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The aim of the current study was to investigate whether the experience of insomnia symptoms per se and symptoms of insomnia due to stress are associated with an increased brain response to the presentation of emotional faces. In addition, we also examined whether the effect of these sleep difficulties on emotional reactivity at the brain level depends on the experience of emotion regulation (ER) difficulties. The current sample consisted of 37 individuals (20 females, 17 males) selected from a larger group of 120 respondents who completed a survey about sleep problems and ER difficulties. Our results indicate that the tendency to experience stress-related insomnia symptoms but not insomnia symptoms per se modulates brain responses to emotional facial expressions, especially in areas of the parietal cortex, insula, and surrounding opercular voxels. Furthermore, difficulties in ER might play an important role, as the effect of stress-related insomnia symptoms on most of these brain regions disappears when controlling for difficulties in ER. However, an effect in the insula was maintained during the presentation of angry faces, suggesting that stress-related insomnia symptoms may increase the brain response to anger in the insula relatively independent from difficulties in ER. These findings suggest that individuals affected by stress-related insomnia symptoms show an enhanced brain response when presented with emotional stimuli (either positive or negative) in brain areas associated with hyperarousal, which could represent a possible ER deficit in these individuals. Thus, interventions that focus on targeting ER difficulties might be effective in reducing the hyperarousal state in individuals affected by stress-related insomnia symptoms.

Objective: To summarize evidence for use of advanced MRI techniques as monitoring biomarkers in the clinic, and to highlight the latest bench-to-bedside developments. Methods: The current evidence regarding the potential for monitoring biomarkers was reviewed and individual modalities of metabolism and/or chemical composition imaging discussed. Perfusion, permeability, and microstructure imaging were similarly analyzed in Part 1 of this two-part review article and are valuable reading as background to this article. We appraise the clinic readiness of all the individual modalities and consider methodologies involving machine learning (radiomics) and the combination of MRI approaches (multiparametric imaging). Results: The biochemical composition of high-grade gliomas is markedly different from healthy brain tissue. Magnetic resonance spectroscopy allows the simultaneous acquisition of an array of metabolic alterations, with choline-based ratios appearing to be consistently discriminatory in treatment response assessment, although challenges remain despite this being a mature technique. Promising directions relate to ultra-high field strengths, 2-hydroxyglutarate analysis, and the use of non-proton nuclei. Labile protons on endogenous proteins can be selectively targeted with chemical exchange saturation transfer to give high resolution images. The body of evidence for clinical application of amide proton transfer imaging has been building for a decade, but more evidence is required to confirm chemical exchange saturation transfer use as a monitoring biomarker. Multiparametric methodologies, including the incorporation of nuclear medicine techniques, combine probes measuring different tumor properties. Although potentially synergistic, the limitations of each individual modality also can be compounded, particularly in the absence of standardization. Machine learning requires large datasets with high-quality annotation; there is currently low-level evidence for monitoring biomarker clinical application. Conclusion: Advanced MRI techniques show huge promise in treatment response assessment. The clinical readiness analysis highlights that most monitoring biomarkers require standardized international consensus guidelines, with more facilitation regarding technique implementation and reporting in the clinic.

