Development of Cognitive and Imaging Biomarkers Predicting Risk of Self-Blaming Bias and Recurrence in Major Depressio
Lead Research Organisation:
King's College London
Department Name: Psychological Medicine
Abstract
Depression is the leading cause of disability world-wide. This is mainly because 50% of people who have had one phase of depression in their life will develop further phases in the future. One explanation is that blaming oneself excessively for failures makes people more vulnerable for developing depression. Recently, we have been able to identify the parts of the brain involved in self-blaming feelings such as guilt. In a preliminary study, we have demonstrated that people with a history of depression, but currently free of symptoms, show abnormalities in the cross-talk within the brain network involved in self-blaming feelings compared with people who have never had depression. In the proposed project we will study people who have fully recovered from a phase of depression and who have stopped their medication as currently recommended by clinical guidelines. We will investigate for the first time, whether the degree of abnormalities in this self-blame-related brain network can predict the risk of developing another depressive phase within the next year. If successful, this project will help to understand what brain changes underlie the tendency to repeatedly develop depression and lead to new approaches to prevention and treatment.
Technical Summary
Importance/Opportunities
Depressive disorders are the leading cause of disability. One prominent reason for this is the recurrence of major depressive (MD) episodes. The neuroanatomical basis of vulnerability to recurrence is unknown. Using fMRI to identify the neuroanatomical basis of cognitive abnormalities is the most promising approach to bridge the gap between clinical and molecular-genetic manifestations of MD. Such imaging biomarkers enable the development of refined disease models and stratification of diagnostic groups.
Background
Current neuroanatomical models explain MD by abnormalities in neural circuits underlying negative emotions. The unresolved conundrum, however, is to explain why excessive negative feelings occur when MD patients blame themselves (manifesting as feeling guilt or self-worthlessness), but not when blaming others. This self-blaming bias cannot be accounted for by models of MD that rely solely on overall increases in negative emotions.
Preliminary data
In an fMRI pilot study in patients with a history of MD (a group with strongly increased vulnerability for MD recurrence), but fully remitted symptoms, we showed that functional decoupling in a specific brain network during self-blaming feelings distinguishes this group from controls. This decoupling was selective for self-blame but did not occur when blaming others and therefore explains vulnerability to self-blaming bias.
Aims/Objectives
Despite these encouraging results, it is unknown whether these fMRI abnormalities prospectively predict risk for MD recurrence. The principle aim of this project is to test this hypothesis. In addition, I will determine whether structural MRI measures, that have demonstrated potential predictive value in one previous study and a novel cognitive test measure can independently predict recurrence.
Methodology
Here, I propose two studies: 1) a case-control study to examine whether our pilot data can be reproduced in a larger group (N=57 patients with remitted MD vs. N=57 never-depressed controls); 2) a longitudinal study to investigate prediction of recurrence in unmedicated patients with fully remitted MD (N=120) within 14 months of clinical follow-up (3 visits) to test whether fMRI abnormalities have predictive value, independently of clinical predictors and in comparison with structural MRI and novel cognitive measures.
Impact/Novelty
Whereas previous research in MD has focused on imaging biomarkers of remission from the depressed state, this project will provide the first fMRI biomarker of recurrence. Without establishing the neuroanatomical traits conferring vulnerability to recurrence, as aimed for here, core pathophysiological aspects of MD will remain unexplained. If successful, this project will lead to improved prevention and development of novel treatments.
Depressive disorders are the leading cause of disability. One prominent reason for this is the recurrence of major depressive (MD) episodes. The neuroanatomical basis of vulnerability to recurrence is unknown. Using fMRI to identify the neuroanatomical basis of cognitive abnormalities is the most promising approach to bridge the gap between clinical and molecular-genetic manifestations of MD. Such imaging biomarkers enable the development of refined disease models and stratification of diagnostic groups.
Background
Current neuroanatomical models explain MD by abnormalities in neural circuits underlying negative emotions. The unresolved conundrum, however, is to explain why excessive negative feelings occur when MD patients blame themselves (manifesting as feeling guilt or self-worthlessness), but not when blaming others. This self-blaming bias cannot be accounted for by models of MD that rely solely on overall increases in negative emotions.
Preliminary data
In an fMRI pilot study in patients with a history of MD (a group with strongly increased vulnerability for MD recurrence), but fully remitted symptoms, we showed that functional decoupling in a specific brain network during self-blaming feelings distinguishes this group from controls. This decoupling was selective for self-blame but did not occur when blaming others and therefore explains vulnerability to self-blaming bias.
Aims/Objectives
Despite these encouraging results, it is unknown whether these fMRI abnormalities prospectively predict risk for MD recurrence. The principle aim of this project is to test this hypothesis. In addition, I will determine whether structural MRI measures, that have demonstrated potential predictive value in one previous study and a novel cognitive test measure can independently predict recurrence.
Methodology
Here, I propose two studies: 1) a case-control study to examine whether our pilot data can be reproduced in a larger group (N=57 patients with remitted MD vs. N=57 never-depressed controls); 2) a longitudinal study to investigate prediction of recurrence in unmedicated patients with fully remitted MD (N=120) within 14 months of clinical follow-up (3 visits) to test whether fMRI abnormalities have predictive value, independently of clinical predictors and in comparison with structural MRI and novel cognitive measures.
Impact/Novelty
Whereas previous research in MD has focused on imaging biomarkers of remission from the depressed state, this project will provide the first fMRI biomarker of recurrence. Without establishing the neuroanatomical traits conferring vulnerability to recurrence, as aimed for here, core pathophysiological aspects of MD will remain unexplained. If successful, this project will lead to improved prevention and development of novel treatments.
