Multimodal Magnetic Resonance Imaging-based Investigation Of Major Depressive Disorder

Major depressive disorder(MDD)is a mood disorder of major mental illness with high prevalence,high recurrence rate,high comorbidity rate and high suicide rate.World Healthy Organization has predicted that MDD will become the largest disease burden of the world in 2030.About 90 million patients with MDD have been estimated in China and bring heavy economic loss and harm for the society and families.The neurobiological mechanisms of MDD remain unclear,and diagnosis of MDD mainly depends on the subjective symptoms,and lacks of objective biomarkers.Magnetic resonance imaging which can non-invasively study the brain structure and function provides an important mean for neuromechanism research of MDD.In the current thesis,we used multimodal neuroimaging approaches to study the MDD to identify its neurophysiological basis.My thesis mainly included the following three parts.Study 1: MDD is a common psychiatric disorder that is characterized by cognitive deficits and affective symptoms.To data,an increasing number of neuroimaging studies have focused on emotion regulation and have consistently shown that emotion dysregulation is one of the central features and underlying mechanisms of MDD.Although gray matter volume in brain regions within emotion regulation networks have changed in MDD,the interactions and relationships between these gray matter structures remain largely unknown.Thus in this study,we adopted a structural covariance method based on gray matter volume to investigate the brain morphological abnormalities within the emotion regulation networks in a large cohort of 65 MDD patients and 65 age-and gegnder-matched healthy controls.A permutation test with P < 0.05 was to identify the significant changes in covariance connectivity strengths between MDD patients and healthy controls.The structural covariance analysis revealed increased correlation strength of gray matter volume between the left angular gyrus and the left amygdala and between the right angular gyrus and the right amygdala,as well as decreased correlation strength of the gray matter volume between the right angular gyrus and the posterior cingulate cortex in MDD.Our findings further supported the notion that emotion dysregulation is an underlying mechanism of MDD by revealing disrupted structural covariance patterns in the emotion regulation network.Study 2: In the brain,the hubs of the brain network play a key role in integrating and transferring information between different functional modules.However,whether the functional network hubs have changed in MDD remains unclear.Using resting-state functional magnetic resonance imaging and graph theory methods,we investigated whether alterations of hubs can be detected in MDD.First,we constructed the whole-brain voxel-wise functional networks and calculated degree centrality in each subject in 34 MDD patients and 34 gender-,age-,and education level-matched healthy controls(HC).Next,the two-sample t-test was applied to compare the degree centrality between HC and MDD patients and identified significant decreased degree centrality in subgenual anterior cingulate cortex in MDD patients.Subsequent functional connectivity analyses of subgenual anterior cingulate cortex showed disruptions in functional connectivity with posterior insula,middle and inferior temporal gyrus,lingual gyrus,and cerebellum in MDD patients.Furthermore,the changed degree centrality of subgenual anterior cingulate cortex and functional connections to subgenual anterior cingulate cortex were significantly correlated with the Hamilton Depression Rating Scale scores in MDD patients.The results of the present study revealed the abnormal hub of subgenual anterior cingulate cortex and its corresponding disrupted frontal-limbic-visual cognitive-cerebellum functional networks in MDD.These findings may provide a new insight for the diagnosis and treatment of MDD.Study 3: The studies of MDD using magnetic resonance imaging have revealed abnormal of structure and function in multiple brain regions.Although several studies have revealed the changes in the brain network level,most of these results are inconsistent resulting from drugs,duration of disease and the nodal definition.Thus in this study,we combined the diffusion magnetic resonance imaging data,a newly constructed Brainnetome Atlas,and the method of graph theory to study the topological alteration of cerebral white matter anatomical network in MDD.The diffusion magntic resonance imaging data of 19 first-episode drug-naive of MDD patients and 30 gender-,age-and education level mathched healthy controls were collected.Deterministic fiber tracking approach was used to construct the whole brain white matter anatomical network in MDD patients and healthy controls.A permutation test with P < 0.05 was to identify the significant changes in network topology properties.We found that the nodal properties were damaged,mainly in the bilateral parahippocampal gyrus,bilateral basal ganglia,bilateral inferior parietal lobule,the left postcentral gyrus,the left paracentral lobule,the right middle temporal gyrus,the right superior parietal lobule,the right insular gyrus,the right medio ventral occipital cortex,the right cingulate gyrus and the right thalamus.Furthermore,we also found the nodal betweenness of right superior parietal lobule and the right insula were positively correlated with the duration of disease,whereas the nodal betweenness of the right ahalamus,the left parahippocampus,and the nodal degree of the left parahippocampus,the right inferior parietal lobule was positively correlated with the Hamilton depression rating scores in MDD patients.These findings provide a new way for understanding the brain network’s mechanisms of MDD.In conclusion,in my current thesis,we used the structural magnetic resonance imaging,resting-state functional magnetic resonance imaging and diffusion magnetic resonance imaging to reveal the structural,functional and anatomical network abnormalities in MDD.These findings provide an important insight for understanding the neurophysiological mechanisms of the MDD.