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Formation anisotropy in complicated geophysical environments can have a significant impact on data interpretation of electromagnetic surveys. To facilitate full 3D modeling of arbitrary anisotropy, we have adopted an [Formula: see text]-version geometric multigrid preconditioned finite-element method (FEM) based on vector basis functions. By using a structured mesh, instead of an unstructured one, our method can conveniently construct the restriction and prolongation operators for multigrid implementation, and then recursively coarsen the grid with the F-cycle coarsening scheme. The geometric multigrid method is used as a preconditioner for the biconjugate-gradient stabilized method to efficiently solve the linear system resulting from the FEM. Our method avoids the need of interpolation for arbitrary anisotropy modeling as in Yee’s grid-based finite-difference method, and it is also more capable of large-scale modeling with respect to the [Formula: see text]-version geometric multigrid preconditioned finite-element method. A numerical example in geophysical well logging is included to demonstrate its numerical performance. Our [Formula: see text]-version geometric multigrid preconditioned FEM is expected to help formation anisotropy characterization with electromagnetic surveys in complicated geophysical environments.

To model the responses of borehole electromagnetic sensing in complicated geological environments, the geometric multigrid preconditioned finite-difference frequency-domain (FDFD) method is examined on its numerical performance as a fast forward modeling solver in this work. The geometric multigrid method is employed as a preconditioner for the biconjugate gradient stabilized method to efficiently solve the linear system resulting from the finite-difference method based on the staggered Yee's cell. The Gauss-Seidel method is utilized as both presmoother and postsmoother for the multigrid method, and the bilinear approach is employed to construct the restriction and prolongation operators between coarse and fine grids. The F-cycle coarsening scheme is used to balance the convergence and computational efficiency of the multigrid method. Numerical examples of borehole electromagnetic sensing are included to examine the performance of the method, which not only demonstrates its accuracy but also shows its capability as a fast solver for practical application. The geometric multigrid preconditioned FDFD method can work as a good candidate for 3-D modeling-based interpretation and evaluation for borehole electromagnetic sensing.

To decipher the organizational logic of complex brain circuits, it is important to chart long-distance pathways while preserving micron-level accuracy of local network. However, mapping the neuronal projections with individual-axon resolution in the large and complex primate brain is still challenging. Herein, we describe a highly efficient pipeline for three-dimensional mapping of the entire macaque brain with subcellular resolution. The pipeline includes a novel poly-N-acryloyl glycinamide (PNAGA)-based embedding method for long-term structure and fluorescence preservation, high-resolution and rapid whole-brain optical imaging, and image post-processing. The cytoarchitectonic information of the entire macaque brain was acquired with a voxel size of 0.32 μm × 0.32 μm × 10 μm, showing its anatomical structure with cell distribution, density, and shape. Furthermore, thanks to viral labeling, individual long-distance projection axons from the frontal cortex were for the first time reconstructed across the entire brain hemisphere with a voxel size of 0.65 μm × 0.65 μm × 3 μm. Our results show that individual cortical axons originating from the prefrontal cortex simultaneously target multiple brain regions, including the visual cortex, striatum, thalamus, and midbrain. This pipeline provides an efficient method for cellular and circuitry investigation of the whole macaque brain with individual-axon resolution, and can shed light on brain function and disorders.

Interleukin-6 (IL-6) has been long considered a key player in cancer-associated cachexia1-15. It is believed that sustained elevation of IL-6 production during cancer progression causes brain dysfunctions, which ultimately result in cachexia16-20. However, how peripheral IL-6 influences the brain remains poorly understood. Here we show that neurons in the area postrema (AP), a circumventricular structure in the hindbrain, mediate the function of IL-6 in cancer-associated cachexia in mice. We found that circulating IL-6 can rapidly enter the AP and activate AP neurons. Peripheral tumor, known to increase circulating IL-61-5,15,18,21-23, leads to elevated IL-6 and neuronal hyperactivity in the AP, and causes potentiated excitatory synaptic transmission onto AP neurons. Remarkably, neutralization of IL-6 in the brain of tumor-bearing mice with an IL-6 antibody prevents cachexia, reduces the hyperactivity in an AP network, and markedly prolongs lifespan. Furthermore, suppression ofIl6ra, the gene encoding IL-6 receptor, specifically in AP neurons with CRISPR/dCas9 interference achieves similar effects. Silencing of Gfral-expressing AP neurons also ameliorates the cancer-associated cachectic phenotypes and AP network hyperactivity. Our study identifies a central mechanism underlying the function of peripheral IL-6, which may serve as a target for treating cancer-associated cachexia.

