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Background: Mild Cognitive Impairment (MCI), as a high risk of Alzheimer's disease (AD), represents a state of cognitive function between normal aging and dementia. Moxibustion may effectively delay the progression of AD, while there is a lack of studies on the treatments in MCI. This study aimed to evaluate the effect of moxibustion treatment revealed by the amplitude of low-frequency fluctuation (ALFF) in MCI. Method: We enrolled 30 MCI patients and 30 matched healthy controls (HCs) in this study. We used ALFF to compare the difference between MCI and HCs at baseline and the regulation of spontaneous neural activity in MCI patients by moxibustion. The Mini-Mental State Examination and Montreal Cognitive Assessment scores were used to evaluate cognitive function. Results: Compared with HCs, the ALFF values significantly decreased in the right temporal poles: middle temporal gyrus (TPOmid), right inferior temporal gyrus, left middle cingulate gyrus, and increased in the left hippocampus, left middle temporal gyrus, right lingual gyrus, and right middle occipital gyrus in MCI patients. After moxibustion treatment, the ALFF values notably increased in the left precuneus, left thalamus, right temporal poles: middle temporal gyrus, right middle frontal gyrus, right inferior temporal gyrus, right putamen, right hippocampus, and right fusiform gyrus, while decreased in the bilateral lingual gyrus in MCI patients. The Mini-Mental State Examination and Montreal Cognitive Assessment scores increased after moxibustion treatment, and the increase in Mini-Mental State Examination score was positively correlated with the increase of ALFF value in the right TPOmid, the right insula, and the left superior temporal gyrus. Conclusion: Moxibustion treatment might improve the cognitive function of MCI patients by modulating the brain activities within the default mode network, visual network, and subcortical network with a trend of increased ALFF values and functional asymmetry of the hippocampus. These results indicate that moxibustion holds great potential in the treatment of MCI. [ABSTRACT FROM AUTHOR]

Objective: This cross-sectional study aimed to distinguish patients with mild cognitive impairment (MCI) from patients with normal controls (NCs) by measuring the levels of N -acetyl aspartate (NAA), total creatinine (tCr), and choline (Cho) in their hippocampus (HIP) and their posterior cingulate gyrus (PCG) by using proton magnetic resonance spectroscopy (MRS) and to predict the cutoff value on the ratios of metabolites. We further aimed to provide a reference for the diagnosis of MCI in elderly patients in China. Methods: About 69 patients who underwent a clinical diagnosis of the MCI group and 67 patients with NCs, the Mini-Mental Status Examination (MMSE) score, the Montreal Cognitive Assessment (MoCA) score, and MRS of the bilateral HIP and bilateral PCG were considered. The ratio of NAA/tCr and Cho/tCr in the bilateral HIP and bilateral PCG was calculated. The relationship between the ratios of metabolites and the scores of MMSE and MoCA was analyzed, and the possible brain metabolite cutoff point for the diagnosis of MCI was evaluated. Results: Compared with the NC group, the scores of MMSE and MoCA in the MCI group decreased significantly (p < 0.05); the ratio of NAA/tCr in the bilateral HIP and bilateral PCG and the ratio of Cho/tCr at the right HIP in the MCI group decreased significantly (p < 0.05); however, there was no significant difference in the ratio of Cho/tCr in the left HIP and bilateral PCG between the two groups (p > 0.05). The correlation coefficient between MMSE/MoCA and the ratio of NAA/tCr was 0.49–0.56 in the bilateral HIP (p < 0.01). The best cutoff value of NAA/creatine (Cr) in the left HIP and the right HIP was 1.195 and 1.19. Sensitivity, specificity, and the Youden index (YDI) in the left HIP and the right HIP were (0.725, 0.803, 0.528) and (0.754, 0.803, 0.557), respectively. Conclusion: The level of metabolites in the HIP and the PCG of patients with MCI and of those with normal subjects has a certain correlation with the score of their MMSE and MoCA. When the value of NAA/tCr in the left HIP and right HIP is <1.19, it suggests that MCI may have occurred. According to this cutoff point, elderly patients with MCI in China could be screened. [ABSTRACT FROM AUTHOR]

