OBJECTIVE:To employ automated fiber quantification (AFQ) for the longitudinal assessment of segmental white matter microstructure in patients with moderate-to-severe obstructive sleep apnea (OSA) before and after short-term continuous positive airway pressure (CPAP) therapy, and to evaluate associations with clinical metrics. METHODS:54 untreated male patients with moderate-to-severe OSA and 51 age- and sex-matched healthy controls (HCs) underwent polysomnography, neuropsychological assessment, and diffusion MRI. To evaluate treatment efficacy, 19 OSA patients underwent a follow-up MRI examination after 3 months of CPAP therapy. AFQ was used to extract fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) values at 100 equidistant nodes along 18 major white matter tracts, allowing precise localization of segmental white matter alterations between groups. Subsequently, correlation analyses were performed between the diffusion metrics of the affected tracts and clinical indicators. RESULTS:At baseline, compared to HCs, point-wise analysis revealed that OSA patients exhibit segmental reductions in MD, AD, and RD values across 9 tracts, alongside elevated AD values in the bilateral cingulum cingulate (CGC). Following 3 months of CPAP, specific segments of the bilateral CGC and the right inferior fronto-occipital fasciculus (IFOF) exhibited increased FA values along with decreased MD and RD values. Conversely, other fiber tracts showed reduced FA values accompanied by elevated MD and RD values. Additionally, baseline AD values in the left CGC correlated with nocturnal oxygen saturation, while post-treatment FA values in the right IFOF correlated with MoCA delayed recall scores. CONCLUSION:AFQ revealed extensive white matter microstructural alterations in untreated male patients with OSA. Short-term CPAP therapy promoted partial recovery in specific tract segments, which may be associated with cognitive amelioration; nevertheless, specific tract segments continue to exhibit signs of chronic neurological damage. These findings may offer novel evidence supporting focal white matter plasticity as a potential neural mechanism underlying CPAP-related cognitive improvement, thereby providing a theoretical basis for personalized therapeutic strategies.

BackgroundPost-COVID-19 pulmonary fibrosis (PCPF) is one of the most common diagnoses after COVID-19 acute infection. However, the interventions for long-term prognosis of PCPF are relatively lacking.AimWe aimed to observe one-year readmission and mortality rates among patients with PCPF, and tried to explore the impact of small-molecule antiviral drugs (SMADs) during hospitalization on the prognosis.MethodA total of 372 patients diagnosed as PCPF were enrolled. 293 of them treated with SMADs and the remaining 79 treated with NO SMADs. One-year readmission and mortality rates were evaluated via regression analysis.ResultsDuring hospitalization, 16 of the 372 patients died. An additional 19 survivors died within one-year postdischarge. Among survivors, 49.62% were readmitted. Compared to the NO-SMAD group, the SMAD group presented a lower one-year readmission rate (45.97%vs. 63.64%, p = 0.020) and a reduced risk of one-year readmission (HR = 0.65, 95% CI: 0.44 to 0.96, p = 0.030).ConclusionNearly half of PCPF patients experienced readmission within one-year following their initial hospitalization for acute COVID-19. Importantly, treatment with SMADs during the acute infection phase was significantly associated with a reduced readmission rate.

