Title: Genetic and symptomatic risks associated with longitudinal brain morphometry in bipolar disorder

Keypoints: 

• Question- Are there distinct brain cortical thickness patterns in adolescents at genetic and symptomatic risk for bipolar disorder, and can these structural brain markers at baseline prospectively predict who will later develop mood disorders?

• Findings- Youth with both genetic risk and subthreshold mood symptoms demonstrated increased cortical thickness across widespread brain regions, potentially reflecting adaptive or compensatory mechanisms that help maintain clinical stability during adolescence. In contrast, reduced baseline cortical thickness in frontotemporal regions was linked to a greater likelihood of progressing to mood disorders over time, mirroring patterns observed in individuals with established bipolar disorder.

• Meaning - Alterations in cortical thickness may serve as prospective biomarkers of both vulnerability and resilience to mood disorders among BD offspring. Recognizing these neurodevelopmental patterns could facilitate early risk stratification, targeted monitoring, and preventive interventions to delay or mitigate illness onset in high-risk individuals.

Title: Prospective longitudinal analysis of physiologic MRIbased tumor habitat predicts short-term patient outcomes in IDH-wildtype glioblastoma

Keypoints: 

• Question - Can multiparametric physiologic MRI-based tumor habitat analysis predict short-term outcomes and progression sites in IDH-wildtype glioblastoma patients?
• Findings - Two key imaging biomarkers were identified: (1) Hypovascular cellular habitat (characterized by low ADC + low CBV) independently predicted shorter time-to-progression (TTP) and overall survival (OS), and progression sites (mean Dice index: 0.31). (2) Habitat risk score (combining increases in hypervascular/hypovascular habitats) stratified patients into low/intermediate/high-risk groups and outperformed tumor volume in predicting 12-month TTP (AUC: 0.762 vs. 0.646).
• Meaning - Spatiotemporal tumor habitat analysis provides clinically actionable biomarkers for early progression prediction and risk stratification in glioblastoma, enabling personalized treatment decisions and improving prognostic accuracy beyond conventional volume-based assessments.