Understanding the neural and cognitive mechanisms of attributional styles and credit assignment in depression
Lead Research Organisation:
University of Oxford
Department Name: Experimental Psychology
Abstract
Depression is the most common psychiatric illness. Yet, its causes remain poorly understood, despite considerable amount of research. I propose a novel approach to understanding depression, namely to investigate the role of credit assignment - or causal attribution - in depression. Credit assignment is about learning the causes of events when there are different potential causes. My experiments will involve measuring credit assignment behaviour in human participants, as well as assessing the underlying neural processes using modern brain imaging techniques.
In a nutshell, I will examine whether in depression, patients have problems with correctly attributing positive or negative outcomes to different sources - do they have a tendency to falsely belief that they are the source of negative outcomes? But that on the other hand, positive outcomes are due to external causes, such as the environment or other people? Do they only have problems identifying the source for outcomes external to them or does this extend to their own emotions, such as low mood? Any of these aberrant credit assignment processes could explain how depression maintains itself by continuously reinforcing negative views and attitudes about the self, thus leading to low mood.
To investigate what types of credit assignment are changed in depression, I propose a set of paradigms each testing experimentally a different aspect of credit assignment in depression. For this, I will use a computational psychiatry approach. This means, I will develop models describing the complex process of credit assignment in my paradigms in terms of mathematically defined aspects. For example, firstly making a decision in the expectation that it will lead to a certain outcome. Then assessing how the actual outcome compares to your expectation. And finally assigning credit to the internal or external causes that seem most likely. I can then assess which of the different aspects are more specifically affected in depression.
To test the neural effects, I will combine these measurements of behaviour with measurements of brain activity using functional magnetic resonance imaging (fMRI). I will use fMRI to identify the brain areas that are particularly engaged by specific aspects of credit assignment. Subsequently, I can test how deficits in credit assignment in depression can be linked to specific brain areas. In the past, cognitive neuroscience has been particularly fruitful in dissociating cognitive sub-processes as they often map onto very distinct brain regions.
To summarise, I am proposing a series of experiments looking at how patients with depression assign credit to different outcomes when there are multiple causes. I will investigate this question neurally and using quantitative computational modelling. Thus my proposal is an attempt to bridge the gap between psychiatric concepts and mechanistic models from cognitive neuroscience and psychology. In particular, my work will contribute to understanding some of the neural and behavioural mechanisms of the changes occurring in depression and attempts to treat it, using a multi-disciplinary approach.
In a nutshell, I will examine whether in depression, patients have problems with correctly attributing positive or negative outcomes to different sources - do they have a tendency to falsely belief that they are the source of negative outcomes? But that on the other hand, positive outcomes are due to external causes, such as the environment or other people? Do they only have problems identifying the source for outcomes external to them or does this extend to their own emotions, such as low mood? Any of these aberrant credit assignment processes could explain how depression maintains itself by continuously reinforcing negative views and attitudes about the self, thus leading to low mood.
To investigate what types of credit assignment are changed in depression, I propose a set of paradigms each testing experimentally a different aspect of credit assignment in depression. For this, I will use a computational psychiatry approach. This means, I will develop models describing the complex process of credit assignment in my paradigms in terms of mathematically defined aspects. For example, firstly making a decision in the expectation that it will lead to a certain outcome. Then assessing how the actual outcome compares to your expectation. And finally assigning credit to the internal or external causes that seem most likely. I can then assess which of the different aspects are more specifically affected in depression.
To test the neural effects, I will combine these measurements of behaviour with measurements of brain activity using functional magnetic resonance imaging (fMRI). I will use fMRI to identify the brain areas that are particularly engaged by specific aspects of credit assignment. Subsequently, I can test how deficits in credit assignment in depression can be linked to specific brain areas. In the past, cognitive neuroscience has been particularly fruitful in dissociating cognitive sub-processes as they often map onto very distinct brain regions.
