Information Bias in Depression
Lead Research Organisation:
University of Oxford
Department Name: Psychiatry
Abstract
Many people will experience clinical depression in their life. While there are treatments which help, they often don't work as well or as reliably as we would like. It is therefore essential that new, more effective treatments are developed. In order to do this we must first understand the full range of interconnected mechanisms, from abnormal brain function to maladaptive habits in thinking, which cause people to become, or to remain, depressed. A better understanding of these multi-level mechanisms, and how they link together, is an essential step in the development of the new treatments which are needed for this illness.
Recently, "computational modelling" techniques have been successfully deployed to investigate the causes of psychiatric symptoms. These techniques use simple computer programs which are adjusted until they mimic the behaviour of humans and are useful at linking symptoms of illnesses to the thinking habits and brain systems which underpin them. A particular insight of computational modelling studies, compared to other nethods of understanding brain function, is that people adjust their thinking habits and brain activity to focus more on those events which provide information about the future. For example, consider a situation in which experiencing a positive event, such as being complimented, means that other positive events are more likely to occur, but in which negative events, such as being insulted, occur at random. In this situation, the positive events contain more information about the future than the negative events and because of this, as predicted by the computational modelling approach, people will tend to focus more on the positive than negative events (e.g. they will pay more attention to the positive events and learn more from them). In depression, patients focus more on negative than positive events, in other words they behave as if negative events provide more information. Indeed, this focus on negative events is believed to be one of the factors which cause people to become depressed in the first place. Computational modelling studies have linked this process of estimating the inforomation content of events with activity in the anterior cingulate cortex of the brain and of the neurotransmitter norepinephrine (NE) system suggesting that these brain systems may also be involved in producing the negative "information bias" of depression. In summary computational modelling provides a novel method for understanding the negative thinking habits which cause depression and for linking these habits to the brain systems which underpin them. In doing so it suggests new ways in which the thinking habits can be altered.
In this project I will use these insights of computational modelling to clarify the cognitive and neural mechanisms which cause depression and explore how these mechanisms may be targeted by novel treatments. Specifically, I will:
a) use computational modelling to precisely describe the information bias of depressed patients
b) use functional neuroimaging to investigate the neural mechanisms by which these information biases are acquired
c) test the degree to which the biases may be modified with a simple training interventions and with the commonly prescribed drug atomoxetine (which changes the activity of the NE system).
This translational project will be completed in the Department of Psychiatry of the University of Oxford and will include studies of healthy participants and depressed patients.
By using recent advances in computational neuroscience to better understand the causes of depression this project aims to guide the development of novel, and more effective, treatments for this devastating illness.
Recently, "computational modelling" techniques have been successfully deployed to investigate the causes of psychiatric symptoms. These techniques use simple computer programs which are adjusted until they mimic the behaviour of humans and are useful at linking symptoms of illnesses to the thinking habits and brain systems which underpin them. A particular insight of computational modelling studies, compared to other nethods of understanding brain function, is that people adjust their thinking habits and brain activity to focus more on those events which provide information about the future. For example, consider a situation in which experiencing a positive event, such as being complimented, means that other positive events are more likely to occur, but in which negative events, such as being insulted, occur at random. In this situation, the positive events contain more information about the future than the negative events and because of this, as predicted by the computational modelling approach, people will tend to focus more on the positive than negative events (e.g. they will pay more attention to the positive events and learn more from them). In depression, patients focus more on negative than positive events, in other words they behave as if negative events provide more information. Indeed, this focus on negative events is believed to be one of the factors which cause people to become depressed in the first place. Computational modelling studies have linked this process of estimating the inforomation content of events with activity in the anterior cingulate cortex of the brain and of the neurotransmitter norepinephrine (NE) system suggesting that these brain systems may also be involved in producing the negative "information bias" of depression. In summary computational modelling provides a novel method for understanding the negative thinking habits which cause depression and for linking these habits to the brain systems which underpin them. In doing so it suggests new ways in which the thinking habits can be altered.
In this project I will use these insights of computational modelling to clarify the cognitive and neural mechanisms which cause depression and explore how these mechanisms may be targeted by novel treatments. Specifically, I will:
a) use computational modelling to precisely describe the information bias of depressed patients
b) use functional neuroimaging to investigate the neural mechanisms by which these information biases are acquired
c) test the degree to which the biases may be modified with a simple training interventions and with the commonly prescribed drug atomoxetine (which changes the activity of the NE system).
