Anterior default mode network and posterior insular connectivity is predictive of depressive symptom reduction following serial ketamine infusion. Issue 12 (17th May 2022)

• Record Type: Journal Article
• Authors: Wade, Benjamin S. C.; Loureiro, Joana; Sahib, Ashish; Kubicki, Antoni; Joshi, Shantanu H.; Hellemann, Gerhard; Espinoza, Randall T.; Woods, Roger P.; Congdon, Eliza; Narr, Katherine L.
• Is Part Of: Psychological medicine. Volume 52:Issue 12(2022)
• Page Start: 2376
• Page End: 2386
• Publication Date: 2022-05-17
• Subjects: Default mode network -- functional connectivity -- machine learning -- major depressive disorder -- serial ketamine infusion; Psychiatry -- Periodicals; Medicine and psychology -- Periodicals; Clinical psychology -- Periodicals; 616.89
• DOI: 10.1017/S0033291722001313
• Languages: English
• ISSNs: 0033-2917

Abstract

Abstract: Background: Ketamine is a rapidly-acting antidepressant treatment with robust response rates. Previous studies have reported that serial ketamine therapy modulates resting state functional connectivity in several large-scale networks, though it remains unknown whether variations in brain structure, function, and connectivity impact subsequent treatment success. We used a data-driven approach to determine whether pretreatment multimodal neuroimaging measures predict changes along symptom dimensions of depression following serial ketamine infusion. Methods: Patients with depression ( n = 60) received structural, resting state functional, and diffusion MRI scans before treatment. Depressive symptoms were assessed using the 17-item Hamilton Depression Rating Scale (HDRS-17), the Inventory of Depressive Symptomatology (IDS-C), and the Rumination Response Scale (RRS) before and 24 h after patients received four (0.5 mg/kg) infusions of racemic ketamine over 2 weeks. Nineteen unaffected controls were assessed at similar timepoints. Random forest regression models predicted symptom changes using pretreatment multimodal neuroimaging and demographic measures. Results: Two HDRS-17 subscales, the HDRS-6 and core mood and anhedonia (CMA) symptoms, and the RRS: reflection (RRSR) scale were predicted significantly with 19, 27, and 1% variance explained, respectively. Increased right medial prefrontal cortex/anterior cingulate and posterior insula (PoI) and lower kurtosis of the superior longitudinal fasciculus predicted reduced HDRS-6 and CMA symptoms following treatment. RRSR change was predicted by global connectivity of the left posterior cingulate, left insula, and right superior parietal lobule. Conclusions: Our findings support that connectivity of the anterior default mode network and PoI may serve as potential biomarkers of antidepressant outcomes for core depressive symptoms.