To summarise, I am proposing a series of experiments looking at how patients with depression assign credit to different outcomes when there are multiple causes. I will investigate this question neurally and using quantitative computational modelling. Thus my proposal is an attempt to bridge the gap between psychiatric concepts and mechanistic models from cognitive neuroscience and psychology. In particular, my work will contribute to understanding some of the neural and behavioural mechanisms of the changes occurring in depression and attempts to treat it, using a multi-disciplinary approach.
Technical Summary
Depression is the most common mental illness. In the past, depression has been linked to negative biases in perception, attention or memory. In particular, cognitive theories of depression have suggested negative attributional styles as the root cause of depression. However, the cognitive and neural mechanisms underlying this are not yet known. Recent advances in computational cognitive neuroscience have put forward quantitative descriptions of credit assignment, i.e. learning about the causes of events. I propose to investigate the cognitive and neural mechanisms of different aspects of credit assignment and test whether it can be used to provide an account of neural activity and behaviour associated with attributional styles in depression.
I will compare the behaviour of depressed patients with non-depressed participants and measure the neural mechanisms associated with the behaviour. The experiments I propose, focus on 1) how positive or negative events are attributed, to either to the self, environmental features or remain unassigned, 2) how attributions are made to the self or other people and how this information influences social decisions, 3) how emotional responses (as opposed to simpler, more concrete outcomes) are attributed, and how they might, if they unattributed or incorrectly attributed, interfere with other behaviours.
To address these questions, I will combine approaches from cognitive neuroscience and psychology. To characterize the behavioural effects, approaches will include computational modelling using approaches from machine learning, Bayesian optimal learning theory and hierarchical modelling. The neural mechanisms and changes in depression will be probed using computational analyses of functional magnetic resonance imaging (fMRI) data involving modelling and pattern classification techniques. The causal involvement of the areas identified using fMRI will be further probed using transcranial direct current stimulation (tDCS).
I will compare the behaviour of depressed patients with non-depressed participants and measure the neural mechanisms associated with the behaviour. The experiments I propose, focus on 1) how positive or negative events are attributed, to either to the self, environmental features or remain unassigned, 2) how attributions are made to the self or other people and how this information influences social decisions, 3) how emotional responses (as opposed to simpler, more concrete outcomes) are attributed, and how they might, if they unattributed or incorrectly attributed, interfere with other behaviours.
To address these questions, I will combine approaches from cognitive neuroscience and psychology. To characterize the behavioural effects, approaches will include computational modelling using approaches from machine learning, Bayesian optimal learning theory and hierarchical modelling. The neural mechanisms and changes in depression will be probed using computational analyses of functional magnetic resonance imaging (fMRI) data involving modelling and pattern classification techniques. The causal involvement of the areas identified using fMRI will be further probed using transcranial direct current stimulation (tDCS).
Planned Impact
10-20% of the population will suffer from depression throughout their lives. As depression can be a very debilitating disorder, it is both a quality of life as well as economic issue. The aim of the research I propose is to understand attributional styles in depression better at the cognitive and neural level. As part of this I will design computerised tasks that can measure different aspects of attributional styles in depression. I can envision several potential practical applications of this work:
Firstly, a commercial application, in the mid-term future (within about 5-10 years), could be to use these tasks that I will develop as a test-battery to screen or refine new potential treatments for depression by e.g. pharmaceutical companies or researchers at universities. As a specific example, based on our neural results, we could suggest, again at some point in the future after appropriate piloting, brain areas that might be a target for non-invasive interventions such as transcranial magnetic stimulation (TMS) or transcranial direct current stimulation (tDCS).
Secondly, the computerized tasks could also be modified in the future to work as 'computer brain-training' interventions; if successful in clinical studies, these tasks could be widely distributed to patients with depression (or other patient groups with problems in attributional styles, such as anxiety) over the internet as an additional treatment. The advantage of this approach is that it is very up-scalable. Again, initial small-scale studies of this approach could be carried out within about 5-10 years. In the longer term then, the results of this work may benefit patients with depression by developing new treatments or complementing and enhancing current treatments.
