Major depressive disorder (MDD) is a globally prevalent and highly disabling disease characterized by dysfunction of large-scale brain networks. Previous studies have found that static functional connectivity is not sufficient to reflect the complicated and time-varying properties of the brain. The underlying dynamic interactions between brain functional networks of MDD remain largely unknown, and it is also unclear whether neuroimaging-based dynamic properties are sufficiently robust to discriminate individuals with MDD from healthy controls since the diagnosis of MDD mainly depends on symptom-based criteria evaluated by clinical observation. Resting-state functional magnetic resonance imaging (fMRI) data of 221 MDD patients and 215 healthy controls were shared by REST-meta-MDD consortium. We investigated the spatial-temporal dynamics of MDD using co-activation pattern analysis and made individual diagnoses using support vector machine (SVM). We found that MDD patients exhibited aberrant dynamic properties (such as dwell time, occurrence rate, transition probability, and entropy of Markov trajectories) in some transient networks including subcortical network (SCN), activated default mode network (DMN), de-activated SCN-cerebellum network, a joint network, activated attention network (ATN), and de-activated DMN-ATN, where some dynamic properties were indicative of depressive symptoms. The trajectories of other networks to deactivated DMN-ATN were more accessible in MDD patients. Subgroup analyses also showed subtle dynamic changes in first-episode drug-naïve (FEDN) MDD patients. Finally, SVM achieved preferable accuracies of 84.69%, 76.77%, and 88.10% in discriminating patients with MDD, FEDN MDD, and recurrent MDD from healthy controls with their dynamic metrics. Our findings reveal that MDD is characterized by aberrant dynamic fluctuations of brain network and the feasibility of discriminating MDD patients using dynamic properties, which provide novel insights into the neural mechanism of MDD.

The aim of this study was to investigate whether pretherapeutic, multiparametric magnetic resonance imaging (MRI) radiomic features can be used for predict

Background: Perineural invasion (PNI) of intrahepatic cholangiocarcinoma (ICC) is a strong independent risk factor for tumour recurrence and long-term patient survival. However, there is a lack of noninvasive tools for accurately predicting the PNI status. The authors develop and validate a combined model incorporating radiomics signature and clinicoradiological features based on machine learning for predicting PNI in ICC, and used the Shapley Additive explanation (SHAP) to visualize the prediction process for clinical application. Methods: This retrospective and prospective study included 243 patients with pathologically diagnosed ICC (training, n=136; external validation, n=81; prospective, n=26, respectively) who underwent preoperative contrast-enhanced computed tomography between January 2012 and May 2023 at three institutions (three tertiary referral centres in Guangdong Province, China). The ElasticNet was applied to select radiomics features and construct signature derived from computed tomography images, and univariate and multivariate analyses by logistic regression were used to identify the significant clinical and radiological variables with PNI. A robust combined model incorporating radiomics signature and clinicoradiological features based on machine learning was developed and the SHAP was used to visualize the prediction process. A Kaplan–Meier survival analysis was performed to compare prognostic differences between PNI-positive and PNI-negative groups and was conducted to explore the prognostic information of the combined model. Results: Among 243 patients (mean age, 61.2 years ± 11.0 (SD); 152 men and 91 women), 108 (44.4%) were diagnosed as PNI-positive. The radiomics signature was constructed by seven radiomics features, with areas under the curves of 0.792, 0.748, and 0.729 in the training, external validation, and prospective cohorts, respectively. Three significant clinicoradiological features were selected and combined with radiomics signature to construct a combined model using machine learning. The eXtreme Gradient Boosting exhibited improved accuracy and robustness (areas under the curves of 0.884, 0.831, and 0.831, respectively). Survival analysis showed the construction combined model could be used to stratify relapse-free survival (hazard ratio, 1.933; 95% CI: 1.093–3.418; P=0.021). Conclusions: We developed and validated a robust combined model incorporating radiomics signature and clinicoradiological features based on machine learning to accurately identify the PNI statuses of ICC, and visualize the prediction process through SHAP for clinical application.

Major depressive disorder (MDD) is a devastating mental disorder that affects up to 17% of the population worldwide. Although brain-wide network-level abnormalities in MDD patients via resting-state functional magnetic resonance imaging (rsfMRI) exist, the mechanisms underlying these network changes are unknown, despite their immense potential for depression diagnosis and management. Here, we show that the astrocytic calcium-deficient mice, inositol 1,4,5-trisphosphate-type-2 receptor knockout mice ( Itpr2 ^−/− mice), display abnormal rsfMRI functional connectivity (rsFC) in depression-related networks, especially decreased rsFC in medial prefrontal cortex (mPFC)–related pathways. We further uncover rsFC decreases in MDD patients highly consistent with those of Itpr2 ^−/− mice, especially in mPFC-related pathways. Optogenetic activation of mPFC astrocytes partially enhances rsFC in depression-related networks in both Itpr2 ^−/− and wild-type mice. Optogenetic activation of the mPFC neurons or mPFC-striatum pathway rescues disrupted rsFC and depressive-like behaviors in Itpr2 ^−/− mice. Our results identify the previously unknown role of astrocyte dysfunction in driving rsFC abnormalities in depression.

Background and Purpose: Lymph node status is a key factor for the recommendation of organ preservation for patients with locally advanced rectal cancer (LARC) following neoadjuvant therapy but generally confirmed post-operation. This study aimed to preoperatively predict the lymph node status following neoadjuvant therapy using multiparametric magnetic resonance imaging (MRI)-based radiomic signature. Materials and Methods: A total of 391 patients with LARC who underwent neoadjuvant therapy and TME were included, of which 261 and 130 patients were allocated to the primary cohort and the validation cohort, respectively. The tumor area, as determined by preoperative MRI, underwent radiomics analysis to build a radiomic signature related to lymph node status. Two radiologists reassessed the lymph node status on MRI. The radiomic signature and restaging results were included in a multivariate analysis to build a combined model for predicting the lymph node status. Stratified analyses were performed to test the predictive ability of the combined model in patients with post-therapeutic MRI T1-2 or T3-4 tumors, respectively. Results: The combined model was built in the primary cohort, and predicted lymph node metastasis (LNM+) with an area under the curve of 0.818 and a negative predictive value (NPV) of 93.7% were considered in the validation cohort. Stratified analyses indicated that the combined model could predict LNM+ with a NPV of 100 and 87.8% in the post-therapeutic MRI T1-2 and T3-4 subgroups, respectively. Conclusion: This study reveals the potential of radiomics as a predictor of lymph node status for patients with LARC following neoadjuvant therapy, especially for those with post-therapeutic MRI T1-2 tumors.