Frontiers of Computational Psychiatry / by Siobhán Cronin

When was the last time you sat down and read someone’s PhD thesis?

Given how much blood, sweat, and tears go into these things, I’m ashamed to admit I’ve only read a few.

However, I recently came across a thesis that was so well written and so well aligned with my interests that it felt as though I had stumbled upon a New York Times popular science bestseller, of the likes of Oliver Sachs or António Demásio. The thesis is by Thomas Viktor Wiecki, and is titled Computational Psychiatry: Combining multiple levels of analysis to understand brain disorders.

Unicorns like these come along only so often, so in honor of Wiecki’s stellar work, I offer this six-part jaunt through his research, lingering on both methods and the broader implications his work has for the fields of psychiatry and computational neuroscience.

Let’s begin!


To best understand why this research is such a breath of fresh air, one must hold in context the impact of DSM (the diagnostic and statistical manual of mental disorders) on focussing field-wide research targets, the shortcomings of which are best summed up in the author’s words:

Although primarily intended to be of value to clinicians, the DSM has also played a substantial role as a classification system for scientific research with the goals of validating the diagnostic categories and translating research results directly into clinical practice. Although these research goals are commendable, decisions regarding systematic classification are often more based on perceptions of clinical utility rather than scientific merit. As a consequence, DSM-based research programs have failed to deliver consistent, replicable, and specific results, and it has been widely observed that the validation of DSM categories has been limited, that DSM categories do not provide well-defined phenotypes, and that they have limited research utility.

One possible solution to this DSM pitfall is to create multidimensional profiles (MPs) from a series of various neuropsychological tasks. Rather than clumping patients together under crude delineations of symptoms, researchers can define neurocognitive phenotypes that provide a more nuanced lens for studying the origins, developmental stages, and response to specific treatments within meaningful subject clusters.

Wiecki gives a great example of this in relationship to ADHD (attention deficit hyperactivity disorder), citing a paper that used MPs to clusters patients, and found that these clusters did not cleave cleanly into groups of ADHD patients and Healthy Controls (HCs), but rather clustered in groups that included both. When a classifier was applied to these clusters, their analysis was better at predicting ADHD subjects than an analysis on the data as a whole. This finding underscores what we lose when we analyze data at the level of symptoms.


Wiecki goes on to provide concrete examples of how computational modeling can help us better understand psychopathology. As several brain disorders affect decision making, Wiecki provides examples from literature on Parkinson’s Disease (PD) and Schizophrenia (SZ) to illustrate how computational models have helped advance our understanding.

Current thinking on PD identifies the disorder as the “cell death of midbrain dopaminergic neurons in the substantia nigra pars compacta”. There are several visible symptoms that result from this degeneration, as it so fundamentally disrupts the motor system. However, the effects of PD on cognition can lead one to classify PD as an “action selection disorder” in which the development of the disorder can be tracked by performance on learning tasks. This is an example of how a model that takes into account behavioral markers can supplement existing models that track physical action; in this case, disruptions to the motor system.


Cognitive tasks can help researchers zero in on what is happening in our brains, yet they are subject to the “task-impurity problem” — the problem of tasks producing results, yet not measuring what we think they are measuring. The limitations of traditional practices in fitting computational models in psychiatric contexts extend from fitting models to individual subjects as well as estimating parameters with an aggregated dataset from multiple subjects. The challenge with the former lies in masking any potential similarities of parameters, while the latter masks individual differences that are the actual research targets. That’s where hierarchical Bayesian estimation can come to the rescue, with its ability to quantify uncertainty and increase sensitivity in parameter estimation.

“Computational models try to deconstruct behavior into its individual components and identify separable cognitive processes”, and this is where the richness lies. Even if null results are produced, I believe the sheer act itself of breaking phenomena down into more discreet cognitive processes is an advance for neuroscience. Such clustering techniques are being demonstrated to be better predictors of subject differences than aggregate performance scores. This is fertile terrain for additional research.

Next up…. Part 2: Modeling Inhibition