Major depressive disorder (MDD) is characterized by dysfunction in higher-order cortical regions involved in emotional and cognitive processes; however, its neurobiological basis remains unclear. Pharmacological treatments, including esketamine and sertraline, produce rapid antidepressant effects. We investigated the local intrinsic neural dynamics underlying these antidepressant effects using intrinsic neural timescales (INT), which measure the duration and capacity of information integration in localized brain regions. A total of 57 healthy controls and 41 patients with MDD were included. All patients with MDD received six intravenous infusions of esketamine (0.25 mg/kg) and two weeks of sertraline treatment. All participants underwent resting-state fMRI to quantify INT alterations at the voxel and brain network levels, followed by spatial correlation analyses linking autocorrelation metrics of INT signals to transcriptomic data from the Allen Human Brain Atlas and PET-derived neurotransmitter receptor maps. The spatial correlation between PLS2 scores and case–control t-statistic maps did not pass rigorous spin permutation testing (r = 0.484, p = 0.068) and did not meet the conventional significance threshold of 0.05. Accordingly, all subsequent enrichment analyses based on PLS2 results are presented as exploratory preliminary observations for hypothesis generation. Compared with healthy controls, patients with MDD exhibited significantly elevated INT levels in the left and right precuneus. Following treatment, patients with MDD showed significantly increased INT in the left occipital midline region and left calcarine cortex. At the brain network level, INT levels were significantly reduced in the default mode network (DMN) in patients with MDD compared with healthy controls. Compared with the pre-treatment state, the cerebellar network (CN) showed significantly elevated INT levels after treatment. Partial least squares regression analysis suggested potential associations between INT alterations and spatial gene expression gradients particularly those associated with immune responses, hormonal regulation, neutrophils, regulatory T cells, and glutamatergic synapses. Cell-type enrichment analysis identified excitatory and inhibitory neurons as key cellular contributors. Alterations in INT also correlated with cortical 5-HT1b and NAT receptor density, suggesting a role for inhibitory neurotransmission in temporal integration deficits. This study advances understanding of treatment-related brain abnormalities in patients with MDD from the perspective of local neural dynamics. These findings support a multiscale pathophysiological framework involving brain connectivity dynamics, molecular architecture, and neurochemical regulation in MDD.

The adoption of artificial intelligence (AI) techniques in medical imaging has led to significant improvements in diagnostic performance, particularly in neurological disorders. However, the limited interpretability of deep learning models, often referred to as the “black box” issue, poses substantial challenges in clinical trust, transparency, and regulatory acceptance. Explainable artificial intelligence (XAI) aims to address these limitations by enhancing model transparency and interpretability. This review systematically analysed 77 eligible studies, selected from an initial pool of 108 publications, focusing on XAI applications in neurological medical imaging. The included approaches were categorised into four primary groups: (1) feature visualisation techniques, (2) hierarchical and causal interpretability methods, (3) self-supervised and federated learning strategies, and (4) dynamic and multimodal interpretability frameworks. Each category was evaluated in terms of technical methodology, clinical applicability, and associated limitations. Feature visualisation methods such as Grad-CAM offer intuitive visual outputs for imaging data but often lack robustness and reproducibility, while attribution methods such as SHAP provide global or local feature importance—mainly for tabular or structured data—and are less frequently applied to medical images. Hierarchical models, including Layer-wise Relevance Propagation, provide more detailed insights but face barriers to clinical integration. Federated and self-supervised learning approaches are increasingly explored for privacy preservation and model generalisation in medical imaging; however, the integration of explainability mechanisms into these frameworks is still at an early stage, and standardised methods for interpretable federated/self-supervised models remain underdeveloped. Dynamic and multimodal frameworks represent a promising direction for comprehensive model explanation but are still in the early stages of exploration. Despite progress, key challenges persist, including the lack of standardised evaluation metrics, limited clinical validation, and unresolved ethical concerns. Future research should focus on integrating interpretability into model development, establishing benchmark evaluation protocols, and promoting effective human–AI collaboration in clinical workflows.

Introduction: Esketamine has demonstrated acute antidepressant effects in patients with major depressive disorder (MDD). This study investigated whether these effects associate with reversible white matter fiber integrity recovery using diffusion imaging. Method: Twenty patients with MDD and 20 healthy controls received 2-week esketamine treatment. Patients received 0.25 mg/kg intravenous esketamine. Emotional and cognitive recovery were assessed. Diffusion tensor imaging and tract-based spatial statistics evaluated white matter fiber integrity pre/post-treatment. Correlation analyses examined associations between white matter changes and clinical scales. Results: Compared to controls, patients with MDD exhibited decreased fractional anisotropy (FA) values of cerebral white matter fibers involving the association fibers, the commissural fibers and projection fibers. Esketamine effectively reduced depression, anxiety, and suicidal ideation scores while improving cognitive function. However, no reversible recovery of compromised white matter integrity was observed after 2 weeks of esketamine treatment. FA reductions in projection fibers correlated with anxiety and suicidal ideation severity. Limitations: Concurrent sertraline use and lack of placebo control limited our ability to isolate esketamine's effects. The wide age range may have introduced response variability. We used minimal effective dosages based on previous research. The small sample size limited statistical power. Larger, more controlled studies are needed to validate these preliminary findings. Discussion: This study enhances MDD neuropathological understanding, with widespread white matter impairment and associations between projection fibers and symptom severity. While producing significant antidepressant effects, short-term esketamine did not recover compromised white matter microstructure.