N-acetyl aspartate, creatine, posterior cingulate gyrus, mild cognitive impairment, choline hippocampus Keywords: N-acetyl aspartate; creatine; choline hippocampus; posterior cingulate gyrus; mild cognitive impairment EN N-acetyl aspartate creatine choline hippocampus posterior cingulate gyrus mild cognitive impairment 1 2 2 10/14/21 20211012 NES 211012 In the original article, there were mistakes in Table 1 and Table 2 as published. Additionally, Table 2 incorrectly showed the data calculation results of 64 patients with MCI and 30 patients with NC. [Extracted from the article]

[Display omitted] • The abnormal local and long-range FCD in MCI compared with HCs. • The reversed FCD was found in MCI after treatments of acupoint-related moxibustion. • After moxibustion treatment, the MoCA scores of MCI were improved. Mild cognitive impairment (MCI) is a general neurodegenerative disease. Moxibustion has been shown to have remarkable effect on cognitive improvement, however, less is known about the effect of moxibustion on MCI and its underlying neural mechanism. This study aimed to investigate the ameliorative brain network in MCI after treatments of acupoint-related moxibustion. Resting-state functional MRI were derived from 47 MCI patients and 30 healthy controls (HCs). Patients were randomized as Tiaoshen YiZhi (TSYZ, n = 27) and sham (SHAM, n = 20) acupoint moxibustion groups. Functional connectivity density (FCD) method and repeated-measures two-way analysis of variance (ANOVA) were performed to ascertain the interaction effects between groups (TSYZ and SHAM) and time (baseline and post-treatment). Abnormal FCD was examined between baseline and post-treatment in TSYZ and SHAM groups, respectively. Compared with HCs, MCI showed altered FCD in the middle frontal cortex (MFC), inferior frontal cortex, temporal pole, thalamus and middle cingulate cortex. After moxibustion treatment in MCI, 1) a significant time-by-groups interaction was observed in the medial prefrontal cortex (mPFC); 2) abnormal long-range FCD (lrFCD) in the mPFC and MFC were modulated in TSYZ group; 3) significantly improved clinical symptoms; 4) changed lrFCD in the MFC was significantly negatively correlated with the increased Montreal Cognitive Assessment scores in TSYZ group. These imaging findings suggest that treatments of acupoint-related moxibustion could improve lrFCD in certain regions related to self-related cognitive and decision making. Our study might promote understanding of MCI neural mechanisms and expand the clinical application of moxibustion in MCI. [ABSTRACT FROM AUTHOR]

• An new multi-task learning framework is devised for differential diagnosis of subjective cognitive decline and mild cognive impairment. • The proposed multi-task learning algorithm combines functional and structural brain information. • The proposed method can discover the most disease-related brain regions and their connectivity. • The extensive experiments demonstrate good classification performance against competing techniques. Early diagnosis and intervention of mild cognitive impairment (MCI) and its early stage (i.e., subjective cognitive decline (SCD)) is able to delay or reverse the disease progression. However, discrimination between SCD, MCI and healthy subjects accurately remains challenging. This paper proposes an auto-weighted centralised multi-task (AWCMT) learning framework for differential diagnosis of SCD and MCI. AWCMT is based on structural and functional connectivity information inferred from magnetic resonance imaging (MRI). To be specific, we devise a novel multi-task learning algorithm to combine neuroimaging functional and structural connective information. We construct a functional brain network through a sparse and low-rank machine learning method, and also a structural brain network via fibre bundle tracking. Those two networks are constructed separately and independently. Multi-task learning is then used to identify features integration of functional and structural connectivity. Hence, we can learn each task's significance automatically in a balanced way. By combining the functional and structural information, the most informative features of SCD and MCI are obtained for diagnosis. The extensive experiments on the public and self-collected datasets demonstrate that the proposed algorithm obtains better performance in classifying SCD, MCI and healthy people than traditional algorithms. The newly proposed method has good interpretability as it is able to discover the most disease-related brain regions and their connectivity. The results agree well with current clinical findings and provide new insights into early AD detection based on the multi-modal neuroimaging technique. [Display omitted] [ABSTRACT FROM AUTHOR]