Brain Connectivity in \u3cem\u3eAteles geoffroyi\u3c/em\u3e: Resting-State Functional Magnetic Resonance Imaging of Working Memory and Executive Control

The objective of this research was to describe the organization and connectivity of the working memory (WM) and executive control (EC) networks in Ateles geoffroyi in resting-state conditions. Recent studies have shown that resting-state activity may underlie rudimentary brain functioning, showing that several brain regions can be tonically active at rest, maximizing the efficiency of information transfer while preserving a low physical connection cost. Whole-brain resting-state images were acquired from three healthy adult Ateles monkeys (2 females, 1 male; mean age 10.5 ± SD 2.5 years). Data were analyzed with independent component analysis, and results were grouped together using the GIFT software. The present study compared the EC and WM networks obtained with human data and with results found in the literature in other primate species. Nine resting-state networks were found, which were similar to resting networks found in healthy human adults in the prefrontal basal portion and frontopolar area. Additionally, components of the WM network were found to be extending into the hypothalamus and the olfactory areas. A key finding was the discovery of connections in the WM and EC networks to the hypothalamus, the motor cortex, and the entorhinal cortex, suggesting that information is integrated from larger brain areas. The correlated areas suggest that many elements of WM and EC may be conserved across primate species. Characterization of these networks in resting-state conditions in nonhuman primate brains is a fundamental prerequisite for understanding of the neural bases underlying the evolution and function of this cognitive system

Intertumoral Heterogeneity within Medulloblastoma Subgroups

While molecular subgrouping has revolutionized medulloblastoma classification, the extent of heterogeneity within subgroups is unknown. Similarity network fusion (SNF) applied to genome-wide DNA methylation and gene expression data across 763 primary samples identifies very homogeneous clusters of patients, supporting the presence of medulloblastoma subtypes. After integration of somatic copy-number alterations, and clinical features specific to each cluster, we identify 12 different subtypes of medulloblastoma. Integrative analysis using SNF further delineates group 3 from group 4 medulloblastoma, which is not as readily apparent through analyses of individual data types. Two clear subtypes of infants with Sonic Hedgehog medulloblastoma with disparate outcomes and biology are identified. Medulloblastoma subtypes identified through integrative clustering have important implications for stratification of future clinical trials

Intertumoral Heterogeneity within Medulloblastoma Subgroups

While molecular subgrouping has revolutionized medulloblastoma classification, the extent of heterogeneity within subgroups is unknown. Similarity network fusion (SNF) applied to genome-wide DNA methylation and gene expression data across 763 primary samples identifies very homogeneous clusters of patients, supporting the presence of medulloblastoma subtypes. After integration of somatic copy-number alterations, and clinical features specific to each cluster, we identify 12 different subtypes of medulloblastoma. Integrative analysis using SNF further delineates group 3 from group 4 medulloblastoma, which is not as readily apparent through analyses of individual data types. Two clear subtypes of infants with Sonic Hedgehog medulloblastoma with disparate outcomes and biology are identified. Medulloblastoma subtypes identified through integrative clustering have important implications for stratification of future clinical trials. [Display omitted] •Medulloblastoma comprises 12 subtypes; 2 WNT, 4 SHH, 3 group 3, and 3 group 4 groups•Heterogeneity within subgroups accounts for previously unexplained variation•Groups 3 and 4 medulloblastoma are molecularly distinct entities•Clinically and biologically relevant subtypes exist for each subgroup Cavalli et al. analyze 763 primary medulloblastoma samples using the similarity network fusion approach. They identify subtypes that have distinct somatic copy-number aberrations, activated pathways, and clinical outcomes within each of the four known subgroups and further delineate group 3 from group 4 MB

Intertumoral Heterogeneity within Medulloblastoma Subgroups

While molecular subgrouping has revolutionized medulloblastoma classification, the extent of heterogeneity within subgroups is unknown. Similarity network fusion (SNF) applied to genome-wide DNA methylation and gene expression data across 763 primary samples identifies very homogeneous clusters of patients, supporting the presence of medulloblastoma subtypes. After integration of somatic copy-number alterations, and clinical features specific to each cluster, we identify 12 different subtypes of medulloblastoma. Integrative analysis using SNF further delineates group 3 from group 4 medulloblastoma, which is not as readily apparent through analyses of individual data types. Two clear subtypes of infants with Sonic Hedgehog medulloblastoma with disparate outcomes and biology are identified. Medulloblastoma subtypes identified through integrative clustering have important implications for stratification of future clinical trials