BACKGROUND:Obstructive sleep apnea (OSA) patients exhibit neurological symptoms, driving research in sleep medicine and clinical neurology. Neurologists and radiologists explore detection methods to identify unique neural features associated with OSA in the atypical nervous system. Neuroimaging research in OSA encompasses studying the structural, functional connectivity (FC), and neurometabolic aspects of the brain. Limited resources and OSA's heterogeneity pose challenges to effective neuroimaging research. This study aims to conduct a bibliometric analysis of OSA neuroimaging research to identify key trends and emerging themes. METHODS:This research utilizes various techniques, including functional MRI, structural MRI, diffusion tensor imaging, magnetic resonance spectroscopy, and electroencephalography, among others. Publications from 1993 to 2023 were retrieved from Web of Science on neuroimaging. Analysis tools included Bibliometric.com, CiteSpace, VOSviewer, and R4.3.2. RESULTS:A total of 714 papers were published in 47 countries, with 651 articles, 55 reviews, and 8 meeting abstracts. The USA led in publications, with the University of California System contributing most, primarily in "Sleep" journal. The study identified 2916 authors, with Macey PM having the highest publication count. Recent years highlighted burst keywords such as network, classification, sleep staging, FC, and brain activity. Analysis of keyword clusters revealed "electroencephalography" with the longest temporal duration. CONCLUSIONS:Neuroimaging in OSA research has gained increased attention. Incorporating behavioral sleep medicine insights could enhance understanding of OSA's impact on brain function and behavior. This study aims to assist researchers in identifying potential collaborators, institutions, and key themes, providing a comprehensive perspective on OSA neuroimaging research and related sleep disorders.

BACKGROUND AND PURPOSE:Obstructive sleep apnea (OSA) is linked to cognitive impairment and altered motor-related brain networks. This study examined functional connectivity (FC) changes in subregions of the primary motor cortex (M1) in patients with OSA and their association with sleep structure, cognition, and clinical features. METHODS:Sixty-five patients with OSA and 65 healthy controls (HC) participants matched in age and educational background were included. Resting-state functional MRI data were acquired for all participants using a 3T MRI system. Based on the Human Brainnetome Atlas, we analyzed FC changes of 12 subregions of M1 across the whole brain in patients with OSA. The two-sample t-tests were conducted to compare FC values between subregions of M1 and other brain regions in two groups. Partial correlation analyses examined the association between FC and clinical variables in patients with OSA. Additionally, we employed three machine learning algorithms-support vector machine (SVM), random forest (RF), and logistic regression (LR)-to distinguish patients with OSA from HC based on FC features. RESULTS:Compared to HC, the OSA group found that significant FC enhancements were identified in right A6cdl with the left inferior parietal lobule (IPL); left A4tl with the left inferior frontal gyrus (IFG), bilateral middle frontal gyrus (MFG), and left IPL; and left A6cvl with the right parahippocampal gyrus, bilateral MFG, left IFG, left superior temporal gyrus, and right cingulate gyrus. After Bonferroni correction, a negative correlation was observed between the FC value of A4tl (L)-IPL (L) and N2 (p < 0.05). Furthermore, SVM yielded the highest area under the receiver operating characteristic (ROC) curve (AUC) among all classifiers, indicating its superior performance in discriminating OSA patients from HC based on FC features. CONCLUSION:The study demonstrates that OSA significantly impacts brain functional networks, particularly affecting motor control through altered FC in subregions of M1. These alterations correlate with upper airway dysfunction and cognitive impairments, increasing accident risks. The high-accuracy SVM classification based on FC patterns demonstrates potential as a diagnostic biomarker for OSA. Future research should explore M1 FC patterns as diagnostic markers and neuromodulation therapies.

