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Impact of social media use in static and dynamic functional network connectivity of social anxiety disorder

Sat, 01 Aug 2026 00:00:00 GMT

Title: Impact of social media use in static and dynamic functional network connectivity of social anxiety disorder Authors: Kim, Hesun Erin; Kim, Byung-Hoon; Cho, Yesol; Ko, Yujin; Kim, Jae-Jin Abstract: Although social media use patterns are related to social anxiety disorder (SAD), their modulatory relationship with functional network connectivity (FNC) change remains unexplored. This study aims to address this question by utilizing static and dynamic FNC analyses. Using resting-state fMRI data from 45 SAD patients and 43 healthy controls (HC) across two acquisition timepoints (baseline, 2-month follow-up), we examined static and dynamic FNC. Standard region-of-interest (ROI)-to-ROI analysis was performed for static FNC. Dynamic FNC was analyzed using sliding-window and kmeans clustering approach. Social media usage was differentiated between messaging (MSG) and social platform (SP) across four time-of-day segments. Influences of digital social behavior were assessed through correlation analysis. Patients showed decreased static connectivity within cognitive control networks, particularly involving the default mode network. Dynamic FNC analysis identified four brain states, with SAD showing altered temporal dynamics. Specifically, patients exhibited abnormalities in the temporal properties of State 1, characterized as an internally focused state linked to self-referential processing. Lowered State 1 occurrence at follow-up was associated with higher social media use-MSG use in HC and SP use in SAD. Greater SP use was related to increased engagements of an externally vigilant state (State 3) in SAD. Aberrant temporal properties in SAD illustrate difficulties disengaging from maladaptive self-referential processing and a propensity toward heightened sensory vigilance. Furthermore, social media use, particularly content-based SP, may modulate brain dynamics related to introspection and external processing among patients. The findings underscore comprehensive neurobiological underpinnings of SAD and impacts of digital social behavior.

Ultrasound Imaging Features Associated With Neoplastic Gallbladder Polyps: A Systematic Review and Meta-Analysis

Wed, 01 Apr 2026 00:00:00 GMT

Title: Ultrasound Imaging Features Associated With Neoplastic Gallbladder Polyps: A Systematic Review and Meta-Analysis Authors: Lee, Sunyoung; Chang, Won; Kim, Yeun-Yoon; Park, Jin Young; Jeon, Sun Kyung; Lee, Jeong Eun; Yoo, Jeongin; Han, Seungchul; Park, So Hyun; Kim, Jae Hyun; Park, Hyo Jung; Zhang, Hyun-Soo; Yoon, Jeong Hee Abstract: Objective: Although most gallbladder polyps are benign, some neoplastic polyps may be malignant or may serve as precursors to malignancy. Distinguishing neoplastic and non-neoplastic polyps using imaging examinations remains a major challenge. This meta-analysis aimed to identify the ultrasound (US) features that are significantly associated with neoplastic polyps. Materials and Methods: The MEDLINE, EMBASE, Cochrane, and KoreaMed databases were searched for articles published up to August 31, 2025. Bivariate random-effects models were used to calculate the meta-analytic pooled diagnostic odds ratios (DORs), sensitivities, and specificities, along with their 95% confidence intervals (CIs), for each US imaging feature in the diagnosis of neoplastic polyps. Results: Thirty studies evaluating 8,953 patients, including 1,216 (13.6%) patients with neoplastic polyps, were included. Among the nine evaluated US imaging features, namely, size >= 10 mm, sessile morphology, single polyp, coexisting gallstones, hypoechogenicity, heterogeneous echogenicity, gallbladder wall thickening (GBWT), absence of hyperechoic spot, and vascularity, eight were significantly associated with neoplastic polyps: size >= 10 mm (DOR: 6.23 [95% CI: 1.86-20.90]), sessile morphology (DOR: 3.54 [1.93-5.97]), single polyp (DOR: 2.21 [1.76-2.74]), coexisting gallstones (DOR: 1.86 [1.29-2.60]), hypoechogenicity (DOR: 3.55 [1.47-7.30]), GBWT (DOR: 9.38 [1.47-32.20]), absence of hyperechoic spots (DOR: 4.23 [2.46-6.83]), and vascularity (DOR: 9.72 [5.81-15.30]). Of these, size >= 10 mm demonstrated the highest pooled sensitivity (0.79 [95% CI: 0.68-0.87]), whereas hypoechogenicity showed the highest pooled specificity (0.93 [95% CI: 0.82-0.98]). Conclusion: Eight US imaging features (size >= 10 mm, sessile morphology, single polyp, coexisting gallstones, hypoechogenicity, GBWT, absence of hyperechoic spots, and vascularity) were significantly associated with the presence of neoplastic polyps. These features may facilitate the management of gallbladder polyps.

