Investigating the role of pericytes in cerebral autoregulation: a modelling study

The brain's inability to store nutrients for more than a few seconds makes it one of the most tightly regulated systems in the body. Driven by metabolic demand, cerebral autoregulation (CA) ensures a constant cerebral blood flow (CBF) over a +/- 50% change in arterial blood pressure (ABP) from baseline. Recent evidence suggests that pericytes, contractile cells in the capillary bed, play a previously-ignored regulatory role. To elucidate the CA phenomenon, the role of oxygen metabolism, pericyte activity and neural signalling in CBF modulation were quantified. Driven by nutrient metabolism in the tissue and pressure sensitivity in the vasculature, the model introduced here successfully replicates CA. To highlight the role of different vessel sizes, vessels with a diameter above 1mm were represented using a lumped parameter model while the microvasculature was illustrated as a branching tree network model. This novel approach elucidated the relationship between the microvasculature's nutrient supply and arterial regulation. Capillary responses to local increases in neuronal activity were experimentally determined, showing that pericytes can increase the diameter of the adjacent vessel by 2.5% in approximately 1s. Their response was quantified and included in the computational model as an active component of the capillary bed. To compare the efficacy model presented here to existing ones, four feedback mechanisms were tested. To simulate dynamic CBF regulation a 10% increase in ABP was imposed. This resulted in a 23.79-34.33% peak increase in CBF, depending on the nature of the feedback mechanism of the model. The four feedback mechanisms that were studied significantly differ in the response time, ultimately highlighting that capillaries play a fundamental role in the rapid regulation of CBF. Conclusively, this study indicates that while pericytes do not greatly alter the peak CBF change, they play a fundamental role in the speed of regulation

Choice of method of place cell classification determines the population of cells identified

Place cells, spatially responsive hippocampal cells, provide the neural substrate supporting navigation and spatial memory. Historically most studies of these neurons have used electrophysiological recordings from implanted electrodes but optical methods, measuring intracellular calcium, are becoming increasingly common. Several methods have been proposed as a means to identify place cells based on their calcium activity but there is no common standard and it is unclear how reliable different approaches are. Here we tested four methods that have previously been applied to two-photon hippocampal imaging or electrophysiological data, using both model datasets and real imaging data. These methods use different parameters to identify place cells, including the peak activity in the place field, compared to other locations (the Peak method); the stability of cells’ activity over repeated traversals of an environment (Stability method); a combination of these parameters with the size of the place field (Combination method); and the spatial information held by the cells (Information method). The methods performed differently from each other on both model and real data. In real datasets, vastly different numbers of place cells were identified using the four methods, with little overlap between the populations identified as place cells. Therefore, choice of place cell detection method dramatically affects the number and properties of identified cells. Ultimately, we recommend the Peak method be used in future studies to identify place cell populations, as this method is robust to moderate variations in place field within a session, and makes no inherent assumptions about the spatial information in place fields, unless there is an explicit theoretical reason for detecting cells with more narrowly defined properties

Gradual not sudden change: multiple sites of functional transition across the microvascular bed

In understanding the role of the neurovascular unit as both a biomarker and target for disease interventions, it is vital to appreciate how the function of different components of this unit change along the vascular tree. The cells of the neurovascular unit together perform an array of vital functions, protecting the brain from circulating toxins and infection, while providing nutrients and clearing away waste products. To do so, the brain’s microvasculature dilates to direct energy substrates to active neurons, regulates access to circulating immune cells, and promotes angiogenesis in response to decreased blood supply, as well as pulsating to help clear waste products and maintain the oxygen supply. Different parts of the cerebrovascular tree contribute differently to various aspects of these functions, and previously, it has been assumed that there are discrete types of vessel along the vascular network that mediate different functions. Another option, however, is that the multiple transitions in function that occur across the vascular network do so at many locations, such that vascular function changes gradually, rather than in sharp steps between clearly distinct vessel types. Here, by reference to new data as well as by reviewing historical and recent literature, we argue that this latter scenario is likely the case and that vascular function gradually changes across the network without clear transition points between arteriole, precapillary arteriole and capillary. This is because classically localized functions are in fact performed by wide swathes of the vasculature, and different functional markers start and stop being expressed at different points along the vascular tree. Furthermore, vascular branch points show alterations in their mural cell morphology that suggest functional specializations irrespective of their position within the network. Together this work emphasizes the need for studies to consider where transitions of different functions occur, and the importance of defining these locations, in order to better understand the vascular network and how to target it to treat disease

