Fronto-limbic effective connectivity as possible predictor of antidepressant response to SSRI administration
The timely selection of the optimal treatment for depressed patients is critical to improve remission rates. The detection of pre-treatment variables able to predict differential treatment response may provide novel approaches for treatment selection. Selective serotonin reuptake inhibitors (SSRIs)...
| Main Authors: | , , , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
Elsevier
2016
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| _version_ | 1797084676279500800 |
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| author | Vai, B Bulgarelli, C Godlewska, B Cowen, P Benedetti, F Harmer, C |
| author_facet | Vai, B Bulgarelli, C Godlewska, B Cowen, P Benedetti, F Harmer, C |
| author_sort | Vai, B |
| collection | OXFORD |
| description | The timely selection of the optimal treatment for depressed patients is critical to improve remission rates. The detection of pre-treatment variables able to predict differential treatment response may provide novel approaches for treatment selection. Selective serotonin reuptake inhibitors (SSRIs) modulate the fronto-limbic functional response and connectivity, an effect preceding the overt clinical antidepressant effects. Here we investigated whether the cortico-limbic connectivity associated with emotional bias measured before SSRI administration predicts the efficacy of antidepressant treatment in MDD patients. fMRI and Dynamic Causal Modeling (DCM) were combined to study if effective connectivity might differentiate healthy controls (HC) and patients affected by major depression who later responded (RMDD, n=21), or failed to respond (nRMDD, n=12), to 6 weeks of escitalopram administration. Sixteen DCMs exploring connectivity between anterior cingulate cortex (ACC), ventrolateral prefrontal cortex (VLPFC), Amygdala (Amy), and fusiform gyrus (FG) were constructed. Analyses revealed that nRMDD had reduced endogenous connectivity from Amy to VLPFC and to ACC, with an increased connectivity and modulation of the ACC to Amy connectivity when processing of fearful emotional stimuli compared to HC. RMDD and HC did not significantly differ among themselves. Pre-treatment effective connectivity in fronto-limbic circuitry could be an important factor affecting antidepressant response, and highlight the mechanisms which may be involved in recovery from depression. These results suggest that fronto-limbic connectivity might provide a neural biomarker to predict the clinical outcome to SSRIs administration in major depression. |
| first_indexed | 2024-03-07T01:58:21Z |
| format | Journal article |
| id | oxford-uuid:9c828cc9-4eb7-4a3c-9c5c-f86c5525da37 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T01:58:21Z |
| publishDate | 2016 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | oxford-uuid:9c828cc9-4eb7-4a3c-9c5c-f86c5525da372022-03-27T00:36:26ZFronto-limbic effective connectivity as possible predictor of antidepressant response to SSRI administrationJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:9c828cc9-4eb7-4a3c-9c5c-f86c5525da37EnglishSymplectic Elements at OxfordElsevier2016Vai, BBulgarelli, CGodlewska, BCowen, PBenedetti, FHarmer, CThe timely selection of the optimal treatment for depressed patients is critical to improve remission rates. The detection of pre-treatment variables able to predict differential treatment response may provide novel approaches for treatment selection. Selective serotonin reuptake inhibitors (SSRIs) modulate the fronto-limbic functional response and connectivity, an effect preceding the overt clinical antidepressant effects. Here we investigated whether the cortico-limbic connectivity associated with emotional bias measured before SSRI administration predicts the efficacy of antidepressant treatment in MDD patients. fMRI and Dynamic Causal Modeling (DCM) were combined to study if effective connectivity might differentiate healthy controls (HC) and patients affected by major depression who later responded (RMDD, n=21), or failed to respond (nRMDD, n=12), to 6 weeks of escitalopram administration. Sixteen DCMs exploring connectivity between anterior cingulate cortex (ACC), ventrolateral prefrontal cortex (VLPFC), Amygdala (Amy), and fusiform gyrus (FG) were constructed. Analyses revealed that nRMDD had reduced endogenous connectivity from Amy to VLPFC and to ACC, with an increased connectivity and modulation of the ACC to Amy connectivity when processing of fearful emotional stimuli compared to HC. RMDD and HC did not significantly differ among themselves. Pre-treatment effective connectivity in fronto-limbic circuitry could be an important factor affecting antidepressant response, and highlight the mechanisms which may be involved in recovery from depression. These results suggest that fronto-limbic connectivity might provide a neural biomarker to predict the clinical outcome to SSRIs administration in major depression. |
| spellingShingle | Vai, B Bulgarelli, C Godlewska, B Cowen, P Benedetti, F Harmer, C Fronto-limbic effective connectivity as possible predictor of antidepressant response to SSRI administration |
| title | Fronto-limbic effective connectivity as possible predictor of antidepressant response to SSRI administration |
| title_full | Fronto-limbic effective connectivity as possible predictor of antidepressant response to SSRI administration |
| title_fullStr | Fronto-limbic effective connectivity as possible predictor of antidepressant response to SSRI administration |
| title_full_unstemmed | Fronto-limbic effective connectivity as possible predictor of antidepressant response to SSRI administration |
| title_short | Fronto-limbic effective connectivity as possible predictor of antidepressant response to SSRI administration |
| title_sort | fronto limbic effective connectivity as possible predictor of antidepressant response to ssri administration |
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