INTRODUCTION:This study aims to explore the relationship between the antidepressant effects of drugs (esketamine combined with sertraline) and brain function changes in patients with major depressive disorder. METHODS:A total of 27 patients with major depressive disorder (MDD) and 27 healthy individuals were included in the study. All MDD patients received a 2-week course of intravenous esketamine infusion (0.25 mg/kg) combined with sertraline. We assessed patients' post-treatment emotional and cognitive recovery using the Hamilton Anxiety Scale, Hamilton Depression Scale, Beck Suicide Ideation Scale, and Montreal Cognitive Assessment Scale. Additionally, all participants underwent magnetic resonance imaging (MRI) scans, with local consistency techniques employed to evaluate changes in brain spontaneous activity before and after treatment. Finally, correlation analysis was used to assess the relationship between the characteristics of spontaneous brain activity in MDD patients and clinical scales. RESULTS:We found that the Reho values of certain brain regions in MDD patients differed from those in the healthy group, primarily manifested as increased regional homogeneity (Reho) values in the right superior frontal gyrus, right precentral gyrus, right middle occipital gyrus, and left middle temporal gyrus; and decreased Reho values in the right middle temporal gyrus, bilateral temporal lobes, and right posterior cerebellar lobe. After two weeks of drug treatment, we observed a reversal in the local consistency of the left middle temporal gyrus (L-MTG), which tended toward normal levels. Additionally, we observed significant improvements in anxiety, depression symptoms, suicidal ideation, and MoCA scores in MDD patients post-treatment. Furthermore, changes in Reho values in the left middle temporal gyrus (L-MTG) were significantly correlated with scores on clinical assessment scales (HAMA). CONCLUSION:Patients with MDD exhibit imbalances in spontaneous brain activity. The L-MTG, as a key hub of the default mode network and salience network, is associated with the severity of depression and recovery after treatment.

Background and Purpose Patients with obstructive sleep apnea (OSA) experience chronic intermittent hypoxia and sleep fragmentation, leading to brain ischemia and neurological dysfunction. Therefore, it is important to identify features that can differentiate patients with OSA from healthy controls (HC) and provide insights into the underlying brain alterations associated with OSA. This study aimed to distinguish patients with OSA from healthy individuals and predict clinical symptom alterations using cerebellum-whole-brain static and dynamic functional connectivity (sFC and dFC, respectively), with the cerebellum as the seed region. Methods Sixty male patients with OSA and 60 male HC matched for age, education level, and sex were included. Using 27 cerebellar seeds, sliding-window analysis was performed to calculate sFC and dFC between the cerebellum and the whole brain. The sFC and dFC values were then combined and used in multiple machine-learning models to distinguish patients with OSA from HC and predict the clinical symptoms of patients with OSA. Results Patients with OSA showed increased dFC between cerebellar subregions and the superior and middle temporal gyri and decreased dFC with the middle frontal gyrus. Conversely, increased sFC was observed between cerebellar subregions and the cerebellar lobule VI, cingulate gyrus, middle frontal gyrus, inferior parietal lobules, insula, and superior temporal gyrus. Combined dynamic-static FC features demonstrated superior classification performance with a support vector machine in discriminating OSA from HC. In clinical symptom prediction, FC alterations contributed up to 30.11% to cognitive impairment, 55.96% to excessive sleepiness, and 27.94% to anxiety and depression. Conclusions Combining cerebrocerebellar sFC and dFC analyses enables high-precision classification and prediction of OSA. Aberrant FC patterns reflect compensatory brain reorganization and disrupted cognitive network integration, highlighting potential neuroimaging markers for OSA.

