I’ve written a very brief piece on embracing the complexity of mental health problems, entitled “Studying mental disorders as systems, not syndromes” (download). I also had the opportunity to give a keynote on the paper, which you can find on youtube. This was a lot of fun to make, given that I created an entirely new presentation (no recycling of old material!).
In this blog, I’ll briefly summarize the core message of the short paper and talk. This will be interspersed with some beautiful images I was able to create using an AI tool making images from text input, for which I used the title of the paper.1
1. Introduction: lack of progress
In the paper, I describe the dire situation we find ourselves in. Over the last decades, many specialists have worked tirelessly to improve the lives of people affected by mental health problems. Mental health has also received increased political and funding priority. Despite these global efforts, however, progress in understanding, predicting, and treating mental disorders remains disappointing. There are many reasons for this lack of progress, and I discuss two roadblocks: diagnostic literalism and reductionism.
2. Diagnostic literalism
I show, using examples from the history of psychiatric nosology, that many categories and thresholds we use for research purposes are somewhat arbitrary, shaped to a considerable degree by historical forces rather than empirical evidence. I demonstrate that current diagnostic categories are manufactured, rather than discovered, and put that into context of many other manufactured nosological systems. Beavers and elephants are not like helium and magnesium: they are not natural kinds with sufficient and necessary properties that allow to uniquely identify them; the idea that species are natural kinds is pre-Darwinian. Because categorization of mental illness is somewhat arbitrary does not imply that categorized things “do not exist”. There is a continuum between normal and abnormal blood pressure, and where to draw the boundary is somewhat arbitrary, but that does not mean high blood pressure (or beavers, or depression) does not exist.
The superimposition of categorical diagnoses on the complex landscape of mental health problems explains the situation we find ourselves in. This makes it obvious that diagnostic categories and thresholds listed in the DSM are meant as clinically useful tools with heuristic value: they don’t have clear thresholds like water and steam, and cannot be neatly separated like helium or magnesium. However, we study them as if this were the case, e.g. in case control studies, which I refer to as diagnostic literalism.
Reductionism is one of the most powerful frameworks for trying to understand systems: figuring out the properties of the whole given the parts. This works well for simple mechanical systems, such as bicycles, but has limits when systems become increasingly complex, such as the stock market, the weather, or the internet. I showcase that research on mental disorders has been conducted largely within strongly reductionist frameworks, and discuss, as an example, biological reductionism. I briefly review research efforts, concluding that biological psychiatry has led to considerable insights about how human genes and brains work, but has told us relatively little about the biology of specific mental disorders.
This lack of progress is not because biology is not crucially involved in predisposing or mediating disease trajectory——it arises because we have focused on studying the biology of particular DSM labels that are likely the wrong targets, and because we have studied biology (at least largely) in isolation. Diagnostic literalism and reductionism have formed a vicious cycle of reification, and continue to do so whenever we talk about “risk factors for schizophrenia”, “genes for major depression”, and “symptoms of PTSD”.
4. Embracing complexity
Reductionism is helpful to fix systems such as bicycles, but mental disorders are not like bicycles: they arise within a person over time, and are best understood as phenomena resulting from interactions of biopsychosocial elements organized in hierarchies of inter-dependent levels. I discuss a few features such systems have, including emergence, stable states, phase transitions, and behaving in ways that aren’t necessarily intuitive. I conclude that understanding mental disorders as dynamic entities that can no longer be decomposed into simple cause-effect relations highlights the importance of studying mental health systems holistically. Doing so successfully will require building more interdisciplinary bridges, and open our ivory towers to theories and methods from network and systems sciences, fields with a long and rich tradition.