Personalized connectivity-based network targeting model of transcranial magnetic stimulation for treatment of psychiatric disorders: computational feasibility and reproducibility.

Repetitive transcranial magnetic stimulation (rTMS) holds promise for treating psychiatric disorders; however, the variability in treatment efficacy among individuals underscores the need for further improvement. Growing evidence has shown that TMS induces a broad network modulatory effect, and its effectiveness may rely on accurate modulation of the pathological network specific to each disorder. Therefore, determining the optimal TMS coil setting that will engage the functional pathway delivering the stimulation is crucial. Compared to group-averaged functional connectivity (FC), individual FC provides specific information about a person's brain functional architecture, offering the potential for more accurate network targeting for personalized TMS. However, the low signal-to-noise ratio (SNR) of FC poses a challenge when utilizing individual resting-state FC. To overcome this challenge, the proposed solutions include increasing the scan duration and employing the cluster method to enhance the stability of FC. This study aimed to evaluate the stability of a personalized FC-based network targeting model in individuals with major depressive disorder or schizophrenia with auditory verbal hallucinations. Using resting-state functional magnetic resonance imaging data from the Human Connectome Project, we assessed the model's stability. We employed longer scan durations and cluster methodologies to improve the precision in identifying optimal individual sites. Our findings demonstrate that a scan duration of 28 minutes and the utilization of the cluster method achieved stable identification of individual sites, as evidenced by the intraindividual distance falling below the ~1cm spatial resolution of TMS. The current model provides a feasible approach to obtaining stable personalized TMS targets from the scalp, offering a more accurate method of TMS targeting in clinical applications.

A radiomics-based brain network in T1 images: construction, attributes, and applications.

T1 image is a widely collected imaging sequence in various neuroimaging datasets, but it is rarely used to construct an individual-level brain network. In this study, a novel individualized radiomics-based structural similarity network was proposed from T1 images. In detail, it used voxel-based morphometry to obtain the preprocessed gray matter images, and radiomic features were then extracted on each region of interest in Brainnetome atlas, and an individualized radiomics-based structural similarity network was finally built using the correlational values of radiomic features between any pair of regions of interest. After that, the network characteristics of individualized radiomics-based structural similarity network were assessed, including graph theory attributes, test-retest reliability, and individual identification ability (fingerprinting). At last, two representative applications for individualized radiomics-based structural similarity network, namely mild cognitive impairment subtype discrimination and fluid intelligence prediction, were exemplified and compared with some other networks on large open-source datasets. The results revealed that the individualized radiomics-based structural similarity network displays remarkable network characteristics and exhibits advantageous performances in mild cognitive impairment subtype discrimination and fluid intelligence prediction. In summary, the individualized radiomics-based structural similarity network provides a distinctive, reliable, and informative individualized structural brain network, which can be combined with other networks such as resting-state functional connectivity for various phenotypic and clinical applications.

Multiple faults diagnosis for ocean-going marine diesel engines based on different neural network algorithms.

Fault diagnosis technologies for ocean-going marine diesel engines play an important role in the safety and reliability of ship navigation. Although many fault diagnosis technologies have achieved acceptable results for single fault of diesel engines, the diagnosis of multiple faults is rarely involved. Due to the strong correlation, non-linearity and randomness of multiple faults, it is extremely difficult to make an accurate diagnosis. In this study, diagnosis methods based on thermal parametric analysis combined with different neural network algorithms were established and used for the diagnosis of multiple faults in the ocean-going marine diesel engine. The results show that the Levenberg Marquardt back propagation neural network has the highest diagnostic accuracy rate of 88.89% and 100% for multiple faults and single faults, respectively, and its diagnostic time is also relatively short, 0.78 s. The Bayesian regularization back propagation neural network can give a diagnostic accuracy rate of 100% for single faults, but for multiple faults, the diagnostic accuracy rate is only 55.56%, and the diagnosis time for the entire sample is the longest. As for the probabilistic neural network, although it has the fastest diagnosis speed, it has the lowest diagnostic accuracy rate for both single faults and multiple faults. The results may provide references for the online diagnosis of single faults and multiple faults in ocean-going marine diesel engines.

MNI2CPC: A probabilistic cortex-to-scalp mapping for non-invasive brain stimulation targeting.

