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Idiopathic intracranial hypertension is a brain disease incorporating cerebrospinal fluid disturbance, increased intracranial pressure and visual failure, but with unknown cause. This study examined a hypothesis that glymphatic function is impaired in idiopathic intracranial hypertension patients. The MRI contrast agent gadobutrol was utilized as a cerebrospinal fluid tracer following intrathecal administration. Consecutive standardized T1 MRI acquisitions over 48 h were done to assess tracer distribution within brain of 15 idiopathic intracranial hypertension patients and 15 reference individuals who were comparable in age and gender distribution. Using FreeSurfer software, we semi-quantified tracer level in multiple brain regions as T1 MRI signal change. The tracer enriched the entire brain of idiopathic intracranial hypertension and reference subjects. In idiopathic intracranial hypertension, tracer enrichment was increased and clearance of tracer delayed from a wide range of brain regions, including both grey and white matter. Differences were most evident in frontal and temporal regions. The pulsatile intracranial pressure was measured overnight and tracer propagation in brain compared between individuals with pathological and normal pulsatile intracranial pressure. In individuals with pathological pulsatile intracranial pressure, tracer enrichment was stronger and clearance from brain delayed, particularly in regions nearby large artery trunks at the brain surface. The present in vivo observations provide evidence for impaired glymphatic function in several brain regions of idiopathic intracranial hypertension patients. Glymphatic failure may imply altered clearance of metabolic byproducts, which may precede neurodegeneration. Further studies are needed to characterize glymphatic failure in idiopathic intracranial hypertension., Intrathecal contrast-enhanced MRI provided evidence for impaired glymphatic function in many brain regions, including both grey and white matter, of patients with idiopathic intracranial hypertension, a brain disease characterized by CSF disturbance and increased intracranial pressure of unknown cause., Graphical Abstract Graphical Abstract

Background Glioblastoma is a rapidly fatal brain cancer that exhibits extensive intra- and intertumoral heterogeneity. Improving survival will require the development of personalized treatment strategies that can stratify tumors into subtypes that differ in therapeutic vulnerability and outcomes. Glioblastoma stratification has been hampered by intratumoral heterogeneity, limiting our ability to compare tumors in a consistent manner. Here, we develop methods that mitigate the impact of intratumoral heterogeneity on transcriptomic-based patient stratification. Methods We accessed open-source transcriptional profiles of histological structures from 34 human glioblastomas from the Ivy Glioblastoma Atlas Project. Principal component and correlation network analyses were performed to assess sample inter-relationships. Gene set enrichment analysis was used to identify enriched biological processes and classify glioblastoma subtype. For survival models, Cox proportional hazards regression was utilized. Transcriptional profiles from 156 human glioblastomas were accessed from The Cancer Genome Atlas to externally validate the survival model. Results We showed that intratumoral histologic architecture influences tumor classification when assessing established subtyping and prognostic gene signatures, and that indiscriminate sampling can produce misleading results. We identified the cellular tumor as a glioblastoma structure that can be targeted for transcriptional analysis to more accurately stratify patients by subtype and prognosis. Based on expression from cellular tumor, we created an improved risk stratification gene signature. Conclusions Our results highlight that biomarker performance for diagnostics, prognostics, and prediction of therapeutic response can be improved by analyzing transcriptional profiles in pure cellular tumor, which is a critical step toward developing personalized treatment for glioblastoma.

Introduction Cellular mechanisms underlying changes in small animal brain lactate concentrations have been investigated for more than 70 years and report sharp reductions in lactate (12-35%) during sleep or anesthesia relative to wakefulness. The goal of this study was to investigate alterations in human cerebral lactate concentrations across sleep-wake cycles. Toward this goal, we developed a novel non-invasive methodology, quantified changes in human cerebral lactate during sleep stages, and investigated potential mechanisms associated with changes in lactate. Methods Nine subjects (four females, five males; 21-27 y-o, mean age 24.2 ±2) were sleep deprived overnight, and underwent (5:45~11:00 am) experiments combining simultaneous MR-spectroscopy (MRS) and polysomnography (PSG) in a 3 T MR instrument using a 64-channel head/neck coil. A single voxel MRS (1H-MRS) acquired signals from a volume of interest (12~24 cm3) for every 7.5-s for 88~180-min. Lactate signal intensity was determined from each 7.5-s spectrum, normalized to corresponding water signal, and averaged over 30-s for each PSG epochs. Artifact corrected PSG data were scored for each 30-s epoch using the standard criteria and classified into one of four stages: W, N1, N2 and N3. Group mean lactate levels were quantified using LCModel. Three subjects returned for lactate diffusivity measurements using diffusion-sensitized PRESS MRS sequence. Results Compared to W, group mean lactate levels within each sleep stage showed a reduction of [4.9 ± 4.9] % in N1, [10.4 ± 5.2] % in N2, and [24.0 ± 5.8] % in N3. We observed a significant decrease in lactate apparent diffusion coefficient (ADC) accompanied by reduced brain lactate in sleep compared to wake (P Conclusion This is the first in-vivo report of alterations in human brain lactate concentrations across sleep-wake cycles. Observed decline in lactate levels during sleep compared to wakefulness is consistent with, and extends results from invasive small animal brain studies first reported more than 70 years ago, and support the notion of altered lactate metabolism and/or increased glymphatic activity in sleeping human brain. Support (if any) The Paul. G. Allen Family Foundation funded the study.

OBJECTIVE Personalized treatment strategies in Glioblastoma multiforme (GBM) has been hampered by intra-tumoral heterogeneity. The goals of this study were to (1) determine the impact of intra-tumoral heterogeneity on established predictive and prognostic transcriptional signatures in human GBM, and (2) develop methods to mitigate the impact of tissue heterogeneity on transcriptomic-based patient stratification. METHODS We analyzed transcriptional profiles of GBM histological structures from the open-source Ivy Glioblastoma Atlas Project. To generate these data, infiltrative tumor, leading edge, cellular tumor [CT], perinecrotic zones, pseudopalisading cells, hyperplastic blood vessels and microvascular proliferation were microdissected from 34 newly diagnosed GBM and underwent RNA sequencing. Data from The Cancer Genome Atlas were used for validation. Principle component analysis, network analysis and gene set enrichment analysis were used to probe gene expression patterns. RESULTS Distinct biological networks were enriched in each tumor histological structure. Classification of patients into GBM molecular subtypes varied based on the structure assessed, with many patients classified as every subtype depending on the structure analyzed. Using only CT to classify subtypes, we identified biologically unique patterns suggesting that proneural and mesenchymal tumors may be more sensitive to chemoradiotherapy and immunotherapy, respectively. Survival outcome predicted by an established multigene panel was confounded by histologic structure. Utilizing CT transcriptomics we developed a novel survival prediction gene signature that identified the highest-risk GBM patients in both CT and bulk tissue gene expression profiles. CONCLUSIONS Histologic structures contribute to intra-tumoral heterogeneity in GBM. Using mixed-structure biopsy samples could incorrectly subtype tumors and produce invalid patient stratification. Limiting transcriptomic analysis to the CT allowed us to develop a new survival prediction gene signature that appears accurate even in mixed tissue samples. The biological patterns uncovered in the subtypes and risk-stratified groups have important implications for guiding the development of precision medicine in GBM.