Data driven clusters derived from resting state functional connectivity: Findings from the EMBARC study

Cherise R Chin Fatt; Abu Minhajuddin; Manish K Jha; Taryn Mayes; A John Rush; Madhukar H Trivedi

doi:10.1016/j.jpsychires.2022.12.002

Data driven clusters derived from resting state functional connectivity: Findings from the EMBARC study

J Psychiatr Res. 2023 Feb:158:150-156. doi: 10.1016/j.jpsychires.2022.12.002. Epub 2022 Dec 27.

Authors

Cherise R Chin Fatt¹, Abu Minhajuddin², Manish K Jha¹, Taryn Mayes¹, A John Rush³, Madhukar H Trivedi⁴

Affiliations

¹ Center for Depression Research and Clinical Care, Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX, USA; Peter O'Donnell Jr. Brain Institute, University of Texas Southwestern Medical Center, Dallas, TX, USA.
² Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA.
³ Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA; Duke-National University of Singapore, Singapore.
⁴ Center for Depression Research and Clinical Care, Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX, USA; Peter O'Donnell Jr. Brain Institute, University of Texas Southwestern Medical Center, Dallas, TX, USA. Electronic address: madhukar.trivedi@utsouthwestern.edu.

Abstract

Background: To address the clinical heterogeneity of Major Depressive Disorder (MDD), this investigation determined whether resting state functional magnetic resonance imaging (fMRI) could be deployed to identify circuit based homogeneous subgroups, and whether subgroups identified show differential treatment outcomes.

Methods: Pretreatment resting state fMRIs obtained from 278 outpatients with nonpsychotic MDD from Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care for Depression Study were used to create data-driven subgroups using CLICK clustering. These subgroups were then compared using baseline clinical data, as well as baseline-to-week 8 changes in depression severity measured using the 17-item Hamilton Rating Scale for Depression (HAMD₁₇) and response/remission rates by treatment group.

Results: Three subgroups were identified. Cluster-1 was characterized by overallhyperconnectivity coupled with profound hypoconnectivity between the supramarginal gyrus (executive control network; ECN) and the superior frontal cortex (dorsal attention network; DAN). Cluster-2 was characterized by overall hypoconnectivity coupled with hyperconnectivity between supramarginal gyrus (ECN) and superior frontal cortex (DAN). Cluster-3 showed hypoconnectivity, especially profound between the angular cortex (default mode network; DMN) and middle frontal cortex (ECN). While baseline clinical measures did not differentiate the three clusters, Cluster-3 had the remission rate (51.6%) compared to Cluster-1 and Cluster-2 (32.7% and 31.9%) when treated with sertraline.

Limitations: Due to the exploratory nature of these analyses, there were no adjustments for multiple comparisons.

Conclusions: Baseline functional connectivity can be used to subgroup patients with MDD that differ in acute phase treatment outcomes. Measures of connectivity may address the heterogeneity of MDD.

Trial registration: ClinicalTrials.gov NCT01407094.

Keywords: CLICK; Data driven; EMBARC; Functional connectivity; Major depressive disorder; Remission.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Brain / diagnostic imaging
Cerebral Cortex
Depressive Disorder, Major* / diagnostic imaging
Depressive Disorder, Major* / drug therapy
Humans
Magnetic Resonance Imaging / methods
Sertraline / therapeutic use

Data driven clusters derived from resting state functional connectivity: Findings from the EMBARC study

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