To further improve the computational efficiency of the existing wave equation-based discontinuous Galerkin time-domain (DGTD) method for modeling electrically small structures, the weighted Laguerre polynomials are incorporated to span the fields in the temporal domain, which finally results in a set of recursive linear equations related to the Laguerre polynomials to be solved. Compared with conventional time integration methods, the proposed approach requires much fewer iterations and thus shows considerable improvement in terms of computational efficiency. With respect to the first-order Maxwell’s curl equation-based DGTD method with weighted Laguerre polynomials, the proposed method reduces the unknowns by more than a half and gives a lighter linear system for solution. In addition, block iterative solvers and the block lower-diagonal-upper (LDU) direct solver can be applied to facilitating the solution at the subdomain level. Three numerical examples are included to demonstrate the accuracy and efficiency of the proposed method. With the weighted Laguerre polynomials, the wave equation-based DGTD method is expected to be more efficient for electrically small structure modeling.

To facilitate the modeling of time-domain controlled-source electromagnetic surveys, we have developed an efficient finite-element method with weighted Laguerre polynomials, which indicates a much lower computational complexity than conventional time integration methods. Our method allows sampling the field at arbitrary time steps, and its accuracy is determined by the number of polynomials, instead of the time sampling interval. An analysis is performed regarding the optimization of the polynomial number to be used and the criterion of selecting the time scale factor. We conduct two numerical tests in marine and land survey environments to demonstrate the superiority of our method over the existing backward Euler time integration method. Our method is expected to facilitate the modeling of transient electromagnetic surveys in the geophysical regime.

Triaxial induction logging tools have been widely applied to formation characterization due to their sensitivity to electric anisotropy. To model triaxial induction logs in multilayered general anisotropic formations, where the anisotropy can be arbitrary, an analytical method is applied to compute the tool responses. For the analytical method, Maxwell’s equations in the spectral domain are written into a compact first-order differential equation. The equation is solved to obtain the spectral-domain fields, which are transformed to the spatial domain through the inverse Fourier transform. The singular issue for the tool located in highly deviated wells is handled by subtracting the singular term in the spectral domain. The singularity treatment makes the integrands in the inverse Fourier transform decay faster, thus making the infinite integration computation faster. Formations with isotropic, transversely isotropic, biaxially anisotropic, and general anisotropic conductivity are modeled and compared to investigate the effects of anisotropy on the tool responses. For a tool in a general anisotropic formation, all of the [Formula: see text] components are nonzero. For a tool in a vertical well in transversely isotropic and biaxially anisotropic formations, only the diagonal components of [Formula: see text] are nonzero. For a tool located in a deviated well, the effects of tool deviation and electric anisotropy are coupled. The diagonal components are more sensitive to the electric anisotropy than the off-diagonal components, and the off-diagonal ones can clearly indicate bed boundaries.

Through synaptic connections, long-range circuits transmit information among neurons and connect different brain regions to form functional motifs and execute specific functions. Tracing the synaptic distribution of specific neurons requires submicron-level resolution information. However, it is a great challenge to map the synaptic terminals completely because these fine structures span multiple regions, even in the whole brain. Here, we develop a pipeline including viral tracing, sample embedding, fluorescent micro-optical sectional tomography, and big data processing. We mapped the whole-brain distribution and architecture of long projections of the parvalbumin neurons in the basal forebrain at the synaptic level. These neurons send massive projections to multiple downstream regions with subregional preference. With three-dimensional reconstruction in the targeted areas, we found that synaptic degeneration was inconsistent with the accumulation of amyloid-β plaques but was preferred in memory-related circuits, such as hippocampal formation and thalamus, but not in most hypothalamic nuclei in 8-month-old mice with five familial Alzheimer’s disease mutations. Our pipeline provides a platform for generating a whole-brain atlas of cell-type-specific synaptic terminals in the physiological and pathological brain, which can provide an important resource for the study of the organizational logic of specific neural circuits and the circuitry changes in pathological conditions.

