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Sleep deprivation (SD) is very common in modern society and regarded as a potential causal mechanism of several clinical disorders. Previous neuroimaging studies have explored the neural mechanisms of SD using magnetic resonance imaging (MRI) from static (comparing two MRI sessions [one after SD and one after resting wakefulness]) and dynamic (using repeated MRI during one night of SD) perspectives. Recent SD researches have focused on the dynamic functional brain organization during the resting‐state scan. Our present study adopted a novel metric (temporal variability), which has been successfully applied to many clinical diseases, to examine the dynamic functional connectivity after SD in 55 normal young subjects. We found that sleep‐deprived subjects showed increased regional‐level temporal variability in large‐scale brain regions, and decreased regional‐level temporal variability in several thalamus subregions. After SD, participants exhibited enhanced intra‐network temporal variability in the default mode network (DMN) and increased inter‐network temporal variability in numerous subnetwork pairs. Furthermore, we found that the inter‐network temporal variability between visual network and DMN was negative related with the slowest 10% respond speed (β = −.42, p = 5.57 × 10−4) of the psychomotor vigilance test after SD following the stepwise regression analysis. In conclusion, our findings suggested that sleep‐deprived subjects showed abnormal dynamic brain functional configuration, which provides new insights into the neural underpinnings of SD and contributes to our understanding of the pathophysiology of clinical disorders. [ABSTRACT FROM AUTHOR]

Recent neuroimaging studies have indicated that abnormalities in brain structure and function may play an important role in the etiology of lifelong premature ejaculation (LPE). LPE patients have exhibited aberrant cortical structure, altered brain network function and abnormal brain activation in response to erotic pictures. However, it remains unclear whether resting-state whole brain functional connectivity (FC) is altered in LPE patients. Machine learning analysis has the advantage of screening the best classification features from high-throughput data (such as FC), which has the potential to identify the pathophysiological targets of disease by establishing classification indicators for patients and healthy controls (HCs). Therefore, the supported vector machine based classification model using FC as features was used in the present study to confirm the most specific FCs that distinguish LPE patients from healthy controls. After feature selection, the remained features were used to build the classification model, with an accuracy 0.85 ± 0.14, sensitivity of 0.92 ± 0.18, specificity of 0.72 ± 0.30, and recall index of 0.85 ± 0.17 across 1000 testing groups (100 times 10-folds cross validation). After that, two-sample t -tests with family-wise error correction were used to compare these features that occur more than 500 times during training steps between LPE patients and HCs. Four FCs, (1) between left medial part of orbital frontal cortex (mOFC) and right mOFC, (2) between the left rectus and right postcentral gyrus, (3) between the right insula and left pallidum, and (4) between the right middle part of temporal pole and right inferior part of temporal gyrus showed significant group difference. These results demonstrate that resting-state brain FC might be a discriminating feature to distinguish LPE patients from HCs. These classification features, especially the FC between bilateral mOFC, provide underlying abnormal central functional targets in LPE etiology, which offers a novel alternative target for future intervention in LPE treatment. [ABSTRACT FROM AUTHOR]

Primary dysmenorrhea (PDM), characterized by cramping pain in the lower abdomen, is a common gynecological disorder in women of child-bearing age. An increasing number of neuroimaging studies have emphasized that PDM is associated with functional and structural abnormalities in the regions related to the default mode network (DMN). Based on resting-state functional magnetic resonance imaging (fMRI), the aim of this study was to use amplitude of low-frequency fluctuation (ALFF) and functional connectivity (FC) to investigate changes of the intrinsic brain activity in the DMN in PDM. Pearson correlation analysis was conducted to assess relationships between the neuroimaging findings and clinical symptoms. Forty-eight PDM patients and thirty-eight matched healthy controls participated in this study. Compared to healthy controls, PDM patients had increased ALFF in the precuneus, dorsomedial prefrontal cortex (dmPFC) and anterior cingulate cortex (ACC) and decreased ALFF in the thalamus. PDM patients also had decreased connectivity between the precuneus and left dmPFC and right ACC, while increased connectivity between the precuneus and left thalamus. In addition, the ALFF in the left dmPFC in PDM patients positively correlated with disease duration. Our findings provide further evidence of the DMN-related abnormalities in PDM patients which might contribute to a better understanding of the pathophysiology of this disease. [ABSTRACT FROM AUTHOR]

