4. Discussion
depression, depressive symptoms, treatment-resistant depression, AFF
Whole-brain computational modeling combined with nonparametric probabilistic fitting applied to the pre-treatment resting-state fMRI data from patients with treatment-resistant depression, disclosed a dichotomy in the attractor dynamics of our depression sample. According to the derived optimal model parameters within the search interval, we could stratify the patients into the DEP1 and DEP2 subtypes based on their pretreatment FC patterns. We indicated that the two detected depression subtypes exhibited different phenotypic behavior related to certain affective and negative symptoms at baseline. We further found that the effect of treatment on the summative scores for the affect subscale of BPRS, motivation and pleasure subscale of CAINS varied as a function of depression subtypes. Nevertheless, the effect on the summative scores was mostly driven by the worsening of DEP2 by active treatment, potentially due to the heterogeneity of DEP2’s response to individual items of the measurements. We followed up on this observation by assessing the individual items of MADRS-S total, BPRS AFF and CAINS using nested ANOVA. We found that the behavior related to mood, initiative/lassitude, suicidality and motivation for work and school activities was indeed improved in the subtype with a lower metastable and synchronous pretreatment intrinsic brain activity (i.e., DEP2), despite worsening of the summative scores. Hence, our results suggested that the summative scores of phenotypic behaviors for depression are not well-suited for dissociating the depression subtypes.There are diverging neurophysiological mechanisms involved in depression, and therefore, large-scale multimodal analysis of depression cohorts has failed to uncover distinctive neurobiological biomarkers [Fortunately, there is a growing momentum in psychiatry research to disclose the heterogeneity in the cohorts and unravel different subtypes based on neuroimaging data [It is worth noting we used a new rTMS protocol, namely the accelerated dmPFC iTBS, compared to commonly used non-invasive stimulation protocols for depression (i.e., high- or low-frequency rTMS over the dlPFC). Specifically based on lesion and meta-analysis studies, stimulating the dmPFC has been suggested to be a promising alternative target for neurostimulation therapy in depression [Nevertheless, applying iTBS over dmPFC has yielded mixed results in treating the overall depressive symptoms [Our study had several limitations, our grid-search spanned a limited interval of the parameter space and therefore the obtained optimal parameter-sets are contingent on our defined search scope. Moreover, the small sample size bounded our model prediction and the statistical analysis. Furthermore, because our nested ANOVA analysis was an exploratory analysis, our findings should be considered as preliminary and should be validated in an independent sample. However, we speculate that with a larger sample, several finer grained depression subtypes might be identified. Another possible limitation of the current study is that instead of using sample-specific structural connectivity, we used the structural connectivity estimated from HCP to minimize the possible errors arising from an under-sampled dataset. Furthermore, we included only 68 cortical regions in our model. Future studies with more realistic computational models based on a higher spatial resolution and larger sample sizes are required to further assess the heterogeneity in depression. Notably, computational modeling based on longitudinal functional data collected during the treatment course as well as the follow-up session are required to shed light on the underlying mechanism of iTBS in depression.In conclusion, we used a novel approach based on the attractor dynamics of whole-brain model to delineate the depression cohort in a clinically relevant manner. Our approach provided a mathematical platform to efficiently combine the tractography and functional imaging data, while taking the nonlinear dynamics of the brain into account. We found improvements in mood, initiative/lassitude, suicidality, motivation for work and schools in response to applying iTBS to dmPFC, in the subgroup with blunted frequency dynamics and less global metastability and synchrony. This finding highlights the importance of designing stratified treatment protocols in order to increase the success rate of rTMS therapies. Nevertheless, it should be noted that this type of stratification of the depression cohort is novel and has to be further validated in independent samples before finding its way into clinical practices.