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A novel resting-state functional magnetic resonance imaging signature of resilience to recurrent depression

BACKGROUND: A high proportion of patients with remitted major depressive disorder (MDD) will experience recurring episodes, whilst some develop resilience and remain in recovery. The neural basis of resilience to recurrence is elusive. Abnormal resting-state connectivity of the subgenual cingulate c...

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Detalles Bibliográficos
Autores principales: Workman, C. I., Lythe, K. E., McKie, S., Moll, J., Gethin, J. A., Deakin, J. F. W., Elliott, R., Zahn, R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cambridge University Press 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5426313/
https://www.ncbi.nlm.nih.gov/pubmed/27821193
http://dx.doi.org/10.1017/S0033291716002567
Descripción
Sumario:BACKGROUND: A high proportion of patients with remitted major depressive disorder (MDD) will experience recurring episodes, whilst some develop resilience and remain in recovery. The neural basis of resilience to recurrence is elusive. Abnormal resting-state connectivity of the subgenual cingulate cortex (sgACC) was previously found in cross-sectional studies of MDD, suggesting its potential pathophysiological importance. The current study aimed to investigate whether resting-state connectivity to a left sgACC seed region distinguishes resilient patients from those developing recurring episodes. METHOD: A total of 47 medication-free remitted MDD patients and 38 healthy controls underwent resting-state functional magnetic resonance imaging (fMRI) at baseline. Over 14 months, 30 patients remained resilient whilst 17 experienced a recurring episode. RESULTS: Attenuated interhemispheric left-to-right sgACC connectivity distinguished the resilient from the recurring-episode and control groups and was not correlated with residual depressive symptoms. CONCLUSIONS: The current study revealed a neural signature of resilience to recurrence in MDD and thereby elucidates the role of compensatory adaptation in sgACC networks.