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Unmanned aerial vehicles (UAVs) are becoming more widely used in various industries, raising growing concerns about their safety and reliability. The flight data of UAVs can directly reflect their flight health status; however, the rarity of abnormal flight data and the spatiotemporal characteristics of these data represent a significant challenge for constructing accurate and reliable anomaly detectors. To address this, this study proposes an anomaly detection framework that fully considers the temporal correlations and distribution characteristics of flight data. This framework first combines a one-dimensional convolutional neural network (1DCNN) with an autoencoder (AE) to establish a feature extraction model. This model leverages the feature extraction capabilities of the 1DCNN and the reconstruction capabilities of the AE to thoroughly extract the spatiotemporal features from UAV flight data. Then, to address the challenge of adaptive anomaly detection thresholds, this research proposes a nonlinear model of support vector data description (SVDD) utilizing a 0/1 soft-margin loss, referred to as L0/1-SVDD. This model replaces the traditional hinge loss function in SVDD with a 0/1 loss function, with the goal of enhancing the accuracy and robustness of anomaly detection. Since the 0/1 loss function is a bounded, non-convex, and non-continuous function, this paper proposes the Bregman ADMM algorithm to solve the L0/1-SVDD. Finally, the difference between the reconstructed and the actual value is employed to train the L0/1-SVDD, resulting in a hypersphere classifier that is capable of detecting UAV anomaly data. The experimental results using real flight data show that, compared with methods such as AE, LSTM, and LSTM-AE, the proposed method exhibits superior performance across five evaluation metrics.

Support vector data description (SVDD) is widely regarded as an effective technique for addressing anomaly detection problems. However, its performance can significantly deteriorate when the training data are affected by outliers or mislabeled observations. This study introduces a universal truncated loss function framework into the SVDD model to enhance its robustness and employs the fast alternating direction method of multipliers (ADMM) algorithm to solve various truncated loss functions. Moreover, the convergence of the fast ADMM algorithm is analyzed theoretically. Within this framework, we developed the truncated generalized ramp, truncated binary cross entropy, and truncated linear exponential loss functions for SVDD. We conducted extensive experiments on synthetic and real-world datasets to validate the effectiveness of these three SVDD models in handling data with different noise levels, demonstrating their superior robustness and generalization capabilities compared to other SVDD models.

IntroductionVisual hallucination is a prevalent psychiatric disorder characterized by the occurrence of false visual perceptions due to misinterpretation in the brain. Individuals with Parkinson’s disease often experience both minor and complex visual hallucinations. The underlying mechanism of complex visual hallucinations in Parkinson’s patients is commonly attributed to dysfunction in the visual pathway and attention network. However, there is limited research on the mechanism of minor hallucinations.MethodsTo address this gap, we conducted an experiment involving 13 Parkinson’s patients with minor hallucinations, 13 Parkinson’s patients without hallucinations, and 13 healthy elderly individuals. We collected and analyzed EEG and MRI data. Furthermore, we utilized EEG data from abnormal brain regions to train a machine learning model to determine whether the abnormal EEG data were associated with minor hallucinations.ResultsOur findings revealed that Parkinson’s patients with minor hallucinations exhibited excessive activation of cortical excitability, an imbalanced interaction between the attention network and the default network, and disruption in the connection between these networks. These findings is similar to the mechanism observed in complex visual hallucinations. The visual reconstruction of one patient experiencing hallucinations yields results that differ from those observed in subjects without such symptoms.DiscussionThe visual reconstruction results demonstrated significant differences between Parkinson’s patients with hallucinations and healthy subjects. This suggests that visual reconstruction techniques may offer a means of evaluating hallucinations.

The tight formation of unmanned aerial vehicles (UAVs) provides numerous advantages in practical applications, increasing not only their range but also their efficiency during missions. However, the wingman aerodynamics are affected by the tail vortices generated by the leading aircraft in a tight formation, resulting in unpredictable interference. In this study, a mathematical model of wake vortex was developed, and the aerodynamic characteristics of a tight formation were simulated using Xflow software. A robust control method for tight formations was constructed, in which the disturbance is first estimated with an extended state observer, and then a sliding mode controller (SMC) was designed, enabling the wingman to accurately track the position under conditions of wake vortex from the leading aircraft. The stability of the designed controller was confirmed. Finally, the controller was simulated and verified in mathematical simulation and semi-physical simulation platforms, and the experimental results showed that the controller has high tight formation accuracy and is robust.