INTRODUCTION:We aimed to investigate the relationship between changes in resting-state functional connectivity in a subregion of the hippocampus and the antidepressant effects of esketamine as well as to identify potential neuroimaging markers of the treatment outcomes. METHODS:Twenty-nine patients with major depressive disorder (MDD) received six intravenous infusions of esketamine. All patients completed the Hamilton Anxiety Scale (HAMA), Hamilton Depression Scale (HAMD), Baker Suicide Ideation Scale (BSI), and Montreal Cognitive Assessment Scale (MoCA) to assess emotional and cognitive recovery after treatment. At the same time, all participants underwent magnetic resonance imaging using seed point functional connectivity analysis to divide the hippocampus into two subregions (rostral hippocampus (rHipp) and caudal hippocampus (cHipp)). RESULTS:We found that 29 patients with MDD responded favorably to esketamine, with significant reductions in HAMA, HAMD, and BSI scores and significant increases in MoCA scores. After two weeks of treatment with esketamine, we found that the FC between the right cHipp and the left cerebellum_6, precuneus, and middle temporal gyrus (MTG) was significantly increased in patients with MDD. Correlation analysis showed that the FC values between the right cHipp subregion and the left MTG were negatively correlated with MoCA scores. DISCUSSION:The altered functional connectivity pattern in the hippocampal subregions in patients with MDD may be related to the regulatory mechanism of esketamine in improving depressive symptoms, mainly involving the default network and cortico-cerebellar loop. This study provides new insights into the antidepressant effects of esketamine and potential targets for the treatment of MDD.

Purpose:Previous studies have shown altered paired brain functional connectivity (FC) in obstructive sleep apnea (OSA) patients, linked to cognitive impairment. This study utilized individual FC analysis to investigate the distinctive FC characteristics in OSA and evaluate their classification efficiency. Methods:We included 82 moderate to severe OSA patients [41 OSA with normal cognition (OSA-NC), 41 OSA with mild cognitive impairments (OSA-MCI)] and 84 healthy control (HC). Resting-state fMRI data and clinical scale data were collected. Individual FC was derived using multi-task learning-based sparse convex alternating structure optimization, with feature selection via the least absolute shrinkage and selection operator. Support vector machine classifiers were used for OSA vs HC and OSA-NC vs OSA-MCI classification. The top 10 FC features contributing to classification were analyzed for group differences. A significance level of p < 0.05 was considered statistically significant. Results:The study results showed that individual FC achieved higher classification accuracy than traditional Pearson-based FC (OSA vs HC: 91.8% vs 79.5%; OSA-NC vs OSA-MCI: 81.3% vs 63.8%). The top 10 individual-specific FC networks contributing to classification were mainly located in the default mode network, attention network, showing significant inter-group differences in connectivity strength between the two groups. Conclusion:This study identified static individualized FC characteristics in OSA patients with varying cognitive impairments. Based on individual FC, the classification accuracy of OSA-NC and OSA-MCI was significantly improved, the individual FC may serve as a potential neuroimaging marker for predicting OSA-MCI, providing an individual clinical diagnosis and treatment evaluation.

INTRODUCTION:Dysfunction in amygdala networks has been implicated in major depressive disorder (MDD). Pharmacological treatments, such as esketamine and sertraline, are believed to exert their antidepressant effects by modulating amygdalar activity. This study aimed to investigate the relationship between changes in dynamic functional connectivity (dFC) within amygdala subregions and treatment outcomes, with a focus on identifying potential neuroimaging markers. METHODS:Twenty-eight patients with MDD received six intravenous infusions of esketamine (0.25 mg/kg) combined with 2 weeks of sertraline treatment. Mood and cognitive recovery were assessed using the HAMA, HAMD, BSI, and MoCA. Seed-based dFC analysis of resting-state MRI focused on the dorsal amygdala (DA), medial amygdala (MA), and ventrolateral amygdala (VA). RESULTS:All patients demonstrated positive responses to treatment, with significant reductions in HAMA and HAMD scores, as well as improvements in MoCA scores and a decrease in suicidal ideation. Following treatment, static functional connectivity (sFC) was reduced between the right DA and paracentral lobule, the left VA and multiple regions (including the lentiform nucleus, thalamus, medial prefrontal cortex, inferior parietal lobule, precentral gyrus, and superior parietal lobule), and the right VA and middle frontal gyrus. Conversely, dFC was significantly increased between the right DA and cuneus/superior parietal lobule, and between the left VA and superior parietal lobule. A decrease in dFC was also observed between the left MA and superior temporal gyrus. Correlation analysis showed that dFC between the left MA and superior temporal gyrus was positively correlated with HAMA score. LIMITATION:The combined use of esketamine and sertraline limits the ability to differentiate their individual effects. A significant proportion of the patients were adolescents, with only a few adults, which may influence the age-related response to treatment. The study utilized the lowest effective dose of esketamine, thereby leaving the dose-response relationships unexamined. The small sample size further restricts the statistical power of the findings, highlighting the need for larger studies to validate these results. CONCLUSION:Altered resting-state and dFC in amygdala subregions may play a role in the mechanisms by which the combination of esketamine and sertraline alleviates depressive symptoms, primarily involving the sensorimotor and default mode networks. Together, the sFC and dFC analyses provide an integrated perspective on depression-related connectivity changes and offer potential targets for treatment.

