IntroductionMulti-modal neuroimaging metrics in combination with advanced machine learning techniques have attracted more and more attention for an effective multi-class identification of Alzheimer’s disease (AD), mild cognitive impairment (MCI) and health controls (HC) recently.MethodsIn this paper, a total of 180 subjects consisting of 44 AD, 66 MCI and 58 HC subjects were enrolled, and the multi-modalities of the resting-state functional magnetic resonance imaging (rs-fMRI) and the structural MRI (sMRI) for all participants were obtained. Then, four kinds of metrics including the Hurst exponent (HE) metric and bilateral hippocampus seed independently based connectivity metrics generated from fMRI data, and the gray matter volume (GMV) metric obtained from sMRI data, were calculated and extracted in each region of interest (ROI) based on a newly proposed automated anatomical Labeling (AAL3) atlas after data pre-processing. Next, these metrics were selected with a minimal redundancy maximal relevance (MRMR) method and a sequential feature collection (SFC) algorithm, and only a subset of optimal features were retained after this step. Finally, the support vector machine (SVM) based classification methods and artificial neural network (ANN) algorithm were utilized to identify the multi-class of AD, MCI and HC subjects in single modal and multi-modal metrics respectively, and a nested ten-fold cross-validation was utilized to estimate the final classification performance.ResultsThe results of the SVM and ANN based methods indicated the best accuracies of 80.36 and 74.40%, respectively, by utilizing all the multi-modal metrics, and the optimal accuracies for AD, MCI and HC were 79.55, 78.79 and 82.76%, respectively, in the SVM based method. In contrast, when using single modal metric, the SVM based method obtained a best accuracy of 72.62% with the HE metric, and the accuracies for AD, MCI and HC subjects were just 56.82, 80.30 and 75.86%, respectively. Moreover, the overlapping abnormal brain regions detected by multi-modal metrics were mainly located at posterior cingulate gyrus, superior frontal gyrus and cuneus.ConclusionTaken together, the SVM based method with multi-modal metrics could provide effective diagnostic information for identifying AD, MCI and HC subjects.

Astrocytes are highly heterogeneous and involved in different aspects of fundamental functions in the central nervous system (CNS). However, whether and how this heterogeneous population of cells reacts to the pathophysiological challenge is not well understood. To investigate the response status of astrocytes in the medial vestibular nucleus (MVN) after vestibular loss, we examined the subtypes of astrocytes in MVN using single-cell sequencing technology in a unilateral labyrinthectomy mouse model. We discovered four subtypes of astrocytes in the MVN with each displaying unique gene expression profiles. After unilateral labyrinthectomy, the proportion of the astrocytic subtypes and their transcriptional features on the ipsilateral side of the MVN differ significantly from those on the contralateral side. With new markers to detect and classify the subtypes of astrocytes in the MVN, our findings implicate potential roles of the adaptive changes of astrocyte subtypes in the early vestibular compensation following peripheral vestibular damage to reverse behavioral deficits.

A brain-computer interface (BCI) based on the electroencephalograph (EEG) signal is a novel technology that provides a direct pathway between human brain and outside world. For a traditional subject-dependent BCI system, a calibration procedure is required to collect sufficient data to build a subject-specific adaptation model, which can be a huge challenge for stroke patients. In contrast, subject-independent BCI which can shorten or even eliminate the pre-calibration is more time-saving and meets the requirements of new users for quick access to the BCI. In this paper, we design a novel fusion neural network EEG classification framework that uses a specially designed generative adversarial network (GAN), called a filter bank GAN (FBGAN), to acquire high-quality EEG data for augmentation and a proposed discriminative feature network for motor imagery (MI) task recognition. Specifically, multiple sub-bands of MI EEG are first filtered using a filter bank approach, then sparse common spatial pattern (CSP) features are extracted from multiple bands of filtered EEG data, which constrains the GAN to maintain more spatial features of the EEG signal, and finally we design a convolutional recurrent network classification method with discriminative features (CRNN-DF) to recognize MI tasks based on the idea of feature enhancement. The hybrid neural network proposed in this study achieves an average classification accuracy of 72.74 ± 10.44% (mean ± std) in four-class tasks of BCI IV-2a, which is 4.77% higher than the state-of-the-art subject-independent classification method. A promising approach is provided to facilitate the practical application of BCI.

