Primary insomnia (PIs) is highly prevalent and can lead to adverse socioeconomic impacts, but the underlying mechanism of its complex brain network impairment remains largely unknown. Functional studies are too few and diverse in methodology, which makes it difficult to glean general conclusions. To answer this question, we first used graph theory-based network analyse, together with seed-based functional connectivity approach, to characterize the topology architecture of whole-brain functional networks associated with PIs. Forty-eight subjects with PIs and 48 age/sex/education-matched good sleepers were recruited. We found PIs is associated with altered connection properties of intra-networks within the executive control network, default mode network and salience network, and inter-network between auditory language comprehension center and executive control network. These complex networks were correlated with negative emotions and insomnia severity in the PIs group. Altered connection properties of these network hubs appeared to be neural risk factors for neuropsychological changes of PIs, and might be used as potential neuroimaging markers to distinguish the PIs from the good sleepers. These findings highlight the role of functional connectivity in the pathophysiology of PIs, and may underlie the neural mechanisms of etiology of PIs.

ObjectiveWhether moderate alcohol consumption has health benefits remains controversial, but the harmful effects of excessive alcohol consumption on behavior and brain function are well recognized. The aim of this study was to investigate alcohol-induced regional brain activities and their relationships with behavioral factors.Subjects and methodsA total of 29 alcohol-dependent subjects (9 females and 20 males) and 29 status-matched healthy controls (11 females and 18 males) were recruited. Severity of alcohol dependence questionnaire (SADQ) and alcohol use disorders identification test (AUDIT) were used to evaluate the severity of alcohol craving. Regional homogeneity (ReHo) analysis was used to explore the alcohol-induced regional brain changes. Receiver operating characteristic (ROC) curve was used to investigate the ability of regional brain activities to distinguish alcohol-dependent subjects from healthy controls. Pearson correlations were used to investigate the relationships between alcohol-induced ReHo differences and behavioral factors.ResultsAlcohol-dependent subjects related to healthy controls showed higher ReHo areas in the right superior frontal gyrus (SFG), bilateral medial frontal gyrus (MFG), left precentral gyrus (PG), bilateral middle temporal gyrus (MTG), and right inferior temporal gyrus (ITG) and lower ReHo areas in the right cerebellum posterior lobe (CPL), left rectal gyrus (RG), and right cluster of pons and cerebellum anterior lobe (CAL). ROC curve revealed high area under the curve (AUC) values (mean ± SD: 0.864 ± 0.028; range: 0.828–0.911) of ReHo differences. Diagnostic analysis showed that these areas alone discriminated alcohol-dependent subjects from healthy controls with high degree of sensitivities (mean ± SD: 81.25% ± 11.49%; range: 62.5%–100%) and specificities (mean ± SD: 81.75% ± 12.36%; range: 67.5%–100%). Years of drink showed negative correlation with left RG (r = −0.493, p = 0.007), the same finding was shown between AUDIT and right CPL (r = −0.52, p = 0.004).ConclusionAlcohol dependence is associated with aberrant regional activities in multiple brain areas. ReHo analysis may be a useful biological indicator for the detection of regional brain activities in individuals with alcohol dependence.

Objective: To investigate acute sleep deprivation (SD)-related regional brain activity changes and their relationships with behavioral performances.Methods: Twenty-two female subjects underwent an MRI scan and an attention network test at rested wakefulness (RW) status and after 24 h SD. The amplitude of low-frequency fluctuations (ALFF) was used to investigate SD-related regional brain activity changes. We used the receiver operating characteristic (ROC) curve to evaluate the ability of the ALFF differences in regional brain areas to distinguish the SD status from the RW status. We used Pearson correlations to evaluate the relationships between the ALFF differences in brain areas and the behavioral performances during the SD status.Results: Subjects at the SD status exhibited a lower accuracy rate and a longer reaction time relative to the RW status. Compared with RW, SD showed significant lower ALFF values in the right cerebellum anterior lobe, and higher ALFF areas in the bilateral inferior occipital gyrus, left thalamus, left insula, and bilateral postcentral gyrus. The area under the curve values of the specific ALFF differences in brain areas were (mean ± std, 0.851 ± 0.045; 0.805–0.93). Further, the ROC curve analysis demonstrated that the ALFF differences in those regional brain areas alone discriminated the SD status from the RW status with high degrees of sensitivities (82.16 ± 7.61%; 75–93.8%) and specificities (81.23 ± 11.39%; 62.5–93.7%). The accuracy rate showed negative correlations with the left inferior occipital gyrus, left thalamus, and left postcentral gyrus, and showed a positive correlation with the right cerebellum.Conclusions: The ALFF analysis is a potential indicator for detecting the excitation–inhibition imbalance of regional cortical activations disturbed by acute SD with high performances.