This guest post was written by Payton Jones (email@example.com) and Haley Elliott (firstname.lastname@example.org), and is based on a recent letter in which they highlight the importance of non-symptom variables in psychological networks. Payton is a graduate student at Harvard University in the Richard J. McNally lab. His research focuses on the etiology of mental disorders and statistical methods. Haley is an undergraduate research assistant, also in the McNally lab at Harvard.
If you are reading this, it is likely that you have heard of the network approach to mental disorders. Since the dawn of medicine, scientists have tried to isolate disorders which give rise to symptoms. Just a few years ago, the network approach to mental disorders flipped this view on its head by asserting that some problems may in fact represent interacting symptoms which give rise to disorders.
This view has been revolutionary, sparking the advent of important new research. However, after several years following the network view, the initial tenets may need to be expanded.
In a recent review of the network approach, Denny Borsboom elucidated four key principles of the initial network view. Principle 2, in particular, states that mental disorders are best represented by networks emerging from interactions among “nodes”, which correspond to symptoms from diagnostic manuals.
It seems evident to clinicians and researchers alike that symptoms do indeed interact with one another, and certainly play a role in the emergence of mental disorders; but can we really blame them for everything?
Where do symptoms in diagnostic manuals come from?
Part of the problem with using symptoms from diagnostic manuals comes from the imperfect history of psychopathology research. Symptoms from the DSM or other manuals are not God-given – they have been carefully chosen by past researchers, often using consensus as a final criterion. Unfortunately, they were not picked with the network perspective in mind. Instead:
1) Symptoms are designed to differentiate between disorders.
Past researchers often gave precedence to “hallmark” symptoms that were unique to a specific problem, and easily differentiable from other problems (such as anhedonia in depression). This means that diagnostic manuals often ignore symptoms that are shared among many disorders, such as concentration problems and sad mood. Ironically, these “transdiagnostic” features seem to be some of the most important nodes in at least some recent network analyses.
2) Symptoms are distressing
Symptoms are generally only identified if a patient or their family identifies them as problematic. But are we so naïve to think that only the most problematic facets of a phenomenon play a role in that phenomenon? Researchers have known for a long time that many causally important aspects of mental disorders are “ego-syntonic” (that is, they are not immediately distressing to the patient). For instance, a patient suffering from OCD is likely to report uncomfortable obsessions and compulsions as potential problems to fix, but may not realize that his or her cognitive style of intolerance of uncertainty is contributing to the problem. This focus on only the visibly distressing aspects of a disorder (similar to a medical check-up) seems to make intuitive sense for client whose distress brought them into therapy in the first place, but may not provide a complete picture of the true causal structure of the problem.
When imagining a causal system of a mental disorder, we can also hypothesize that there may be some protective factors involved. These potential “brakes” in the system will obviously be missed by symptom manuals, which focus only on the negatives.
3) Symptoms are not supposed to interact with each other
Ironically, many symptom manuals were designed in direct opposition to the network view. That is, those designing the measures intentionally tried to pick symptoms which did not interact with one another, for psychometric reasons. For instance, clinicians are often trained in diagnostic interviews to attempt to separate “simple tiredness from sleep issues” from “a different, more profound tiredness associated with depression”, and only count the latter as a symptom. This is obviously problematic if we are attempting to put symptoms into a network, which focuses on the relatedness of symptoms to one another in the emergence of a disorder.
In addition, some manualized symptoms really do seem to be reflective of problems, rather than causally important. For instance, “weight loss” is often included as a symptom of depression. It is easy to see how weight loss could be reflective of other depression symptoms. However, it is somewhat difficult to imagine that weight loss is responsible for causing depression (at least in a majority of people). If networks are supposedly causal systems, it doesn’t make much sense to include this particular symptom.
The expanded network approach
Recently, our lab published a commentary on Borsboom’s review, proposing an expanded view which may help with some of these problems. Instead of relying wholly on symptoms from diagnostic manuals, we propose that networks should include variables that are plausible causal candidates in the etiology or maintenance of mental disorders. This still includes symptoms, but also includes other variables such as cognitive factors, biological factors, and protective factors.
We aren’t the only ones who have thought along these lines. In Eiko Fried and Angelique Cramer‘s recent comprehensive summary of challenges to the network approach, they discuss problems with the term “symptom” and how focusing primarily on symptoms has led network researchers to ignore important elements of mental disorders. Related, the paper includes a discussion on how more static components that play a role in mental disorders (e.g., an initial trauma that sparked PTSD or personality traits) might be conceptualized from a dynamic systems point of view.
By focusing network analyses only on symptoms, what have we missed? Between the commentary and the review, here are some suggested possibilities: impairment of functioning, information processing biases, maladaptive (or adaptive) schemas, metacognitive beliefs, life events, self-esteem, social interactions, rejection events, physical activities, and substance abuse.
In the meantime, some researchers have already been putting the rubber to the road by including non-symptom nodes in network analyses. Here are some prime examples of nodes in recent empirical publications that follow the expanded network approach:
- Attentional control as a node in social anxiety disorder
- Resilience as a protective node in depression
- Access to food as a node in trauma-related problems among war survivors
- Childhood sexual abuse as a node in psychotic disorders
- Impairment of mental and physical functioning in PTSD networks of trauma-exposed veterans
Are symptoms sufficient for network analyses going forward? Or are other causal candidates also important to include as nodes? The future is up to you!
Written by: Payton Jones (email@example.com) and Haley Elliott (firstname.lastname@example.org)