Macroscopic resting state model predicts theta burst stimulation response: A randomized trial

Repetitive transcranial magnetic stimulation (rTMS) is a promising alternative therapy for treatment-resistant depression, although its limited remission rate indicates room for improvement. As depression is a phenomenological construction, the biological heterogeneity within this syndrome needs to be considered to improve the existing therapies. Whole-brain modeling provides an integrative multi-modal framework for capturing disease heterogeneity in a holistic manner.Computational modelling combined with probabilistic nonparametric fitting was applied to the resting-state fMRI data from 42 patients (21 women), to parametrize baseline brain dynamics in depression. All patients were randomly assigned to two treatment groups, namely active (i.e., rTMS, n = 22) or sham (n = 20). The active treatment group received rTMS treatment with an accelerated intermittent theta burst protocol over the dorsomedial prefrontal cortex. The sham treatment group underwent the identical procedure but with the magnetically shielded side of the coil.We stratified the depression sample into distinct covert subtypes based on their baseline attractor dynamics captured by different model parameters. Notably, the two detected depression subtypes exhibited different phenotypic behaviors at baseline. Our stratification could predict the diverse response to the active treatment that could not be explained by the sham treatment. Critically, we further found that one group exhibited more distinct improvement in certain affective and negative symptoms. The subgroup of patients with higher responsiveness to treatment exhibited blunted frequency dynamics for intrinsic activity at baseline, as indexed by lower global metastability and synchrony.Our findings suggested that whole-brain modeling of intrinsic dynamics may constitute a determinant for stratifying patients into treatment groups and bringing us closer towards precision medicine.Author summary: There are multiple therapeutic protocols with a limited remission rate, for treatment-resistant depression, using repetitive transcranial magnetic stimulation (rTMS). It is still unclear how we match different rTMS protocols to patients to optimize the therapy. Currently, the process of determining the best rTMS protocol for each individual involves trial and error. Whole-brain computational modelling paves the way to find the optimal therapeutic protocol for each patient, by integrating multi-modal neuroimaging through theoretical models of brain dynamics. In this work, whole-brain modelling helped us identify two covert clinically relevant subtypes in our depression cohort, exhibiting different responses to the same rTMS therapy applied over the dorsomedial prefrontal cortex. Patients who were assigned to the subtype with blunted resting-state frequency dynamics showed a greater improvement in specific affective and negative symptoms. Moreover, we further indicated that the summative scores of phenotypic behaviors for depression are not well-suited for dissociating the depression subtypes and measuring the treatment outcome. In conclusion, our results suggest that studying whole-brain dynamics could have profound implications for identifying reliable biomarkers and neurostimulation targets for the treatment of psychiatric disorders.