A multi-hit hypothesis for an APOE4-dependent pathophysiological state

The APOE gene encoding the Apolipoprotein E protein is the single most significant genetic risk factor for late-onset Alzheimer's disease. The APOE4 genotype confers a significantly increased risk relative to the other two common genotypes APOE3 and APOE2. Intriguingly, APOE4 has been associated with neuropathological and cognitive deficits in the absence of Alzheimer's disease-related amyloid or tau pathology. Here, we review the extensive literature surrounding the impact of APOE genotype on central nervous system dysfunction, focussing on preclinical model systems and comparison of APOE3 and APOE4, given the low global prevalence of APOE2. A multi-hit hypothesis is proposed to explain how APOE4 shifts cerebral physiology towards pathophysiology through interconnected hits. These hits include the following: neurodegeneration, neurovascular dysfunction, neuroinflammation, oxidative stress, endosomal trafficking impairments, lipid and cellular metabolism disruption, impaired calcium homeostasis and altered transcriptional regulation. The hits, individually and in combination, leave the APOE4 brain in a vulnerable state where further cumulative insults will exacerbate degeneration and lead to cognitive deficits in the absence of Alzheimer's disease pathology and also a state in which such pathology may more easily take hold. We conclude that current evidence supports an APOE4 multi-hit hypothesis, which contributes to an APOE4 pathophysiological state. We highlight key areas where further study is required to elucidate the complex interplay between these individual mechanisms and downstream consequences, helping to frame the current landscape of existing APOE-centric literature

A Recommendation System for Meta-modeling: A Meta-learning Based Approach

Various meta-modeling techniques have been developed to replace computationally expensive simulation models. The performance of these meta-modeling techniques on different models is varied which makes existing model selection/recommendation approaches (e.g., trial-and-error, ensemble) problematic. To address these research gaps, we propose a general meta-modeling recommendation system using meta-learning which can automate the meta-modeling recommendation process by intelligently adapting the learning bias to problem characterizations. The proposed intelligent recommendation system includes four modules: (1) problem module, (2) meta-feature module which includes a comprehensive set of meta-features to characterize the geometrical properties of problems, (3) meta-learner module which compares the performance of instance-based and model-based learning approaches for optimal framework design, and (4) performance evaluation module which introduces two criteria, Spearman\u27s ranking correlation coefficient and hit ratio, to evaluate the system on the accuracy of model ranking prediction and the precision of the best model recommendation, respectively. To further improve the performance of meta-learning for meta-modeling recommendation, different types of feature reduction techniques, including singular value decomposition, stepwise regression and ReliefF, are studied. Experiments show that our proposed framework is able to achieve 94% correlation on model rankings, and a 91% hit ratio on best model recommendation. Moreover, the computational cost of meta-modeling recommendation is significantly reduced from an order of minutes to seconds compared to traditional trial-and-error and ensemble process. The proposed framework can significantly advance the research in meta-modeling recommendation, and can be applied for data-driven system modeling

Dopamine and Glutamate in Antipsychotic-Responsive Compared With Antipsychotic-Nonresponsive Psychosis: A Multicenter Positron Emission Tomography and Magnetic Resonance Spectroscopy Study (STRATA)

The variability in the response to antipsychotic medication in schizophrenia may reflect between-patient differences in neurobiology. Recent cross-sectional neuroimaging studies suggest that a poorer therapeutic response is associated with relatively normal striatal dopamine synthesis capacity but elevated anterior cingulate cortex (ACC) glutamate levels. We sought to test whether these measures can differentiate patients with psychosis who are antipsychotic responsive from those who are antipsychotic nonresponsive in a multicenter cross-sectional study. 1H-magnetic resonance spectroscopy (1H-MRS) was used to measure glutamate levels (Glucorr) in the ACC and in the right striatum in 92 patients across 4 sites (48 responders [R] and 44 nonresponders [NR]). In 54 patients at 2 sites (25 R and 29 NR), we additionally acquired 3,4-dihydroxy-6-[18F]fluoro-L-phenylalanine (18F-DOPA) positron emission tomography (PET) to index striatal dopamine function (Kicer, min−1). The mean ACC Glucorr was higher in the NR than the R group after adjustment for age and sex (F1,80 = 4.27; P = .04). This was associated with an area under the curve for the group discrimination of 0.59. There were no group differences in striatal dopamine function or striatal Glucorr. The results provide partial further support for a role of ACC glutamate, but not striatal dopamine synthesis, in determining the nature of the response to antipsychotic medication. The low discriminative accuracy might be improved in groups with greater clinical separation or increased in future studies that focus on the antipsychotic response at an earlier stage of the disorder and integrate other candidate predictive biomarkers. Greater harmonization of multicenter PET and 1H-MRS may also improve sensitivity