Skip to main content Accessibility help
×
Home

Functional connectivity predictors of acute depression treatment outcome

  • David C. Steffens (a1), Lihong Wang (a1) (a2) (a3) and Godfrey D. Pearlson (a3) (a4)

Abstract

Few studies have examined functional connectivity (FC) patterns using functional magnetic resonance imaging (fMRI) to predict outcomes in late-life depression. We hypothesized that FC within and between frontal and limbic regions would be associated with 12-week depression outcome in older depressed adults. Seventy-one subjects with major depression were enrolled in the study. A study geriatric psychiatrist performed a clinical interview and completed a Montgomery-Åsberg Depression Rating Scale (MADRS). All study participants were free of medication at baseline and had a brain fMRI scan. Using a regions of interest (ROI) atlas (including 164 ROIs), we conducted ROI-to-ROI resting-state FC analyses for each participant. In terms of treatment participants were offered sertraline initially, although in this naturalistic study, other medications were also prescribed. Subjects were evaluated every 2 weeks up to 12 weeks by the study psychiatrist, who followed a flexible, clinically based medication dosing schedule. Multivariate regression analysis was used to examine correlation between change of MADRS score over 12 weeks and baseline FC between brain regions, controlling for age, gender, mean head motion, and baseline MADRS. We found greater FC between the left inferior frontal gyrus pars triangularis and the left frontal eye field and FC of these two regions with a number of brain regions related to reward, salience, and sensorimortor function were correlated with change in MADRS score over 12 weeks. Our results highlight the important role of between inner speech-reward, attention-salience, and attention-sensorimotor network synchronization in predicting acute treatment response in late-life depression.

Copyright

Corresponding author

Correspondence should be addressed to: David C. Steffens, Department of Psychiatry, UConn Health, 264 Farmington Ave, Farmington, CT 06030-1410. Phone: 860-679-4282; Fax: 860-67-1296. Email: steffens@uchc.edu.

References

Hide All
Alexopoulos, G. S., Hoptman, M. J., Kanellopoulos, D., Murphy, C. F., Lim, K. O. and Gunning, F. M. (2012). Functional connectivity in the cognitive control network and the default mode network in late-life depression. Journal of Affective Disorders, 139, 5665.
Cieri, F., Esposito, R., Cera, N., Pieramico, V., Tartaro, A. and Di Giannantonio, M. (2017). Late-life depression: modifications of brain resting state activity. Journal of Geriatric Psychiatry and Neurology, 30, 140150.
Drysdale, A.T. et al. (2017). Resting-state connectivity biomarkers define neurophysiological subtypes of depression. Nature Medicine, 23, 2838.
Han, K. M. et al. (2017). Influence of FKBP5 polymorphism and DNA methylation on structural changes of the brain in major depressive disorder. Scientific Reports, 7, 42621.
Na, K. S. et al. (2016). Brain-derived neurotrophic factor promoter methylation and cortical thickness in recurrent major depressive disorder. Scientific Reports, 6, 21089.
Steffens, D. C., Manning, K. J., Wu, R., Grady, J. J., Fortinsky, R. H. and Tennen, H. A. (2015). Methodology and preliminary results from the neurobiology of late-life depression study. International Psychogeriatrics, 27, 19871997.
Steffens, D. C., Wu, R., Grady, J. J. and Manning, K. J. (2018). Presence of neuroticism and antidepressant remission rates in late-life depression: results from the Neurobiology of Late-Life Depression (NBOLD) study. International Psychogeriatrics, 30, 10691074.
Tadayonnejad, R., Yang, S., Kumar, A. and Ajilore, O. (2014). Multimodal brain connectivity analysis in unmedicated late-life depression. PLoS ONE, 9, e96033.
Vu, N. Q. and Aizenstein, H. J. (2013). Depression in the elderly: brain correlates, neuropsychological findings, and role of vascular lesion load. Current Opinion in Neurology, 26, 656661.
Wu, M., Andreescu, C., Butters, M. A., Tamburo, R., Reynolds, C. F., 3rd and Aizenstein, H. (2011). Default-mode network connectivity and white matter burden in late-life depression. Psychiatry Research, 194, 3946.
Yang, J. et al. (2018). Development and evaluation of a multimodal marker of major depressive disorder. Human Brain Mapping, 39, 44204439.

Keywords

Metrics

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed