The triple‐network model has been widely applied in neuropsychiatric disorders including autism spectrum disorder (ASD). However, the mechanism of causal regulations within the triple‐network and their relations with symptoms of ASD remains unclear. 81 male ASD and 80 well matched typically developing control (TDC) were included in this study, recruited from Autism Brain Image Data Exchange‐I datasets. Spatial reference‐based independent component analysis was used to identify the anterior and posterior part of default‐mode network (aDMN and pDMN), salience network (SN), and bilateral executive‐control network (ECN) from resting‐state functional magnetic resonance imaging data. Spectral dynamic causal model and parametric empirical Bayes with Bayesian model reduction/average were adopted to explore the effective connectivity (EC) within triple‐network and the relationship between EC and autism diagnostic observation schedule (ADOS) scores. After adjusting for age and site effect, ASD and TDC groups both showed inhibition patterns. Compared with TDC, ASD group showed weaker self‐inhibition in aDMN and pDMN, stronger inhibition in pDMN→aDMN, weaker inhibition in aDMN→LECN, pDMN→SN, LECN→SN, and LECN→RECN. Furthermore, negative relationships between ADOS scores and pDMN self‐inhibition strength, as well as with the EC of pDMN→aDMN were observed in ASD group. The present study reveals imbalanced effective connections within triple‐networks in ASD children. More attentions should be focused at the pDMN, which modulates the core symptoms of ASD and may serve as an important region for ASD diagnosis and the target region for ASD treatments.

To investigate lung changes in patients with polymyositis/dermatomyositis-associated interstitial lung disease (PM/DM-ILD) using quantitative CT and to construct a diagnostic model to evaluate the application of quantitative CT and machine learning in diagnosing PM/DM-ILD. Chest CT images from 348 PM/DM individuals were quantitatively analyzed to obtain the lung volume (LV), mean lung density (MLD), and intrapulmonary vascular volume (IPVV) of the whole lung and each lung lobe. The percentage of high attenuation area (HAA %) was determined using the lung density histogram. Patients hospitalized from 2016 to 2021 were used as the training set (n=258), and from 2022 to 2023 were used as the temporal test set (n=90). Seven classification models were established, and their performance was evaluated through ROC analysis, decision curve analysis, calibration, and precision-recall curve. The optimal model was selected and interpreted with Python's SHAP model interpretation package. Compared to the non-ILD group, the mean lung density and percentage of high attenuation area in the whole lung and each lung lobe were significantly increased, and the lung volume and intrapulmonary vessel volume were significantly decreased in the ILD group. The Random Forest (RF) model demonstrated superior performance with the test set area under the curve of 0.843 (95% CI: 0.821-0.865), accuracy of 0.778, sensitivity of 0.784, and specificity of 0.750. Quantitative CT serves as an objective and precise method to assess pulmonary changes in PM/DM-ILD patients. The RF model based on CT quantitative parameters displayed strong diagnostic efficiency in identifying ILD, offering a new and convenient approach for evaluating and diagnosing PM/DM-ILD patients.

Background: Acute exacerbation (AE) of idiopathic inflammatory myopathy-associated interstitial lung disease (IIM-ILD) is fatal. Infection is one of the most important triggers of the AE of IIM-ILD. We evaluated the clinical features and prognosis of idiopathic (I-AE) and infection-triggered (iT-AE) acute exacerbation in IIM-ILD patients. Methods: We retrospectively reviewed 278 consecutive patients with IIM admitted to our hospital between January 2014 and December 2020. Among them, 69 patients experienced AE of IIM-ILD, including 34 with I-AE and 35 with iT-AE. Clinical features and short- and long-term outcomes were analyzed in this preliminary study. Results: Compared with I-AE, patients with iT-AE presented with lower hemoglobin and PaO₂/FiO₂ ratios but higher pulse, body temperature, white blood cell count, neutrophil percentage (NEU), C-reactive protein, erythrocyte sedimentation rates, lactate dehydrogenase, and hydroxybutyrate dehydrogenase levels. They also had more extensive ground-glass opacities (GGOs) on high-resolution computed tomography (all p < 0.05). Mortality was significantly higher in iT-AE than that in I-AE at 30 days (28.6% vs. 5.9%), 90 days (34.3% vs. 14.9%), and 1 year (54.3% vs. 17.6%; log-rank test, p = 0.002). Multivariate logistic regression showed that the combination of NEU and GGO extent could help discriminate iT-AE from I-AE (area under the receiver operating characteristic curve: 0.812; 95% confidence interval: 0.711–0.913; sensitivity: 71.4%, specificity: 73.5%, accuracy: 72.5%). Conclusion: This study found that iT-AE patients exhibited more severe hyperinflammation and markedly worse survival than I-AE patients. Combining NEU and GGO extent may assist in differentiating AE subtypes. Larger prospective studies are required to validate these findings.