IntroductionTranscranial magnetic stimulation (TMS) is a non-invasive intervention that holds promise for improving cognitive function in individuals with Alzheimer's disease (AD). However, the effectiveness of this therapy and the optimal TMS parameters has not reached a consensus. The purpose of the meta-analysis was to systematically discern the effectiveness of different components of TMS protocols on cognitive improvement in patients with mild cognitive impairment (MCI) and AD.MethodsThe meta-analysis was preregistered on Prospero (registration number: CRD42022345482). PubMed, Web of Science, Science Direct, and Cochrane Library databases were used to search, screen and identify eligible studies with the following keywords: Transcranial Magnetic Stimulation OR TMS OR theta burst stimulation AND Alzheimer OR Alzheimers OR Alzheimer's OR mild cognitive impairment OR MCI. Randomized controlled trials (RCTs) of participants with accepted standardized diagnostic criteria were searched by two authors independently. The risk of bias was assessed using an adapted Cochrane Risk of Bias tool. Standardized mean difference (SMD) and 95% confidence interval (CI) were calculated using the random-effects models. Subgroup analyses were performed to investigate the influential factors.ResultsA total of 21 studies and 25 trials were included in this meta-analysis. The findings revealed a significant overall cognition improvement of real stimulation compared with sham stimulation (short-term effects: SMD, 0.91; 95% CI 0.44–1.38; P < 0.01; long-lasting effects: SMD, 0.91; 95% CI 0.27–1.55; P < 0.01). Subgroup analysis demonstrated that stimulation of the left dorsolateral prefrontal cortex and bilateral cerebellums, as well as moderate frequency stimulation (5 Hz and 10 Hz) on mild and moderate cognitive impairment patients, were more effective than other TMS protocols. However, the additional application of cognitive training showed no significant improvement.ConclusionCognitive improvement effect of TMS was demonstrated in MCI and AD patients in both short-term assessment and long-lasting outcomes, and the efficiency of TMS is affected by the stimulation frequency, stimulation site, and participant characteristics. Further RCTs are needed to validate the findings of our subgroup analysis.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022345482, identifier: CRD42022345482.

ObjectiveTo provide a rigorous comparison between patients with mild cognitive impairment due to Alzheimer’s disease (MCI-AD) and healthy elderly, as well as to assess the value of electroencephalography (EEG) in terms of early diagnosis, we conducted a neutral image recognition memory task involving individuals with positive biomarkers including β amyloid deposition, pathologic tau or neurodegeneration.MethodsThe task involving study and test blocks was designed to evaluate participants’ recognition memory. Electroencephalogram was recorded synchronously to elicit event-related potentials in patients with MCI-AD and healthy control subjects. We further analyzed differences between groups or conditions in terms of behavioral performance, time domain, and time-frequency domain.ResultsThe MCI-AD cohort showed a slower response time to old/new images and had low accuracy regarding behavioral performance. The amplitude of the late positive complex for the old/new effects was significantly suppressed in the MCI-AD cohort when compared with that in the HC cohort. The amplitude of the late old/new effects was correlated with the Auditory Verbal Learning Test recognition score in all participants. The time-frequency domain analysis revealed that correct recognition of old items elicited a decrease in beta power, mainly limited to the HC cohort. Moreover, the combination of behavioral (processing speed and accuracy) and electrophysiological (average amplitude and relative power of delta band) measures contributes to classifying patients with MCI-AD from healthy elderly people.ConclusionChanges of old/new effects, accuracy and response time are sensitive to the impairment of recognition memory in patients with MCI-AD and have moderate value in predicting the incipient stage of AD.

Introduction: Evidence indicates that the cerebellum is involved in cognitive processing. However, the specific mechanisms through which the cerebellum repetitive transcranial magnetic stimulation (rTMS) contributes to the cognitive state are unclear. Methods: In the current randomized, double-blind, sham-controlled trial, 27 patients with Alzheimer's disease (AD) were randomly allotted to one of the two groups: rTMS-real or rTMS-sham. We investigated the efficacy of a four-week treatment of bilateral cerebellum rTMS to promote cognitive recovery and alter specific cerebello-cerebral functional connectivity. Results: The cerebellum rTMS significantly improves multi-domain cognitive functions, directly associated with the observed intrinsic functional connectivity between the cerebellum nodes and the dorsolateral prefrontal cortex (DLPFC), medial frontal cortex, and the cingulate cortex in the real rTMS group. In contrast, the sham stimulation showed no significant impact on the clinical improvements and the cerebello-cerebral connectivity. Conclusion: Our results depict that 5 Hz rTMS of the bilateral cerebellum is a promising, non-invasive treatment of cognitive dysfunction in AD patients. This cognitive improvement is accompanied by brain connectivity modulation and is consistent with the pathophysiological brain disconnection model in AD patients.

In various applications, such as disease diagnosis, surgical navigation, human brain atlas analysis, and other neuroimage processing scenarios, brain extraction is typically regarded as the initial stage in MRI image processing. Whole-brain semantic segmentation algorithms, such as U-Net, have demonstrated the ability to achieve relatively satisfactory results even with a limited number of training samples. In order to enhance the precision of brain semantic segmentation, various frameworks have been developed, including 3D U-Net, slice U-Net, and auto-context U-Net. However, the processing methods employed in these models are relatively complex when applied to 3D data models. In this article, we aim to reduce the complexity of the model while maintaining appropriate performance. As an initial step to enhance segmentation accuracy, the preprocessing extraction of full-scale information from magnetic resonance images is performed with a cluster tool. Subsequently, three multi-input hybrid U-Net model frameworks are tested and compared. Finally, we propose utilizing a fusion of two-dimensional segmentation outcomes from different planes to attain improved results. The performance of the proposed framework was tested using publicly accessible benchmark datasets, namely LPBA40, in which we obtained Dice overlap coefficients of 98.05%. Improvement was achieved via our algorithm against several previous studies.

