Citizen seismology helps decipher the 2021 Haiti earthquake

The August 14, Mw7.2, Nippes earthquake in Haiti occurred within the same fault zone as its devastating, Mw7.0, 2010 predecessor but struck the country when field access was limited by insecurity and conventional seismometers from the national network were inoperative. A network of citizen seismometers installed in 2019 provided near-field data critical to rapidly understand the mechanism of the mainshock and monitor its aftershock sequence. Their real-time data define two aftershock clusters that coincide with two areas of coseismic slip derived from inversions of conventional seismological and geodetic data. Machine learning applied to data from the citizen seismometer closest to the mainshock allows us to forecast aftershocks as accurately as with the network-derived catalog. This shows the utility of citizen science contributing to the understanding of a major earthquake.

Impulsive Source of the 2017 MW=7.3 Ezgeleh, Iran, Earthquake

On 12 November 2017, a M W=7.3 earthquake struck near the Iranian town of Ezgeleh, at the Iran-Iraq border. This event was located within the Zagros fold and thrust belt which delimits the continental collision between the Arabian and Eurasian Plates. Despite a high seismic risk, the seismogenic behavior of the complex network of active faults is not well documented in this area due to the long recurrence interval of large earthquakes. In this study, we jointly invert interferometric synthetic aperture radar and near-field strong motions to infer a kinematic slip model of the rupture. The incorporation of these near-field observations enables a fine resolution of the kinematic rupture process. It reveals an impulsive seismic source with a strong southward rupture directivity, consistent with significant damage south of the epicenter. We also show that the slip direction does not match plate convergence, implying that some of the accumulated strain must be partitioned onto other faults.

Microglial Ramification, Surveillance, and Interleukin-1β Release Are Regulated by the Two-Pore Domain K+ Channel THIK-1

Microglia exhibit two modes of motility: they constantly extend and retract their processes to survey the brain, but they also send out targeted processes to envelop sites of tissue damage. We now show that these motility modes differ mechanistically. We identify the two-pore domain channel THIK-1 as the main K⁺ channel expressed in microglia in situ. THIK-1 is tonically active, and its activity is potentiated by P2Y₁₂ receptors. Inhibiting THIK-1 function pharmacologically or by gene knockout depolarizes microglia, which decreases microglial ramification and thus reduces surveillance, whereas blocking P2Y₁₂ receptors does not affect membrane potential, ramification, or surveillance. In contrast, process outgrowth to damaged tissue requires P2Y₁₂ receptor activation but is unaffected by blocking THIK-1. Block of THIK-1 function also inhibits release of the pro-inflammatory cytokine interleukin-1β from activated microglia, consistent with K⁺ loss being needed for inflammasome assembly. Thus, microglial immune surveillance and cytokine release require THIK-1 channel activity.

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The two-pore domain channel THIK-1 is the main K⁺ channel in “resting” microglia

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Tonic activity of THIK-1 maintains the microglial resting potential

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Blocking THIK-1 reduces microglial ramification, surveillance, and IL-1β release

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Surveillance depends on THIK-1, not P2Y₁₂; chemotaxis depends on P2Y₁₂, not THIK-1

Non-signalling energy use in the developing rat brain

Energy use in the brain constrains its information processing power, but only about half the brain's energy consumption is directly related to information processing. Evidence for which non-signalling processes consume the rest of the brain's energy has been scarce. For the first time, we investigated the energy use of the brain's main non-signalling tasks with a single method. After blocking each non-signalling process, we measured oxygen level changes in juvenile rat brain slices with an oxygen-sensing microelectrode and calculated changes in oxygen consumption throughout the slice using a modified diffusion equation. We found that the turnover of the actin and microtubule cytoskeleton, followed by lipid synthesis, are significant energy drains, contributing 25%, 22% and 18%, respectively, to the rate of oxygen consumption. In contrast, protein synthesis is energetically inexpensive. We assess how these estimates of energy expenditure relate to brain energy use in vivo, and how they might differ in the mature brain.

Constraining the kinematics of metropolitan Los Angeles faults with a slip-partitioning model

Due to the limited resolution at depth of geodetic and other geophysical data, the geometry and the loading rate of the ramp-décollement faults below the metropolitan Los Angeles are poorly understood. Here we complement these data by assuming conservation of motion across the Big Bend of the San Andreas Fault. Using a Bayesian approach, we constrain the geometry of the ramp-décollement system from the Mojave block to Los Angeles and propose a partitioning of the convergence with 25.5 ± 0.5 mm/yr and 3.1 ± 0.6 mm/yr of strike-slip motion along the San Andreas Fault and the Whittier Fault, with 2.7 ± 0.9 mm/yr and 2.5 ± 1.0 mm/yr of updip movement along the Sierra Madre and the Puente Hills thrusts. Incorporating conservation of motion in geodetic models of strain accumulation reduces the number of free parameters and constitutes a useful methodology to estimate the tectonic loading and seismic potential of buried fault networks.

