Whole-brain turbulent dynamics predict responsiveness to pharmacological treatment in mahor depressive disorder

Escrichs, Anira; Sanz Perl, Yonatan; Fisher, Patrick M.; Martínez-Molina, Noelia; G-Guzman, Elvira; Frokjaer, Vibe G.; Kringelbach, Morten L.; Knudsen, Gitte M.; Deco, Gustavo

doi:http://dx.doi.org/10.1038/s41380-024-02690-7

Whole-brain turbulent dynamics predict responsiveness to pharmacological treatment in mahor depressive disorder

Llicència

Citació

Escrichs A, Sanz Perl Y, Fisher PM, Martínez-Molina N, G-Guzman E, Frokjaer VG, et al. Whole-brain turbulent dynamics predict responsiveness to pharmacological treatment in mahor depressive disorder. Mol Psychiatry. 2025;30:1069-79. DOI: 10.1038/s41380-024-02690-7

Resum

Depression is a multifactorial clinical syndrome with a low pharmacological treatment response rate. Therefore, identifying predictors of treatment response capable of providing the basis for future developments of individualized therapies is crucial. Here, we applied model-free and model-based measures of whole-brain turbulent dynamics in resting-state functional magnetic resonance imaging (fMRI) in healthy controls and unmedicated depressed patients. After eight weeks of treatment with selective serotonin reuptake inhibitors (SSRIs), patients were classified as responders and non-responders according to the Hamilton Depression Rating Scale 6 (HAMD6). Using the model-free approach, we found that compared to healthy controls and responder patients, non-responder patients presented disruption of the information transmission across spacetime scales. Furthermore, our results revealed that baseline turbulence level is positively correlated with beneficial pharmacological treatment outcomes. Importantly, our model-free approach enabled prediction of which patients would turn out to be non-responders. Finally, our model-based approach provides mechanistic evidence that non-responder patients are less sensitive to stimulation and, consequently, less prone to respond to treatment. Overall, we demonstrated that different levels of turbulent dynamics are suitable for predicting response to SSRIs treatment in depression.