PurposeInvestigating the changes of regional homogeneity (ReHo) values and both static and dynamic functional connectivity (FC) before and after Traditional Chinese Manual Therapy (Tuina) in patients with lumbar disk herniation (LDH) through resting-state functional magnetic resonance imaging (RS-fMRI). Based on this, we observe the effect of Tuina on the above abnormal changes.MethodsPatients with LDH (n = 27) and healthy controls (HCs) (n = 28) were recruited. The functional magnetic resonance imaging (fMRI) scanning was performed two times in LDH patients, before Tuina (time point 1, LDH-pre) and after the sixth Tuina (time point 2, LDH-pos). And for one time in HCs which received no intervention. The ReHo values were compared between LDH-pre and HCs. The significant clusters detected by ReHo analysis were selected as seeds to calculate static functional connectivity (sFC). We also applied the sliding-window to perform dynamic functional connectivity (dFC). To evaluate the Tuina effect, the mean ReHo and FC values (both static and dynamic) were extracted from significant clusters and compared between LDH and HCs.ResultsIn comparison to HCs, LDH patients displayed decreased ReHo in the left orbital part middle frontal gyrus (LO-MFG). For sFC analysis, no significant difference was found. However, we found decreased dFC variance between LO-MFG and the left Fusiform, and increased dFC variance in the left orbital inferior frontal gyrus and left precuneus. Both ReHo and dFC values revealed after Tuina, the brain activities in LDH patients were similar to HCs.ConclusionThe present study characterized the altered patterns of regional homogeneity in spontaneous brain activity and those of functional connectivity in patients with LDH. Tuina can reshape the function of the default mode network (DMN) in LDH patients, which may contribute to the analgesic effect of Tuina in LDH patients.

Proper mitochondrial performance is imperative for the maintenance of normal neuronal function to prevent the development of neurodegenerative diseases. Persistent accumulation of damaged mitochondria plays a role in prion disease pathogenesis, which involves a chain of events that culminate in the generation of reactive oxygen species and neuronal death. Our previous studies have demonstrated that PINK1/Parkin-mediated mitophagy induced by PrP106−126 is defective and leads to an accumulation of damaged mitochondria after PrP106−126 treatment. Externalized cardiolipin (CL), a mitochondria-specific phospholipid, has been reported to play a role in mitophagy by directly interacting with LC3II at the outer mitochondrial membrane. The involvement of CL externalization in PrP106−126-induced mitophagy and its significance in other physiological processes of N2a cells treated with PrP106−126 remain unknown. We demonstrate that the PrP106−126 peptide caused a temporal course of mitophagy in N2a cells, which gradually increased and subsequently decreased. A similar trend in CL externalization to the mitochondrial surface was seen, resulting in a gradual decrease in CL content at the cellular level. Inhibition of CL externalization by knockdown of CL synthase, responsible for de novo synthesis of CL, or phospholipid scramblase-3 and NDPK-D, responsible for CL translocation to the mitochondrial surface, significantly decreased PrP106−126-induced mitophagy in N2a cells. Meanwhile, the inhibition of CL redistribution significantly decreased PINK1 and DRP1 recruitment in PrP106−126 treatment but had no significant decrease in Parkin recruitment. Furthermore, the inhibition of CL externalization resulted in impaired oxidative phosphorylation and severe oxidative stress, which led to mitochondrial dysfunction. Our results indicate that CL externalization induced by PrP106−126 on N2a cells plays a positive role in the initiation of mitophagy, leading to the stabilization of mitochondrial function.

BackgroundApolipoprotein-E (APOE) ε4 is a major genetic risk factor for Alzheimer’s disease (AD). Current studies, which were mainly based on the clinical diagnosis rather than biomarkers, come to inconsistent conclusions regarding the associations of APOE ε4 homozygotes (APOE ε4/ε4) and cerebrospinal fluid (CSF) biomarkers of AD. In addition, few studies have explored the associations of APOE ε4/ε4 with plasma biomarkers. Therefore, we aimed to investigate the associations of APOE ε4/ε4 with fluid biomarkers in dementia and biomarker-diagnosed AD.MethodsA total of 297 patients were enrolled. They were classified into Alzheimer’s continuum, AD, and non-AD, according to CSF biomarkers and/or β amyloid PET results. AD was a subgroup of the AD continuum. Plasma Amyloid β (Aβ) 40, Aβ42, glial fibrillary acidic protein (GFAP), neurofilament light chain (NFL), and phosphorylated tau (P-tau)181 were quantified in 144 of the total population using an ultra-sensitive Simoa technology. We analyzed the associations of APOE ε4/ε4 on CSF and plasma biomarkers in dementia and biomarker diagnosed AD.ResultsBased on the biomarker diagnostic criteria, 169 participants were diagnosed with Alzheimer’s continuum and 128 individuals with non-AD, and among the former, 120 patients with AD. The APOE ε4/ε4 frequencies were 11.8% (20/169), 14.2% (17/120), and 0.8% (1/128) in Alzheimer’s continuum, AD and non-AD, respectively. Only CSF Aβ42 was shown to be decreased in APOE ε4/ε4 carriers than in non-carriers for patients with AD (p = 0.024). Furthermore, we did not find any associations of APOE ε4 with plasma biomarkers of AD and non-AD. Interestingly, we found that in non-AD patients, APOE ε4 carriers had lower CSF Aβ42 (p = 0.018) and higher T-tau/Aβ42 ratios (p < 0.001) and P-tau181/Aβ42 ratios (p = 0.002) than non-carriers.ConclusionOur data confirmed that of the three groups (AD continuum, AD, and non-AD), those with AD had the highest frequency of APOE ɛ4/ɛ4 genotypes. The APOE ɛ4/ɛ4 was associated with CSF levels of Aβ42 but not tau for AD and non-AD, suggesting that APOE ɛ4/ɛ4 affected the Aβ metabolism of both. No associations between APOE ε4/ɛ4 and plasma biomarkers of AD and non-AD were found.