Brain structural network alterations related to serum cortisol levels in drug-naïve, first-episode major depressive disorder patients: a source-based morphometric study
Abstract Higher cortisol levels due to a hyperactive hypothalamic–pituitary–adrenal axis have been reported in patients with major depressive disorder (MDD). Increased cortisol levels change both the brain morphology and function in MDD patients. The multivariate source-based morphometry (SBM) techn...
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| Autores principales: | , , , , , , , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Nature Portfolio
2020
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/8af2624420b54979aa1e3819b0e86dce |
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| Sumario: | Abstract Higher cortisol levels due to a hyperactive hypothalamic–pituitary–adrenal axis have been reported in patients with major depressive disorder (MDD). Increased cortisol levels change both the brain morphology and function in MDD patients. The multivariate source-based morphometry (SBM) technique has been applied to investigate neuroanatomical changes in some neuropsychiatric diseases, but not MDD. We aimed to examine the alterations in gray matter (GM) networks and their relationship with serum cortisol levels in first-episode, drug-naïve MDD patients using SBM. Forty-two patients with MDD and 39 controls were recruited via interviews. Morning serum cortisol levels were measured, and high-resolution T1-weighted imaging followed by SBM analysis was performed in all participants. The patients had significantly higher serum cortisol levels than the controls. We found two GM sources, which were significantly different between patients with MDD and controls (prefrontal network, p < .01; insula-temporal network, p < .01). Serum cortisol levels showed a statistically significant negative correlation with the loading coefficients of the prefrontal network (r = − 0.354, p = 0.02). In conclusion, increased serum cortisol levels were associated with reductions in the prefrontal network in the early stage of MDD, and SBM may be a useful approach for analyzing structural MRI data. |
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