BACKGROUND: Synthesis of neural imaging information from many studies is valuable for identifying stable cortical targets for non-invasive brain stimulation (NIBS). Typically, these targets are specified in Montreal Neurological Institute (MNI) standard brain space. However, in practical NIBS applications, localizing MNI cortical targets often relies on the International 10-20 system or heuristic scalp approaches, which often lacks precision or applies only to specific targets. OBJECTIVE/HYPOTHESIS: We aim to establish a probabilistic mapping from any cortical target in MNI space to continuous proportional coordinate (CPC) standard scalp space (MNI2CPC) and assess the performance of this mapping for NIBS targeting. METHODS: The MNI2CPC mapping was calculated based on a large MRI dataset (n = 114). Its targeting error was evaluated via cross-individual validation using a leave-one-out approach, as well as through independent validation across race (n = 27) and across patient (n = 58) cohorts. RESULTS: The cross-individual validation demonstrated targeting errors of 4.03 ± 0.69 mm on the scalp and 3.30 ± 0.59 mm in the cortex. For independent cohorts, targeting errors were 4.71 ± 0.81 mm (scalp) and 3.85 ± 0.64 mm (cortex) across race, and 4.66 ± 0.77 mm (scalp) and 3.77 ± 0.61 mm (cortex) across patient. We publish a free online tool to enable querying of the CPC coordinate for any given MNI cortical target. The resulting CPC coordinates enable rapid and accurate manual localization on the scalp in a user-friendly manner. CONCLUSIONS: The MNI2CPC mapping developed in this study allows for manual localization of any MNI cortical target, which improves the accessibility and ease of application of NIBS in diverse settings.

Therapeutic potential and protective role of GRK6 overexpression in pulmonary arterial hypertension.

Abnormal proliferation of pulmonary arterial smooth muscle cells (PASMCs) is a key mechanism in the development of pulmonary arterial hypertension (PAH). Signal transducer and activator of transcription 3 (STAT3) signalling plays a critical role in modulating PASMC proliferation, and G-protein-coupled receptor kinase 6 (GRK6) regulates the STAT3 pathway. However, the mechanism underlying the relationship between GRK6 and PAH remains unclear. In this study, we aimed to investigate the role of GRK6 in PAH and determine its potential as a therapeutic target. We utilised hypoxia- and SU5416-induced PAH mouse models and a monocrotaline-induced PAH rat model to analyse the involvement of GRK6. We conducted gain- and loss-of-function experiments using mouse PASMCs. Modulation of GRK6 expression was achieved via a lentiviral vector in vitro and an adeno-associated virus serotype 1 encoding GRK6 in vivo. GRK6 was significantly downregulated in the lung tissues of PAH mice and rats, predominantly in PASMCs. Knockout of GRK6 exacerbated PAH, while both therapeutic and prophylactic overexpression of GRK6 alleviated PAH, as evidenced by a reduction in right ventricular systolic pressure, right ventricular wall to left ventricular wall plus ventricular septum ratio, pulmonary vascular media thickness, and pulmonary vascular muscularisation. Mechanistically, GRK6 overexpression attenuated hypoxia-induced PASMC proliferation and STAT3 phosphorylation. Conversely, knockdown of GRK6 promoted hypoxia-induced proliferation, which was mitigated by a STAT3 inhibitor. Our findings highlight the potential protective and beneficial roles of GRK6 in PAH; we propose a lung-targeted GRK6 gene therapy utilizing adeno-associated virus serotype 1 as a potential treatment approach for patients with PAH.

Determining four confounding factors in individual cognitive traits prediction with functional connectivity: an exploratory study.

Resting-state functional connectivity (RSFC) has been widely adopted for individualized trait prediction. However, multiple confounding factors may impact the predicted brain-behavior relationships. In this study, we investigated the impact of 4 confounding factors including time series length, functional connectivity (FC) type, brain parcellation choice, and variance of the predicted target. The data from Human Connectome Project including 1,206 healthy subjects were employed, with 3 cognitive traits including fluid intelligence, working memory, and picture vocabulary ability as the prediction targets. We compared the prediction performance under different settings of these 4 factors using partial least square regression. Results demonstrated appropriate time series length (300 time points) and brain parcellation (independent component analysis, ICA100/200) can achieve better prediction performance without too much time consumption. FC calculated by Pearson, Spearman, and Partial correlation achieves higher accuracy and lower time cost than mutual information and coherence. Cognitive traits with larger variance among subjects can be better predicted due to the well elaboration of individual variability. In addition, the beneficial effects of increasing scan duration to prediction partially arise from the improved test-retest reliability of RSFC. Taken together, the study highlights the importance of determining these factors in RSFC-based prediction, which can facilitate standardization of RSFC-based prediction pipelines going forward.

