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Abstract Background The spatial layout of large-scale functional brain networks exhibits considerable inter-individual variability, especially in the association cortex. Research has demonstrated a link between early socioeconomic status (SES) and variations in both brain structure and function, which are further associated with cognitive and mental health outcomes. However, the extent to which SES is associated with individual differences in personalized functional network topography during childhood remains largely unexplored. Methods We used a machine learning approach—spatially regularized non-negative matrix factorization (NMF)—to delineate 17 personalized functional networks in children aged 9–10 years, utilizing high-quality functional MRI data from 6001 participants in the Adolescent Brain Cognitive Development study. Partial least square regression approach with repeated random twofold cross-validation was used to evaluate the association between the multivariate pattern of functional network topography and three SES factors, including family income-to-needs ratio, parental education, and neighborhood disadvantage. Results We found that individual variations in personalized functional network topography aligned with the hierarchical sensorimotor-association axis across the cortex. Furthermore, we observed that functional network topography significantly predicted the three SES factors from unseen individuals. The associations between functional topography and SES factors were also hierarchically organized along the sensorimotor-association cortical axis, exhibiting stronger positive associations in the higher-order association cortex. Additionally, we have made the personalized functional networks publicly accessible. Conclusions These results offer insights into how SES influences neurodevelopment through personalized functional neuroanatomy in childhood, highlighting the cortex-wide, hierarchically organized plasticity of the functional networks in response to diverse SES backgrounds.

Abstract Background The different symptoms of major depressive disorder (MDD) in adolescents compared to adults suggested there may be differences in the pathophysiology between adolescents and adults with MDD. However, despite the amygdala being considered critical in the pathophysiology, there was limited knowledge about the commonalities and differences in the resting-state functional connectivity (rsFC) of amygdala subregions in MDD patients of different age groups. Methods In the current study, 65 adolescents (46 with MDD and 19 controls) and 91 adults (35 with MDD and 56 controls) were included. A seed-based functional connectivity analysis was performed for each of the amygdala subregions. A 2 × 2 ANOVA was used to analyze the main effect of age, diagnosis, and their interaction on the rsFC of each subregion. Results A significant main effect of age was revealed in the rsFC of bilateral centromedial (CM) subregions and right laterobasal (LB) subregion with several brain regions in the limbic system and frontoparietal network. The significant main effect of diagnosis showed MDD patients of different ages showed higher connectivity than controls between the right LB and left middle frontal gyrus (MFG). Conclusions The rsFC of specific amygdala subregions with brain regions in the limbic system and frontoparietal network is affected by age, indicating a distinct amygdala connectivity profile in adolescents. The decreased rsFC between the right LB and the left MFG in adolescents and adults with MDD could serve as a diagnostic biomarker and a target of nonpharmacological treatment for MDD.

Abstract While functional MRI (fMRI) studies have mainly focused on gray matter, recent studies have consistently found that blood-oxygenation-level-dependent (BOLD) signals can be reliably detected in white matter, and functional connectivity (FC) has been organized into distributed networks in white matter. Nevertheless, it remains unclear whether this white matter FC reflects underlying electrophysiological synchronization. To address this question, we employ intracranial stereotactic-electroencephalography (SEEG) and resting-state fMRI data from a group of 16 patients with drug-resistant epilepsy. We find that BOLD FC is correlated with SEEG FC in white matter, and this result is consistent across a wide range of frequency bands for each participant. By including diffusion spectrum imaging data, we also find that white matter FC from both SEEG and fMRI are correlated with white matter structural connectivity, suggesting that anatomical fiber tracts underlie the functional synchronization in white matter. These results provide evidence for the electrophysiological and structural basis of white matter BOLD FC, which could be a potential biomarker for psychiatric and neurological disorders.

BackgroundGliomas are the most common primary tumors of the central nervous system, with high heterogeneity and highly variable survival rates. Accurate classification and prognostic assessment are key to the selection of treatment strategies. One hallmark of the tumor is resistance to cell death. PANoptosis, a novel mode of programmed cell death, has been frequently reported to be involved in the innate immunity associated with pathogen infection and played an important role in cancers. However, the intrinsic association of PANoptosis with glioma requires deeper investigation.MethodsThe genetics and expression of the 17 reported PANoptosome-related genes were analyzed in glioma. Based on these genes, patients were divided into two subtypes by consensus clustering analysis. After obtaining the differentially expressed genes between clusters, a prognostic model called PANopotic score was constructed after univariate Cox regression, LASSO regression, and multivariate Cox regression. The expression of the 5 genes included in the PANopotic score was also examined by qPCR in our cohort. The prognostic differences, clinical features, TME infiltration status, and immune characteristics between PANoptotic clusters and score groups were compared, some of which even extended to pan-cancer levels.ResultsGene mutations, CNVs and altered gene expression of PANoptosome-related genes exist in gliomas. Two PANoptotic clusters were significantly different in prognosis, clinical features, immune characteristics, and mutation landscapes. The 5 genes included in the PANopotic score had significantly altered expression in glioma samples in our cohort. The high PANoptotic score group was inclined to show an unfavorable prognosis, lower tumor purity, worse molecular genetic signature, and distinct immune characteristics related to immunotherapy. The PANoptotic score was considered as an independent prognostic factor for glioma and showed superior prognostic assessment efficacy over several reported models. PANopotic score was included in the nomogram constructed for the potential clinical prognostic application. The associations of PANoptotic score with prognostic assessment and tumor immune characteristics were also reflected at the pan-cancer level.ConclusionMolecular subtypes of glioma based on PANoptosome-related genes were proposed and PANoptotic score was constructed with different clinical characteristics of anti-tumor immunity. The potential intrinsic association between PANoptosis and glioma subtypes, prognosis, and immunotherapy was revealed.