Multimodal Brain Network Abnormalities In Majore Depressive Disorder Based On Graph Signal Processing

Major depressive disorder(MDD),referred to as depression,is a mental illness.Due to its high prevalence,high disability rate,high recurrence rate and other properties,it brings great inconvenience to the lives of patients.However,the pathogenesis of depression is complex,and a consistent theoretical concept has not yet been formed.Magnetic resonance imaging(MRI)provides a safe and non-invasive method to detect brain structure and function,and has been widely used in the research of various mental and neurological diseases.Based on graph signal processing(GSP),this study combines functional MRI(f MRI)and structural MRI(s MRI)data,constraining functional activities from brain structural networks and different constraints of structural networks.To explore the extent of abnormalities in MDD patients from the perspective of dynamic activity of brain function.In this study,firstly,the GSP method was used to decompose the brain function signal into the part coupled with the structural network and the part decoupled from the structural network from the perspective of the underlying structural network of the brain constraining the functional activity of the brain.The degree of decoupling measure quantifies the degree to which brain functional activity deviates from structural networks and analyzes differences between MDD and normal controls.The study found that in the MDD patient group,the degree of constraint of brain structure networks on functional activities in multiple brain regions was significantly changed.The study also explored the relationship between abnormal patterns and behaviors of MDD patients,using gene expression profiles to link the abnormal patterns of MDD patients’ coupling degrees with gene expression,and further enrichment analysis found that genes with negative expression weights were mainly enriched in Among the entries related to ion regulation and secretion regulation.Reveals a link between abnormal patterns and gene expression in MDD patients.In addition,this study continues to divide brain functional activities into lowfrequency parts and high-frequency parts based on the GSP method,which represent functional activities under different degrees of structural network constraints.A clustering method based on brain spatial activity at each moment was then used to capture the dynamic patterns of the brain’s transient functional network.The study found that the brain mode mainly activated by the default mode network(DMN)has an increased probability of occurrence in both high-frequency and low-frequency activities of brain function.In high-frequency activities,the frontoparietal network(FPN)and limbic network co-activation-based brain states are significantly reduced,which means that MDD patients have specific brain macroscopic network organization destruction in different levels of brain dynamic activities.Abnormal activity to make some supplements.In summary,this study used the graph signal processing method,combined with multi-modal image data,to explore the changes in the restriction ability of the underlying structural network of the brain to functional activities in MDD patients and the abnormality of brain dynamic activities under different constraints of the structural network.Understanding the pathological mechanisms of MDD patients provides new perspectives.