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The purpose of this study is to decode different thinking states from the brain activation patterns and identify the neural circuits that disengage from these thinking patterns using functional magnetic resonance imaging (fMRI) measurement in individuals with major depressive disorder.
This study aims to identify brain activation patterns associated with successful disengagement from negative thinking for MDD-affected participants. The investigators will use a machine learning classifier to decode thinking states from participants' fMRI signals. The decoder is utilized to trace the thinking state's time course as a measure of regulation performance. Investigating the brain activation correlated with the time course of the regulation success can indicate the neural circuits contributing to disengaging from negative thinking. The investigators will also explore the most effective regulation strategy for individual participants. Participants will be instructed to use three regulation strategies: mindfulness by focusing on breathing, distraction with positive thinking, and reinterpretation of a negative thing in a positive way. The investigators expect that the effective strategy could vary across participants, which could be associated with the variability of brain activation patterns in negative thinking.
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| functional magnetic resonance imaging (fMRI) | Behavioral | The investigators will utilize standard BOLD fMRI in blocked-design tasks and resting state (participant is given no overt task) in the study. Anatomical scans with T1-weighted contrast, quantitative measurement of spin relaxation times, and diffusion tensor imaging (DTI) are also used as an anatomical reference for functional activation as well as to investigate a brain structural relationship with the participants' task performance, including successful emotion regulation. |
| Measure | Description | Time Frame |
|---|---|---|
| Classification accuracy of different internal thoughts | fMRI brain images captured during various internal thought processes are used as input to a machine learning classifier. This classifier is trained to discriminate between different mental states. In this context, "classification accuracy" refers to the classifier's ability to accurately identify or predict the specific mental state based on the fMRI data. This measures the classifier's effectiveness in differentiating between mental states by analyzing patterns of brain activity. Classification accuracy is assessed using the area under the receiver operating characteristic curve (AUC), which is robust to imbalances in sample size for each mental state by incorporating the relationship between true positive and false positive rates. A higher AUC indicates better classifier performance. | 2 weeks |
| Changes in blood oxygen level-dependent (BOLD) signals across the whole brain during different internal thoughts and emotion regulation strategies. | Differences in whole brain activation patterns, elicited by various internal thoughts and emotion regulation strategies, will be quantified based on changes in blood oxygen level-dependent (BOLD) signals in the whole brain. An increase in BOLD signal indicates a greater brain activation. | 2 weeks |
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| Measure | Description | Time Frame |
|---|---|---|
| Correlations of Ruminative Responses Scale (RRS) Brooding subscores with classification accuracy and BOLD signal changes | The RRS Brooding subscale is a self-report scale to measure brooding ruminative responses. A higher score indicates higher brooding ruminative responses with a maximum score of 20 and a minimum score of 5. Classification accuracy and whole brain activation pattern differences in BOLD signals from brain imaging will be correlated with the RRS Brooding subscale. |
Inclusion Criteria:
Exclusion Criteria:
MRI Contraindications
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Individuals with major depressive disorders
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Laureate Institute for Brain Research | Tulsa | Oklahoma | 74136 | United States |
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| ID | Term |
|---|---|
| D003865 | Depressive Disorder, Major |
| ID | Term |
|---|---|
| D003866 | Depressive Disorder |
| D019964 | Mood Disorders |
| D001523 | Mental Disorders |
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| 2 weeks |
| Correlations of Ruminative Responses Scale (RRS) Depression subscores with classification accuracy and BOLD signal changes | The RRS Depression subscale is a self-report scale to measure depressive ruminative responses. A higher score indicates higher ruminative responses with a maximum score of 48 and a minimum score of 12. Classification accuracy and whole brain activation pattern differences in BOLD signals from brain imaging will be correlated with the RRS Depression subscale. | 2 weeks |
| Correlations of Ruminative Responses Scale (RRS) Reflection subscores with classification accuracy and BOLD signal changes | The RRS Reflection subscale is a self-report scale to measure reflective ruminative responses. A higher score indicates higher ruminative responses with a maximum score of 20 and a minimum score of 5. Classification accuracy and whole brain activation pattern differences in BOLD signals from brain imaging will be correlated with the RRS Reflection subscale. | 2 weeks |
| Correlations of Ruminative Responses Scale (RRS) total score with classification accuracy and BOLD signal changes | The RRS is a self-report scale to measure ruminative responses. A higher score indicates higher ruminative responses with a maximum score of 88 and a minimum score of 22. Classification accuracy and whole brain activation pattern differences in BOLD signals from brain imaging will be correlated with the RRS total scores. | 2 weeks |
| Correlations of Montgomery-Asberg Depression Rating Scale (MADRS) scores with classification accuracy and BOLD signal changes | The MADRS is an interviewer-rated scale to measure the severity of depressive symptoms. A higher score indicates severer depression with a maximum score of 60 and a minimum score of 0. Classification accuracy and whole brain activation pattern differences in BOLD signals from brain imaging will be correlated with the MADRS scores. | 2 weeks |
| Correlations of Hamilton Anxiety Rating Scale (HAM-A) scores with classification accuracy and BOLD signal changes | The HAMA is an interviewer-rated scale to measure the severity of anxiety symptoms. A higher score indicates severer anxiety with a maximum score of 56 and a minimum score of 0. Classification accuracy and whole brain activation pattern differences in BOLD signals from brain imaging will be correlated with the HAM-A scores. | 2 weeks |
| Correlations of Brief State Rumination Inventory (BSRI) scores with classification accuracy and BOLD signal changes | The BSRI is a self-report scale to measure state rumination. A higher score indicates higher state rumination with a maximum score of 800 and the minimum score of 0. Classification accuracy and whole brain activation pattern differences in BOLD signals from brain imaging will be correlated with the BSRI scores. | 2 weeks |