Background: Connective tissue disease-associated interstitial lung disease (CTD-ILD) is triggered by pulmonary vascular injury. However, early subclinical lung changes in polymyositis/dermatomyositis (PM/DM) patients without ILD (PM/DM-non-ILD) remain undetectable by conventional imaging. Objective: To evaluate microvascular integrity and parenchymal features in PM/DM-non-ILD patients using quantitative computed tomography (QCT). Additionally, this study aims to identify associations between QCT parameters and laboratory markers of inflammation and autoimmunity, and evaluate the performance of QCT metrics in detecting subclinical lung pathology. Method: This retrospective study included 71 PM/DM-non-ILD patients and 71 age- and sex-matched healthy controls were enrolled. Chest high-resolution CT (HRCT) images, laboratory data, and clinical records were collected. All HRCT scans were analyzed using the “Digital Lung” platform to obtain QCT parameters, including lung volume, density, and intrapulmonary vascular volumes (IPVV) for the whole lung and individual lobes. Standardized IPVV was calculated by dividing the vascular volume by the corresponding lobe volume to account for lung volume differences. QCT parameters and laboratory results were compared, their correlation analyzed, and diagnostic performance evaluated using receiver operating characteristic (ROC) analysis. Results: In PM/DM-non-ILD patients, standardized IPVV was notably lower in the entire lung and each lobe compared to healthy controls ( P < 0.05). Hematological analysis of PM/DM-non-ILD patients showed a pattern consistent with immune dysregulation, characterized by significantly reduced levels of hemoglobin, lymphocytes, eosinophils, and basophils (including their percentages), alongside elevated monocytes, monocyte percentage, neutrophil percentage, neutrophil-to-lymphocyte ratio, and platelet-to-lymphocyte ratio compared to controls (all P < 0.05). QCT parameters showed significant correlations with key hematologic inflammatory indicators ( P < 0.05). Conclusion: The decreased standardized intrapulmonary vascular volume (IPVV), across all lung lobes (whole lung, right lung, left lung, right upper lobe, right middle lobe, right lower lobe, left upper lobe, left lower lobe) indicates its potential as a sensitive marker for early pulmonary involvement in patients with polymyositis/dermatomyositis without interstitial lung disease. The distinct vascular changes in the right middle lung lobe may serve as a discriminative indicator for subclinical lung pathology in this population, potentially reflecting underlying immune dysregulation.

Background: Chronic obstructive pulmonary disease (COPD) is frequently complicated by cognitive impairment, yet the underlying large-scale structural network alterations remain poorly understood. This study aimed to investigate individualized structural covariance networks (ISCNs) based on sulcal depth in COPD patients and to explore their relationship with cognitive performance. Methods: Seventy-two patients with stable COPD and 68 age- and sex-matched healthy controls underwent 3T T1-weighted MRI. ISCNs were constructed using Jensen–Shannon divergence-based similarity of sulcal depth across 148 cortical regions. Graph-theoretical metrics, network-based statistics, and partial least squares regression were employed to characterize network topology, identify altered connectivity, and examine network associations with cognition assessed via Montreal Cognitive Assessment (MoCA) scores. Results: COPD patients exhibited region-specific, bidirectional sulcal depth changes. At the network level, morphological similarity was reduced between the cingulo-opercular network and both the ventral multimodal and frontoparietal networks, whereas default mode–auditory network similarity was increased. Graph analysis revealed significantly higher gamma and lambda in COPD patients, indicating excessive local modularity alongside impaired global integration. Nodal efficiency was widely reduced in bilateral insular and frontal regions, but paradoxically increased in the left superior circular sulcus of the insula and right anterior cingulate gyrus. Partial least squares regression identified 14 interregional connections that collectively explained 28.3% of the variance in MoCA scores, with both positively and negatively weighted connections. Conclusion: COPD is associated with a multi-level reorganization of brain structural networks, spanning from local sulcal depth abnormalities to global topological imbalance and bidirectional connectivity changes. These bidirectional network shifts may be associated with the cognitive impairment in this population, offering novel network-based biomarkers for early detection and intervention.