Caspase-8 Promotes Pulmonary Hypertension by Activating Macrophage-Associated Inflammation and IL-1ß (Interleukin 1ß) Production.

BACKGROUND: Macrophages are involved in the pathogenesis of pulmonary arterial hypertension (PAH). Caspase-8, an apical component of cell death pathways, is significantly upregulated in macrophages of PAH animal models. However, its role in PAH remains unclear. Caspase-8 plays a critical role in regulating inflammatory responses via inflammasome activation, cell death, and cytokine induction. This study investigated the mechanism of regulation of IL-1ß (interleukin 1ß) activation in macrophages by caspase-8. METHODS: A hypoxia + SU5416-induced PAH mouse model and monocrotaline-induced rat model of PAH were constructed and the role of caspase-8 was analyzed. RESULTS: Caspase-8 and cleaved-caspase-8 were significantly upregulated in the lung tissues of SU5416 and hypoxia-treated PAH mice and monocrotaline-treated rats. Pharmacological inhibition of caspase-8 alleviated PAH compared with wild-type mice, observed as a significant reduction in right ventricular systolic pressure, ratio of right ventricular wall to left ventricular wall plus ventricular septum, pulmonary vascular media thickness, and pulmonary vascular muscularization; caspase-8 ablated mice also showed significant remission. Mechanistically, increased proliferation of pulmonary arterial smooth muscle cellss is closely associated with activation of the NLRP3 (NOD [nucleotide oligomerization domain]-, LRR [leucine-rich repeat]-, and PYD [pyrin domain]-containing protein 3) inflammasome and the IL-1ß signaling pathway. Although caspase-8 did not affect extracellular matrix synthesis, it promoted inflammatory cell infiltration and pulmonary arterial smooth muscle cell proliferation via NLRP3/IL-1ß activation during the development stage of PAH. CONCLUSIONS: Taken together, our study suggests that macrophage-derived IL-1ß via caspase-8-dependent canonical inflammasome is required for macrophages to play a pathogenic role in pulmonary perivascular inflammation.

Circulating ANGPTL3 and ANGPTL4 levels predict coronary artery atherosclerosis severity.

BACKGROUND: We investigated the role of ANGPTL3 and ANGPTL4 in atherosclerosis development and determined whether plasma concentrations of ANGPTL3 and ANGPTL4 are related to the degree of coronary stenosis. METHODS: A total of 305 consecutive patients with angina who underwent diagnostic coronary angiography were enrolled in the study between August 2017 and August 2018. The levels of ANGPTL3 and ANGPTL4 were measured by using competitive ELISA kits. RESULTS: According to the degree of coronary artery stenosis, patients were classified into four types: coronary artery stenosis of < 10%, 10-50%, 50-75, and > 75%. The plasma ANGPTL3 level was higher (51.71 ± 52.67 vs. 24.65 ± 10.32 ng/mL, P < 0.001) and that of ANGPTL4 was lower (454.66 ± 269.05 vs. 875.49 ± 961.15 ng/mL, P < 0.001) in the coronary artery stenosis ≥ 10% group than in the < 10% group. ANGPTL3 and ANGPTL4 levels were significantly associated with the severity of coronary vascular stenosis. ROC curve analyses indicated that ANGPTL3 concentrations above 30.5 ng/mL can predict atherosclerosis with a sensitivity of 71.2% and specificity of 75.3%, and that ANGPTL4 levels below 497.5 ng/mL can predict atherosclerosis with a sensitivity of 63.9% and specificity of 74.5%. ANGPTL3 and ANGPTL4 were determined to be independent risk factors for coronary atherosclerosis with odds ratios (ORs) of 0.189 (95% CI 0.097-0.368, P < 0.001) and 3.625 (95% CI 1.873-7.016, P < 0.001), respectively. CONCLUSIONS: Increased ANGPTL3 or decreased ANGPTL4 shows an association with coronary atherosclerosis and, may become a predictor of coronary atherosclerosis in the future.