This translational project will be completed in the Department of Psychiatry of the University of Oxford and will include studies of healthy participants and depressed patients.
By using recent advances in computational neuroscience to better understand the causes of depression this project aims to guide the development of novel, and more effective, treatments for this devastating illness.
Technical Summary
Depression is a common and debilitating illness. While a range of treatments are available, remission rates are low and relapse common. It is therefore essential that new, more effective treatments are developed. In order to do this we must first understand the full range of interconnected mechanisms, from abnormal brain function to maladaptive cognitive habits, which cause people to become, or to remain, depressed.
Recently, computational modelling techniques have been successfully used to link cognitive and neurobiological function with symptoms of psychiatric illness. One of the insights of this approach is that events with a high "information content", defined as the degree to which the event improves prediction of the future, are preferentially processed. Activity of the central norepinephrine (NE) system and of the anterior cingulate cortex covaries with the information content of stimuli indicating that these systems may underpin this phenomenon.
Depression is characterised by negative cognitive biases which are causally implicated in the illness. In terms of the information theoretic account described above, depressed patients behave as if negative events provided more information than positive events. In the current proposal I will use computational modelling to investigate why patients show this "information bias", what neural systems support it and how it can be altered by cognitive and pharmacological interventions.
Specifically, I will:
a) characterise the computationally defined information bias of depressed patients
b) investigate the neural mechanisms by which an information bias is acquired
c) test the degree to which it may be modified with a simple training intervention or by manipulation of the NE system using atomoxetine.
By using recent advances in computational neuroscience to better understand the causes of depression this project will guide the development of the novel treatments which are needed for the illness.
Recently, computational modelling techniques have been successfully used to link cognitive and neurobiological function with symptoms of psychiatric illness. One of the insights of this approach is that events with a high "information content", defined as the degree to which the event improves prediction of the future, are preferentially processed. Activity of the central norepinephrine (NE) system and of the anterior cingulate cortex covaries with the information content of stimuli indicating that these systems may underpin this phenomenon.
Depression is characterised by negative cognitive biases which are causally implicated in the illness. In terms of the information theoretic account described above, depressed patients behave as if negative events provided more information than positive events. In the current proposal I will use computational modelling to investigate why patients show this "information bias", what neural systems support it and how it can be altered by cognitive and pharmacological interventions.
Specifically, I will:
a) characterise the computationally defined information bias of depressed patients
b) investigate the neural mechanisms by which an information bias is acquired
c) test the degree to which it may be modified with a simple training intervention or by manipulation of the NE system using atomoxetine.
By using recent advances in computational neuroscience to better understand the causes of depression this project will guide the development of the novel treatments which are needed for the illness.
Planned Impact
Depression is a common, chronic and disabling disorder with enormous costs to individual patients and to society. The World Health Organisation (WHO) has projected that unipolar depressive disorders will be the second leading cause of worldwide disability-adjusted life years (DALYs) by 2030. While a range of both psychological and pharmacological interventions have been proven effective in the treatment of depression, treatment response tends to be in the order of 50% (remission rates are significantly lower) with recurrence after treatment being the norm. There is therefore a strong rationale for developing interventions which treat and prevent this disorder. In order to achieve this goal it is necessary to understand the multi-level mechanisms which confer vulnerability to and maintain the illness-- and the interventions which target and modify these mechanisms. The current application, which applies recent advances in computational neuroscience in order to better understand the aetiology of depression and to guide the initial develop of novel interventions for the illness, represents the first step in this process. Ultimately therefore, the primary beneficiaries of the research are future patients. However, as described above there is also a strong economic case (both nationally and globally) for the development of more effective treatments and preventative interventions in depression.
From the point of view of industry, a key difficulty in the development of novel interventions in psychiatry has been the limited aetiological understanding of mental disorders and role of individual neurotransmiter systems in that aetiology. Such understanding is essential to reliably identify promising interventions for clinical development. The current application seeks to use recent advances in computational neuroscience to improve the aetiological understanding of depression and, more specifically, to initially test methods for altering causal processes in depression vulnerability. A better understanding of these processes increases the prospects of successfully identifying future effective treatments and therefore provides a benefit to those who seek to develop novel pharmacological and cognitive interventions.