BACKGROUND:The estrogen receptor (ER) serves as a pivotal indicator for assessing endocrine therapy efficacy and breast cancer prognosis. Invasive biopsy is a conventional approach for appraising ER expression levels, but it bears disadvantages due to tumor heterogeneity. To address the issue, a deep learning model leveraging mammography images was developed in this study for accurate evaluation of ER status in patients with breast cancer. OBJECTIVES:To predict the ER status in breast cancer patients with a newly developed deep learning model leveraging mammography images. MATERIALS AND METHODS:Datasets comprising preoperative mammography images, ER expression levels, and clinical data spanning from October 2016 to October 2021 were retrospectively collected from 358 patients diagnosed with invasive ductal carcinoma. Following collection, these datasets were divided into a training dataset (n = 257) and a testing dataset (n = 101). Subsequently, a deep learning prediction model, referred to as IP-SE-DResNet model, was developed utilizing two deep residual networks along with the Squeeze-and-Excitation attention mechanism. This model was tailored to forecast the ER status in breast cancer patients utilizing mammography images from both craniocaudal view and mediolateral oblique view. Performance measurements including prediction accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curves (AUCs) were employed to assess the effectiveness of the model. RESULTS:In the training dataset, the AUCs for the IP-SE-DResNet model utilizing mammography images from the craniocaudal view, mediolateral oblique view, and the combined images from both views, were 0.849 (95% CIs: 0.809-0.868), 0.858 (95% CIs: 0.813-0.872), and 0.895 (95% CIs: 0.866-0.913), respectively. Correspondingly, the AUCs for these three image categories in the testing dataset were 0.835 (95% CIs: 0.790-0.887), 0.746 (95% CIs: 0.793-0.889), and 0.886 (95% CIs: 0.809-0.934), respectively. A comprehensive comparison between performance measurements underscored a substantial enhancement achieved by the proposed IP-SE-DResNet model in contrast to a traditional radiomics model employing the naive Bayesian classifier. For the latter, the AUCs stood at only 0.614 (95% CIs: 0.594-0.638) in the training dataset and 0.613 (95% CIs: 0.587-0.654) in the testing dataset, both utilizing a combination of mammography images from the craniocaudal and mediolateral oblique views. CONCLUSIONS:The proposed IP-SE-DResNet model presents a potent and non-invasive approach for predicting ER status in breast cancer patients, potentially enhancing the efficiency and diagnostic precision of radiologists.

Previous studies have shown that the structural and functional impairments of hippocampal subregions in patients with obstructive sleep apnea (OSA) are related to cognitive impairment. Continuous positive airway pressure (CPAP) treatment can improve the clinical symptoms of OSA. Therefore, this study aimed to investigate functional connectivity (FC) changes in hippocampal subregions of patients with OSA after six months of CPAP treatment (post-CPAP) and its relationship with neurocognitive function. We collected and analyzed baseline (pre-CPAP) and post-CPAP data from 20 patients with OSA, including sleep monitoring, clinical evaluation, and resting-state functional magnetic resonance imaging. The results showed that compared with pre-CPAP OSA patients, the FC between the right anterior hippocampal gyrus and multiple brain regions, and between the left anterior hippocampal gyrus and posterior central gyrus were reduced in post-CPAP OSA patients. By contrast, the FC between the left middle hippocampus and the left precentral gyrus was increased. The changes in FC in these brain regions were closely related to cognitive dysfunction. Therefore, our findings suggest that CPAP treatment can effectively change the FC patterns of hippocampal subregions in patients with OSA, facilitating a better understanding of the neural mechanisms of cognitive function improvement, and emphasizing the importance of early diagnosis and timely treatment of OSA.

