The Research Of Resting-state FMRI In Primary Insomnia Patients

Part One:To investigate abnormal spontaneous regional brain activity in primary insomnia using ALFF algorithm.Objective:Investigating functional specialization is crucial for a complete understanding of the neural mechanisms of primary insomnia(PI).Resting-state functional magnetic resonance imaging(fMRI)is a useful tool to explore the functional specialization of PI.However,only a few studies have focused on the functional specialization of PI using resting-state fMRI and results of these studies were far from consistent.Thus,the current study aimed to investigate functional specialization of PI using resting-state fMRI with amplitude of low frequency fluctuations(ALFFs)algorithm.Methods and Materials:In this study,55 PI patients and 44 healthy controls were included.ALFF values were compared between the two groups using two-sample t-test.The relationship of abnormal ALFF values with clinical characteristics and duration of insomnia was investigated using Pearson’s correlation analysis.Results:PI patients showed lower ALFF values in the left orbitofrontal cortex/inferior frontal gyrus,right middle frontal gyrus,left inferior parietal lobule,and bilateral cerebellum posterior lobes,while higher ALFF values in the right middle/inferior temporal that extended to the right occipital lobe.In addition,we found that the duration of PI negatively correlated with ALFF values in the left orbitofrontal cortex/inferior frontal gyrus,and the Pittsburgh Sleep Quality Index score negatively correlated with ALFF values in the left inferior parietal lobule.Conclusion:The present study added information to limited studies on functional specialization and provided evidence for hyperarousal hypothesis in PI.Part Two:Abnormal whole-brain functional connectivity in patients with primary insomniaObjective:The investigation of the mechanism of insomnia could provide the basis for improved understanding and treatment of insomnia.The aim of this study is to investigate the abnormal functional connectivity throughout the entire brain of insomnia patients,and analyze the global distribution of these abnormalities.Methods and Materials:Whole brains of 50 patients with insomnia and 40 healthy controls were divided into116 regions and abnormal connectivities were identified by comparing the Pearson’s correlation coefficients of each pair using general linear model analyses with covariates of age,sex,and duration of education.Results:In patients with insomnia,regions that relate to wakefulness,emotion,worry/rumination,saliency/attention,and sensory-motor showed increased positive connectivity with each other;however,regions that often restrain each other,such as regions in salience network with regions in default mode network,showed decreased positive connectivity.Correlation analysis indicated that some increased positive functional connectivity was associated with the Self-Rating Depression Scale,Insomnia Severity Index,and Pittsburgh Sleep Quality Index scores.Conclusion:According to our findings,increased and decreased positive connectivities suggest function strengthening and function disinhibition,respectively,which offers a parsimonious explanation for the hyperarousal hypothesis in the level of the whole-brain functional connectivity in patients with insomnia.Part Three:Effective connectivity of the ventral right anterior insula in primary insomnia patients:a resting-state fMRI study with multivariate pattern analysisObjective:To investigate right anterior insula-based effective connectivity in primary insomnia using multivariate pattern analysis.Materials and Methods:Fifty patients with insomnia and 40 healthy controls were scanned using resting-state functional magnetic resonance imaging.Granger causality analysis was applied to explore the effective connectivity between the right anterior insula and the whole brain.We took the effective connectivity as feature vector,and then a pattern classifier was designed using principal component analysis and a linear support vector machine to identify brain areas that had distinct effective connectivity between groups.Results:(1)Effective connectivity of the ventral right anterior insula could accurately classify insomnia patients from health controls(P≤0.008;permutation test).(2)Areas in the default mode and central executive networks as well as the visual/auditory-related and affective-related areas showed altered effective connectivity with the ventral right anterior insula(voxel P<0.02;cluster P<0.001;permutation test).(3)Effective connectivity from ventral right anterior insula to key nodes of default mode network and right fusiform gyrus exhibited significant correlations with insomnia severity(P<0.05).Conclusion:Our finding highlights the role of the ventral right AIns,which is the hub node of SN,in insomnia.