From the point of view of industry, a key difficulty in the development of novel interventions in psychiatry has been the limited aetiological understanding of mental disorders and role of individual neurotransmiter systems in that aetiology. Such understanding is essential to reliably identify promising interventions for clinical development. The current application seeks to use recent advances in computational neuroscience to improve the aetiological understanding of depression and, more specifically, to initially test methods for altering causal processes in depression vulnerability. A better understanding of these processes increases the prospects of successfully identifying future effective treatments and therefore provides a benefit to those who seek to develop novel pharmacological and cognitive interventions.
People |
ORCID iD |
Michael Browning (Principal Investigator / Fellow) |
Publications
Browning M
(2017)
443. Characterisation of a Computationally Defined Treatment Target for Anxiety and Depression
in Biological Psychiatry
Walsh AEL
(2018)
A Dissociation of the Acute Effects of Bupropion on Positive Emotional Processing and Reward Processing in Healthy Volunteers.
in Frontiers in psychiatry
Huys QJM
(2021)
Advances in the computational understanding of mental illness.
in Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology
Pulcu E
(2017)
Affective bias as a rational response to the statistics of rewards and punishments.
in eLife
Reinecke A
(2018)
Angiotensin Regulation of Amygdala Response to Threat in High-Trait-Anxiety Individuals.
in Biological psychiatry. Cognitive neuroscience and neuroimaging
Hilland E
(2020)
Attentional bias modification is associated with fMRI response toward negative stimuli in individuals with residual depression: a randomized controlled trial.
in Journal of psychiatry & neuroscience : JPN
Description | Biomedical Resources Grant |
Amount | £1,200,000 (GBP) |
Funding ID | 212952/Z/18/Z |
Organisation | Wellcome Trust |
Sector | Charity/Non Profit |
Country | United Kingdom |
Start | 08/2018 |
End | 09/2023 |
Description | Erdem BRC |
Amount | £3,600 (GBP) |
Organisation | Oxford University Hospitals NHS Foundation Trust |
Department | NIHR Oxford Biomedical Research Centre |
Sector | Academic/University |
Country | United Kingdom |
Start | 02/2018 |
End | 02/2020 |
Description | Funding for an experts meeting in Cold Spring Harbor |
Amount | $10,000 (USD) |
Organisation | Brown University |
Sector | Academic/University |
Country | United States |
Start | 02/2019 |
End | 02/2019 |
Description | Funding for an experts meeting in Cold Spring Harbor |
Amount | $20,000 (USD) |
Organisation | Laureate Institute for Brain Research |
Sector | Learned Society |
Country | United States |
Start | 02/2019 |
End | 02/2019 |
Description | Funding for an experts meeting in Cold Spring Harbor |
Amount | $20,000 (USD) |
Organisation | Society of Biological Psychiatry |
Sector | Learned Society |
Country | United States |
Start | 02/2019 |
End | 02/2019 |
Description | MICA: Application for a Mental Health Data Pathfinder award (Oxford) |
Amount | £1,000,000 (GBP) |
Funding ID | MC_PC_17215 |
Organisation | Medical Research Council (MRC) |
Sector | Public |
Country | United Kingdom |
Start | 03/2018 |
End | 03/2020 |
Description | NIHR EME Scheme |
Amount | £2,600,000 (GBP) |
Organisation | NIHR Evaluation, Trials and Studies Coordinating Centre (NETSCC) |
Sector | Public |
Country | United Kingdom |
Start | 03/2019 |
End | 04/2023 |
Description | Wellcome Trust Clinical Training Fellowship |
Amount | £150,000 (GBP) |
Organisation | Wellcome Trust |
Sector | Charity/Non Profit |
Country | United Kingdom |
Start | 01/2019 |
End | 01/2022 |
Description | Emotional Disorders Translational Research Collaboration |
Organisation | National Institute for Health Research |
Country | United Kingdom |
Sector | Public |
PI Contribution | I am the chair of the emotional disorders translational research collaboration (TRC) a grouping of UK based researchers who are active in emotional disorders research. The group meets four times a year and is used to: discuss and shape new collaborative funding applications (and recruit co-investigators), respond to industrial partners interested in early stage (i.e. Experimental Medicine) studies in emotional disorders, feedback to the NIHR in terms of areas of research interest. |
Collaborator Contribution | The TRC is supported by the NIHR (largely through the BRC and CRF infrastructure of those who are members of the TRC). This resource is used to employ an administrator who supports mental health TRC activity. |
Impact | The collaboration has led to a number of funding applications that are currently being considered (e.g. I am involved in a NIHR-EME application, led from Kings College, that is in the second stage of assessment). These applications have not yet been formly agreed-- I would expect this to start happening this year. |