Recent studies have discovered that functional connections are impaired in patients with Parkinson’s disease (PD) accompanied by hallucinations (PD-H), even at the preclinical stage. The cerebellum has been implicated in playing a role in cognitive processes. However, the functional connectivity (FC) between the cognitive sub-regions of the cerebellum in PD patients with hallucinations needs further clarification. Resting-state functional magnetic resonance imaging (rs-fMRI) data were collected from three groups (17 PD-H patients, 13 patients with Parkinson’s disease not accompanied by hallucinations (PD-NH), and 26 healthy controls (HC)). The data were collected in this study to investigate the impact of cerebellar FC changes on cognitive performance. Additionally, we define cerebellar FC as a training feature for classifying all subjects using Support Vector Machines (SVMs). We found that in the PD-H patients, there was an increase in FC within the left side of the precuneus (PCUN) compared to the HC. Additionally, there was an increase in FC within the bilateral opercular part of the inferior frontal gyrus (IFGoprec) and triangular part of the inferior frontal gyrus (IFCtriang), as well as the left side of the postcentral gyrus (PoCG), inferior parietal lobe (IPL), and PCUN compared to the PD-NH patients. In the machine learning training results, cerebellar FC has also been proven to be an effective biomarker feature, achieving a recognition rate of over 90% for PD-H. These findings indicate that the cortico-cerebellar FC in PD-H and PD-NH patients was significantly disrupted, with different patterns of distribution. The proposed pipeline offers a promising, low-cost alternative for diagnosing preclinical PD-H and may also be beneficial for other degenerative brain disorders.

Abstract Background Migraine is a common and disabling primary headache, which is associated with a wide range of psychiatric comorbidities. However, the mechanisms of emotion processing in migraine are not fully understood yet. The present study aimed to investigate the neural network during neutral, positive, and negative emotional stimuli in the migraine patients. Methods A total of 24 migraine patients and 24 age- and sex-matching healthy controls were enrolled in this study. Neuromagnetic brain activity was recorded using a whole-head magnetoencephalography (MEG) system upon exposure to human facial expression stimuli. MEG data were analyzed in multi-frequency ranges from 1 to 100 Hz. Results The migraine patients exhibited a significant enhancement in the effective connectivity from the prefrontal lobe to the temporal cortex during the negative emotional stimuli in the gamma frequency (30–90 Hz). Graph theory analysis revealed that the migraine patients had an increased degree and clustering coefficient of connectivity in the delta frequency range (1–4 Hz) upon exposure to positive emotional stimuli and an increased degree of connectivity in the delta frequency range (1–4 Hz) upon exposure to negative emotional stimuli. Clinical correlation analysis showed that the history, attack frequency, duration, and neuropsychological scales of the migraine patients had a negative correlation with the network parameters in certain frequency ranges. Conclusions The results suggested that the individuals with migraine showed deviant effective connectivity in viewing the human facial expressions in multi-frequencies. The prefrontal-temporal pathway might be related to the altered negative emotional modulation in migraine. These findings suggested that migraine might be characterized by more universal altered cerebral processing of negative stimuli. Since the significant result in this study was frequency-specific, more independent replicative studies are needed to confirm these results, and to elucidate the neurocircuitry underlying the association between migraine and emotional conditions.

Abstract Background Growing evidence indicates that inflammasome-mediated inflammation plays important roles in the pathophysiology of amnestic mild cognitive impairment (aMCI) and Alzheimer’s disease (AD). Pyroptosis induced by inflammasome, and Gasdermin D (GSDMD) is involved in several neurodegenerative disorders. However, it is not clear whether peripheral inflammasome and pyroptosis are activated in aMCI and AD patients, influencing on neuroinflammation. The aim of this study was to examine the association between systemic inflammasome-induced pyroptosis and clinical features in aMCI and AD. Methods A total of 86 participants, including 33 subjects with aMCI, 33 subjects with AD, and 20 cognitively normal controls, in this study. The Mini Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA) scale were used for cognitive assessment. Levels of inflammasome-related genes/proteins in peripheral blood mononuclear cells (PBMCs) were determined using quantitative polymerase chain reaction and Western blotting. The levels of IL-1β, Aβ1-42, Aβ1-40, p-tau, and t-tau in cerebrospinal fluid (CSF), as well as the plasma IL-1β level, were measured by enzyme-linked immunosorbent assay. Finally, lipopolysaccharides (LPS) were used to investigate the effects of systemic inflammasome-induced pyroptosis in an AD mice model. Results Several genes involved in the inflammatory response were enriched in PBMCs of AD patients. The mRNA and protein levels of NLRP3, caspase-1, GSDMD, and IL-1β were increased in PBMCs of aMCI and AD patients. The IL-1β level in plasma and CSF of aMCI and AD patients was significantly higher than that in controls and negatively correlated with the CSF Aβ1-42 level, as well as MMSE and MoCA scores. Furthermore, there was a positive correlation between the IL-1β level in plasma and CSF of aMCI or AD patients. In vivo experiments showed that systemic inflammasome-induced pyroptosis aggravated neuroinflammation in 5 × FAD mice. Conclusions Our findings showed that canonical inflammasome signaling and GSDMD-induced pyroptosis were activated in PBMCs of aMCI and AD patients. In addition, the proinflammatory cytokine IL-1β was strongly associated with the pathophysiology of aMCI and AD. As such, targeting inflammasome-induced pyroptosis may be a new approach to inhibit neuroinflammation in aMCI and AD patients.

