ObjectiveInvestigating 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.MethodsIn 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.ResultsPI 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.ConclusionThe present study added information to limited studies on functional specialization and provided evidence for hyperarousal hypothesis in PI.

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. Whole brains of 50 patients with insomnia and 40 healthy controls were divided into 116 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. 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. 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.

Dynamic functional connectivity (DFC) analysis can capture time‐varying properties of connectivity. However, studies on large samples using DFC to investigate transdiagnostic dysconnectivity across schizophrenia (SZ), bipolar disorder (BD), and major depressive disorder (MDD) are rare. In this study, we used resting‐state functional magnetic resonance imaging and a sliding‐window method to study DFC in a total of 610 individuals (150 with SZ, 100 with BD, 150 with MDD, and 210 healthy controls [HC]) at a single site. Using k‐means clustering, DFCs were clustered into three functional connectivity states: one was a more frequent state with moderate positive and negative connectivity (State 1), and the other two were less frequent states with stronger positive and negative connectivity (State 2 and State 3). Significant 4‐group differences (SZ, BD, MDD, and HC groups; q < .05, false‐discovery rate [FDR]‐corrected) in DFC were nearly only in State 1. Post hoc analyses (q < .05, FDR‐corrected) in State 1 showed that transdiagnostic dysconnectivity patterns among SZ, BD and MDD featured consistently decreased connectivity within most networks (the visual, somatomotor, salience and frontoparietal networks), which was most obvious in both range and extent for SZ. Our findings suggest that there is more common dysconnectivity across SZ, BD and MDD than we previously expected and that such dysconnectivity is state‐dependent, which provides new insights into the pathophysiological mechanism of major psychiatric disorders.

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ObjectiveDaytime cognitive impairment is an essential symptom of primary insomnia (PI). However, the underlying neural substrate remains largely unknown. Many studies have shown that the right anterior insula (rAI) as a key node of salience network (SN) plays a critical role in switching between the executive control network (ECN) and the default mode network (DMN) for better performance of cognitively demanding tasks. Aberrant effective connectivity (directional functional connectivity) of rAI with ECN or DMN may be one reason for daytime cognitive impairment in PI patients. Up to now, no effective connectivity study has been conducted on patients with PI during resting state. Our aim is to investigate the effective connectivity between the rAI and the other voxels in the whole brain in PI.Materials and methodsFifty drug-naive patients with PI and forty age- and sex-matched healthy controls were scanned using resting-state functional MRI. Seed-based Granger causality analysis was used to examine effective connectivity between the rAI, including ventral and dorsal part, and the whole brain. The effective connectivity was compared between the two groups and was correlated with clinical characteristics.ResultsCompared with controls, patients showed decreased effective connectivity from the rAI to the bilateral precuneus, the left postcentral gyrus (extending to bilateral precuneus) and the bilateral cerebellum posterior lobe, and decreased effective connectivity from the bilateral orbitofrontal cortex (OFC) to the rAI (single voxel P < 0.001, AlphaSim corrected with P < 0.01). In addition, effective connectivity from the ventral rAI to the left postcentral gyrus and from the left OFC to the ventral rAI were significantly negatively correlated with Insomnia Severity Index scores (r = −0.28/P = 0.046 and r = −0.29/P = 0.038, respectively).ConclusionThe present study is the first to reveal aberrant effective connectivity between the SN hub (rAI) and the posterior DMN hub (precuneus) as well as decision-making region (OFC) and sensori-motor region in PI. These findings suggest an aberrant salience processing system of the rAI in PI patients.

