NESPRED - version 1
Research type
Research Study
Full title
Neurocognitive signatures predicting risk of recurrent depression (NESPRED)
IRAS ID
292362
Contact name
Roland Zahn
Contact email
Sponsor organisation
King's College London
Duration of Study in the UK
4 years, 7 months, 0 days
Research summary
The development of new treatments that prevent recurrence of depressive episodes has been hampered by a lack of knowledge about psychological and brain changes that are risk factors. We have identified such risk factors in recovered patients that predict, on an individual basis, which patient will have another episode in the next year. By adding functional MRI scans, and a novel test of being inclined to self-blame to standard measures, we achieved over 80% accuracy. In contrast, standard measures alone were no better than chance guessing who would develop another episode. Patients were scanned whilst they experienced self-blame, which is thought to play an important role in depression by decreasing self-worth. We have demonstrated that recovered patients who go on to develop depression show altered connections in the self-blame-related brain network which differed from those who remained well.Despite these encouraging results, it is unknown:
1) whether we can confirm these results in a larger independent group
2) whether MRI is needed for predicting who will develop depression at an individual level or could be replaced by other more widely available measures (such as digital health or chemical markers)
3) whether the brain networks found to be disrupted when blaming oneself in depression are linked to abnormal stress hormones, the only established chemical risk factor for recurrence
4) whether the disruption in brain networks when blaming oneself makes people more vulnerable to develop depression after a stressful life eventTo answer these questions, we propose to enrol 150 patients recovered from depression who have stopped their antidepressant medication in accordance with guidelines. An initial MRI and psychological tests will be used to predict recurrence after one year. This will deliver much needed evidence for reproducible
risk factors to 1) inform novel treatment approaches and 2) develop a so-called "prognostic marker" to predict recurrence risk for affected individuals.Lay summary of study results: Each week, around two million people in the UK experience a depressive episode. Most recover but are at a higher risk of developing symptoms again in the future compared with people who have never experienced depression. Why some individuals develop another episode of depression whilst others remain in stable recovery is poorly understood. Identifying these risk factors is essential to develop better treatments as current treatments reduce the risk of recurrence but are often insufficient to do so.
During everyday dealings with other people, we automatically think about their behaviour and our own and this affects how we feel about ourselves and other people. We have previously shown that brain magnetic resonance imaging (MRI) scans and computer tests whilst thinking about our interactions with others can predict risk of recurring depression. As part of the Neurocognitive Signatures Predicting Risk of Recurrent Depression (NESPRED) project, we aimed to see whether we could confirm these patterns of brain activation in a larger group of people and whether we might be able replace MRI scans with cheaper measures.
To investigate this, we recruited 150 participants who had recovered from depression, were not currently taking antidepressant medications and had no signs of a bipolar disorder. We also recruited 40 control participants with no personal or family history of a mood disorder for comparison. After the baseline session, participants were asked to attend an MRI session in which images of their brain were taken, chemical measures were collected and participants completed tasks probing different cognitive functions. Participants were also invited to attend an optional electroencephalography (EEG) session, which is a low-cost electrical neuroimaging technique and might offer a more scalable alternative to MRI. We followed our participants over a 14-month period to see whether any of our measures could predict whether they would remain stable or develop another episode of depression. We made specific predictions and planned our analysis, all of which we registered publicly before analysing our data.
One of the most striking findings of the study was that we found the same brain regions predicting risk of depression recurrence as in our previous work. We showed participants blame-evoking statements of hypothetical social scenarios in which they were either behaving negatively towards their friend or their friend towards them. After the brain scan, we also asked participants to rate the degree of self-blame and blaming of their friend. By probing this “finger of blame”, we confirmed our previous findings that people with a history of depression were more likely to blame themselves rather than their friend even when their friend was described to have done something wrong. We focused on a measure of functional MRI connectedness between two brain areas previously found to be relevant to unhelpful self-blame in depression.
One area is called the “right anterior temporal lobe” and sits just beneath our temple, while the other area, more widely studied in depression, is called the “subgenual cortex” and sits in the depth of the midline of the frontal part of our brain.
Our previously supported hypothesis is that a helpful pattern of connectedness between these brain areas is important for integrating information that helps us to come up with a nuanced interpretation of failure and protects us against overgeneral self-critical thinking often observed in people with depression. Previous studies have shown that the right anterior temporal lobe is important for understanding the social meaning of situations and helps differentiate the interpretation of when something goes wrong in a social interaction. In contrast, the subgenual region is relevant to how much an individual tends to attribute blame to either themselves or others (“finger of blame”).
Like in our previous study, a disruption in the connectedness between these two brain areas whilst feeling self-blame was associated with a higher risk of developing another episode of depression. We think that this disruption in the connectedness leads to overgeneralisations and a tendency to blame oneself even if it is not one’s fault, potentially making someone more prone to develop another episode of depression in response to certain life events or usual fluctuations in mood. More research is required to understand what this connectedness means and if it is something we could target with novel treatment approaches.