PurposeTo explore changes in the brain structural network in patients with cerebellar infarction on different sides and their correlations with changes in cognitive function.MethodsNineteen patients with acute left posterior cerebellar infarction and 18 patients with acute right posterior cerebellar infarction seen from July 2016 to September 2019 in the Department of Neurology, Affiliated Brain Hospital of Nanjing Medical University, were selected. A total of 27 healthy controls matched for sex, age, and years of education were recruited. The subjects underwent head diffusion magnetic resonance imaging examination and neuropsychological cognitive scale evaluation, and we analyzed changes in brain structural network properties in patients with cerebellar infarction and their correlation with changes in patients' cognitive function.ResultsThe Mini-Mental Status Examination (MMSE), Montreal Cognitive Assessment (MOCA) and the Rey auditory verbal learning test (RAVLT) scores in the left and right cerebellar infarction groups were significantly lower than those in the healthy control group (p < 0.05). In addition, the digit span test (DST) scores were lower in the left cerebellar infarction group (p < 0.05); the trail-making test (TMT) times in the right cerebellar infarction group were significantly higher than those in the left cerebellar infarction group (p < 0.05). Meanwhile, the left and right cerebellar infarction groups had abnormal brain topological properties, including clustering coefficient, shortest path length, global efficiency, local efficiency and nodal efficiency. After unilateral cerebellar infarction, bilateral cerebral nodal efficiency was abnormal. Correlation analysis showed that there was a close correlation between decreased processing speed in patients with left cerebellar infarction and decreased efficiency of right cerebral nodes (p < 0.05), and there was a close relationship between executive dysfunction and decreased efficiency of left cerebral nodes in patients with right cerebellar infarction (p < 0.05).ConclusionPatients with cerebellar infarction have cognitive impairment. Unilateral cerebellar infarction can reduce the network efficiency of key regions in the bilateral cerebral hemispheres, and these abnormal changes are closely related to patient cognitive impairment. The results of this study provide evidence for understanding the underlying neural mechanisms of cerebellar cognitive impairment and suggest that brain topological network properties may be markers of cerebellar cognitive impairment.

Transcranial magnetic stimulation is a widely used brain intervention technique in clinical settings. In recent years, the role of the cerebellum in learning and memory has become one of the hotspots in the field of cognitive neuroscience. In this study, we recruited 36 healthy college or graduate students as subjects and divided them into groups, with 10 to 14 subjects in each group. We performed 5 Hz and 20 Hz repeated transcranial magnetic stimulation and sham stimulation on the Crus II subregion of the cerebellum in different groups, then let them complete the 2-back working memory task before and after the stimulation. We simultaneously recorded the electroencephalogram in the experiment and analyzed the data. We found that after repeated transcranial magnetic stimulation of the cerebellum at 5 Hz and 20 Hz, the N170 and P300 event-related potential components in the prefrontal cortex showed significant differences compared to those in the sham stimulation group. Using phase-locked values to construct brain networks and conduct further analysis, we discovered that stimulation frequencies of 5 Hz and 20 Hz had significant effects on the local and global efficiency of brain networks in comparison to the sham stimulation group. The results showed that repeated transcranial magnetic stimulation on cerebellar targets can effectively affect the subjects’ working memory tasks. Repeated transcranial magnetic stimulation at 5 Hz and 20 Hz could enhance the excitatory responses of the frontal lobes. After stimulation at 5 Hz and 20 Hz, the efficiency of the brain network significantly improved.

Abstract Background and Purpose We aimed to determine whether demographic information, clinical characteristics, laboratory tests, and imaging features are associated with orofacial dyskinesias (OFLD) in patients with anti‐N‐methyl‐D‐aspartate receptor (NMDAR) encephalitis. Methods In this retrospective study, patients who were diagnosed with anti‐NMDAR encephalitis were enrolled. All patients’ factors, including demographic information, clinical characteristics, laboratory tests, and imaging features, were obtained at the time of hospitalization. The neurological function was assessed using the modified Rankin scale (mRS). Univariate and multivariate logistic regressions were used to examine the associations between clinical factors and OFLD. Results In total, 119 patients (median age: 28.0 [19.0–41.0] years; 67 females) were recruited. Of 119 patients, 44 (37.0%) had OFLD. OFLD was associated with increased mRS at admission, serum sodium, lumbar puncture pressure, female biologic sex, fever, psychiatric symptoms, seizures, impaired consciousness, autonomic dysfunction, and central hypoventilation in univariate logistic regression, respectively. Multivariate regression analysis revealed that female biologic sex (odds ratios [OR], 4.73; 95% confidence interval [CI], 1.27–17.64; p = .021), increased mRS at admission (OR, 2.09; 95% CI, 1.18–3.71; p = .011), psychiatric symptoms (OR, 7.27; 95% CI, 1.20–43.91; p = .031), and seizures (OR, 5.11; 95% CI, 1.22–21.43; p = .026) were associated with OFLD, after adjusting for confounding factors. Conclusions Our analysis suggests that the following clinical factors are associated with OFLD: female biologic sex, increased mRS at admission, psychiatric symptoms, and seizures.

