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PurposeDaytime complaints such as memory and attention deficits and failure to accomplish daily tasks are common in insomnia patients. However, objective psychological tests to detect cognitive impairment are equivocal. Neural function associated with cognitive performance may explain the discrepancy. The aim of this study was to investigate the hemodynamic response patterns of patients with chronic insomnia disorder (CID) using the noninvasive and low-cost functional neuroimaging technique of multichannel near-infrared spectroscopy (NIRS) in order to identify changes of neural function associated with cognitive performance.Patients and methodsTwenty-four CID patients and twenty-five healthy controls matched for age, right-hand dominance, educational level, and gender were examined during verbal fluency tasks (VFT) using NIRS. A covariance analysis was conducted to analyze differences of oxygenated hemoglobin (oxy-Hb) changes in prefrontal cortex (PFC) between the two groups and reduce the influence of the severity of depression. Pearson correlation coeffcients were calculated to examine the relationship between the oxy-Hb changes, with the severity of insomnia and depressive symptoms assessed by the Pittsburgh Sleep Quality Index (PSQI) and the Hamilton Rating Scale for Depression (HAMD).ResultsThe number of words generated during the VFT in CID groups showed no statistical differences with healthy controls. CID patients showed hypoactivation in the PFC during the cognitive task. In addition, we found that the function of left orbitofrontal cortex (OFC) during the VFT was significantly negatively correlated with the PSQI scores and the function of right dorsolateral PFC (DLPFC) was significantly negatively correlated with the HAMD scores.ConclusionThe present study detected dysfunctions in PFC in spite of intact performance which indicates the role of PFC in the neurophysiological underpinnings. Left OFC function is associated with insomnia symptoms and right DLPFC function is associated with depressive symptoms.

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Background/Objective. Menopausal depression (MD) is characterized by depressive symptoms along with hormonal fluctuations. We investigate brain function alteration between major depressive disorder (MDD) and MD. Methods. The difference in oxygenated hemoglobin (Oxy-Hb) for the prefrontal cortex (PFC) was compared retrospectively among 90 females presented with 30 MDD, 30 MD, and 30 healthy controls (HCs) using verbal fluency task (VFT) with near-infrared spectroscopy (NIRS). Results. We observed a significant difference in Oxy-Hb alteration in the left dorsolateral PFC (DLPFC) using VFT with NIRS (channel 18, P = 0.007) between the MD and MDD groups. A significant difference in Oxy-Hb levels was observed among the three groups in the bilateral DLPFC (channels 18, 27, 33, 39, 41, and 45; P < 0.05). Compared to the HCs, the MD group presented lower Oxy-Hb activation in the right DLPFC (channel 41; P = 0.048) and the left DLPFC (channels 18, 39, and 45; P < 0.05), and the MDD group presented lower Oxy-Hb activation in the right DLPFC (channels 27, 33, and 41; P < 0.05) and the left DLPFC (channels 39 and 45; P < 0.05). Conclusion. Abnormal hemodynamics of the left DLPFC can differentiate MD from MDD by NIRS.

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Alzheimer's disease (AD) has raised extensive concern in healthcare and academia as one of the most prevalent health threats to the elderly. Due to the irreversible nature of AD, early and accurate diagnoses are significant for effective prevention and treatment. However, diverse clinical symptoms and limited neuroimaging accuracy make diagnoses challenging. In this article, we built a brain network for each subject, which assembles several commonly used neuroimaging data simply and reasonably, including structural magnetic resonance imaging (MRI), diffusion-weighted imaging (DWI), and amyloid positron emission tomography (PET). Based on some existing research results, we applied statistical methods to analyze (i) the distinct affinity of AD burden on each brain region, (ii) the topological lateralization between left and right hemispheric sub-networks, and (iii) the asymmetry of the AD attacks on the left and right hemispheres. In the light of advances in graph convolutional networks for graph classifications and summarized characteristics of brain networks and AD pathologies, we proposed a regional brain fusion-graph convolutional network (RBF-GCN), which is constructed with an RBF framework mainly, including three sub-modules, namely, hemispheric network generation module, multichannel GCN module, and feature fusion module. In the multichannel GCN module, the improved GCN by our proposed adaptive native node attribute (ANNA) unit embeds within each channel independently. We not only fully verified the effectiveness of the RBF framework and ANNA unit but also achieved competitive results in multiple sets of AD stages' classification tasks using hundreds of experiments over the ADNI clinical dataset.