Energy-Efficient Information Transfer by Visual Pathway Synapses

The architecture of computational devices is shaped by their energy consumption. Energetic constraints are used to design silicon-based computers but are poorly understood for neural computation. In the brain, most energy is used to reverse ion influxes generating excitatory postsynaptic currents (EPSCs) and action potentials. Thus, EPSCs should be small to minimize energy use, but not so small as to impair information transmission. We quantified information flow through the retinothalamic synapse in the visual pathway in brain slices, with cortical and inhibitory input to the postsynaptic cell blocked. Altering EPSC size with dynamic clamp, we found that a larger-than-normal EPSC increased information flow through the synapse. Thus, the evolutionarily selected EPSC size does not maximize retinal information flow to the cortex. By assessing the energy used on postsynaptic ion pumping and action potentials, we show that, instead, the EPSC size optimizes the ratio of retinal information transmitted to energy consumed. These data suggest maximization of information transmission per energy used as a synaptic design principle.

Multi-timescale modeling of activity-dependent metabolic coupling in the neuron-glia-vasculature ensemble

Glucose is the main energy substrate in the adult brain under normal conditions. Accumulating evidence, however, indicates that lactate produced in astrocytes (a type of glial cell) can also fuel neuronal activity. The quantitative aspects of this so-called astrocyte-neuron lactate shuttle (ANLS) are still debated. To address this question, we developed a detailed biophysical model of the brain’s metabolic interactions. Our model integrates three modeling approaches, the Buxton-Wang model of vascular dynamics, the Hodgkin-Huxley formulation of neuronal membrane excitability and a biophysical model of metabolic pathways. This approach provides a template for large-scale simulations of the neuron-glia-vasculature (NGV) ensemble, and for the first time integrates the respective timescales at which energy metabolism and neuronal excitability occur. The model is constrained by relative neuronal and astrocytic oxygen and glucose utilization, by the concentration of metabolites at rest and by the temporal dynamics of NADH upon activation. These constraints produced four observations. First, a transfer of lactate from astrocytes to neurons emerged in response to activity. Second, constrained by activity-dependent NADH transients, neuronal oxidative metabolism increased first upon activation with a subsequent delayed astrocytic glycolysis increase. Third, the model correctly predicted the dynamics of extracellular lactate and oxygen as observed in vivo in rats. Fourth, the model correctly predicted the temporal dynamics of tissue lactate, of tissue glucose and oxygen consumption, and of the BOLD signal as reported in human studies. These findings not only support the ANLS hypothesis but also provide a quantitative mathematical description of the metabolic activation in neurons and glial cells, as well as of the macroscopic measurements obtained during brain imaging.

The brain has remarkable information processing capacity, yet is also very energy efficient. How this metabolic efficiency is achieved given the spatial and metabolic constraints inherent to the designs and energy requirements of brain cells is a fundamental question in neurobiology. The major cell classes in mammalian nervous systems include neurons, glia and the microvasculature that supplies the molecular substrates of energy and metabolism. Together, this neuron-glia-vasculature (NGV) ensemble constitutes the functional unit that underlies the cost infrastructure of computation. In spite of its importance, a comprehensive understanding of this dynamic system remains elusive. While it is well established that glucose feeds the brain, few of the details regarding the destiny of glucose intermediates in metabolic pathways are known. Controversy remains regarding the degree of cooperativity between glia and neurons in sharing lactate, the product of aerobic glycolysis (Warburg effect) and one of the substrates for further energy extraction by oxidative processes. Specifically, while experimental data support the occurrence of a flow of lactate from glia to neurons, the astrocyte-neuron lactate shuttle (ANLS), some theoretical considerations have been proposed to support the occurrence of lactate transport in the other direction (NALS). Our computational model is the first to integrate multiple timescales of the NGV unit. It provides a quantitative mathematical description of metabolic activation in neurons and astrocytes, and of the macroscopic measurements obtained during brain imaging that uses metabolism as a proxy for neuronal activity.

Multimodal imaging in rats reveals impaired neurovascular coupling in sustained hypertension

BACKGROUND AND PURPOSE

Arterial hypertension is an important risk factor for cerebrovascular diseases, such as transient ischemic attacks or stroke, and represents a major global health issue. The effects of hypertension on cerebral blood flow, particularly at the microvascular level, remain unknown.