The prediction of dispersion curves is an essential part of understanding borehole acoustic modes. Traditional dispersion curve calculation requires finding zeros of the determinant of the characteristic matrix of a borehole system. This approach is subject to interpretational ambiguity because the determinant can be scaled arbitrarily. We have developed an alternative approach based on the reciprocal condition number (RCN) of the characteristic matrix. The RCN quantifies the singularity of a matrix on a fixed scale between zero and unity; thus, it represents wave modal strengths. We find that our method and traditional methods give identical dispersion-curve locations for simple models and that our method provides more insights on the wave modal amplitudes of complicated models with multiple layers, such as those incorporating logging tools and casing strings. Finally, we determine how our method can be used as the basis of sensitivity analysis to indicate how the dispersion curves are dependent on the model parameters.

Imaging hydraulic fractures is of paramount importance to subsurface resource extraction, geologic storage, and hazardous waste disposal. The use of electrically conductive proppants and current energized steel casing provides a promising approach to monitor the distribution of fractures. In this article, a borehole-to-surface system is employed to energize the steel casing and measure electric and magnetic fields on the ground. A convolutional neural network (CNN) is then trained to learn the relationship between the measured field pattern and the parameterized fracture, namely, the lateral extent and direction. To accelerate the generation of training data with limited accuracy loss, an approximate hollow casing is modeled by the impedance transition boundary condition with a tenfold magnified radius and reduced conductivity. Two training strategies are then presented with a grid search of the network’s hyperparameters. The well-trained CNN shows good generalization to unseen fracture conductivity, the true casing model, as well as white Gaussian noise. Finally, we apply the CNN to image irregular fractures and obtain reliable results even under strong noise, indicating a promising imaging technique for more complicated fractures.

Bioelectromagnetism focuses on the study of electromagnetic fields in biological tissues from direct current (DC) to optical frequencies. It is challenging to develop an electromagnetics (EM) simulation method to cover this entire frequency band due to the electrically small/large scattering problem at extremely low/high frequencies. This paper focuses on the band from DC to microwave frequencies in bioelectromagnetism. Its main research objective is to develop a method that can overcome the low frequency breakdown problem at low frequencies (practically DC) and still stay stable at microwave frequencies. Based on the scattered field vector Helmholtz equation, the mixed finite element method (mixed FEM) is developed for the broadband electromagnetic field simulation in biological tissues. By imposing Gauss' law as the constraint condition, the mixed FEM overcomes the low frequency breakdown problem without resorting to the quasi-static approximation and remains effective and accurate at high frequencies. Extremely low frequency and high frequency numerical results are demonstrated to verify that the mixed FEM is a stable full-wave electromagnetic field simulation method for the full-bandwidth bioelectromagnetism.

This letter presents a fast and memory-efficient 3-D electromagnetic solver for logging-while-drilling (LWD) tools based on the flipped total field/scattered field-based discontinuous Galerkin frequency-domain (TF/SF DGFD) method. The new method inherits the Riemann transmission condition (RTC) with surface current sources from the mixed TF/SF DGFD method to couple nonconformal meshes as well as TF/SF solvers; it then extends to the moving-tool scenarios for the LWD application with a solver flipping technology and delivers a single global matrix of dramatically reduced dimension for all source positions, which can be solved directly on a normal-memory computer. By incorporating the tetrahedral/hexahedral mixed mesh and the curved domain decomposition method, the new solver is applied to two LWD cases and about 70% of unknowns can be saved for the flipped TF/SF DGFD remeshed regions.

Traditional time-domain finite-element and spectral-element methods are known to suffer from the so-called low-frequency breakdown problem, where the system matrix can become ill-conditioned, and results can be unstable for electrically fine structures. In this article, a new mixed spectral-element time-domain (SETD) and finite-element time-domain (FETD) methods are proposed to overcome this low-frequency breakdown problem by constraint equations and tree-cotree splitting. Two forms of gradient matrices or the null space of the stiffness matrix of high-order basis functions are calculated to construct the constraint equations. Then, the constraint equations are directly applied in the time-stepping matrix equation by tree-cotree splitting to improve the properties of system matrices when the implicit Newmark-beta algorithm is adopted in two different forms. In the calculation of the first form of gradient matrix, the electric field is expanded by high-order edge basis functions; in the second form of gradient matrix, high-order edge basis functions associated with cotree edges and nodal basis functions defined on the free nodes are both employed to expand the electric field. Several numerical examples demonstrate that the new mixed SETD and mixed FETD methods can effectively overcome the low-frequency breakdown with high accuracy and without increasing the number of degrees of freedom over the conventional SETD and FETD methods.