Association of T2-Weighted Imaging Features in Invasive Breast Cancer With Clinicopathologic Features and Neoadjuvant Treatment Outcomes

Wed, 01 Apr 2026 00:00:00 GMT

Title: Association of T2-Weighted Imaging Features in Invasive Breast Cancer With Clinicopathologic Features and Neoadjuvant Treatment Outcomes Authors: Youn, Inyoung; Roh, Yun Ho; Kim, Min Jung; Yoon, Jung Hyun; Kwon, Mi-ri; Park, Vivian Youngjean Abstract: Objective: To investigate the associations between T2-weighted imaging (T2WI) features and clinicopathologic characteristics in invasive breast cancer, as well as their relationship with treatment response to neoadjuvant chemotherapy (NAC). Materials and Methods: This retrospective study included 179 women with invasive breast cancer who underwent preoperative 3T breast MRI between November 2020 and February 2021. Intratumoral T2 signal intensity (SI) and peritumoral edema were graded on T2WI, and T2 relaxation times were calculated both including and excluding necrotic or cystic areas. T2 relaxation times were compared across T2 SI grades using the Kruskal-Wallis test. Associations between T2 features and clinicopathologic factors were assessed using chi-square tests and logistic regression analyses. In patients who received NAC (n = 68), associations between T2 features and NAC outcomes were also evaluated. Results: Higher intratumoral T2 SI and peritumoral edema grades were significantly associated with longer T2 relaxation times (P < 0.001). Intratumoral T2 SI grades were associated with higher clinical T category, axillary lymph node metastasis, and tumor multiplicity (all P < 0.05). Longer intratumoral T2 relaxation times were associated with higher clinical T category, hormone receptor (HR) negativity, and the triple-negative subtype (all P < 0.05), even after excluding necrotic or cystic areas. Higher peritumoral edema grades were associated with advanced clinical T category, HR negativity, and the triple-negative subtype (all P < 0.05). T2 relaxation times of peritumoral edema showed no significant associations, except with higher clinical T category (P = 0.005) and estrogen receptor status (P = 0.030). In the NAC subgroup, higher intratumoral T2 SI grades and longer T2 relaxation times were significantly associated with disease progression during NAC (P < 0.05), but not with non-pathologic complete response. Peritumoral edema showed no significant association with NAC outcomes (P > 0.05). Conclusion: T2-weighted MRI features were associated with clinicopathologic factors, including clinical T category, HR status, triple-negative subtype, and disease progression during NAC.

Key Measures for Evaluating Diagnostic Accuracy in Multi-Class Classification: An Overview and Simulation-Based Comparison

Wed, 01 Apr 2026 00:00:00 GMT

Title: Key Measures for Evaluating Diagnostic Accuracy in Multi-Class Classification: An Overview and Simulation-Based Comparison Authors: Ryu, Leeha; Han, Kyunghwa; Jung, Inkyung; Park, Yae Won; Ahn, Sung Soo; Hwang, Dosik; 박예원 Abstract: Recent advancements in artificial intelligence have led to increased interest in predictive modeling across various domains, including medicine. Although numerous metrics have been established for binary classification, the growing adoption of multi-class systems necessitates robust evaluation measures. However, comprehensive simulation studies comparing the performance of existing multi-class metrics under diverse data conditions remain limited. In this study, we first provide a concise overview of commonly used accuracy metrics for multi-class classification. Then, we report a simulation study that systematically evaluates several diagnostic accuracy measures under a wide range of scenarios, including three-and five-class settings, balanced and imbalanced sample sizes, and different distributional assumptions for predictors. We assessed each metric's performance in terms of bias and 95% confidence interval coverage. Under balanced conditions, most metrics demonstrated stable and unbiased performance, closely approximating the true values. However, under imbalanced conditions, greater bias was observed, with the M-index and polytomous discrimination index exhibiting comparatively more stable performance across various scenarios. The micro-averaged receiver operating characteristic curve area consistently showed higher bias under class imbalance. Finally, we applied these metrics to a glioma tumor grading task using external datasets. This study provides a systematic comparison of commonly used metrics and offers practical guidance for selecting appropriate measures in multi-class classification tasks.