ObjectiveSummarize evidence for use of advanced MRI techniques as monitoring biomarkers in the clinic, and highlight the latest bench-to-bedside developments.MethodsExperts in advanced MRI techniques applied to high-grade glioma treatment response assessment convened through a European framework. Current evidence regarding the potential for monitoring biomarkers in adult high-grade glioma is reviewed, and individual modalities of perfusion, permeability, and microstructure imaging are discussed (in Part 1 of two). In Part 2, we discuss modalities related to metabolism and/or chemical composition, appraise the clinic readiness of the individual modalities, and consider post-processing methodologies involving the combination of MRI approaches (multiparametric imaging) or machine learning (radiomics).ResultsHigh-grade glioma vasculature exhibits increased perfusion, blood volume, and permeability compared with normal brain tissue. Measures of cerebral blood volume derived from dynamic susceptibility contrast-enhanced MRI have consistently provided information about brain tumor growth and response to treatment; it is the most clinically validated advanced technique. Clinical studies have proven the potential of dynamic contrast-enhanced MRI for distinguishing post-treatment related effects from recurrence, but the optimal acquisition protocol, mode of analysis, parameter of highest diagnostic value, and optimal cut-off points remain to be established. Arterial spin labeling techniques do not require the injection of a contrast agent, and repeated measurements of cerebral blood flow can be performed. The absence of potential gadolinium deposition effects allows widespread use in pediatric patients and those with impaired renal function. More data are necessary to establish clinical validity as monitoring biomarkers. Diffusion-weighted imaging, apparent diffusion coefficient analysis, diffusion tensor or kurtosis imaging, intravoxel incoherent motion, and other microstructural modeling approaches also allow treatment response assessment; more robust data are required to validate these alone or when applied to post-processing methodologies.ConclusionConsiderable progress has been made in the development of these monitoring biomarkers. Many techniques are in their infancy, whereas others have generated a larger body of evidence for clinical application.

Little is known about the natural growth characteristics of untreated glioblastoma before surgical or therapeutic intervention, because patients are rapidly treated after preliminary radiographic diagnosis. Understanding the growth characteristics of uninhibited human glioblastoma may be useful for characterizing changes in response to therapy. Thus, the objective of the current study was to explore tumor growth dynamics in a cohort of patients with untreated glioblastoma before surgical or therapeutic intervention.Ninety-five patients with glioblastoma who had measurable enhancing disease on2 magnetic resonance imaging scans before surgery were identified. Tumor growth rates were quantified in 4 different ways (the percentage change per day, the absolute rate of change per day, the estimated volumetric doubling time, and the radial expansion rate) using 3 different approaches (bidirectional product, enhancing disease, and total lesion volume).The median volumetric doubling time was 21.1 days, the percentage change in tumor volume was 2.1% per day, and the rate of change in total lesion volume was 0.18 cc per day. The length of follow-up between magnetic resonance imaging examinations should be28 days to detect progressive disease with high specificity. Small initial tumor sizes (3 cm in greatest dimension) are biased toward a large percentage change at follow-up.Presurgical, treatment-naive glioblastoma growth dynamics can be estimated in a variety of ways with similar results. The percentage changes in tumor size and volume depend on baseline tumor size and the time interval between scans. Cancer 2016;122:1718-27. © 2016 American Cancer Society.

Cancer therapy for both central nervous system (CNS) and non-CNS tumors has been previously associated with transient and long-term cognitive deterioration, commonly referred to as 'chemo fog'. This therapy-related damage to otherwise normal-appearing brain tissue is reported using post-mortem neuropathological analysis. Although the literature on monitoring therapy effects on structural magnetic resonance imaging (MRI) is well established, such macroscopic structural changes appear relatively late and irreversible. Early quantitative MRI biomarkers of therapy-induced damage would potentially permit taking these treatment side effects into account, paving the way towards a more personalized treatment planning.This systematic review (PROSPERO number 224196) provides an overview of quantitative tomographic imaging methods, potentially identifying the adverse side effects of cancer therapy in normal-appearing brain tissue. Seventy studies were obtained from the MEDLINE and Web of Science databases. Studies reporting changes in normal-appearing brain tissue using MRI, PET, or SPECT quantitative biomarkers, related to radio-, chemo-, immuno-, or hormone therapy for any kind of solid, cystic, or liquid tumor were included. The main findings of the reviewed studies were summarized, providing also the risk of bias of each study assessed using a modified QUADAS-2 tool. For each imaging method, this review provides the methodological background, and the benefits and shortcomings of each method from the imaging perspective. Finally, a set of recommendations is proposed to support future research. ispartof: MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE vol:35 issue:1 pages:163-186 ispartof: location:Germany status: published