s function but its function in complex situations,” says Quentin Huys of the Swiss Federal Institute of Technology (E.T.H. Zurich) and the University of Zurich, lead author of a review of the emerging field of computational psychiatry, published this month inNature Neuroscience. Computational psychiatry sets forth the ambitious goal of using sophisticated numerical tools to understand and treat mental illness. [Scientific American is part of Springer Nature.]

Psychiatry currently defines disorders using lists of symptoms. Researchers have been devoting enormous energies to find biological markers that make diagnosis more objective with only halting success. Part of the problem is there is usually no one-to-one correspondence between biological causes and disorders defined by their symptoms, such as those in the Diagnostic and Statistical Manual of Mental Disorders,Fifth Edition (DSM-5). A specific disorder, like depression or schizophrenia, may result from a range of different underlying causes (biological or otherwise). On the other hand, the same cause might ultimately lead to different disorders in different people, depending on anything from their genetics to their life experiences. One of the goals of computational psychiatry is to draw connections between symptomsand causes, regardless of diagnoses.

The variability that exists within a single disorder means two people can have the same diagnosis but share no symptoms. Furthermore, significant overlap exists between diagnoses: Many symptoms are shared among numerous conditions, and multiple conditions often occur together. “To deal with this complexity we need more powerful tools,” Huys says.

In the age of big data neuroscientists routinely handle extremely high-dimensional data sets. There are many types of data, including neural anatomy or activity as well as cognitive, clinical, genetic and more. The data generated by an fMRI scan alone can consist of many series of values changing over time, in which each numerical series represents the activity of a single unit of brain volume. One of the two main branches of computational psychiatry involves applying machine-learning techniques to these large data sets to find patterns without referring to theories about cognitive dysfunction or mental illness.

Initially these “data-driven” efforts focused on developing automatic tools for objective diagnosis. For instance, numerous studies have attempted to use the average structural and functional brain differences seen, in magnetic resonance imaging (MRI) scans of people with a given psychiatric diagnosis to distinguish between those with and without the disorder.

The moderate accuracy obtained in some of these studies indicates the disorders are indeed reflected in the brain, but there are problems to overcome before such tools are clinically useful. For instance, many clinical cases are ambiguous, and it is not clear how useful classification systems, which tend to be developed using clear-cut cases, would be in those situations. Also, as symptoms increase in severity, the number of co-occurring conditions tends to increase whereas classification systems tend to treat disorders as mutually exclusive. Techniques to allow for complex multiple diagnoses are far more challenging.

Researchers are working on these problems, and performance is likely to improve both as the tools themselves are refined and more types of data are added. But the difficulty of connecting biology to disorders defined by clusters of symptoms may prove to be a fundamental limit to progress until such time as psychiatry’s classification system undergoes drastic changes.