De Groene Amsterdammer Blog Post by Sanne Bloemink
10 June 2024
For years I have believed in the 'law of the spiral'. I came up with this law myself and it dictates the following: you are either in an upward spiral or in a downward spiral. When I am cheerful, others enjoy interacting with me more. They validate me, which makes me happier. Which means I have more energy and get more done. Which means I do more work and get more recognition. Which gives me more money. Which gives me money to eat healthy and exercise. Which makes me happier again. The spiral shoots up!
Unfortunately, the reverse also applies. If I am sad, people are more likely to turn away from me. Which means I get less validation. This makes me insecure, I start isolating myself and perhaps this will all keep me awake at night. As a result, I am less able to concentrate during the day and may lose my job. Which causes me to lose money and recognition. Which causes my self-esteem to collapse and makes me even more down. Which means I have even fewer people surrounding me. And before you know it, I don't feel that much like living that life of me anymore. I've ended up at the bottom of the vortex.
Of course, this spiral is never absolute. If you have a lot of friends, a large network that can quickly help you find a new job if you become unemployed, or if you have a bizarrely positive genetic mindset, a negative spiral can be averted and you can quickly zoom back up. And conversely, any beginnings of a positive spiral can be suppressed by inhibiting circumstances such as a minimal network, a drafty house or a lack of money.
Recently I found a piece of evidence for my intuitive spiral law. In recent months I have attended a number of lectures for a module on 'complexity' at the UvA. One of the guest lectures of this module was given by Gaby Lunansky, a scientist with dark curls, blue eyes and a calm, confident voice. She explains the network model of psychopathology with great clarity to students and I listen, my excitement growing.
In psychiatry, models have always been based on the idea of ‘mood disorders', such as anxiety and depression, being the cause of various symptoms. In this model, depression first occurs and then causes a range of symptoms such as fatigue, lack of appetite and sad feelings. Where exactly the depression itself comes from is unclear in this model, but the widespread idea was for a long time that this had to do with a chemical imbalance in the brain, or a missing substance. However, no evidence has ever been found for such an imbalance and this is not due to inadequate funding of research, because for a long time most money in psychiatry actually went to this type of research. To diagnose depression according to the DSM, the psychiatric bible, it is necessary to determine all symptoms. If sufficient symptoms can be identified, a diagnosis of depression can be made. The more symptoms and and the more severe the symptoms, the more severe the depression.
Network theory was developed by Denny Borsboom and Angelique Cramer and this theory does not view depression or anxiety as the underlying cause but rather as an outcome: a particular state of a complex system of symptoms. Symptoms such as loss of concentration, insomnia, sad feelings and changes in appetite all influence each other in a complex system and that system can at a certain point become stuck in a certain state that we then call depression. The whole thing is dynamic in the sense that the symptoms can continuously change in intensity over time and that they can influence each other. A single major event such, as the loss of a loved one or job, can throw the system out of balance and cause the system to enter a depressive state. Lunansky gives the example of the fictional Jake who first loses his job, but soon can't sleep well anymore, which makes him less able to concentrate. He now also has more and more financial worries and is starting to feel more and more worthless. His sad feelings spread and eventually he develops suicidal thoughts.
You can draw such a network model as a large cloud in which points are connected by lines. The points of this cloud are the symptoms. The lines between the symptoms are the mutual relationships and these can be thicker or thinner depending on the strength of the relationship. Lunansky builds statistical models that include the relationships between all those variables. Different models are possible. She explains: 'For example, you can take a cross-section of society. Then you work with a group of people in whom you individually measure symptoms at a certain point in time. You aggregate those numbers and calculate an average .' The outcome is the average of a group, but the measurements are taken individually. The mutual relationships within a group are therefore not included in such a model. Lunansky: 'Ideally you would want that, because people do not live in a vacuum, but that of course immediately makes the model much more complex.'
It is also possible to conduct longitudinal research, in which people are followed individually over a longer period of time. There is now even an app that maps out such a cloud of symptoms and their mutual relationships over a certain time for a single person. 'You can use this app to ask how someone feels several times a day. After about two weeks, the app has enough data to create an individual network for a client. The therapist and client can then use that information to gain more insight. This would make it easier to see which symptoms have the strongest effect on throwing the system out of balance. Then you can focus primarily on those symptoms.
The network model can work well not only in a therapeutic setting but also in everyday life. “This model, even without statistical content, is often seen as very valuable for educational purposes,” says Lunansky. Increasingly, physiological factors are also included in a model in addition to psychological factors. Think of heart rate or movement. This leads to another advantage: in this network model there is much more room for the intimate intertwining of body and mind. The network model has therefore become extremely popular in the clinical setting of mental health care. “It fits much better with what clinicians see in their practice,” Lunansky thinks. Clients themselves also have a better emotional understanding of how it works. 'The classic categorical approach to diagnoses such as depression and anxiety is therefore increasingly difficult to sell.' Lunansky conducted extensive research into resilience. She developed a new model for this. The idea of network theory is that a system with fewer connections between symptoms should have more resilience, because the network cannot be shaken that easily by the change of one single symptom. 'But my latest research showed that this was not exactly correct. A possible explanation could be that more connections between symptoms could indicate increased vulnerability on the one hand, but perhaps also increased flexibility at the same time.'
Lunansky points out that the network approach is not suitable for all conditions. 'It works well for mood disorders, but if you think, for example, of a surgeon who has to act in acute situations, then this model is less obvious.' An important criticism is that the model still uses the same symptoms that the DSM uses. “I think that's fair criticism,” says Lunansky. “We are now working on this to see how this can be changed.” Another criticism is that the model ultimately does not make that much difference to clinical practice. Lunansky disagrees because just understanding the mental state as a system can contribute to recovery. However, she is aware that the great litmus test is now coming. 'The idea of the network model is firmly established in science, but now the research is starting to focus on interventions and that is of course what it is ultimately about. Can we really provide better help to people with this?'
Published in De Groene Amsterdammer on June 10, 2024. Translated by the Institute for Advanced Study.