Meta-analytic evidence for neuroimaging models of depression: state or trait?
BACKGROUND: Major Depressive Disorder (MDD) is a leading cause of disease burden worldwide. With the rapid growth of neuroimaging research on relatively small samples, meta-analytic techniques are becoming increasingly important. Here, we aim to clarify the support in fMRI literature for three leadi...
| Main Authors: | , , , , , , |
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| Format: | Journal article |
| Language: | English |
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2013
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| _version_ | 1797077443109978112 |
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| author | Graham, J Salimi-Khorshidi, G Hagan, C Walsh, N Goodyer, I Lennox, B Suckling, J |
| author_facet | Graham, J Salimi-Khorshidi, G Hagan, C Walsh, N Goodyer, I Lennox, B Suckling, J |
| author_sort | Graham, J |
| collection | OXFORD |
| description | BACKGROUND: Major Depressive Disorder (MDD) is a leading cause of disease burden worldwide. With the rapid growth of neuroimaging research on relatively small samples, meta-analytic techniques are becoming increasingly important. Here, we aim to clarify the support in fMRI literature for three leading neurobiological models of MDD: limbic-cortical, cortico-striatal and the default mode network. METHODS: Searches of PubMed and Web of Knowledge, and manual searches, were undertaken in early 2011. Data from 34 case-control comparisons (n=1165) and 6 treatment studies (n=105) were analysed separately with two meta-analytic methods for imaging data: Activation Likelihood Estimation and Gaussian-Process Regression. RESULTS: There was broad support for limbic-cortical and cortico-striatal models in the case-control data. Evidence for the role of the default mode network was weaker. Treatment-sensitive regions were primarily in lateral frontal areas. LIMITATIONS: In any meta-analysis, the increase in the statistical power of the inference comes with the risk of aggregating heterogeneous study pools. While we believe that this wide range of paradigms allows identification of key regions of dysfunction in MDD (regardless of task), we attempted to minimise such risks by employing GPR, which models such heterogeneity. CONCLUSIONS: The focus of treatment effects in frontal areas indicates that dysregulation here may represent a biomarker of treatment response. Since the dysregulation in many subcortical regions in the case-control comparisons appeared insensitive to treatment, we propose that these act as trait vulnerability markers, or perhaps treatment insensitivity. Our findings allow these models of MDD to be applied to fMRI literature with some confidence. |
| first_indexed | 2024-03-07T00:18:04Z |
| format | Journal article |
| id | oxford-uuid:7b88e442-dbe3-4b67-b768-255e0ff12ef9 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T00:18:04Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | oxford-uuid:7b88e442-dbe3-4b67-b768-255e0ff12ef92022-03-26T20:51:19ZMeta-analytic evidence for neuroimaging models of depression: state or trait?Journal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:7b88e442-dbe3-4b67-b768-255e0ff12ef9EnglishSymplectic Elements at Oxford2013Graham, JSalimi-Khorshidi, GHagan, CWalsh, NGoodyer, ILennox, BSuckling, JBACKGROUND: Major Depressive Disorder (MDD) is a leading cause of disease burden worldwide. With the rapid growth of neuroimaging research on relatively small samples, meta-analytic techniques are becoming increasingly important. Here, we aim to clarify the support in fMRI literature for three leading neurobiological models of MDD: limbic-cortical, cortico-striatal and the default mode network. METHODS: Searches of PubMed and Web of Knowledge, and manual searches, were undertaken in early 2011. Data from 34 case-control comparisons (n=1165) and 6 treatment studies (n=105) were analysed separately with two meta-analytic methods for imaging data: Activation Likelihood Estimation and Gaussian-Process Regression. RESULTS: There was broad support for limbic-cortical and cortico-striatal models in the case-control data. Evidence for the role of the default mode network was weaker. Treatment-sensitive regions were primarily in lateral frontal areas. LIMITATIONS: In any meta-analysis, the increase in the statistical power of the inference comes with the risk of aggregating heterogeneous study pools. While we believe that this wide range of paradigms allows identification of key regions of dysfunction in MDD (regardless of task), we attempted to minimise such risks by employing GPR, which models such heterogeneity. CONCLUSIONS: The focus of treatment effects in frontal areas indicates that dysregulation here may represent a biomarker of treatment response. Since the dysregulation in many subcortical regions in the case-control comparisons appeared insensitive to treatment, we propose that these act as trait vulnerability markers, or perhaps treatment insensitivity. Our findings allow these models of MDD to be applied to fMRI literature with some confidence. |
| spellingShingle | Graham, J Salimi-Khorshidi, G Hagan, C Walsh, N Goodyer, I Lennox, B Suckling, J Meta-analytic evidence for neuroimaging models of depression: state or trait? |
| title | Meta-analytic evidence for neuroimaging models of depression: state or trait? |
| title_full | Meta-analytic evidence for neuroimaging models of depression: state or trait? |
| title_fullStr | Meta-analytic evidence for neuroimaging models of depression: state or trait? |
| title_full_unstemmed | Meta-analytic evidence for neuroimaging models of depression: state or trait? |
| title_short | Meta-analytic evidence for neuroimaging models of depression: state or trait? |
| title_sort | meta analytic evidence for neuroimaging models of depression state or trait |
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