The pontomesencephalic tegmentum, comprising the pedunculopontine nucleus and laterodorsal tegmental nucleus, is involved in various functions via complex connections; however, the organizational structure of these circuits in the whole brain is not entirely clear. Here, combining viral tracing with fluorescent micro-optical sectional tomography, we comprehensively investigated the input and output circuits of two cholinergic subregions in a continuous whole-brain dataset. We found that these nuclei receive abundant input with similar spatial distributions but with different quantitative measures and acquire similar neuromodulatory afferents from the ascending reticular activation system. Meanwhile, these cholinergic nuclei project to similar targeting areas throughout multiple brain regions and have different spatial preferences in 3D. Moreover, some cholinergic connections are unidirectional, including projections from the pedunculopontine nucleus and laterodorsal tegmental nucleus to the ventral posterior complex of the thalamus, and have different impacts on locomotion and anxiety. These results reveal the integrated cholinergic connectome of the midbrain, thus improving the present understanding of the organizational structure of the pontine-tegmental cholinergic system from its anatomical structure to its functional modulation.

This letter extends the discontinuous Galerkin frequency domain (DGFD)-based domain decomposition method (DDM) to model the open borehole environment accurately with a newly developed hierarchical sudoku mesh. Both the borehole and mud invasion effects can be modeled by this framework efficiently. Furthermore, deep reading measurements can be modeled conveniently by inserting arbitrarily shaped objects into the hierarchical sudoku mesh; the capability to distinguish a cavity saturated with either oil or water with different borehole-object distances are then studied for a deep reading tool in an open borehole carbonate environment. The DGFD with the hierarchical sudoku mesh shows six times faster speed than the traditional finite-element method (FEM) for the deep reading case.

A periodic perfectly matched layer (PML) is proposed for the spectral element method (SEM) for the first time, which not only simplifies the recognition process of a PML domain, but also saves degrees of freedom (DoFs) when squeezing its layer number to 1. An objective function to evaluate and optimize its performance has been theoretically derived. In the use of this objective function, the PML parameters are optimized under different conditions; the relationships between the PML's performance and the scaling factor profile, the source position, the basis functions’ order, and the PML layer number are studied, which can help guide the practical applications of PML in the SEM modeling. An estimation method is also introduced to facilitate the selection of the scaling factor.

A nonconformal hybrid finite difference time domain (FDTD)/finite element time domain (FETD) method was previously introduced, which implemented the hybridization through a buffer zone. Although this method has been demonstrated to be accurate and long-time stable, further efforts are still desirable to remove the buffer zone and to implement an implicit–explicit time integration from the perspective of practical applications. In this paper, a novel hybrid method is proposed, which not only successfully eliminates the necessity of the buffer zone without compromising the featured advantage (e.g., nonconformal mesh) but also effectively applies an implicit–explicit time integration scheme to improve the computational efficiency. Furthermore, the new method extends the hybridization to a broader level by incorporating the spectral element time domain (SETD) method based on the discontinuous Galerkin and domain decomposition techniques, resulting in a more general hybrid FDTD/SETD/FETD framework. The framework employs the explicit leapfrog time integration for the FDTD region while it employs the implicit Crank–Nicolson time integration for the FETD region. For the SETD region, either the implicit or explicit time integration can be employed, depending on the mesh sizes in it. When the implicit region becomes large, it can be further split into multiple subdomains to reduce computational complexity. Numerical examples are included to demonstrate the performance of the proposed hybrid method, which is accurate, long-time stable and more efficient than the hybrid method with a buffer zone.

