Efficacy Analysis Of Repetitive Transcranial Magnetic Stimulation Treatment On Depression

Depressive disorder is the most common and difficult-to-treat mental illness,and also an important factor leading to disability in individuals worldwide.Its core symptoms are depressed mood,decreased energy,and decreased interest lasting for more than two weeks.At present,about 350 million people around the world suffer from depression,and the risk of disease is still increasing year by year with the rapid development of society.This takes a huge toll on the social functioning of the patient himself,his family,and even society due to high medical costs and lost productivity.Therefore,the exploration of effective treatment methods for depression has gradually attracted the wide attention of scholars.The existing treatment methods mainly include drug therapy,psychotherapy,and physical therapy.Among them,repetitive transcranial magnetic stimulation(r TMS)has been widely studied and used as a non-invasive,safe,and effective treatment method.However,existing studies have shown that there is high heterogeneity in the therapeutic effect of r TMS among patients with depression.Hence,exploring the mechanism of r TMS and discovering neuroimaging markers that can predict the efficacy of r TMS treatment have high theoretical research and application value.The existing neuroimaging studies mostly focused on patients with treatment-resistant depression(TRD),and the results have confirmed that r TMS treatment can change the gray matter volume and diffusion measurements(such as fractional anisotropy)in the frontal lobe,subcortical and limbic regions of depressed individuals.RTMS treatment can change the local metabolism of individual stimulation sites and the association between the site and remote brain areas,such as the FC between the dorsolateral prefrontal cortex(DLPFC)and distal subcortical regions,default mode network(DMN)and central executive network(CEN).The therapeutic effect is also related to the FC between the anterior cingulate cortex(ACC)and DMN,which confirmed the network effect of the treatment.Combined with previous studies,we found that the treatment effect of depression is closely related to the function and structure of individual brain networks.However,its neurobiological mechanism is still unclear.Secondly,most of the existing studies have explored the changes in brain regions’ connectivity before and after treatment,and fewer have explored the predictive role of brain regions and their connectivity on treatment response.The research on individual differences in treatment response is still advancing.In addition,although more researchers have begun to pay attention to the effectiveness of r TMS as a first-line treatment,relevant empirical studies are still scarce.Therefore,based on the evidence of thalamo-cortical neuroimaging,this study explores the predictive role of brain structure and function in non-TRD patients with r TMS treatment,to provide theoretical and application support for personalized treatment of patients with depression.Firstly,based on the preliminary verification of r TMS treatment effect in non-TRD patients,we investigated the role of thalamic nucleus volume in predicting r TMS treatment effect.Secondly,some researchers have found that the occurrence of depression is related to the change of thalamo-cortical connection,and r TMS treatment can reset this connection.Furthermore,different nuclei in the thalamus are connected to specific parts of the cerebral cortex,and regional heterogeneity of nuclei may be related to differences in treatment effects in different patients.Therefore,Study II mainly focused on the functional and structural connectivity of the thalamo-cortex,to explore its predictive value for r TMS treatment,and to explore the difference between the early improvement group and the early non-improvement group.A total of 33 patients with depression diagnosed by structured clinical interview were recruited in this study.The 17-item Hamilton Depression Scale(HAMD-17)was used to measure the severity of clinical symptoms.The patients’ resting-state data,structural image data,and diffusion tensor imaging(DTI)data were collected before treatment.We defined the rate of reduction in depressive symptoms(i.e.,the rate of r TMS reduction)as the ratio of the difference between pre-and post-treatment HAMD-17 scores to the HAMD-17 score before treatment.During the r TMS treatment phase,the left DLPFC was selected as the stimulation site,and the treatment was conducted 5 times a week for 2 weeks.After the treatment,the severity of depressive symptoms was measured again.At the same time,depression patients were divided into an early improvement group(HAMD score reduction greater than 20%after treatment)and an early non-improvement group(HAMD score reduction less than 20% after treatment).In Study 1,a pared-samples t-test was used to analyze changes in HAMD-17 scores before and after r TMS treatment,and then we obtained thalamic templates from AAL templates to calculate gray matter volume images for each subject.The regression analysis of the reduction rate of depressive symptoms and the volume of the left and right thalamus was performed to explore whether the thalamic gray matter volume could predict the treatment effect of r TMS.The results showed that the HAMD-17 score was significantly lower after r TMS treatment than before treatment(before r TMS treatment: 22.2±5.3;After r TMS: 14.2±4.9;t =-7.2,p < 0.001).Compared with the early non-improvement group,the early improvement group had a higher HAMD-17 score before treatment and a lower HAMD-17 score after treatment.Regression analysis showed that the volume of the left and right thalamus did not predict the decline rate of depressive symptoms,but the dorsolateral thalamus nuclei did.If the threshold was relaxed,the left dorsolateral thalamic nucleus,left anterior occipital nucleus/lateral posterior nucleus and right medial and ventral occipital nuclei could also be found to predict r TMS efficacy.In Study 2,Pearson correlation analysis was used to calculate the FC between each thalamic nucleus(seed)and the corresponding cortex(target)for each subject.Subsequently,the Pearson correlation coefficients were r-z transformed.Secondly,the diffusion toolbox in the FSL toolbox was used to calculate the structural connectivity of the thalamo-cortex.Finally,the Libsvm tool was used to perform support vector machine regression(SVR)analysis based on the functional and structural connectivity maps of the thalamo-cortex.Leave-one-out cross-validation(LOOCV)was used to estimate the prediction accuracy of SVR.At the same time,an independent sample t-test was used to explore the differences in the functional and structural connectivity of thalamo-cortex between the early improvement group and the early non-improvement group.The results showed that the FC between dorsomedial thalamic nuclei and the prefrontal cortex could predict the therapeutic effect of r TMS,while the FC between other thalamic nuclei and the cerebral cortex could not predict the therapeutic effect.Among them,the brain regions that contributed more to prediction are the dorsolateral prefrontal cortex(DLPFC),medial prefrontal cortex(m PFC),ventrolateral prefrontal cortex(VLPFC),and orbitofrontal cortex contributed(OFC).Subsequently,we similarly found significant predictive effects of thalamo-prefrontal structural connectivity(SC)for treatment,but we did not observe statistical differences in thalamo-cortical functional or structural connectivity between early improvers and early non-improvers.In conclusion,this study explored the predictive effects of thalamic volume and thalamo-cortical functional and structural connectivity on r TMS efficacy in non-TRD patients using machine learning techniques from a multimodal perspective.The results of this study show that r TMS has a significant therapeutic effect on non-TRD patients,and the volume of some thalamic nuclei and the functional and structural connectivity between the thalamus and prefrontal lobe is of great significance in predicting the effectiveness of r TMS treatment in depression.The results of this study not only reveal the mechanism of r TMS in the treatment of patients with depression,but also provide a theoretical basis for guiding patients to choose more effective personalized treatment methods,and play a certain clinical reference significance and practical value in optimizing the treatment plan of patients.Follow-up studies can use a larger treatment sample to further explore the synergistic brain regions that effectively mediate patient improvement,and more effectively reveal the complex neurobiological mechanisms of individual heterogeneity in r TMS treatment.