Strength of association of classical vascular risk factors in young patients with ischaemic stroke: a case-control study

INTRODUCTION

Recent studies have reported an increasing incidence of ischaemic stroke among young adults. However, the strength of the association between traditional vascular risk factors has not been fully established.

METHODS

We compared 120 patients with a first ischaemic stroke before the age of 55 years admitted to the stroke unit of our centre with 600 healthy non-stroke controls from a population-based cohort study (HERMEX), matched for sex. Risk factors assessed included: hypertension, obesity, auricular fibrillation, current smoking, estimated glomerular filtration rate (eGFR), total cholesterol, low-density lipoprotein cholesterol (LDL-C), triglycerides, high-density lipoprotein cholesterol (HDL-C) and diabetes mellitus. We used logistic regression analysis and calculated population attributable risk. We performed an overall analysis, by sex and aetiological subgroup.

RESULTS

Using logistic regression analysis, we found that overall, the significant risk factors were: hypertension (OR: 1.58; 95%CI: 1.01-2.50), atrial fibrillation (OR: 4.77; 95%CI: 1.20-19.00), low eGFR (OR: 4.74; 95%CI: 1.3-21.94) and low HDL-C (OR: 5.20; 95%CI: 3.29-8.21), as well as smoking for males (OR: 1.86; 95%CI: 1.14-3.03). LDL-C showed an inverse association with stroke. The population attributable risk for HDL-C was 37.8% and for hypertension 21.1%. In terms of aetiological subgroups, only low HDL-C was associated with stroke of undetermined aetiology.

CONCLUSIONS

Hypertension, auricular fibrillation, low eGFR, and low HDL-C, plus tobacco use in men, are the main risk factors among patients under 55 years of age with a first ischaemic stroke. We believe that it would be of particular interest to further explore the management of low HDL-C levels as part of preventive strategies in young stroke patients.

Modulation of intercolumnar synchronization by endogenous electric fields in cerebral cortex

Neurons synaptically interacting in a conductive medium generate extracellular endogenous electric fields (EFs) that reciprocally affect membrane potential. Exogenous EFs modulate neuronal activity, and their clinical applications are being profusely explored. However, whether endogenous EFs contribute to network synchronization remains unclear. We analyzed spontaneously generated slow-wave activity in the cerebral cortex network in vitro, which allowed us to distinguish synaptic from nonsynaptic mechanisms of activity propagation and synchronization. Slow oscillations generated EFs that propagated independently of synaptic transmission. We demonstrate that cortical oscillations modulate spontaneous rhythmic activity of neighboring synaptically disconnected cortical columns if layers are aligned. We provide experimental evidence that these EF-mediated effects are compatible with electric dipoles. With a model of interacting dipoles, we reproduce the experimental measurements and predict that endogenous EF-mediated synchronizing effects should be relevant in the brain. Thus, experiments and models suggest that electric-dipole interactions contribute to synchronization of neighboring cortical columns.

Slow Waves in Cortical Slices: How Spontaneous Activity is Shaped by Laminar Structure

Cortical slow oscillations (SO) of neural activity spontaneously emerge and propagate during deep sleep and anesthesia and are also expressed in isolated brain slices and cortical slabs. We lack full understanding of how SO integrate the different structural levels underlying local excitability of cell assemblies and their mutual interaction. Here, we focus on ongoing slow waves (SWs) in cortical slices reconstructed from a 16-electrode array designed to probe the neuronal activity at multiple spatial scales. In spite of the variable propagation patterns observed, we reproducibly found a smooth strip of loci leading the SW fronts, overlapping cortical layers 4 and 5, along which Up states were the longest and displayed the highest firing rate. Propagation modes were uncorrelated in time, signaling a memoryless generation of SWs. All these features could be modeled by a multimodular large-scale network of spiking neurons with a specific balance between local and intermodular connectivity. Modules work as relaxation oscillators with a weakly stable Down state and a peak of local excitability to model layers 4 and 5. These conditions allow for both optimal sensitivity to the network structure and richness of propagation modes, both of which are potential substrates for dynamic flexibility in more general contexts.