The skills that I will develop during this fellowship will also have a wider impact on further mental health research that I am planning to carry out. The computational modeling and neuroimaging techniques will be applicable to other problems as they are a general approach for putting questions about symptoms of mental illnesses into experiments. This approach allows understanding symptoms that patients report in mechanistic terms and find out more about their basis in the brain. Thus the skills I will learn during this fellowship will be very valuable for my future career as a researcher.
Firstly, a commercial application, in the mid-term future (within about 5-10 years), could be to use these tasks that I will develop as a test-battery to screen or refine new potential treatments for depression by e.g. pharmaceutical companies or researchers at universities. As a specific example, based on our neural results, we could suggest, again at some point in the future after appropriate piloting, brain areas that might be a target for non-invasive interventions such as transcranial magnetic stimulation (TMS) or transcranial direct current stimulation (tDCS).
Secondly, the computerized tasks could also be modified in the future to work as 'computer brain-training' interventions; if successful in clinical studies, these tasks could be widely distributed to patients with depression (or other patient groups with problems in attributional styles, such as anxiety) over the internet as an additional treatment. The advantage of this approach is that it is very up-scalable. Again, initial small-scale studies of this approach could be carried out within about 5-10 years. In the longer term then, the results of this work may benefit patients with depression by developing new treatments or complementing and enhancing current treatments.
The skills that I will develop during this fellowship will also have a wider impact on further mental health research that I am planning to carry out. The computational modeling and neuroimaging techniques will be applicable to other problems as they are a general approach for putting questions about symptoms of mental illnesses into experiments. This approach allows understanding symptoms that patients report in mechanistic terms and find out more about their basis in the brain. Thus the skills I will learn during this fellowship will be very valuable for my future career as a researcher.
People |
ORCID iD |
Jacqueline Scholl (Principal Investigator / Fellow) |
Publications
Petitet P
(2021)
The relationship between apathy and impulsivity in large population samples.
in Scientific reports
Scholl J
(2022)
The effect of apathy and compulsivity on planning and stopping in sequential decision-making
in PLOS Biology
Van Der Velden A
(2023)
Mindfulness Training Changes Brain Dynamics During Depressive Rumination: A Randomized Controlled Trial
in Biological Psychiatry
Description | DPhil studentship from Experimental Psychology, Oxford University, to Lisa Spiering |
Amount | £70,000 (GBP) |
Organisation | University of Oxford |
Sector | Academic/University |
Country | United Kingdom |
Start | 10/2020 |
End | 09/2023 |
Description | DPhil studentship from Medical Sciences, Oxford University to Hailey Trier |
Amount | £125,000 (GBP) |
Organisation | University of Oxford |
Sector | Academic/University |
Country | United Kingdom |
Start | 10/2018 |
End | 09/2021 |
Description | Medical Sciences Internal Fund: Pump Priming |
Amount | £10,000 (GBP) |
Funding ID | 0006378 |
Organisation | University of Oxford |
Sector | Academic/University |
Country | United Kingdom |
Start | 01/2019 |
End | 12/2020 |
Description | WIN Seed Award |
Amount | £11,900 (GBP) |
Organisation | Wellcome Centre for Integrative Neuroimaging |
Sector | Public |
Country | United Kingdom |
Start | 04/2020 |
End | 04/2022 |
Title | Should I stick with it or go? |
Description | Roughly 750 (confirmation) and 400 (discovery) participants on sequential decision making task together with clinical symptoms. |
Type Of Material | Database/Collection of data |
Year Produced | 2022 |
Provided To Others? | Yes |
Impact | Data set has only just been made available, but it has already been used in one additional publication and is currently part of another study. |
URL | https://osf.io/dfg2u/ |
Description | Computational modelling of gambling disorder biases |
Organisation | Lyon Neuroscience Research Center |
Country | France |
Sector | Public |
PI Contribution | Collected data, supervised MSc student during analysis. |
Collaborator Contribution | Experimental design, supervised MSc student |
Impact | No outputs yet. |
Start Year | 2021 |
Description | Effects of mood instability on decision-making and learning |
Organisation | University of Oxford |
Department | Structural Genomics Consortium (SGC) |
Country | United Kingdom |