Objective: To explore whether or not functional connectivity (FC) could be used as a potential biomarker for classification of primary insomnia (PI) at the individual level by using multivariate pattern analysis (MVPA).Methods: Thirty-eight drug-naive patients with PI, and 44 healthy controls (HC) underwent resting-state functional MR imaging. Voxel-wise functional connectivity strength (FCS), large-scale functional connectivity (large-scale FC) and regional homogeneity (ReHo) were calculated for each participant. We used support vector machine (SVM) with the three types of metrics as features separately to classify patients from healthy controls. Then we evaluated its classification performances. Finally, FC metrics with significant high classification performance were compared between the two groups and were correlated with clinical characteristics, i.e., Insomnia Severity Index (ISI), Pittsburgh Sleep Quality Index (PSQI), Self-rating Anxiety Scale (SAS), Self-rating Depression Scale (SDS) in the patients' group.Results: The best classifier could reach up to an accuracy of 81.5%, with a sensitivity of 84.9%, specificity of 79.1%, and area under the receiver operating characteristic curve (AUC) of 83.0% (all P < 0.001). Right anterior insular cortex (BA48), left precuneus (BA7), and left middle frontal gyrus (BA8) showed high classification weights. In addition, the right anterior insular cortex (BA48) and left middle frontal gyrus (BA8) were the overlapping regions between MVPA and group comparison. Correlation analysis showed that FCS in left middle frontal gyrus and head of right caudate nucleus were correlated with PSQI and SDS, respectively.Conclusion: The current study suggests abnormal FCS in right anterior insular cortex (BA48) and left middle frontal gyrus (BA8) might serve as a potential neuromarkers for PI.

Objective: In generalized anxiety disorder (GAD), abnormal top-down control from prefrontal cortex (PFC) to amygdala is a widely accepted hypothesis through which "emotional dysregulation model" may be explained. However, whether and how the PFC directly exerted abnormal top-down control on amygdala remained largely unknown. We aim to investigate the amygdala-based effective connectivity by using Granger causality analysis (GCA).Methods: Thirty-five drug-naive patients with GAD and thirty-six healthy controls (HC) underwent resting-state functional MR imaging. We used seed-based Granger causality analysis to examine the effective connectivity between the bilateral amygdala and the whole brain. The amygdala-based effective connectivity was compared between the two groups.Results: In HC, the left middle frontal gyrus exerted inhibitory influence on the right amygdala, while in GAD group, this influence was disrupted (single voxel P < 0.001, Gaussian random field corrected with P < 0.01). Conclusion:Our finding might provide new insight into the "insufficient top-down control" hypothesis in GAD.

PurposeTo establish and validate a radiomics model to estimate the malignancy of mediastinal lymph nodes (LNs) based on contrast-enhanced CT imaging.MethodIn total, 201 pathologically confirmed mediastinal LNs from 129 patients were enrolled and assigned to training and test sets. Radiomics features were extracted from the region of interest (ROI) delineated on venous-phase CT imaging of LN. Feature selection was performed with least absolute shrinkage and selection operator (LASSO) binary logistic regression. Multivariate logistic regression was performed with the backward stepwise elimination. A model was fitted to associate mediastinal LN malignancy with selected features. The performance of the model was assessed and compared to that of five other machine learning algorithms (support vector machine, naive Bayes, random forest, decision tree, K-nearest neighbor) using receiver operating characteristic (ROC) curves. Calibration curves and Hosmer-Lemeshow tests were used to assess the calibration degree. Decision curve analysis (DCA) was used to assess the clinical usefulness of the logistic regression model in both the training and test sets. Stratified analysis was performed for different scanners and slice thicknesses.ResultAmong the six machine learning methods, the logistic regression model with the eight strongest features showed a significant association with mediastinal LN status and the satisfactory diagnostic performance for distinguishing malignant LNs from benign LNs. The accuracy, sensitivity, specificity and area under the ROC curve (AUC) were 0.850/0.803, 0.821/0.806, 0.893/0.800, and 0.922/0.850 in the training/test sets, respectively. The Hosmer-Lemeshow test showed that the P value was &gt; 0.05, indicating good calibration, and the calibration curves showed good agreement between the classifications and actual observations. DCA showed that the model would obtain more benefit when the threshold probability was between 30% and 90% in the test set. Stratified analysis showed that the performance was not affected by different scanners or slice thicknesses. There was no significant difference (DeLong test, P &gt; 0.05) between any two subgroups, which showed the generalization of the radiomics score across different factors.ConclusionThe model we built could help assist the preoperative estimation of mediastinal LN malignancy based on contrast-enhanced CT imaging, with stability for different scanners and slice thicknesses.