Highlights • Decreased connectivity of the anterior and anterolateral hippocampal subregions was identified after sleep deprivation. • The functional connectivity of the middle hippocampal subregion was significantly increased after sleep deprivation. • The effect of sleep deprivation on connectivity in different hippocampal subregions was not identical. Abstract Previous studies have revealed that sleep deprivation (SD) alters hippocampal functional connectivity (FC). However, the effects of SD on the FC of hippocampal subregions are still unknown. In this study, we used a masked independent component analysis (mICA) to partition the hippocampus into several small regions and investigated the changes in the FC of each small region within the whole brain after 24 h of SD in 40 normal young subjects. First, we determined the optimal number of hippocampal subregions in a data-driven manner using a reproducibility analysis and chose 17 as the optimal number of hippocampal subregions. Second, we compared the FC of each subregion between rested wakefulness and SD states using mCIA. Reduced FC was found between the left junction of anterior and anterolateral hippocampal region and the default mode network and bilateral thalamus after SD (p < 0.05/17, Threshold-Free Cluster Enhancement correction, Bonferroni's corrected for the number of subregions). The FC between the left posterior of the anterolateral and the left lateral posterior of the anterior hippocampal regions and somatomotor network changed more negative after SD. However, increased FC was identified between the left middle hippocampal region and vision-related regions after SD. Our results reflect differential effects of SD on the FC in specific hippocampal regions and provide new insights into the impact of SD on the resting-state functional organization in the human brain. [ABSTRACT FROM AUTHOR]

Studies using the flanker task have reported that response conflict is detected by the medial frontal cortex (MFC). As a conflict alert system, the MFC shows enhanced functional communication with task-related regions. Previous studies have revealed individual differences in functional connectivity during cognitive task performance. However, the mechanisms underlying these individual differences remain unclear. In the current study, electroencephalography (EEG) was recorded while 30 subjects performed a flanker task that was modified to exclude feature integration and contingency learning. The diffusion tensor imaging (DTI) data were collected the day before the EEG session. FCz-P3/4 theta phase synchronization was used to measure functional connectivity between the MFC and posterior parietal cortex (PPC). Hierarchical regression analyses were used to assess the relationship between MFC-PPC conflict-induced theta phase synchronization and white matter integrity in significant regions derived from tract-based spatial statistics (TBSS) analysis. As expected, MFC-PPC theta phase synchronization was significantly enhanced during conflict, suggesting a conflict-induced functional connectivity. However, these findings were only found in the right hemisphere, which may be related to the asymmetrical role of the bilateral PPC in response conflict processing. Furthermore, hierarchical regression analyses revealed that 44% of individual variability in FCz-P4 conflict-induced theta phase synchronization could be explained by variations in axial diffusivity (AD) in the genu of the corpus callosum (gCC). These results demonstrated that structural integrity in the gCC predicts conflict-induced functional connectivity between the MFC and right PPC. [ABSTRACT FROM AUTHOR]

Abstract: Majority of previous heroin fMRI studies focused on abnormal brain function in heroin-dependent individuals. However, few fMRI studies focused on the resting-state abnormalities in heroin-dependent individuals and assessed the relationship between the resting-state functional connectivity changes and duration of heroin use. In the present study, discrete cosine transform (DCT) was employed to explore spatial distribution of low frequency BOLD oscillations in heroin-dependent individuals and healthy subjects during resting-state; meanwhile resting-state functional connectivity analysis was used to investigate the temporal signatures of overlapping brain regions obtained in DCT analysis among these two groups. Main finding of the present study is that the default mode network (DMN) and rostral anterior cingulate cortex (rACC) network of heroin-dependent individuals were changed compared with healthy subjects. More importantly, these changes negatively correlated with duration of heroin use. These resting-state functional abnormalites in heroin-dependent individuals provided evidence for abnormal functional organization in heroin-dependent individuals, such as functional impairments in decision-making and inhibitory control. [Copyright &y& Elsevier]