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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