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Functional connectivity predictors of acute depression treatment outcome

Published online by Cambridge University Press:  03 January 2019

David C. Steffens ,
Lihong Wang  and
Godfrey D. Pearlson
David C. Steffens*
Affiliation:
Department of Psychiatry, University of Connecticut School of Medicine, Farmington, CT, USA
Lihong Wang
Affiliation:
Department of Psychiatry, University of Connecticut School of Medicine, Farmington, CT, USA Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, USA Olin Neuropsychiatry Research Center, Institute of Living of Hartford Hospital, Hartford, CT, USA
Godfrey D. Pearlson
Affiliation:
Olin Neuropsychiatry Research Center, Institute of Living of Hartford Hospital, Hartford, CT, USA Departments of Psychiatry and Neuroscience, Yale University School of Medicine, New Haven, CT, USA
*
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: [email protected].
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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.

Keywords

depressionelderlytreatmentfunctional magnetic resonance imagingconnectivity
Type
Brief Report
Information
International Psychogeriatrics , Volume 31 , Issue 12: Issue Theme: Assessment of Cognitive Impairment in East Asia , December 2019 , pp. 1831 - 1835
Copyright
© International Psychogeriatric Association 2018 

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References

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, 56–65.CrossRefGoogle ScholarPubMed
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, 140–150.CrossRefGoogle ScholarPubMed
Drysdale, A.T. et al. (2017). Resting-state connectivity biomarkers define neurophysiological subtypes of depression. Nature Medicine, 23, 28–38.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Na, K. S. et al. (2016). Brain-derived neurotrophic factor promoter methylation and cortical thickness in recurrent major depressive disorder. Scientific Reports, 6, 21089.CrossRefGoogle ScholarPubMed
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, 1987–1997.CrossRefGoogle ScholarPubMed
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, 1069–1074.CrossRefGoogle ScholarPubMed
Tadayonnejad, R., Yang, S., Kumar, A. and Ajilore, O. (2014). Multimodal brain connectivity analysis in unmedicated late-life depression. PLoS ONE, 9, e96033.CrossRefGoogle ScholarPubMed
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, 656–661.CrossRefGoogle ScholarPubMed
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, 39–46.CrossRefGoogle ScholarPubMed
Yang, J. et al. (2018). Development and evaluation of a multimodal marker of major depressive disorder. Human Brain Mapping, 39, 4420–4439.CrossRefGoogle ScholarPubMed