BackgroundUveal melanoma(UVM) is the most common intraocular malignancy and has a poor prognosis. The clinical significance of necroptosis(NCPS) in UVM is unclear.MethodsWe first identified necroptosis genes in UVM by single-cell analysis of the GSE139829 dataset from the GEO database and weighted co-expression network analysis of TCGA data. COX regression and Lasso regression were used to construct the prognostic model. Then survival analysis, immune microenvironment analysis, and mutation analysis were carried out. Finally, cell experiments were performed to verify the role of ITPA in UVM.ResultBy necroptosis-related prognostic model, UVM patients in both TCGA and GEO cohorts could be classified as high-NCPS and low-NCPS groups, with significant differences in survival time between the two groups (P

BackgroundThe pathophysiology of keloid formation is not yet understood, so the identification of biomarkers for kelod can be one step towards designing new targeting therapies which will improve outcomes for patients with keloids or at risk of developing keloids.MethodsIn this study, we performed single-cell RNA sequencing analysis, weighted co-expression network analysis, and differential expression analysis of keloids based on public databases. And 3 RNA sequencing data from keloid patients in our center were used for validation. Besides, we performed QRT-PCR on keloid tissue and adjacent normal tissues from 16 patients for further verification.ResultsWe identified the sensitive biomarker of keloid: Tenascin-C (TNC). Then, Pseudotime analysis found that the expression level of TNC decreased first, then stabilized and finally increased with fibroblast differentiation, suggesting that TNC may play an potential role in fibroblast differentiation. In addition, there were differences in the infiltration level of macrophages M0 between the TNC-high group and the TNC-low group. Macrophages M0 had a higher infiltration level in low TNC- group (P

With the improvement of UAV performance and intelligence in recent years, it is particularly important for unmanned aerial vehicles (UAVs) to improve the ability of autonomous air combat. Aiming to solve the problem of how to improve the autonomous air combat maneuver decision ability of UAVs so that it can be close to manual manipulation, this paper proposes an autonomous air combat maneuvering decision method based on the combination of simulated operation command and the final reward value deep deterministic policy gradient (FRV-DDPG) algorithm. Firstly, the six-degree-of-freedom (6-DOF) model is established based on the air combat process, UAV motion, and missile motion. Secondly, a prediction method based on the Particle swarm optimization radial basis function (PSO-RBF) is designed to simulate the operation command of the enemy aircraft, which makes the training process more realistic, and then an improved DDPG strategy is proposed, which returns the final reward value to the previous reward value in a certain proportion of time for offline training, which can improve the convergence speed of the algorithm. Finally, the effectiveness of the algorithm is verified by building a simulation environment. The simulation results show that the algorithm can improve the autonomous air combat maneuver decision-making ability of UAVs.

The cerebellar region has four times as many brain cells as the brain, but whether the cerebellum functions in cognition, and how it does so, remain unexplored. In order to verify whether the cerebellum is involved in cognition, we chose to investigate whether the cerebellum is involved in the process of error judgment. We designed an experiment in which we could activate the subject’s error-related potentials (ErrP). We recruited 26 subjects and asked them to wear EEG caps with cerebellar regions designed by us to participate in the experiment so that we could record their EEG activity throughout the experiment. We successfully mitigated the majority of noise interference after a series of pre-processing of the data collected from each subject. Our analysis of the preprocessed data revealed that our experiment successfully activated ErrP, and that the EEG signals, including the cerebellum, were significantly different when subjects made errors compared to when they made correct judgments. We designed a feature extraction method that requires selecting channels with large differences under different classifications, firstly by extracting the time-frequency features of these channels, and then screening these features with sequence backward feature (SBS) selection. We use the extracted features as the input and different event types in EEG data as the labels for multiple classifiers to classify the data in the executive and feedback segments, where the average accuracy for two-class classification of executive segments can reach 80.5%. The major contribution of our study is the discovery of the presence of ErrP in cerebellar regions and the extraction of an effective feature extraction method for EEG data.

Background: Recent studies have discovered that functional connections are impaired among patients with Alzheimer's disease (AD), even at the preclinical stage. The cerebellum has been implicated as playing a role in cognitive processes. However, functional connectivity (FC) among cognitive sub-regions of the cerebellum in patients with AD and mild cognitive impairment (MCI) remains to be further elucidated.Objective: Our study aims to investigate the FC changes of the cerebellum among patients with AD and MCI, compared to healthy controls (HC). Additionally, we explored the role of cerebellum FC changes in the cognitive performance of all subjects.Materials: Resting-state functional magnetic resonance imaging (rs-fMRI) data from three different groups (28 AD patients, 26 MCI patients, and 30 HC) was collected. We defined cerebellar crus II and lobule IX as seed regions to assess the intragroup differences of cortico-cerebellar connectivity. Bias correlational analysis was performed to investigate the relationship between changes in FC and neuropsychological performance.Results: Compared to HC, AD patients had decreased FC within the caudate, limbic lobe, medial frontal gyrus (MFG), middle temporal gyrus, superior frontal gyrus, parietal lobe/precuneus, inferior temporal gyrus, and posterior cingulate gyrus. Interestingly, MCI patients demonstrated increased FC within inferior parietal lobe, and MFG, while they had decreased FC in the thalamus, inferior frontal gyrus, and superior frontal gyrus. Further analysis indicated that FC changes between the left crus II and the right thalamus, as well as between left lobule IX and the right parietal lobe, were both associated with cognitive decline in AD. Disrupted FC between left crus II and right thalamus, as well as between left lobule IX and right parietal lobe, was associated with attention deficit among subjects with MCI.Conclusion: These findings indicate that cortico-cerebellar FC in MCI and AD patients was significantly disrupted with different distributions, particularly in the default mode networks (DMN) and fronto-parietal networks (FPN) region. Increased activity within the fronto-parietal areas of MCI patients indicated a possible compensatory role for the cerebellum in cognitive impairment. Therefore, alterations in the cortico-cerebellar FC represent a novel approach for early diagnosis and a potential therapeutic target for early intervention.

