Functional Connectome Gradient Of Brain Networks In Major Depressive Disorder

Major depressive disorder(MDD)is a common mental illness characterized by persistent depression,accompanied by brain function damage and behavioral changes.The disease makes patients suffer from the disease and causes social and family burdens.However,the pathophysiological changes of the onset and development of MDD have not been proved,and its clinical diagnosis is poor and the effect of antidepressant intervention is unstable.MDD-related brain dysfunction involves a wide range of brain networks,such as the passivation of visual function,the abnormality of default mode network function activity and the damage of reward system,but the specific organization distribution pattern behind these dysfunctions is still unclear.Therefore,the purpose of this study is to explore the gradient pattern and hierarchical structure of continuous transformation of large-scale brain functional space in patients with MDD by using manifold learning algorithm in machine learning,and to find potential imaging markers for diagnosis and prognosis of MDD.The first part including a total of 113 subjects,including 70 MDD patients and 43age-and sex-matched healthy controls.All subjects underwent magnetic resonance scanning and scale evaluation at baseline.Among them,36 MDD patients who received selective serotonin reuptake inhibitors for 12 weeks received magnetic resonance scanning and scale evaluation again after treatment.Based on the functional connectivity between the default mode network(DMN)and the cerebral cortex,the diffusion mapping embedding method is applied to identify the spatial organization axis of all voxel’s functional changes in DMN,and the functional gradient of DMN is obtained.The results showed that the functional gradient of DMN in MDD patients showed abnormal changes,the functional gradient of the medial core subsystem decreased and the functional gradient of the lateral dorsomedial prefrontal lobe system increased.After antidepressant treatment,the abnormal DMN functional gradient of MDD patients was reconfigured to normal level.The functional gradient of DMN before treatment significantly predicted the improvement of symptoms after treatment,and the predictive weight analysis showed that the functional gradient of medial prefrontal lobe played an important role in predicting the treatment effect of MDD patients.This study enhanced our understanding of abnormal functional tissues of DMN in MDD patients,and emphasized the application potential of brain network functional gradient in antidepressant treatment.The second part included 651 subjects from the public dataset of Rest-meta-MDD project,including 323 MDD patients and 328 age-and sex-matched healthy controls.In this study,firstly,we constructed the functional connectivity of cerebral cortex and obtain the primary-transmodal functional gradient of cerebral cortex by diffusion mapping embedding method,which is used to represent the functional hierarchical structure of sensorimotor-default mode network in cerebral cortex.The results showed that compared with healthy controls,the primary-transmodal functional gradient of MDD patients is significantly compressed,the functional gradient of primary functional network is increased and the functional gradient of DMN is decreased,and the abnormal functional gradient score is significantly related to the clinical symptoms of MDD patients.Secondly,we weighted the subcortical-cortical functional connectivity to the cortical primary-multimodal functional gradient,and constructed a subcortical weighted functional gradient to study the parallel functional hierarchy damage of subcortical structures.The results showed that the weighted functional gradient of caudate,amygdala and thalamus was decreased in MDD patients.This study provided a new insight into abnormal brain function hierarchy and the co-occurrence of lowlevel perception and high-level cognitive dysfunction in MDD patients.The third part is an exploratory study based on the second part,which aims to explore the potential molecular and cellular basis behind the primary-transmodal functional gradient of MDD patients.Molecular imaging of neurotransmitter system and Allen human brain atlas gene expression data from publicly shared datasets were used for correlation analysis with abnormal patterns of primary-transmodal functional gradient in MDD patients.The results showed that the abnormal primary-transmodal gradient is related to the molecular density of neurotransmitter system including serotonin and dopamine receptor,and to the MDD-related risk genes found in previous studies.We further found that the astrocytes and neurons specific transcriptional signatures is related to the abnormal pattern of primary-transmodel functional gradient in MDD.This study provides a multi-faceted perspective for exploring the abnormal pattern of brain function in MDD patients.To sum up,this study uses the brain network functional gradient analysis technology to find the continuous spatial transformation of brain function and the abnormal pattern of hierarchical structure in DMN and cortex of MDD patients and its associations with clinical symptoms and microscale molecular structure,and the brain network functional gradient is helpful for the diagnosis and treatment effect prediction of MDD.The functional gradient analysis of brain network emphasizes the representation of brain function with functional space layout as the main feature,and explores the organizational pattern of continuous spatial transformation in macroscale brain function,which is helpful to understand the pathophysiological mechanism of MDD to some extent.