Novel PET/CT tracers for targeted imaging of membrane receptors to evaluate cardiomyocyte apoptosis and tissue repair process in a rat model of myocardial infarction.

The purpose of this study was to employ novel tracers PET imaging approach to define the time course and intensity of myocardial repair after apoptosis and to correlate the imaging signal to immunohistochemical staining in myocardial infarction (MI). We designed novel αVß3-targeted and radio-functionalized tracers for detection of apoptosis in H9C2 cells and myocardial tissue. MI rats were imaged with [18F]FDG, [18F]ANP-Cin or [18F]ANP-RGD2 using a small-animal PET/CT device. Rats were sacrificed, and tissue samples from viable and injured myocardial areas were sectioned for TUNEL assay and histology. The uncorrected radiochemical yield of [18F]ANP-Cin and [18F]ANP-RGD2 were 41.3 ± 5.4% and 21.17 ± 4.7%, respectively. Two tracers meet many criteria for cardiac imaging, including high stability, high binding, no toxicity, fast renal clearance and excellent biodistribution in rat models. The uptake of [18F]ANP-Cin was significantly higher on the 1st and 3rd day than the 7th or 28th day after MI induction, a timeframe associated with increased cardiomyocyte apoptosis. Higher uptake of [18F]ANP-Cin was observed in MI rats than in N-acetylcysteine (NAC)-treated rats on the 3rd days. In contrast with [18F]ANP-Cin, no hot-spots was observed with [18F]ANP-RGD2 on the 1st day and more hot-spots was observed from the 3rd day to the 7th day, then less on the 28th days in the high apoptotic site. There was no uptake of [18F]FDG in or around the apoptotic region. On the 7th day the uptake of [18F]ANP-RGD2 was higher in NAC-treated rats than MI rats. [18F]ANP-Cin and [18F]ANP-RGD2 are superior to [18F]FDG for PET/CT imaging for evaluation of cardiomyocyte apoptosis and tissue repair processes in the MI rats.

Impact of Long-Term Home Quarantine on Mental Health and Physical Activity of People in Shanghai During the COVID-19 Pandemic.

This study aimed to investigate the effects of long-term home quarantine on the mental health of people during the COVID-19 epidemic in Shanghai. We conducted an online questionnaire survey on March 26 2020 and collected data on demographics, level of physical activity (PA), and mental health status of the participants. We assessed the mental health status using the Patient Health Questionnaire (PHQ-9) and Generalized Anxiety Disorder Scale (GAD-7), whereas PA was assessed using International Physical Activity Questionnaire Short Form (IPAQ-SF). Of all 2,409 valid samples, participants reported performing a total of 2015.20 metabolic equivalent of task (MET)-minutes/week of total PA before the outbreak period and 1720.29 MET-minutes/week of total PA during the outbreak period (p < 0.001). Participants who spent a longer time at home reported to have a better performance on the PHQ-9 (p = 0.087) and GAD-7 (p < 0.001). A high level of PA was considered an protective factor against depression (OR = 0.755, 95% CI 0.603-0.944, p < 0.001). Additionally, a high level of PA had a preventative effect on anxiety (OR = 0.741, 95% CI 0.568-0.967, p < 0.001), and a longer working period during the outbreak was shown to be a risk factor for anxiety (11-29 days, OR 1.455, 95% CI 1.110-1.909; 30-60 days OR 1.619, 95% CI 1.227-2.316). Home confinement during the pandemic might not have a negative effect on mental health provided that people engage in more PA indoors. This study encourages interventions for mental health problems through physical exercise.

Bootstrapping promotes the RSFC-behavior associations: An application of individual cognitive traits prediction.