The medial prefrontal cortex (mPFC) contains populations of GABAergic interneurons that play different roles in cognition and emotion. Their local and long-range inputs are incompletely understood. We used monosynaptic rabies viral tracers in combination with fluorescence micro-optical sectioning tomography to generate a whole-brain atlas of direct long-range inputs to GABAergic interneurons in the mPFC of male mice. We discovered that three subtypes of GABAergic interneurons in two areas of the mPFC are innervated by same upstream areas. Input from subcortical upstream areas includes cholinergic neurons from the basal forebrain and serotonergic neurons (which co-release glutamate) from the raphe nuclei. Reconstruction of single-neuron morphology revealed novel substantia innominata-anteromedial thalamic nucleus-mPFC and striatum-anteromedial thalamic nucleus-mPFC circuits. Based on the projection logic of individual neurons, we classified cortical and hippocampal input neurons into several types. This atlas provides the anatomical foundation for understanding the functional organization of the mPFC.

To model the responses of electromagnetic surveys for geophysical subsurface sensing, a mixed total field/scattered field-based discontinuous Galerkin frequency-domain (TF/SF DGFD) method is proposed in this paper. The proposed TF/SF DGFD method is implemented at a subdomain level based on the domain decomposition technique. Different subdomains can employ either the TF DGFD framework or the SF DGFD framework, which are then coupled through the Riemann transmission condition. To balance the computation efficiency and accuracy for practical applications, the proposed method prefers to using the SF DGFD framework for subdomains with sources and using the TF DGFD framework for the remaining subdomains. At the interfaces between total field and scattered field subdomains, the Riemann transmission condition is slightly modified by incorporating the background fields due to the physically imposed sources in the background media. In this way, the proposed method only requires surface integrals of the background fields as extra overhead instead of elementwise integration of the scattering objects for the purely scattered field-based method, which can improve the computational efficiency. Also, it is more accurate than the purely TF DGFD method given the same mesh. Numerical examples are studied to examine the performance of the proposed method, which is proven to have better accuracy than the TF DGFD method. The TF/SF DGFD method will facilitate modeling of electromagnetic surveys under complicated geophysical environments for subsurface sensing.

Short-term memory deficits have been associated with prefrontal cortex (PFC) dysfunction in Alzheimer’s disease (AD) and AD mouse models. Extratelencephalic projection (ET) neurons in the PFC play a key role in short-term working memory, but the mechanism between ET neuronal dysfunction in the PFC and short-term memory impairment in AD is not well understood. Here, using fiber photometry and optogenetics, we found reduced neural activity in the ET neurons in the medial prefrontal cortex (mPFC) of the 5×FAD mouse model led to object recognition memory (ORM) deficits. Activation of ET neurons in the mPFC of 5×FAD mice rescued ORM impairment, and inhibition of ET neurons in the mPFC of wild type mice impaired ORM expression. ET neurons in the mPFC that project to supramammillary nucleus were necessary for ORM expression. Viral tracing and in vivo recording revealed that mPFC ET neurons received fewer cholinergic inputs from the basal forebrain in 5×FAD mice. Furthermore, activation of cholinergic fibers in the mPFC rescued ORM deficits in 5×FAD mice, while acetylcholine deficiency reduced the response of ET neurons in the mPFC to familiar objects. Taken together, our results revealed a neural mechanism behind ORM impairment in 5×FAD mice.

The dorsal raphe nucleus (DR) and median raphe nucleus (MR) contain populations of glutamatergic and GABAergic neurons regulating diverse behavioral functions. Their whole-brain input-output circuits remain incompletely understood. We used viral tracing combined with fluorescence micro-optical sectioning tomography to generate a comprehensive whole-brain atlas of inputs and outputs of glutamatergic and GABAergic neurons in the DR and MR. We discovered that these neurons receive inputs from similar upstream brain regions. The glutamatergic and GABAergic neurons in the same raphe nucleus have divergent projection patterns with differences in critical brain regions. Specifically, MR glutamatergic neurons project to the lateral habenula via multiple pathways. Correlation and cluster analysis indicated that glutamatergic and GABAergic neurons in the same raphe nucleus receive inputs from heterogeneous neurons in upstream brain regions and send different collateral projections. This connectivity atlas provides insights into the cell heterogeneity, anatomical connectivity and behavioral functions of the raphe nucleus.