Start Year | 2018 |
Description | Expert meeting on Computational Psychiatry at Cold Spring Harbor |
Organisation | Brown University |
Country | United States |
Sector | Academic/University |
PI Contribution | I was the lead organiser for an expert meeting on computational psychiatry held in 2019 in Cold Spring Harbor. The three day meeting involved 40 expert researchers and was the basis for a white paper published in 2020 (Browning et al. Biological Psychiatry). The event was funded by three organisations: The society for biological psychiatry, Brown University and the Laureate Institute in Tulsa. |
Collaborator Contribution | The partners provided funding for the meeting |
Impact | White paper published 2020: Browning et al. Biological Psychiatry. The collaboration is multi disciplinary: Psychiatrists, Psychologists, Neuroscientists, Statisticians, Modellers. |
Start Year | 2018 |
Description | Expert meeting on Computational Psychiatry at Cold Spring Harbor |
Organisation | Laureate Institute for Brain Research |
Country | United States |
Sector | Learned Society |
PI Contribution | I was the lead organiser for an expert meeting on computational psychiatry held in 2019 in Cold Spring Harbor. The three day meeting involved 40 expert researchers and was the basis for a white paper published in 2020 (Browning et al. Biological Psychiatry). The event was funded by three organisations: The society for biological psychiatry, Brown University and the Laureate Institute in Tulsa. |
Collaborator Contribution | The partners provided funding for the meeting |
Impact | White paper published 2020: Browning et al. Biological Psychiatry. The collaboration is multi disciplinary: Psychiatrists, Psychologists, Neuroscientists, Statisticians, Modellers. |
Start Year | 2018 |
Description | Expert meeting on Computational Psychiatry at Cold Spring Harbor |
Organisation | Society of Biological Psychiatry |
Country | United States |
Sector | Learned Society |
PI Contribution | I was the lead organiser for an expert meeting on computational psychiatry held in 2019 in Cold Spring Harbor. The three day meeting involved 40 expert researchers and was the basis for a white paper published in 2020 (Browning et al. Biological Psychiatry). The event was funded by three organisations: The society for biological psychiatry, Brown University and the Laureate Institute in Tulsa. |
Collaborator Contribution | The partners provided funding for the meeting |
Impact | White paper published 2020: Browning et al. Biological Psychiatry. The collaboration is multi disciplinary: Psychiatrists, Psychologists, Neuroscientists, Statisticians, Modellers. |
Start Year | 2018 |
Description | Transcontinental Computational Psychiatry Workgroup |
Organisation | Laureate Institute for Brain Research |
Country | United States |
Sector | Learned Society |
PI Contribution | I am a co-founder and organiser of this workgroup. We organise monthly webinars for researchers interested in computational psychiatry as well as educational courses at conferences. |
Collaborator Contribution | Partners (Prof Martin Paulus and Dr Quenin Huys) co-organise the meetings and workshops. |
Impact | Educational meeting at SOBP 2017 conference. Monthly webinars (research data and educational) -- can be found at https://www.cmod4mh.com/ |
Start Year | 2016 |
Description | Transcontinental Computational Psychiatry Workgroup |
Organisation | Laureate Institute for Brain Research |
Country | United States |
Sector | Learned Society |
PI Contribution | I am a co-founder and organiser of this workgroup. We organise monthly webinars for researchers interested in computational psychiatry as well as educational courses at conferences. |
Collaborator Contribution | Partners (Prof Martin Paulus and Dr Quenin Huys) co-organise the meetings and workshops. |
Impact | Educational meeting at SOBP 2017 conference. Monthly webinars (research data and educational) -- can be found at https://www.cmod4mh.com/ |
Start Year | 2016 |
Title | Information bias in depression trial |
Description | We are currently finishing off an initial small trial of a novel computational intervention for depression (the focus of the current award). |
Type | Therapeutic Intervention - Psychological/Behavioural |
Current Stage Of Development | Initial development |
Year Development Stage Completed | 2019 |
Development Status | Under active development/distribution |
Clinical Trial? | Yes |
Impact | The intevention being tested is a novel approach to cognitive interventions in depression, it is based on computational models of learning in depression and is testing the impact of altering a specific, computationally defined, process. |
URL | https://clinicaltrials.gov/show/NCT02913898 |
Description | Appointed to head of NOCRI TRC in treatment resistant depression |