Sector | Public |
PI Contribution | I have designed two tasks measuring decision-making and learning that will be used as part of a larger Wellcome Trust Funded project (COMET) to measure psychological and neural effects of mood instability. I am supervising the analysis of this data by a PhD student. |
Collaborator Contribution | They have designed the larger project, acquired funding and employed a PhD student who is currently collecting and analysing the data. |
Impact | We hope to submit the first result for publication by the end of 2017. |
Start Year | 2015 |
Description | Losartan and fear processing |
Organisation | University of Oxford |
Department | Department of Psychiatry |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | My DPhil student and I are providing a computerised tasks and will provide analysis of the data. |
Collaborator Contribution | Acquisition of funds, design of the drug study, collection of data. |
Impact | Not yet. |
Start Year | 2020 |
Description | Mindfulness and psychosis |
Organisation | University of Plymouth |
Department | Psychology Plymouth |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | Supervised PhD student |
Collaborator Contribution | Supervised PhD student, acquired funding. |
Impact | No outputs yet. |
Start Year | 2021 |
Description | Mindfulness based cognitive therapy |
Organisation | Aarhus University |
Country | Denmark |
Sector | Academic/University |
PI Contribution | I have analysed FMRI data supplied by the collaborator. |
Collaborator Contribution | The collaborator has acquired funds for the study and collected the data. |
Impact | Publication is currently being prepared for submission. |
Start Year | 2018 |
Description | Susceptibility to peer feedback and its relationship with emotional resilience following bullying experiences |
Organisation | University of Oxford |
Department | Department of Psychiatry |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | Development of task design and analysis methods. |
Collaborator Contribution | Acquisition of data, development of task design and analysis methods. |
Impact | The project has only just started. |
Start Year | 2019 |
Description | Understanding credit assignment in drug and alcohol addiction |
Organisation | University of Plymouth |
Department | Psychology Plymouth |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | Collaboration on experimental design and analysis strategy. |
Collaborator Contribution | Acquisition of funding, data collection, experimental design and analysis |
Impact | Not yet. |
Start Year | 2020 |
Description | Understanding treatment mechanisms of tanscranial direct current brain stimulation in depression |
Organisation | University of Oxford |
Department | Department of Psychiatry |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | DPhil student and I have contributed computerised tasks and will analyse the data once collected. |
Collaborator Contribution | Acquisition of funds, experimental design, collection of data. |
Impact | Not yet. |
Start Year | 2019 |
Description | Understanding treatment mechanisms of tanscranial direct current brain stimulation in depression |
Organisation | University of Toronto |
Country | Canada |
Sector | Academic/University |
PI Contribution | DPhil student and I have contributed computerised tasks and will analyse the data once collected. |
Collaborator Contribution | Acquisition of funds, experimental design, collection of data. |
Impact | Not yet. |
Start Year | 2019 |
Description | Bayesian data analysis workshop |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | Regional |
Primary Audience | Postgraduate students |
Results and Impact | Designed workshop teaching Bayesian methods for data analysis. Workshop was based on training I undertook/ skills and techniques I have acquired as part of my MRC skills development fellowship. |
Year(s) Of Engagement Activity | 2017 |
Description | Computational modeling for anlaysing behaviour and neural data |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | Local |
Primary Audience | Postgraduate students |
Results and Impact | Co-organization of a 4 session long workshop on how to use the computational methods I have been developing in my work for analysing behavioural and neural data. |
Year(s) Of Engagement Activity | 2018 |
Description | PSYNAPPS - Emotional enhancement |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | Local |
Primary Audience | Undergraduate students |
Results and Impact | Talk at symposium organised by Oxford University Psychology and Neuroscience Applications Society (open to general public and members of the University) discussing mechanisms of psychiatric medications in healthy people and how people use drugs to affect their emotions. Talk led to discussion afterwards. |
Year(s) Of Engagement Activity | 2016 |