Even though we were delighted to confirm the same brain regions and their connections predicting subsequent risk of recurrence at the group level, it is important to ask the question whether this can be used to advise people clinically on their individual risk of recurrence.
Currently, clinical advice is linked to standard measures, such as scores on depressive symptom scales and number of previous depressive episodes. When using these standard clinical measures to make predictions for each individual, we confirmed previous studies showing that the prediction was performing better than flipping a coin (i.e. chance), but overall, was not accurate enough to tell who will develop another episode of depression. Adding information from the functional MRI, unfortunately, also failed to improve the level of accuracy in prediction unlike our previous work had suggested.
As mentioned before, we were also interested to understand the brain structure including the white matter (i.e. the “cables” connecting nerve cells and conveying the electrical signals between them) and the grey matter which contains the nerve cells thought to represent information we are storing in our brains. Previous work has shown that certain differences in brain structure and organisation of connections within the brain, particularly in regions involved in social and emotional functions, might leave an individual more at risk of developing another episode. Even though we did not find any differences in the brain structure, the group who went on to experience another episode of depression showed differences in the organisation of a major white matter bundle, called the “cingulum bundle”, which has been known to be relevant to mood disorders since the early 20th century. We have not yet investigated whether the structural measures are able to make predictions at an individual level, which would be important to establish ahead of any future clinical application.
Moreover, participants completed a virtual reality task which probed the “finger of blame” in a similar way as the functional brain scans. Participants were shown immersive scenes, while a narrator described a hypothetical social scenario that happened between the participants and their friends. They were asked how they would react to the hypothetical scenario and how much they would blame themselves and their friend for the behaviour. Interestingly, people who blamed themselves for their friend’s bad behaviour (unhelpful self-blame), were much more likely to develop another episode of depression over the next year. When trying to make predictions for each individual, the model was performing similarly to the model using standard clinical measures. This is promising as it points to the potential of replacing the long, in-depth clinical interview with a short, self-administered computer test, yielding a similar performance.
How people respond to stressful life events, plays an important role in depression as well. As part of the NESPRED study, we collected chemical measures linked to the stress response (cortisol) and social bonding (oxytocin) to better understand the interaction between these hormonal markers, stressful life events and vulnerability to depression. Notably, we found that the impact of stressful life events on subsequent depression recurrence depended on oxytocin rather than cortisol responses. Participants with higher levels of oxytocin who experienced stressful life events during the study, were more likely to experience recurrent depression.Interestingly, participants with a history of early life neglect and abuse had lower oxytocin levels. We also confirmed our hypothesis that people with a lower pain sensitivity used as a measure of high levels of internal production of opioids (“endogenous opioids”) were more likely to develop a loss of feeling over the next year. These findings contribute to a better understanding of previously unknown roles of oxytocin and opioids in depression risk and their complex interaction with stressful life events.
Lastly, we used portable EEG, which measures the electrical activity of the brain. The brain is made up of billions of neurons, which are specialised cells that transmit information through electrical and chemical signals. Neurons often work in large networks, where many neurons fire together in a synchronised manner. This synchronised activity generates rhythmic patterns of electrical activity, sometimes known as brain waves. We investigated if those brain waves, i.e. EEG activity, could predict risk of recurrence. As with the other tasks, we asked participants to imagine social interactions in the context of blame. We found that people with a history of depression showed differences in brain activity related to social emotions, suggesting that their EEG did not distinguish between themselves and their friends in terms of who was initiating a social action (agency), compared to people without a history of depression. Moreover, when asked to recall emotional memories about themselves or their friend, participants with a history of depression who showed decreased “signal complexity”, i.e. a reduced degree of chaos-like and a more predictable nature of the EEG signal, were less likely to have another episode of depression, potentially a compensation mechanism to remain well which could be used for future brain training approaches if confirmed. We interpreted this as a compensation mechanism as participants with no history of depression had the same level of EEG signal complexity for emotional memories as participants who developed a recurrent episode and it was those remaining stable who showed an “abnormal” EEG signal complexity. This alerts us to being careful in interpreting group differences, sometimes an “abnormal” signal can be helpful and point to strategies people’s brain might have developed to remain resilient.
We hope this report on the main findings of our research gives you an idea of how much we have learned from this project already and how many new clues for future research the field will gain from this project. We are currently preparing a series of research papers to publish the results in much more detail. This process will take time, but once the results are published in a scientific journal, they will be accessible to the public.
REC name
London - South East Research Ethics Committee
REC reference
21/LO/0137
Date of REC Opinion
2 Mar 2021
REC opinion
Further Information Favourable Opinion