ERC Starting Grant on predicting depression onset

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I’m extremely happy, proud, humbled, and somewhat nervous announce that my application for an European Research Council (ERC) Starting Grant was successful (on my first try!), following the announcement by the ERC today. The ERC Starting Grant is part of EU’s Research and Innovation programme, called Horizon 2020. The scheme was quite competitive this year, with 436 out of 3354 applications (13%) being funded. You can find a full list of awardees here, and I want to congratulate everybody. Special congratulations to my four colleagues at Leiden University who also received the grant.

ERC Starting Grant

What is an ERC Starting Grant? Mariya Gabriel, European Commissioner for Innovation, Research, Culture, Education and Youth, summarizes the grant scheme as follows:

“the EU is leveraging the talent and curiosity of some of the best young researchers in Europe. Their ideas are set to break fresh ground and open new ways to deal with pressing challenges in the areas of health, energy and digital technologies, as well as many other fields. Our ambition to effectively tackle current and future crises depends on our strong will to continuously and increasingly support top research at the frontiers of our knowledge.”

Those who work in academia know how much this means: it’s a 1,500,000€ grant for 5-years that finances 2-3 PhD students, 1-2 postdocs, and myself. For those of who you are not in academia: I’m 38 years old, and this grant means that I may obtain my very first permanent contract, after many short-term contracts (3 years phd, 2 years postdoc, 2 years postdoc, 5 years assistant professor of which I’ve done about half). In this blog post, I will briefly describe the topic of my ERC.

WARN-D: predicting depression in young adults before it occurs

In summary, I will try to build the early warning system WARN-D that reliably predicts depression in young adults before it occurs. Why depression, and why prediction?

Mood disorders like depression are common, debilitating, and often chronic, and depression is considered among the most pressing health-related problems of modern society. Unfortunately, compared to breakthroughs in treating diseases like certain cancers, we haven’t made a lot of progress in increasing efficacy of depression treatments over the last 2 decades, and young people are disproportionately affected.

This is why experts agree that prevention is the most effective way to change depression’s global disease burden. And the biggest barrier to successful prevention is to identify people at risk for depression in the near future — and we cannot do that at the moment.

3 interdisciplinary pillars of the grant

My proposal aims to solve the challenge who should receive prevention, and when, by developing the personalized early warning system WARN-D. To figure out the problem of personalized detection, my team and I will follow 2,000 young adults over 2 years, and integrate emerging theoretical, measurement, and modelling approaches from different scientific fields so far unconnected.

  1. Regarding theory, I conceptualize depression as a complex, dynamical, biopsychosocial system in which causal relations and vicious cycles between a host of variables can move the system from a healthy to a clinical state, consistent with the Network Approach to Psychopathology that I co-developed. This borrows heavily from the growing discipline of complexity science that has led to massive breakthroughs in other disciplines.
  2. Regarding measurement, in addition to traditional mental health surveys every few months, we will assess how young adults are doing in their daily lives. To do so, we will use smart-phone based ecological momentary assessment (EMA) and collect temporal dynamics of variables like mood, anxiety, stress, and impairment. Further, we will collect smart-watch based digital phenotype data that is somewhat more objective, such as sleep patterns, activity, sunlight exposure, and weight changes.
  3. Regarding statistical modeling, we will use dynamical network models to study the relations among these within-person mood systems, and use parameters of these models, combined with baseline, EMA, and digital phenotype data, to construct the prediction model WARN-D via state-of-the-art machine learning models.

Further goals

Overall, my ERC Starting Grant combines and integrates numerous modern tools to develop a tailored early warning system with the goal to forecast depression reliably before it occurs. I believe that improving detection of future onset is a necessary next step to enable tailored prevention programs to kick in. The advantage of WARN-D is that if it works reasonably well, it can be moved to other populations and problems, after validation. For instance, it could be used to forecast PTSD in soldiers going into a war zone, or to forecast burnout in teachers or medical professionals during stressful periods, or to forecast upcoming manic or depressive episodes in patients with a history of bipolar disorder. So keep your eyes open for future WARN-X projects!

More specific goals include, among others, to:

  • Map the (healthy) biopsychosocial mood system, and clinical deviations from it
  • Integrate self-report EMA data with more objective digital phenotype data
  • Explore interindividual differences in within-person mood systems
  • Find out how variables in the external field (e.g. one-time stressors or personality traits) affect the mood systems
  • Test the long-term stability of mood systems
  • Find early warning signals of depression onset
  • Understand the nature of depression onset, using a broad set of novel data

Above: Hypothetical examples of simplified versions of mood systems that can be estimated via dynamic network models of EMA (circle) & digital phenotype (square) data. Arrows depict that a variable at time t predicts another variable at t+1, controlling for the rest of the system at t (green=positive, dashed/red=negative).

Short video

The amazing staff at Leiden University made a short 4-minute video in which I introduce the grant:

Like science in general, grant writing is a team sport, and I want to thank everybody who helped me get this application funded, with special thanks to the scientific advisory board of my ERC Starting Grant, Hester Bergsma at Leiden University, and our Clinical Psychology Unit. For any ideas related to the grant — or if you think your CV fits exceptionally well and you’d like to work with me (I’ll start hiring early 2021!) — you can reach me via

10 thoughts on “ERC Starting Grant on predicting depression onset

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  3. Kristen Syme

    Congratulations on a successful grant app on a very important topic! How will environmental adversities/stressors be measured? Will the measures capture the quality of social relationships and events and circumstances such as assault, ongoing abuse, conflicts, etc.? Do you think there will be some suggestibility occurring when you inform participants of their depression risk?

    1. Eiko Post author

      Missed this because it was marked as spam Kristen, sorry. We have plans on assessing stressors and other events in what I think of the external field. How do to that exactly is something we’re working on. Suggestibility is a good point that is also going to come up in ethics I am sure, and we will try to mitigate this.

  4. rik

    Congrats! Could you say what the target population is for WARN-D (i mean not in the trial but in the final application)? young adults at risk for depression? How do you find them and why would they use WARN-D?

    1. Eiko Post author

      Sorry, the comment was flagged as spam, just saw it. Excellent question Rik, not straight forward to answer. Given the large sample and that we want WARN-D to generalize to normal student samples (of which a substantial proportion are at risk, some are at high risk for mood disorders), we will not be too selective in the recruitment procedures. The reasons for using WARN-D will be to get a personalized risk score for upcoming transitions into depression.


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