Bistability, Causality, and Complexity in Cortical Networks: An In Vitro Perturbational Study

Measuring the spatiotemporal complexity of cortical responses to direct perturbations provides a reliable index of the brain's capacity for consciousness in humans under both physiological and pathological conditions. Upon loss of consciousness, the complex pattern of causal interactions observed during wakefulness collapses into a stereotypical slow wave, suggesting that cortical bistability may play a role. Bistability is mainly expressed in the form of slow oscillations, a default pattern of activity that emerges from cortical networks in conditions of functional or anatomical disconnection. Here, we employ an in vitro model to understand the relationship between bistability and complexity in cortical circuits. We adapted the perturbational complexity index applied in humans to electrically stimulated cortical slices under different neuromodulatory conditions. At this microscale level, we demonstrate that perturbational complexity can be effectively modulated by pharmacological reduction of bistability and, albeit to a lesser extent, by enhancement of excitability, providing mechanistic insights into the macroscale measurements performed in humans.

Quantification of Signal-to-Noise Ratio in Cerebral Cortex Recordings Using Flexible MEAs With Co-localized Platinum Black, Carbon Nanotubes, and Gold Electrodes

Developing new standardized tools to characterize brain recording devices is critical to evaluate neural probes and for translation to clinical use. The signal-to-noise ratio (SNR) measurement is the gold standard for quantifying the performance of brain recording devices. Given the drawbacks with the SNR measure, our first objective was to devise a new method to calculate the SNR of neural signals to distinguish signal from noise. Our second objective was to apply this new SNR method to evaluate electrodes of three different materials (platinum black, Pt; carbon nanotubes, CNTs; and gold, Au) co-localized in tritrodes to record from the same cortical area using specifically designed multielectrode arrays. Hence, we devised an approach to calculate SNR at different frequencies based on the features of cortical slow oscillations (SO). Since SO consist in the alternation of silent periods (Down states) and active periods (Up states) of neuronal activity, we used these as noise and signal, respectively. The spectral SNR was computed as the power spectral density (PSD) of Up states (signal) divided by the PSD of Down states (noise). We found that Pt and CNTs electrodes have better recording performance than Au electrodes for the explored frequency range (5–1500 Hz). Together with two proposed SNR estimators for the lower and upper frequency limits, these results substantiate our SNR calculation at different frequency bands. Our results provide a new validated SNR measure that provides rich information of the performance of recording devices at different brain activity frequency bands (<1500 Hz).

Beta and Gamma Oscillations in Prefrontal Cortex During NMDA Hypofunction: An In Vitro Model of Schizophrenia Features

NMDA receptor (NMDAr) hypofunction has been widely used as a schizophrenia model. Decreased activation of NMDAr is associated with a disrupted excitation/inhibition balance in the prefrontal cortex and with alterations in gamma synchronization. Our aim was to investigate whether this phenomenon could be reproduced in the spontaneous oscillatory activity generated by the local prefrontal network in vitro and, if so, to explore the effects of antipsychotics on the resulting activity. Extracellular recordings were obtained from prefrontal cortex slices bathed in in vivo-like ACSF solution. Slow (<1 Hz) oscillations consisting of interspersed Up (active) and Down (silent) states spontaneously emerged. Fast-frequency oscillations (15-90 Hz) occurred during Up states. We explored the effects of the NMDAr antagonist MK-801 on the spontaneously generated activity. Bath-applied MK-801 induced a dose-dependent decrease in Up-state duration and in the frequency of Up states. However, the beta/gamma power during Up states significantly increased; this increase was in turn prevented by the antipsychotic drug clozapine. The increased beta/gamma power with NMDAr blockade implies that NMDAr activation in physiological conditions prevents hypersynchronization in this frequency range. High-frequency hypersynchronization following NMDAr blockade occurring in cortical slices suggests that-at least part of-the underlying mechanisms of this schizophrenia feature persist in the local cortical circuit, even in the absence of long-range cortical or subcortical inputs. The observed action of clozapine decreasing hypersynchronization in the local circuit may be one of the mechanisms of action of clozapine in preventing schizophrenia symptoms derived from NMDA hypofunction.