Aiming at the difficulty of post-stall maneuvering control modeling and control of advanced aircraft under unsteady aerodynamics, a control method with high control accuracy and fast computation speed is proposed based on Radial Basis Function (RBF) network with minimum parameters learning (MPL) and dynamic surface control (DSC) method. Firstly, the aerodynamic characteristics of post-stall maneuvers are analyzed based on the experimental data of large-scale oscillation wind tunnels, and the key factors affecting the unsteady aerodynamic forces are obtained. Then, an accurate unsteady aerodynamic model is established based on the improved extreme learning machine (ELM) method. Secondly, the influence of unsteady aerodynamic forces on the control of post-stall maneuvers is considered. For the uncertainty of advanced aircraft model, high angle of attack flight control laws based on RBF-DSC are designed. In order to improve the calculation speed of the above control law and optimize the parameters, a post-stall maneuver control law method based on MPL-RBF-DSC is designed, and the stability of the method is proved. The coordinated allocation of the conventional aerodynamic surfaces and thrust vectors is realized based on the daisy chain method. Finally, the typical maneuver simulation of “Cobra” is carried out, which highlights the advantages of the design method in this paper, such as high control accuracy, short calculation time and strong robustness.

Abstract Background Although altered neural networks have been demonstrated in recent MEG (magnetoencephalography) research in migraine patients during resting state, it is unknown whether this alteration can be detected in task-related networks. The present study aimed to investigate the abnormalities of the frequency-specific somatosensory-related network in migraine patients by using MEG. Methods Twenty-two migraineurs in the interictal phase and twenty-two sex- and age-matched healthy volunteers were studied using a whole-head magnetoencephalography (MEG) system. Electrical stimuli were delivered alternately to the median nerve on the right wrists of all subjects. MEG data were analyzed in a frequency range of 1–1000 Hz in multiple bands. Results The brain network patterns revealed that the patients with migraine exhibited remarkably increased functional connectivity in the high-frequency (250–1000 Hz) band between the sensory cortex and the frontal lobe. The results of quantitative analysis of graph theory showed that the patients had (1) an increased degree of connectivity in the theta (4–8 Hz), beta (13–30 Hz) and gamma (30–80 Hz) bands; (2) an increased connectivity strength in the beta (13–30 Hz) and gamma (30–80 Hz) bands; (3) an increased path length in the beta (13–30 Hz), gamma (30–80 Hz) and ripple (80–250 Hz) bands; and (4) an increased clustering coefficient in the theta (4–8 Hz), beta (13–30 Hz) and gamma (30–80 Hz) bands. Conclusions The results indicate that migraine is associated with aberrant connections from the somatosensory cortex to the frontal lobe. The frequency-specific increases in connectivity in terms of strength, path length and clustering coefficients support the notion that migraineurs have elevated cortical networks. This alteration in functional connectivity may be involved in somatosensory processing in migraine patients and may contribute to understanding migraine pathophysiology and to providing convincing evidence for a spatially targeted migraine therapy.

With recent advances in airborne weapons, air combat tends to occur in the form of beyond-visual-range (BVR) combat and multi-aircraft cooperation. Target assignment is critical in multi-aircraft BVR air combat decision-making. Most previous research on target assignment for multi-aircraft cooperative BVR air combat has focused on centralized algorithms, which can be time-consuming and unreliable. This paper proposes an efficient distributed target assignment algorithm called the multi-target consensus-based auction algorithm (MTCBAA). First, by analyzing the main geometric aspects of BVR air combat, a target assignment model for cooperative BVR air combat was established. Next, based on a consensus-based auction algorithm (CBAA), the MTCBAA was developed to solve the target assignment problem by introducing a cooperative decision-making variable. Although the MTCBAA is based on a greedy mechanism, it can guarantee at least 50% global optimization performance, which was proven through a demonstration of the minimum optimization performance of a centralized target assignment algorithm, since the centralized algorithm is equivalent to the MTCBAA. Finally, experiments were conducted, including an experiment that illustrates the operation of the proposed algorithm, Monte Carlo comparisons with a centralized target assignment method based on the immune algorithm, and deployment experiments on a semi-physical simulation platform. Compared with the heuristic target assignment algorithm, the proposed algorithm significantly improved the target assignment efficiency. The practicality of the proposed algorithm was further verified through a distributed semi-physical simulation experiment.

