Elevated plasma ceramide levels in post-menopausal women: a cross-sectional study.

Circulating ceramide levels are abnormally elevated in age-dependent pathologies such as cardiovascular diseases, obesity and Alzheimer's disease. Nevertheless, the potential impact of age on plasma ceramide levels has not yet been systematically examined. In the present study, we quantified a focused panel of plasma ceramides and dihydroceramides in a cohort of 164 subjects (84 women) 19 to 80 years of age. After adjusting for potential confounders, multivariable linear regression analysis revealed a positive association between age and ceramide (d18:1/24:0) (β (SE) = 5.67 (2.38); p = .0198) and ceramide (d18:1/24:1) (β (SE) = 2.88 (.61); p < .0001) in women, and between age and ceramide (d18:1/24:1) in men (β (SE) = 1.86 (.77); p = .0179). In women of all ages, but not men, plasma ceramide (d18:1/24:1) was negatively correlated with plasma estradiol (r = -0.294; p = .007). Finally, in vitro experiments in human cancer cells expressing estrogen receptors showed that incubation with estradiol (10 nM, 24 h) significantly decreased ceramide accumulation. Together, the results suggest that aging is associated with an increase in circulating ceramide levels, which in post-menopausal women is at least partially associated with lower estradiol levels

Organization and hierarchy of the human functional brain network lead to a chain-like core

The brain is a paradigmatic example of a complex system: its functionality emerges as a global property of local mesoscopic and microscopic interactions. Complex network theory allows to elicit the functional architecture of the brain in terms of links (correlations) between nodes (grey matter regions) and to extract information out of the noise. Here we present the analysis of functional magnetic resonance imaging data from forty healthy humans at rest for the investigation of the basal scaffold of the functional brain network organization. We show how brain regions tend to coordinate by forming ahighly hierarchical chain-like structure of homogeneously clustered anatomical areas. A maximum spanning tree approach revealed the centrality of the occipital cortex and the peculiar aggregation of cerebellar regions to form a closed core. We also report the hierarchy of network segregation and the level of clusters integration as a function of the connectivity strength between brain regions

Influence of APOE and RNF219 on Behavioral and Cognitive Features of Female Patients Affected by Mild Cognitive Impairment or Alzheimer's Disease.

The risk for Alzheimer's disease (AD) is associated with the presence of the ?4 allele of Apolipoprotein E (APOE) gene and, recently, with a novel genetic variant of the RNF219 gene. This study aimed at evaluating interactions between APOE-?4 and RNF219/G variants in the modulation of behavioral and cognitive features of two cohorts of patients suffering from mild cognitive impairment (MCI) or AD. We enrolled a total of 173 female MCI or AD patients (83 MCI; 90 AD). Subjects were screened with a comprehensive set of neuropsychological evaluations and genotyped for the APOE and RNF219 polymorphic variants. Analysis of covariance was performed to assess the main and interaction effects of APOE and RNF219 genotypes on the cognitive and behavioral scores. The analysis revealed that the simultaneous presence of APOE-?4 and RNF219/G variants results in significant effects on specific neuropsychiatric scores in MCI and AD patients. In MCI patients, RNF219 and APOE variants worked together to impact the levels of anxiety negatively. Similarly, in AD patients, the RNF219 variants were found to be associated with increased anxiety levels. Our data indicate a novel synergistic activity APOE and RNF219 in the modulation of behavioral traits of female MCI and AD patients

Neurovascular and neuroinflammatory mechanisms associated with mood disorders

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Loss of function mutations in the progranulin gene are related to pro-inflammatory cytokine dysregulation in frontotemporal lobar degeneration patients

The progranulin gene (PGRN) encodes a pleiotropic molecule with anti-inflammatory actions and neuronal protective effects. Accordingly, PGRN-deficient mice have been demonstrated to develop enhanced inflammation and progressive neurodegeneration. Loss of function mutations of the PGRN gene have been also reported to cause frontotemporal lobar degeneration (FTLD), a neurodegenerative disease leading to dementia generally in the presenium. Since neurodegeneration might be negatively impacted by chronic inflammation, the possible influence of PGRN defects on inflammatory pathways appears to be of great relevance for the understanding of neurodegeneration pathogenic processes in those patients. However, no data about the inflammatory profile of PGRN-defective subjects have been so far provided. In this study, we analyzed serum levels of the pro-inflammatory mediators IL-6, TNF-α and IL-18 in FTLD patients with or without PGRN mutations, at both pre-symptomatic and symptomatic stages. We provide evidence that circulating IL-6 is increased in PGRN-mutated FTLD patients, as compared to both PGRN non-mutated FTLD patients and controls. In contrast, levels of IL-6 were not altered in asymptomatic subjects carrying the PGRN mutations. Finally, TNF-α and IL-18 serum levels did not differ among all groups of included subjects. We conclude that the profile of circulating pro-inflammatory cytokines is altered in PGRN-related symptomatic FTLD. Thus, our findings point to IL-6 as a possible specific mediator and a potential therapeutic target in this monogenic disease, suggesting that an enhanced inflammatory response might be indeed involved in its progression

Neuroinflammation and excitatory symptoms in bipolar disorder

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Metastable States of Multiscale Brain Networks Are Keys to Crack the Timing Problem

The dynamics of the environment where we live in and the interaction with it, predicting events, provided strong evolutionary pressures for the brain functioning to process temporal information and generate timed responses. As a result, the human brain is able to process temporal information and generate temporal patterns. Despite the clear importance of temporal processing to cognition, learning, communication and sensory, motor and emotional processing, the basal mechanisms of how animals differentiate simple intervals or provide timed responses are still under debate. The lesson we learned from the last decade of research in neuroscience is that functional and structural brain connectivity matter. Specifically, it has been accepted that the organization of the brain in interacting segregated networks enables its function. In this paper we delineate the route to a promising approach for investigating timing mechanisms. We illustrate how novel insight into timing mechanisms can come by investigating brain functioning as a multi-layer dynamical network whose clustered dynamics is bound to report the presence of metastable states. We anticipate that metastable dynamics underlie the real-time coordination necessary for the brain's dynamic functioning associated to time perception. This new point of view will help further clarifying mechanisms of neuropsychiatric disorders

The ENIGMA Stroke Recovery Working Group: Big data neuroimaging to study brain–behavior relationships after stroke

The goal of the Enhancing Neuroimaging Genetics through Meta‐Analysis (ENIGMA) Stroke Recovery working group is to understand brain and behavior relationships using well‐powered meta‐ and mega‐analytic approaches. ENIGMA Stroke Recovery has data from over 2,100 stroke patients collected across 39 research studies and 10 countries around the world, comprising the largest multisite retrospective stroke data collaboration to date. This article outlines the efforts taken by the ENIGMA Stroke Recovery working group to develop neuroinformatics protocols and methods to manage multisite stroke brain magnetic resonance imaging, behavioral and demographics data. Specifically, the processes for scalable data intake and preprocessing, multisite data harmonization, and large‐scale stroke lesion analysis are described, and challenges unique to this type of big data collaboration in stroke research are discussed. Finally, future directions and limitations, as well as recommendations for improved data harmonization through prospective data collection and data management, are provided