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Using resting-state intrinsic network connectivity to identify suicide risk in mood disorders

Published online by Cambridge University Press:  10 October 2019

Jonathan P. Stange
Affiliation:
University of Illinois at Chicago, Chicago, IL, USA
Lisanne M. Jenkins
Affiliation:
Northwestern University, Chicago, IL, USA
Stephanie Pocius
Affiliation:
University of Utah, Salt Lake City, UT, USA
Kayla Kreutzer
Affiliation:
University of Illinois at Chicago, Chicago, IL, USA
Katie L. Bessette
Affiliation:
University of Illinois at Chicago, Chicago, IL, USA
Sophie R. DelDonno
Affiliation:
University of Illinois at Chicago, Chicago, IL, USA
Leah R. Kling
Affiliation:
University of Illinois at Chicago, Chicago, IL, USA
Runa Bhaumik
Affiliation:
University of Illinois at Chicago, Chicago, IL, USA
Robert C. Welsh
Affiliation:
University of Utah, Salt Lake City, UT, USA
John G. Keilp
Affiliation:
Columbia University, New York, NY, USA
K. Luan Phan
Affiliation:
The Ohio State University, Columbus, OH, USA
Scott A. Langenecker
Affiliation:
University of Utah, Salt Lake City, UT, USA
Corresponding

Abstract

Background

Little is known about the neural substrates of suicide risk in mood disorders. Improving the identification of biomarkers of suicide risk, as indicated by a history of suicide-related behavior (SB), could lead to more targeted treatments to reduce risk.

Methods

Participants were 18 young adults with a mood disorder with a history of SB (as indicated by endorsing a past suicide attempt), 60 with a mood disorder with a history of suicidal ideation (SI) but not SB, 52 with a mood disorder with no history of SI or SB (MD), and 82 healthy comparison participants (HC). Resting-state functional connectivity within and between intrinsic neural networks, including cognitive control network (CCN), salience and emotion network (SEN), and default mode network (DMN), was compared between groups.

Results

Several fronto-parietal regions (k > 57, p < 0.005) were identified in which individuals with SB demonstrated distinct patterns of connectivity within (in the CCN) and across networks (CCN-SEN and CCN-DMN). Connectivity with some of these same regions also distinguished the SB group when participants were re-scanned after 1–4 months. Extracted data defined SB group membership with good accuracy, sensitivity, and specificity (79–88%).

Conclusions

These results suggest that individuals with a history of SB in the context of mood disorders may show reliably distinct patterns of intrinsic network connectivity, even when compared to those with mood disorders without SB. Resting-state fMRI is a promising tool for identifying subtypes of patients with mood disorders who may be at risk for suicidal behavior.

Type
Original Articles
Copyright
Copyright © Cambridge University Press 2019

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