Major Depressive Disorder (MDD) affects millions globally, with approximately 30% of patients experiencing treatment-resistant depression (TRD). While ketamine has emerged as a rapid-acting intervention, response rates remain variable, underscoring the need to refine precision medicine approaches for ketamine treatment. Network theories of mental health disorders have been promoted as framework for better conceptualizing the structure of symptoms, as well as affording potential insights to bolster personalized treatment approaches. This study sought to leverage a network analytic approach to compare the symptom architecture and density of TRD patients who responded to ketamine treatment versus those who did not. In the current study, 447 patients receiving acute-phase intravenous ketamine or intranasal esketamine at the MGH Ketamine Clinic were included. Gaussian graphical models were estimated using the graphical LASSO method to derive pre- and post-treatment symptom networks (11 nodes) using the QIDS-SR-16. Network density and node centrality were compared between responders and non-responders using permutation-based Network Comparison Tests (NCT). Pre-treatment network density was significantly higher in non-responders (global strength = 4.03) compared to responders (global strength = 1.06; p < 0.01). Following treatment, the responder group showed a significant increase in network strength (to 2.66; p < 0.01), while non-responders showed a significant decrease (to 2.59; p < 0.05). Thus, patients with sparsely connected symptom networks at baseline appear more likely to benefit from ketamine, potentially because their symptoms are more amenable to reorganization. Pre-treatment network density serves as a potential correlate of treatment outcomes of ketamine for TRD.