METHODS

Using the spontaneously hypertensive rat (SHR) model, we examined cortical hemodynamic responses on whisker stimulation applying a multimodal imaging approach (multiwavelength spectroscopy, laser speckle imaging, and 2-photon microscopy). We assessed the effects of hypertension in 10-, 20-, and 40-week-old male SHRs and age-matched male Wistar Kyoto rats (CTRL) on hemodynamic responses, histology, and biochemical parameters. In 40-week-old animals, losartan or verapamil was administered for 10 weeks to test the reversibility of hypertension-induced impairments.

RESULTS

Increased arterial blood pressure was associated with a progressive impairment in functional hyperemia in 20- and 40-week-old SHRs; baseline capillary red blood cell velocity was increased in 40-week-old SHRs compared with age-matched CTRLs. Antihypertensive treatment reduced baseline capillary cerebral blood flow almost to CTRL values, whereas functional hyperemic signals did not improve after 10 weeks of drug therapy. Structural analyses of the microvascular network revealed no differences between normo- and hypertensive animals, whereas expression analyses of cerebral lysates showed signs of increased oxidative stress and signs of impaired endothelial homeostasis upon early hypertension.

CONCLUSIONS

Impaired neurovascular coupling in the SHR evolves upon sustained hypertension. Antihypertensive monotherapy using verapamil or losartan is not sufficient to abolish this functional impairment. These deficits in neurovascular coupling in response to sustained hypertension might contribute to accelerate progression of neurodegenerative diseases in chronic hypertension.

Synaptic energy use and supply

Neuronal computation is energetically expensive. Consequently, the brain's limited energy supply imposes constraints on its information processing capability. Most brain energy is used on synaptic transmission, making it important to understand how energy is provided to and used by synapses. We describe how information transmission through presynaptic terminals and postsynaptic spines is related to their energy consumption, assess which mechanisms normally ensure an adequate supply of ATP to these structures, consider the influence of synaptic plasticity and changing brain state on synaptic energy use, and explain how disruption of the energy supply to synapses leads to neuropathology.

Comment on recent modeling studies of astrocyte-neuron metabolic interactions

Recent years have seen a surge in mathematical modeling of the various aspects of neuron-astrocyte interactions, and the field of brain energy metabolism is no exception in that regard. Despite the advent of biophysical models in the field, the long-lasting debate on the role of lactate in brain energy metabolism is still unresolved. Quite the contrary, it has been ported to the world of differential equations. Here, we summarize the present state of this discussion from the modeler's point of view and bring some crucial points to the attention of the non-mathematically proficient reader.

Deciphering neuron-glia compartmentalization in cortical energy metabolism

Energy demand is an important constraint on neural signaling. Several methods have been proposed to assess the energy budget of the brain based on a bottom-up approach in which the energy demand of individual biophysical processes are first estimated independently and then summed up to compute the brain's total energy budget. Here, we address this question using a novel approach that makes use of published datasets that reported average cerebral glucose and oxygen utilization in humans and rodents during different activation states. Our approach allows us (1) to decipher neuron-glia compartmentalization in energy metabolism and (2) to compute a precise state-dependent energy budget for the brain. Under the assumption that the fraction of energy used for signaling is proportional to the cycling of neurotransmitters, we find that in the activated state, most of the energy (∼80%) is oxidatively produced and consumed by neurons to support neuron-to-neuron signaling. Glial cells, while only contributing for a small fraction to energy production (∼6%), actually take up a significant fraction of glucose (50% or more) from the blood and provide neurons with glucose-derived energy substrates. Our results suggest that glycolysis occurs for a significant part in astrocytes whereas most of the oxygen is utilized in neurons. As a consequence, a transfer of glucose-derived metabolites from glial cells to neurons has to take place. Furthermore, we find that the amplitude of this transfer is correlated to (1) the activity level of the brain; the larger the activity, the more metabolites are shuttled from glia to neurons and (2) the oxidative activity in astrocytes; with higher glial pyruvate metabolism, less metabolites are shuttled from glia to neurons. While some of the details of a bottom-up biophysical approach have to be simplified, our method allows for a straightforward assessment of the brain's energy budget from macroscopic measurements with minimal underlying assumptions.

Improved in vivo two-photon imaging after blood replacement by perfluorocarbon

Two-photon microscopy is a powerful method in biomedical research that allows functional and anatomical imaging at a subcellular resolution in vivo. The technique is seriously hampered by absorption and scattering of light by blood, which prevents imaging through large vessels. Here, we demonstrate in the rat cerebral cortex that blood replacement by perfluorocarbon emulsion, a compound also used in human critical care medicine, yields superior image quality, while preserving neuronal integrity. Shadows of large superficial vessels disappear completely and cells can be imaged underneath them. For the first time, it is possible to image complete populations of neurons and astrocytes in the upper layers of neocortex in vivo.