Background: Impaired gait and balance are associated with severity of leukoaraiosis. Evaluation of balance is based on neurological examination using Romberg's test with bipedal standing, assessment scale, and posturographic parameters. The goal of this study was to determine the relationship between static equilibrium and grades of white matter hyperintensities (WMHs) using static posturography as a quantitative technical method.Method: One hundred and eighteen (118) patients with lacunar infarct were recruited and assessed on MRI with Fazekas's grading scale into four groups. On admission, age, gender, height, weight, Berg Balance Scale (BBS), mini-mental state examination (MMSE), and static posturography parameters were recorded, and their correlations with WMHs were determined.Results: Age was significantly and positively correlated with severity of WMHs (r = 0.39, p < 0.05). WMH score was negatively correlated with BBS score (r = −0.65, p < 0.05) and MMSE score (r = −0.79, p < 0.05). There was a significant positive correlation between track length anteroposterior (AP, with eyes closed) and severity of WMHs (r = 0.70, p < 0.05). Partial correlation analysis and multiple logistic regression analysis indicated that track length AP with eyes closed, was a predictor for the severity of WMHs (p< 0.05).Conclusion: The severity of WHMs is associated with age, cognitive decline, and impairment in balance. Posturography parameter in track length in AP direction with eyes closed in relation to cognition and balance, may be a potential marker for disease progression in WMHs.

Normal brain function requires steady blood supply to maintain stable energy state. When blood supply to the brain becomes suboptimal for a long period of time, chronic cerebral hypoperfusion (CCH) and a variety of brain changes may occur. CCH causes white matter injury and cognitive impairment. The present study investigated the effect of nicotinamide (NAM) on CCH-induced cognitive impairment and white matter damage in mice. Male C57Bl/6J mice aged 10–12 weeks (mean age = 11 ± 1 weeks) and weighing 24 - 29 g (mean weight = 26.5 ± 2.5 g) were randomly assigned to three groups (eight mice/group): sham group, CCH group and NAM group. Chronic cerebral hypoperfusion (CCH) was induced using standard methods. The treatment group mice received intraperitoneal injection of NAM at a dose of 200 mg/kg body weight (bwt) daily for 30 days. Learning, memory, anxiety, and depression-like behaviors were measured using Morris water maze test (MWMT), open field test (OFT), sucrose preference test (SPT), and forced swim test (FST), respectively. White matter damage and remodeling were determined via histological/ immunohistochemical analyses, and western blotting, respectively. The results showed that the time spent in target quadrant, number of crossings and escape latency were significantly lower in CCH group than in sham group, but they were significantly increased by NAM (p < 0.05). Mice in NAM group moved significantly faster and covered longer distances, when compared with those in CCH group (p < 0.05). The percentage of time spent in open arms and the number of entries to the open arms were significantly lower in CCH group than in NAM group (p < 0.05). Moreover, anhedonia and histologic scores (index of myelin injury) were significantly higher in CCH group than in sham group, but they were significantly reduced by NAM (p < 0.05). The results of immunohistochemical staining and Western blotting showed that the protein expressions of 2′, 3′-cyclic-nucleotide 3′-phosphodiesterase (CNPase) and synaptophysin were significantly downregulated in CCH group, relative to sham group, but they were significantly upregulated by NAM (p < 0.05). These results indicate that NAM improves cognitive function in mice with CCH.

This paper proposes an approach of target tracking of a ground target for UAVs using Optimal Two-Stage Cubature Kalman Filter and Improved Coordinated Lateral Guidance Law. Firstly, the Optimal Two-Stage Cubature Kalman Filter (OTSCKF) is proposed to estimate the target motion. The OTSCKF combines two-stage filtering technology with CKF to improve the estimation accuracy. Secondly, to keep a constant distance between the UAV and the target, a new guidance law based on the lateral turning equation is proposed and its asymptotic stability is proven. On this basis, a distributed tracking algorithm is designed to balance the phase difference and achieve cooperation among multi-UAVs. Thirdly, numerical experiments are performed for the tracking problems of moving targets and the results verify the effectiveness of the proposed guidance algorithm.

Cerebellar lesions can lead to a series of cognitive and emotional disorders by influencing cerebral activity via cerebro-cerebellar loops. To explore changes in cognitive function and structural brain networks in patients with posterior cerebellar infarction, we conducted the current study using diffusion-weighted MRI (32 cerebellar infarction patients, 29 controls). Moreover, a series of neuropsychological tests were used to assess the subject's cognitive performance. We found cognitive impairment following cerebellar infarction involving multiple cognitive domains, including memory, executive functions, visuospatial abilities, processing speed and language functions, and brain topological abnormalities, including changes in clustering coefficients, shortest path lengths, global efficiency, local efficiencies, betweenness centrality and nodal efficiencies. Our results indicated that measures of local efficiency, mainly in the precuneus, cingulate gyrus and frontal-temporal cortex, were significantly reduced with posterior cerebellar infarction. At the same time, The correlation analysis suggested thatthe abnormal alterations in the right PCG, bilateral DCG, right PCUN may play a core role in the cognitive impairment following cerebellar infarctions. The differences in topological features of the structural brain networks within the cerebro-cerebellar circuits may provide a new approach to explore the pathophysiological mechanisms of cognitive impairment following cerebellar infarction.