Form Of Engagement Activity | A formal working group, expert panel or dialogue |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Professional Practitioners |
Results and Impact | I was elected as the chair of the NIHR NOCRI Translational Research Collaboration in Treatment Resistant Depression. The collaboration includes clinical academics from throughout the UK, The goal of the collaboration is to increase the efficiency with with clinical studies of depressed patients may be carried out in the UK. The first meeting of the collaboration was in Feburary 2019. |
Year(s) Of Engagement Activity | 2019 |
Description | Award talk at British Association of Psychopharmacology Meeting |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Professional Practitioners |
Results and Impact | This was a talk associated with winning the senior clinical psychopharmacology award from the BAP. During the talk I described work I have completed. The audience included clinical and non-clinical researchers active in psychopharmacology and was a useful method for raising awareness of the computational techniques that I use. |
Year(s) Of Engagement Activity | 2016 |
URL | https://www.bap.org.uk/pdfs/biogs/awards2016_MichaelBrowning.pdf |
Description | BAP clinical talk |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Professional Practitioners |
Results and Impact | Clinically focussed talk on using cognitive neuroscience to predict treatment response in depression. Part of the applied clinical stream of the BAP conference. Participants are intended to be practising clinicians rather than researchers. I was approached by a number of clinicians about the possibility of applying these techniques in their practice. |
Year(s) Of Engagement Activity | 2017 |
URL | https://www.bap.org.uk/pdfs/2017_Summer_Meeting_Programme.pdf |
Description | Computational Psychiatry Meeting At Cold Spring Harbor |
Form Of Engagement Activity | A formal working group, expert panel or dialogue |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Professional Practitioners |
Results and Impact | I organised an experts meeting, together with 3 other colleagues, on Compuational Psychiatry at Cold Spring Harbor which took place in Feburary 2019. The focus of the meeting was steps which need to be taken to facilitate the clinical translation of computational techniques. The meeting was attended by 35 international experts and was funded by grants we secured from three US institutions (SOBP, Brown University and the Laureate Institution). The meeting took place over 2.5 days and involved a series of breakout groups and discussion sessions. I am currently authoring a paper based on the meeting. |
Year(s) Of Engagement Activity | 2019,2020 |
URL | https://www.cshl.edu/wp-content/uploads/2019/02/COMP19_Agenda.pdf |
Description | Interview for Wired UK |
Form Of Engagement Activity | A press release, press conference or response to a media enquiry/interview |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Media (as a channel to the public) |
Results and Impact | It is an article about a recent study which came from my MRC fellowship. The study uses computational modelling of behaviour and the magazine (Wired UK) is technology focussed-- so they wrote about using these sorts of novel technologies to improve mental health. |
Year(s) Of Engagement Activity | 2017 |
URL | http://www.wired.co.uk/article/human-behaviour-learning-computer-model-depression-negative-bias-trea... |
Description | Oxford Neuroscience Open Day |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | Local |
Primary Audience | Postgraduate students |
Results and Impact | Presentation of my work at the Oxford Neurosceince open day-- neuroscience researchers from throughout the University attended |
Year(s) Of Engagement Activity | 2019 |
URL | https://www.neuroscience.ox.ac.uk/about/oxford-neuroscience-symposium/ |
Description | Press article about BAP prize |
Form Of Engagement Activity | A press release, press conference or response to a media enquiry/interview |
Part Of Official Scheme? | No |
Geographic Reach | Local |
Primary Audience | Public/other audiences |
Results and Impact | A media article covering an award made to a group member. |
Year(s) Of Engagement Activity | 2016 |
URL | http://www.oxfordhealth.nhs.uk/news/oxford-health-consultant-psychiatrist-wins-prestigious-prize |
Description | SOBP talk |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Professional Practitioners |
Results and Impact | Talk about the work funded by my fellowship at the Society for Biological Psychiatry in San Diego. I was approached by a number of researchers from my and related fields after the talk to discuss how they may use similar techniques. On the back of this I have been invited to collaborate with a number of other researchers on projects which are using my tasks/techniques. |
Year(s) Of Engagement Activity | 2017 |