Resting-state functional connectivity (RSFC) records enormous functional interaction information between any pair of brain nodes, which enriches the individual-phenotypic prediction. To reduce high-dimensional features, correlation analysis is a common way for feature selection. However, resting state fMRI signal exhibits typically low signal-to-noise ratio and the correlation analysis is sensitive to outliers and data distribution, which may bring unstable features to prediction. To alleviate this problem, a bootstrapping-based feature selection framework was proposed and applied to connectome-based predictive modeling, support vector regression, least absolute shrinkage and selection operator, and Ridge regression to predict a series of cognitive traits based on Human Connectome Project data. To systematically investigate the influences of different parameter settings on the bootstrapping-based framework, 216 parameter combinations were evaluated and the best performance among them was identified as the final prediction result for each cognitive trait. By using the bootstrapping methods, the best prediction performances outperformed the baseline method in all four prediction models. Furthermore, the proposed framework could effectively reduce the feature dimension by retaining the more stable features. The results demonstrate that the proposed framework is an easy-to-use and effective method to improve RSFC prediction of cognitive traits and is highly recommended in future RSFC-prediction studies.

Integrin ß3 promotes cardiomyocyte proliferation and attenuates hypoxia-induced apoptosis via regulating the PTEN/Akt/mTOR and ERK1/2 pathways.

Objective: Integrin ß3 is one of the main integrin heterodimer receptors on the surface of cardiac myocytes. Our previous studies showed that hypoxia induces apoptosis and increases integrin ß3 expression in cardiomyocytes. However, the exact mechanism by which integrin ß3 protects against apoptosis remains unclear. Hence, the present investigation aimed to explore the mechanism of integrin ß3 in cardiomyocyte proliferation and hypoxia-induced cardiomyocyte apoptosis. Methods: Stable cells and in vivo acute and chronic heart failure rat models were generated to reveal the essential role of integrin ß3 in cardiomyocyte proliferation and apoptosis. Western blotting and immunohistochemistry were employed to detect the expression of integrin ß3 in the stable cells and rat cardiac tissue. Flow cytometer was used to investigate the role of integrin ß3 in hypoxia-induced cardiomyocyte apoptosis. Confocal microscopy was used to detect the localization of integrin ß3 and integrin αv in cardiomyocytes. Results: A cobaltous chloride-induced hypoxic microenvironment stimulated cardiomyocyte apoptosis and increased integrin ß3 expression in H9C2 cells, AC16 cells, and cardiac tissue from acute and chronic heart failure rats. The overexpression of integrin ß3 promoted cardiomyocyte proliferation, whereas silencing integrin ß3 expression resulted in decreased cell proliferation in vitro. Furthermore, knocking down integrin ß3 expression using shRNA or the integrin ß3 inhibitor cilengitide exacerbated cobaltous chloride-induced cardiomyocyte apoptosis, whereas overexpression of integrin ß3 weakened cobaltous chloride-induced cardiomyocytes apoptosis. We found that integrin ß3 promoted cardiomyocytes proliferation through the regulation of the PTEN/Akt/mTOR and ERK1/2 signaling pathways. In addition, we found that knockdown of integrin αv or integrin ß1 weakened the effect of integrin ß3 in cardiomyocyte proliferation. Conclusion: Our findings revealed the molecular mechanism of the role of integrin ß3 in cardiomyocyte proliferation and hypoxia-induced cardiomyocyte apoptosis, providing new insights into the mechanisms underlying myocardial protection.

Altered Temporal Organization of Brief Spontaneous Brain Activities in Patients with Alzheimer's Disease.

The alterations of dynamic brain functions in Alzheimer's disease (AD) remain far from well understood. In this study, using functional magnetic resonance imaging (fMRI) data, we adopted a co-activation pattern (CAP) approach, which relies on very few assumptions, to explore the differences of brain dynamics among healthy elderly, patients with early amnestic mild cognitive impairment (MCI) and patients with AD. Briefly, k-means clustering was applied to all fMRI frames from the three groups and generated a set of reproducible CAPs. We found the obtained CAPs showed high correspondence to the well acknowledged functional networks including default mode network (DMN), executive control network and visual networks, etc. Different from previous CAP-based studies, we further quantitatively analyzed the temporal dependence of the CAPs using multiple parameters. Primary findings include, for AD and MCI compared with NC, the decreased mean fraction of occurrence and persistence of DMN related CAPs, which indicates the typical DMN damage; the increased/decreased mean persistence of ventral/dorsal visual network related CAPs, which may associate with the visuospatial disorder of patients with AD pathology; the elevated transition and CAP entropies and multiple alterations of CAP transition probabilities, which imply the altered mode of information flow and lifted system uncertainty in AD brains. We also found correlations of proposed measurements to cerebrospinal fluid biomarkers and neuropsychological scores. This study verified the AD-related alteration found by traditional FC analysis, and proposed several new biomarkers which have the potential for assisting AD treatment and early diagnosis.