The quantitative single-neuron modeling competition

As large-scale, detailed network modeling projects are flourishing in the field of computational neuroscience, it is more and more important to design single neuron models that not only capture qualitative features of real neurons but are quantitatively accurate in silico representations of those. Recent years have seen substantial effort being put in the development of algorithms for the systematic evaluation and optimization of neuron models with respect to electrophysiological data. It is however difficult to compare these methods because of the lack of appropriate benchmark tests. Here, we describe one such effort of providing the community with a standardized set of tests to quantify the performances of single neuron models. Our effort takes the form of a yearly challenge similar to the ones which have been present in the machine learning community for some time. This paper gives an account of the first two challenges which took place in 2007 and 2008 and discusses future directions. The results of the competition suggest that best performance on data obtained from single or double electrode current or conductance injection is achieved by models that combine features of standard leaky integrate-and-fire models with a second variable reflecting adaptation, refractoriness, or a dynamic threshold.

A benchmark test for a quantitative assessment of simple neuron models

Several methods and algorithms have recently been proposed that allow for the systematic evaluation of simple neuron models from intracellular or extracellular recordings. Models built in this way generate good quantitative predictions of the future activity of neurons under temporally structured current injection. It is, however, difficult to compare the advantages of various models and algorithms since each model is designed for a different set of data. Here, we report about one of the first attempts to establish a benchmark test that permits a systematic comparison of methods and performances in predicting the activity of rat cortical pyramidal neurons. We present early submissions to the benchmark test and discuss implications for the design of future tests and simple neurons models.

Predicting spike timing of neocortical pyramidal neurons by simple threshold models

Neurons generate spikes reliably with millisecond precision if driven by a fluctuating current--is it then possible to predict the spike timing knowing the input? We determined parameters of an adapting threshold model using data recorded in vitro from 24 layer 5 pyramidal neurons from rat somatosensory cortex, stimulated intracellularly by a fluctuating current simulating synaptic bombardment in vivo. The model generates output spikes whenever the membrane voltage (a filtered version of the input current) reaches a dynamic threshold. We find that for input currents with large fluctuation amplitude, up to 75% of the spike times can be predicted with a precision of +/-2 ms. Some of the intrinsic neuronal unreliability can be accounted for by a noisy threshold mechanism. Our results suggest that, under random current injection into the soma, (i) neuronal behavior in the subthreshold regime can be well approximated by a simple linear filter; and (ii) most of the nonlinearities are captured by a simple threshold process.

Predicting spike times of a detailed conductance-based neuron model driven by stochastic spike arrival

Reduced models of neuronal activity such as integrate-and-fire models allow a description of neuronal dynamics in simple, intuitive terms and are easy to simulate numerically. We present a method to fit an integrate-and-fire-type model of neuronal activity, namely a modified version of the spike response model, to a detailed Hodgkin-Huxley-type neuron model driven by stochastic spike arrival. In the Hogkin-Huxley model, spike arrival at the synapse is modeled by a change of synaptic conductance. For such conductance spike input, more than 70% of the postsynaptic action potentials can be predicted with the correct timing by the integrate-and-fire-type model. The modified spike response model is based upon a linearized theory of conductance-driven integrate-and-fire neurons.

Generalized Integrate-and-Fire Models of Neuronal Activity Approximate Spike Trains of a Detailed Model to a High Degree of Accuracy

We demonstrate that single-variable integrate-and-fire models can quantitatively capture the dynamics of a physiologically detailed model for fast-spiking cortical neurons. Through a systematic set of approximations, we reduce the conductance-based model to 2 variants of integrate-and-fire models. In the first variant (nonlinear integrate-and-fire model), parameters depend on the instantaneous membrane potential, whereas in the second variant, they depend on the time elapsed since the last spike [Spike Response Model (SRM)]. The direct reduction links features of the simple models to biophysical features of the full conductance-based model. To quantitatively test the predictive power of the SRM and of the nonlinear integrate-and-fire model, we compare spike trains in the simple models to those in the full conductance-based model when the models are subjected to identical randomly fluctuating input. For random current input, the simple models reproduce 70–80 percent of the spikes in the full model (with temporal precision of ±2 ms) over a wide range of firing frequencies. For random conductance injection, up to 73 percent of spikes are coincident. We also present a technique for numerically optimizing parameters in the SRM and the nonlinear integrate-and-fire model based on spike trains in the full conductance-based model. This technique can be used to tune simple models to reproduce spike trains of real neurons.