PURPOSE:To investigate dynamic functional connectivity (dFC) within the cerebellar-whole brain network and dynamic topological properties of the cerebellar network in obstructive sleep apnea (OSA) patients. METHODS:Sixty male patients and 60 male healthy controls were included. The sliding window method examined the fluctuations in cerebellum-whole brain dFC and connection strength in OSA. Furthermore, graph theory metrics evaluated the dynamic topological properties of the cerebellar network. Additionally, hidden Markov modeling validated the robustness of the dFC. The correlations between the abovementioned measures and clinical assessments were assessed. RESULTS:Two dynamic network states were characterized. State 2 exhibited a heightened frequency, longer fractional occupancy, and greater mean dwell time in OSA. The cerebellar networks and cerebrocerebellar dFC alterations were mainly located in the default mode network, frontoparietal network, somatomotor network, right cerebellar CrusI/II, and other networks. Global properties indicated aberrant cerebellar topology in OSA. Dynamic properties were correlated with clinical indicators primarily on emotion, cognition, and sleep. CONCLUSION:Abnormal dFC in male OSA may indicate an imbalance between the integration and segregation of brain networks, concurrent with global topological alterations. Abnormal default mode network interactions with high-order and low-level cognitive networks, disrupting their coordination, may impair the regulation of cognitive, emotional, and sleep functions in OSA.

This study investigated the abnormal dynamic functional connectivity (dFC) variability of the thalamo-cortical circuit in patients with obstructive sleep apnea (OSA) and explored the relationship between these changes and the clinical characteristics of patients with OSA. A total of 91 newly diagnosed patients with moderate-to-severe OSA and 84 education-matched healthy controls (HCs) were included. All participants underwent neuropsychological testing and a functional magnetic resonance imaging scan. We explored the thalamo-cortical dFC changes by dividing the thalamus into 16 subregions and combining them using a sliding-window approach. Correlation analysis assessed the relationship between dFC variability and clinical features, and the support vector machine method was used for classification. The OSA group exhibited increased dFC variability between the thalamic subregions and extensive cortical areas, compared with the HCs group. Decreased dFC variability was observed in some frontal-occipital-temporal cortical regions. These dFC changes positively correlated with daytime sleepiness, disease severity, and cognitive scores. Altered dFC variability contributed to the discrimination between patients with OSA and HCs, with a classification accuracy of 77.8%. Our findings show thalamo-cortical overactivation and disconnection in patients with OSA, disrupting information flow within the brain networks. These results enhance understanding of the temporal variability of thalamo-cortical circuits in patients with OSA.