The dorsal raphe nucleus (DR) and median raphe nucleus (MR) contain populations of glutamatergic and GABAergic neurons that regulate diverse behavioral functions. However, their whole-brain input-output circuits remain incompletely elucidated. We used viral tracing combined with fluorescence micro-optical sectioning tomography to generate a comprehensive whole-brain atlas of inputs and outputs of glutamatergic and GABAergic neurons in the DR and MR. We found that these neurons received inputs from similar upstream brain regions. The glutamatergic and GABAergic neurons in the same raphe nucleus had divergent projection patterns with differences in critical brain regions. Specifically, MR glutamatergic neurons projected to the lateral habenula through multiple pathways. Correlation and cluster analysis revealed that glutamatergic and GABAergic neurons in the same raphe nucleus received heterogeneous inputs and sent different collateral projections. This connectivity atlas further elucidates the anatomical architecture of the raphe nuclei, which could facilitate better understanding of their behavioral functions.

Summary Resin embedding is widely used to dissect the fine structure of bio-tissue with electron and optical microscopy. However, it is difficult to embed large-volume tissues with resin. Here, we modified the formula of LR-White resin to prevent the sample cracking during polymerization process and applied this method to the intact brains of mouse, ferret, and macaque. Meanwhile, we increased the fluorescence preservation rate for green fluorescent protein (GFP) from 73 ± 4.0% to 126 ± 3.0% and tdTomato from 60 ± 3.3% to 117 ± 2.8%. Combined with the whole-brain imaging system, we acquired the cytoarchitectonic information and the circuit information such as individual axon and boutons which were labeled with multiple fluorescent proteins. This method shows great potential in the study of continuous fine microstructure information in large-volume tissues from different species, which can facilitate the neuroscience research and help the understanding of the structure-function relationship in complex bio-tissues., Graphical Abstract, Highlights • Modified LR-White resin embedding was proposed to embed large-volume tissues • Retarder α-methyl-styrene was added to prevent cracking during polymerization • Resin formula was modified to preserve multiple fluorescent proteins • Microstructure information was acquired from the brains of different species, Techniques in Neuroscience; Tissue Engineering; Biomedical Materials

SummaryWe normally regard sleep and wake as two distinct opposing brain states, where sleep requires silence of wake-promoting structures such as the locus coeruleus (LC)-norepinephrine (NE) system. We set out to investigate how cortical NE dynamics and NE-related astrocytic activity relates to LC population activity during sleep states.We show that LC displays regular phasic activity bouts during NREM sleep leading to a slow oscillatory pattern of prefrontal NE levels of which the majority of NE increases does not lead to awakening. NE troughs link to sleep spindles and continued NE decline transitions into REM sleep. Last, we show that prefrontal astrocytes have reduced sensitivity towards NE during sleep.Our results suggest that dynamic changes in the activity of wake-promoting systems during sleep create alternation between crucial sleep processes and broadening of sensitivity towards incoming sensory input.HighlightsExtracellular levels of norepinephrine display dynamic changes during NREM and REM sleepPhasic activity of locus coeruleus neurons during NREM underlies slow norepinephrine oscillationsSpindles occur at norepinephrine troughs and are abolished by norepinephrine increasesIncreased spindles prior to REM reflect the beginning of a long-lasting norepinephrine declineREM episodes are characterized by a sub-threshold continuous norepinephrine declineThe responsiveness of astrocytic Ca2+ to norepinephrine is reduced during sleep

Optical clearing methods are widely used for three-dimensional biological information acquisition in the whole organ. However, the imaging quality of cleared tissues is often limited by ununiformed tissue clearing. By combining tissue clearing with mechanical sectioning based whole organ imaging system, we can reduce the influence of light scattering and absorption on the tissue to get isotropic and high resolution in both superficial and deep layers. However, it remains challenging for optical cleared biological tissue to maintain good sectioning property. Here, we developed a clearing method named M-CUBIC (machinable CUBIC), which combined a modified CUBIC method with PNAGA (poly-N-acryloyl glycinamide) hydrogel embedding to transparentize tissue while improving its sectioning property. With high-throughput light-sheet tomography platform (HLTP) and fluorescent micro-optical sectioning tomography (fMOST), we acquired continuous datasets with subcellular resolution from intact mouse brains for single neuron tracing, as well as the fine vascular structure of kidneys. This method can be used to acquire microstructures of multiple types of biological organs with subcellular resolutions, which can facilitate biological research.