Predicting Antidepressant Citalopram Treatment Response via Changes in Brain Functional Connectivity After Acute Intravenous Challenge

Abstract

Introduction: The early and therapy-specific prediction of treatment success in major depressive disorder is of paramount importance due to high lifetime prevalence, and heterogeneity of response to standard medication and symptom expression. Hence, this study assessed the predictability of long-term antidepressant effects of escitalopram based on the short-term influence of citalopram on functional connectivity. Methods: Twenty nine subjects suffering from major depression were scanned twice with resting-state functional magnetic resonance imaging under the influence of intravenous citalopram and placebo in a randomized, double-blinded cross-over fashion. Symptom factors were identified for the Hamilton depression rating scale (HAM-D) and Beck's depression inventory (BDI) taken before and after a median of seven weeks of escitalopram therapy. Predictors were calculated from whole-brain functional connectivity, fed into robust regression models, and cross-validated. Results: Significant predictive power could be demonstrated for one HAM-D factor describing insomnia and the total score ( r = 0.45–0.55). Remission and response could furthermore be predicted with an area under the receiver operating characteristic curve of 0.73 and 0.68, respectively. Functional regions with high influence on the predictor were located especially in the ventral attention, fronto-parietal, and default mode networks. Conclusion: It was shown that medication-specific antidepressant symptom improvements can be predicted using functional connectivity measured during acute pharmacological challenge as an easily assessable imaging marker. The regions with high influence have previously been related to major depression as well as the response to selective serotonin reuptake inhibitors, corroborating the advantages of the current approach of focusing on treatment-specific symptom improvements.

MRI predictors of pharmacotherapy response in major depressive disorder

“…A large study employing NBS found the DMN was the primary distinguisher between remitters and non-remitters with the following associated with remission: greater within DMN connectivity, greater connectivity between the DMN, FPN, and SMN, greater connectivity between the DMN and visual, limbic, auditory and VAN, and greater connectivity between the FPN/SMN and C-ON/dorsal attention network ( Korgaonkar et al, 2020 ). Another study employing NBS noted that the DMN, VAN, and FPN were the primary predictors remission and response, though the method precluded an overall directionality of the effect ( Klöbl, 2020 ). A large study investigating intra- and inter-network connectivity strength reported that greater internal DMN connectivity and lower external ECN connectivity predicted symptom improvement ( Fan et al, 2020 ).…”

Predicting escitalopram treatment response from pre-treatment and early response resting state fMRI in a multi-site sample: A CAN-BIND-1 report

“…Improved predictive accuracy observed in the delta models, which include imaging data for each patient from two separate timepoints, requires yet additional resources. Results from Klöbl et al (2020) , however, suggest medication-induced FC alterations may be achieved in a much shorter timeframe by an acute intravenous SSRI challenge. This may allow the week 2 timepoint used in delta models to be acquired at the baseline visit following an intravenous challenge, eliminating the need for a second scanning session and 2-week medication trial before response prediction can be made.…”

Connecting the Dots: Approaching a Standardized Nomenclature for Molecular Connectivity Combining Data and Literature

“…fMRI preprocessing and filtering fMRI data were preprocessed in SPM12 and ArtRepair toolbox 36 , following an established protocol 37 . In brief, the data underwent correction for transient slice artifacts and slice-timing discrepancies, followed by realignment and reslicing of the realigned images.…”