Abstract Background Lung adenocarcinoma, a leading cause of cancer-related mortality, demands precise prognostic indicators for effective management. The presence of spread through air space (STAS) indicates adverse tumor behavior. However, comparative differences between 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography(PET)/computed tomography(CT) and CT in predicting STAS in lung adenocarcinoma remain inadequately explored. This retrospective study analyzes preoperative CT and 18F-FDG PET/CT features to predict STAS, aiming to identify key predictive factors and enhance clinical decision-making. Methods Between February 2022 and April 2023, 100 patients (108 lesions) who underwent surgery for clinical lung adenocarcinoma were enrolled. All these patients underwent 18F-FDG PET/CT, thin-section chest CT scan, and pathological biopsy. Univariate and multivariate logistic regression was used to analyze CT and 18F-FDG PET/CT image characteristics. Receiver operating characteristic curve analysis was performed to identify a cut-off value. Results Sixty lesions were positive for STAS, and 48 lesions were negative for STAS. The STAS-positive was frequently observed in acinar predominant. However, STAS-negative was frequently observed in minimally invasive adenocarcinoma. Univariable analysis results revealed that CT features (including nodule type, maximum tumor diameter, maximum solid component diameter, consolidation tumor ratio, pleural indentation, lobulation, spiculation) and all 18F-FDG PET/CT characteristics were statistically significant difference in STAS-positive and STAS-negative lesions. And multivariate logistic regression results showed that the maximum tumor diameter and SUVmax were the independent influencing factors of CT and 18F-FDG PET/CT in STAS, respectively. The area under the curve of maximum tumor diameter and SUVmax was 0.68 vs. 0.82. The cut-off value for maximum tumor diameter and SUVmax was 2.35 vs. 5.05 with a sensitivity of 50.0% vs. 68.3% and specificity of 81.2% vs. 87.5%, which showed that SUVmax was superior to the maximum tumor diameter. Conclusion The radiological features of SUVmax is the best model for predicting STAS in lung adenocarcinoma. These radiological features could predict STAS with excellent specificity but inferior sensitivity.

Introduction: Continuous positive airway pressure (CPAP) therapy improves clinical symptoms in patients with obstructive sleep apnea (OSA); however, the mechanism of this clinical improvement and how it may be associated with the restoration of white matter (WM) structures in the brain is unclear. Therefore, this study investigated the relationship between the structural recovery of brain WM and improvements in cognitive function and emotion after long-term (12 months) CPAP treatment in patients with OSA. Methods: We collected data from 17 patients with OSA before and 12 months after CPAP treatment, including sleep monitoring, clinical assessment, and diffusion tensor imaging (DTI) magnetic resonance imaging. Results: We observed a partial reversible recovery of brain WM (mean and radial diffusion coefficients) after treatment. This recovery involved the commissural fibers (cingulum, body of corpus callosum), projection fibers (retrolenticular part of the internal capsule, posterior thalamic radiation, posterior limb of the internal capsule, superior corona radiata, posterior corona radiata), association fibers (external capsule, superior longitudinal fasciculus, inferior longitudinal fasciculus), and other regions. In addition, the improvements in WM fibers in one part of the brain significantly were correlated with the Hamilton Anxiety Scale and Hamilton Depression Scale scores. Discussion: Our results suggest that reversible recovery of reduced brain WM integrity due to OSA may require longer CPAP treatment. Moreover, changes in the integrity of the commissural fibers were associated with emotion regulation. These restored WM areas may explain the cognitive and mood improvements observed after OSA treatment.

Purpose: This study is to evaluate the altered number of functional connection (s) in patients with obstructive sleep apnea (OSA) by functional connectivity density (FCD), to investigate its relationship with cognitive function, and to explore whether these features could be used to distinguish OSA from healthy controls (HCs). Methods: Seventy-six OSA patients and 72 HCs were included in the analysis. All participants underwent resting-state functional magnetic resonance imaging scan. Subsequently, intergroup differences between long-and short-range FCD groups were obtained in the Matlab platform by using the degree centrality option with a 75 mm cutoff. The partial correlation analysis were used to assess the relationship between the altered FCD value and clinical assessments in OSA patients. The FCD values of the different brain regions were used as classification features to distinguish the two groups by support vector machine (SVM). Results: Compared to HCs, OSA patients had decreased long-range FCD in the right superior frontal gyrus (SFG), right precuneus, and left middle frontal gyrus (MFG). Simultaneously, increased long-range FCD in the right cingulate gyrus (CG). Meanwhile, the short-range FCD were decreased in the right postcentral gyrus (PoCG), right SFG, left MFG, and right CG. The short-range FCD values of the right PoCG were correlated with the Montreal Cognitive Assessment scores in OSA patients. SVM analysis showed that FCD in differential brain regions could differentiate OSA patients from HCs. Conclusion: Long- and short-range FCD values in different brain regions of OSA patients may be related to cognitive decline, and also be effective in distinguishing OSA patients from HCs. These findings provide new perspectives on neurocognition in OSA patients.