Here, we aimed to investigate brain activity in migraineurs in response to emotional stimulation. Magnetoencephalography (MEG) was used to examine 20 patients with episodic migraine (EM group), 15 patients with chronic migraine (CM group), and 35 healthy participants (control group). Neuromagnetic brain activity was elicited by emotional stimulation using photographs of facial expressions. We analyzed the latency and amplitude of M100 and M170 components and used Morlet wavelet and beamformers to analyze the spectral and spatial signatures of MEG signals in gamma band (30–100 Hz). We found that the timing and frequency of MEG activity differed across the three groups in response negative emotional stimuli. First, peak M170 amplitude was significantly lower in the CM group than in the control group. Second, compared with the control group, the average spectral power was significantly lower in the EM group and CM group at M100 and M170. Third, the average spectral powers of the M100 and M170 in the CM group were negatively correlated with either HAM-D scores or migraine attack frequency. No significant differences across groups was found for positive or neutral emotional stimuli. Furthermore, after negative emotional stimuli, the MEG source analysis demonstrated that the CM group showed a significantly higher percentage of amygdala activation than the control group for M100 and M170. Thus, during headache free phases, migraineurs have abnormal brain activity in the gamma band in response to negative emotional stimuli.Trial Registration:ChiCTR-RNC-17012599. Registered 7 September, 2017.

Lesions of the cerebellum lead to motor and non-motor deficits by influencing cerebral cortex activity via cerebello-cortical circuits. It remains unknown whether the cerebello-cortical “disconnection” underlies motor and non-motor impairments both in the parkinsonian variant of multiple system atrophy (MSA-P) and Parkinson’s disease (PD). In this study, we investigated both the functional and effective connectivity of the cerebello-cortical circuits from resting-state functional magnetic resonance imaging (rs-fMRI) data of three groups (26 MSA-P patients, 31 PD patients, and 30 controls). Correlation analysis was performed between the causal connectivity and clinical scores. PD patients showed a weakened cerebellar dentate nucleus (DN) functional coupling in the posterior cingulate cortex (PCC) and inferior parietal lobe compared with MSA-P or controls. MSA-P patients exhibited significantly enhanced effective connectivity from the DN to PCC compared with PD patients or controls, as well as declined causal connectivity from the left precentral gyrus to right DN compared with the controls, and this value is significantly correlated with the motor symptom scores. Our findings demonstrated a crucial role for the cerebello-cortical networks in both MSA-P and PD patients in addition to striatal-thalamo-cortical (STC) networks and indicated that different patterns of cerebello-cortical loop degeneration are involved in the development of the diseases.

A robust adaptive neural control scheme based on a back-stepping technique is developed for the longitudinal dynamics of a flexible hypersonic flight vehicle, which is able to ensure the state tracking error being confined in the prescribed bounds, in spite of the existing model uncertainties and actuator constraints. Minimal learning parameter technique–based neural networks are used to estimate the model uncertainties; thus, the amount of online updated parameters is largely lessened, and the prior information of the aerodynamic parameters is dispensable. With the utilization of an assistant compensation system, the problem of actuator constraint is overcome. By combining the prescribed performance function and sliding mode differentiator into the neural back-stepping control design procedure, a composite state tracking error constrained adaptive neural control approach is presented, and a new type of adaptive law is constructed. As compared with other adaptive neural control designs for hypersonic flight vehicle, the proposed composite control scheme exhibits not only low-computation property but also strong robustness. Finally, two comparative simulations are performed to demonstrate the robustness of this neural prescribed performance controller.

BACKGROUND: Stress and various stress hormones, including catecholamines and glucocorticoids, have recently been implicated in the pathogenesis of Alzheimer's disease (AD), which represents the greatest unresolved medical challenge in neurology. Angiotensin receptor blockers have shown benefits in AD and prone-to-AD animals. However, the mechanisms responsible for their efficacy remain unknown, and no studies have directly addressed the role of central angiotensin II (Ang II), a fundamental stress hormone, in the pathogenesis of AD. The present study focused on the role of central Ang II in amyloidogenesis, the critical process in AD neuropathology, and aimed to provide direct evidence for the role of this stress hormone in the pathogenesis of AD. METHODOLOGY/PRINCIPAL FINDINGS: Increased central Ang II levels during stress response were modeled by intracerebroventricular (ICV) administration of graded doses of Ang II (6 ng/hr low dose, 60 ng/hr medium dose, and 600 ng/hr high dose, all delivered at a rate of 0.25 µl/hr) to male Sprague Dawley rats (280-310 g) via osmotic pumps. After 1 week of continuous Ang II infusion, the stimulation of Ang II type 1 receptors was accompanied by the modulation of amyloid precursor protein, α-, β-and γ-secretase, and increased β amyloid production. These effects could be completely abolished by concomitant ICV infusion of losartan, indicating that central Ang II played a causative role in these alterations. CONCLUSIONS/SIGNIFICANCE: Central Ang II is essential to the stress response, and the results of this study suggest that increased central Ang II levels play an important role in amyloidogenesis during stress, and that central Ang II-directed stress prevention and treatment might represent a novel anti-AD strategy.