Identifying Mild Cognitive Impairment with Random Forest by Integrating Multiple MRI Morphological Metrics.

Mild cognitive impairment (MCI) exhibits a high risk of progression to Alzheimer's disease (AD), and it is commonly deemed as the precursor of AD. It is important to find effective and robust ways for the early diagnosis of MCI. In this paper, a random forest-based method combining multiple morphological metrics was proposed to identify MCI from normal controls (NC). Voxel-based morphometry, deformation-based morphometry, and surface-based morphometry were utilized to extract morphological metrics such as gray matter volume, Jacobian determinant value, cortical thickness, gyrification index, sulcus depth, and fractal dimension. An initial discovery dataset (56 MCI/55 NC) from the ADNI were used to construct classification models and the performances were testified with 10-fold cross validation. To test the generalization of the proposed method, two extra validation datasets including longitudinal ADNI data (30 MCI/16 NC) and collected data from Xuanwu Hospital (27 MCI/32 NC) were employed respectively to evaluate the performance. No matter whether testing was done on the discovery dataset or the extra validation datasets, the accuracies were about 80% with the combined morphological metrics, which were significantly superior to single metric (accuracy: 45% ∼76%) and also displayed good generalization across datasets. Additionally, gyrification index and cortical thickness derived from surface-based morphometry outperformed other features in MCI identification, suggesting they were some key morphological biomarkers for early MCI diagnosis. Combining the multiple morphological metrics together resulted in a significantly better and reliable identification model, which may be helpful to assist in the clinical diagnosis of MCI.

Integrin ß3 inhibits hypoxia-induced apoptosis in cardiomyocytes.

Hypoxia-induced apoptosis plays an important role in cardiovascular diseases. Integrin ß3 is one of the main integrin heterodimer receptors on the surface of cardiac myocytes. However, despite the important role that integrin ß3 plays in the cardiovascular disease, its exact role in the hypoxia response remains unclear. Hence, in the present investigation we aimed to study the role of integrin ß3 in hypoxia-induced apoptosis in H9C2 cells and primary rat myocardial cells. MTT assay, flow cytometry and TUNEL assay results showed that hypoxia inhibited cardiomyocyte proliferation and induced cardiomyocyte apoptosis. The expression levels of integrin ß3 and HIF1α were upregulated in hypoxia-induced cardiomyocytes as revealed by real-time PCR and western blot analysis. Furthermore, knockdown of integrin ß3 expression by siRNA increased hypoxia-induced cardiomyocyte apoptosis. In addition, integrin ß3 overexpression weakened hypoxia-induced cardiomyocyte apoptosis. The protein expressions of integrin ß3 and HIF1α were upregulated in acute myocardial infarction rat cardiac tissues compared with the control rat cardiac tissues. Our data suggest that integrin ß3 plays a protective role in cardiomyocytes during hypoxia-induced apoptosis.

A fuzzy logic urea dosage controller design for two-cell selective catalytic reduction systems.

Diesel engines have dominated in the heavy-duty vehicular and marine power source. However, the induced air pollution is a big problem. As people's awareness of environmental protection increasing, the emission regulations of diesel-engine are becoming more stringent. In order to achieve the emission regulations, the after-treatment system is a necessary choice. Specifically, the selective catalytic reduction (SCR) system has been widely applied to reduce the NOX emissions of diesel engine. Different from single-cell SCR systems, the two-cell systems have various benefits from the modeling and control perspective. In this paper, the urea dosage controller design for two-cell SCR systems was investigated. Firstly, the two-cell SCR modeling was introduced. Based on the developed model, the design procedure for the fuzzy logic urea dosage controller was well addressed. Secondly, simulations and comparisons were employed via an experimental verification of the whole vehicle simulator. And the results showed that the designed controller simultaneously achieved high NOX reduction rate and low tail-pipe ammonia slip.