scholarly journals Network structure of the mouse brain connectome with voxel resolution

2020 ◽  
Vol 6 (51) ◽  
pp. eabb7187
Author(s):  
Ludovico Coletta ◽  
Marco Pagani ◽  
Jennifer D. Whitesell ◽  
Julie A. Harris ◽  
Boris Bernhardt ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Mouse Brain ◽  
Network Structure ◽  
Brain Connectivity ◽  
Spatial Scales ◽  
Mammalian Brain ◽  
Fine Grained ◽  
Brain Connectome ◽  
Neural Information ◽  
Organizational Principles

Fine-grained descriptions of brain connectivity are required to understand how neural information is processed and relayed across spatial scales. Previous investigations of the mouse brain connectome have used discrete anatomical parcellations, limiting spatial resolution and potentially concealing network attributes critical to connectome organization. Here, we provide a voxel-level description of the network and hierarchical structure of the directed mouse connectome, unconstrained by regional partitioning. We report a number of previously unappreciated organizational principles in the mammalian brain, including a directional segregation of hub regions into neural sink and sources, and a strategic wiring of neuromodulatory nuclei as connector hubs and critical orchestrators of network communication. We also find that the mouse cortical connectome is hierarchically organized along two superimposed cortical gradients reflecting unimodal-transmodal functional processing and a modality-specific sensorimotor axis, recapitulating a phylogenetically conserved feature of higher mammals. These findings advance our understanding of the foundational wiring principles of the mammalian connectome.

scholarly journals Network structure of the mouse brain connectome with voxel resolution

2020 ◽  
Author(s):  
Ludovico Coletta ◽  
Marco Pagani ◽  
Jennifer D. Whitesell ◽  
Julie A. Harris ◽  
Boris Bernhardt ◽  
...  
Keyword(s):  
Mouse Brain ◽  
Functional Organization ◽  
Brain Connectivity ◽  
Spatial Scales ◽  
Structural Features ◽  
Fine Grained ◽  
Brain Connectome ◽  
Neural Information ◽  
Brain States ◽  
Spatial Topography

AbstractFine-grained descriptions of brain connectivity are fundamental for understanding how neural information is processed and relayed across spatial scales. Prior investigations of the mouse brain connectome have employed discrete anatomical parcellations, limiting spatial resolution and potentially concealing network attributes critical to the organization of the mammalian connectome. Here we provide a voxel-level description of the network and hierarchical structure of the directed mouse connectome, unconstrained by regional partitioning. We show that integrative hub regions can be directionally segregated into neural sinks and sources, defining a hierarchical axis. We describe a set of structural communities that spatially reconstitute previously described fMRI networks of the mouse brain, and document that neuromodulatory nuclei are strategically wired as critical orchestrators of inter-modular and network communicability. Notably, like in primates, the directed mouse connectome is organized along two superimposed cortical gradients reflecting unimodal-transmodal functional processing and a modality-specific sensorimotor axis. These structural features can be related to patterns of intralaminar connectivity and to the spatial topography of dynamic fMRI brain states, respectively. Together, our results reveal a high-resolution structural scaffold linking mesoscale connectome topography to its macroscale functional organization, and create opportunities for identifying targets of interventions to modulate brain function in a physiologically-accessible species.

Carbon Monitor Gridded Dataset (CMGD), a near-real-time high resolution gridded CO2 emission estimates dataset

2021 ◽  
Author(s):  
Xinyu Dou ◽  
Zhu Liu
Keyword(s):  
Real Time ◽  
Spatial Resolution ◽  
Energy Use ◽  
High Spatial Resolution ◽  
Spatial Scales ◽  
Energy Transition ◽  
High Temporal Resolution ◽  
Fine Grained ◽  
Gridded Dataset ◽  
Emission Changes

<p>The COVID-19 pandemic is impacting human activities, and in turn energy use and carbon dioxide (CO<sub>2</sub>) emissions. This research first presented near-real-time high-spatial-resolution(0.1°*0.1°) and high-temporal-resolution(daily) gridded estimates of CO<sub>2</sub> emissions for different sectors named Carbon Monitor Gridded Dataset(CMGD). This dataset responds to the growing and urgent need for high-quality, fine-grained CO<sub>2</sub> emission estimates to support global emissions monitoring on the refined spatial scale. CMGD is derived from our Carbon Monitor, a near-real-time daily dataset of global CO<sub>2</sub> emission from fossil fuel and cement production, including detailed information in 6 sectors and main countries. Based on EDGAR v5.0 gridded CO<sub>2</sub> emissions map and other geospatial proxies, we finally constructed CMGD with a high spatial resolution of 0.1 degree. Here, we provided the total emissions of specific countries and analyzed the countries with larger emissions (including the EU). Furthermore, we analyzed the daily emission changes of several typical cities around the world and provided insights on the contributions of various sectors. Through CMGD, we can get a much faster and more fine-grained overview, including timelines that show where and how emissions decreases have corresponded to lockdown measures at the finer spatial scales. The fine-grain and timeliness of CMGD emissions estimates will facilitate more local and adaptive management of CO<sub>2</sub> emissions during both the pandemic recovery and the ongoing energy transition.</p>

scholarly journals Multimodal Magnetic Resonance Histology and Light Sheet Imaging for Quantitative Neurogenetics of the Mouse

2021 ◽  
Author(s):  
G. Allan Johnson ◽  
Gary Cofer ◽  
James Cook ◽  
James Gee ◽  
Adam Hall ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Spatial Resolution ◽  
Mouse Brain ◽  
Angular Resolution ◽  
Imaging Techniques ◽  
Brain Connectivity ◽  
Light Sheet ◽  
Whole Brain ◽  
Light Sheet Microscopy

Paul Lauterbur closed his seminal paper on MRI with the statement that "zeugmatographic (imaging) techniques should find many useful applications in studies of the internal structures, states and composition of microscopic objects" {Lauterbur, 1973 #967}. Magnetic resonance microscopy was subsequently demonstrated in 1986 by three groups{Aguayo, 1986 #968}{Eccles, 1986 #969}{Johnson, 1986 #970}. The application of MRI to the study of tissue structure, i.e. magnetic resonance histology (MRH) was suggested in 1993 {Johnson, 1993 #957}. MRH, while based on the same physical principals as MRI is something fundamentally different than the clinical exams which are typically limited to voxel dimensions of ~ 1 mm3. Preclinical imaging systems can acquire images with voxels ~ 1000 times smaller. The MR histology images presented here have been acquired at yet another factor of 1000 increase in spatial resolution. Figure S1 in the supplement shows a comparison of a state-of-the-art fractional anisotropy images of a C57 mouse brain in vivo @ 150 um resolution (voxel volume of 3.3 x10-3 mm3) with the atlas we have generated for this work at 15 um spatial resolution (voxel volume of 3.3 x 10-6 mm3). In previous work, we have demonstrated the utility of MR histology in neurogenetics at spatial/angular resolution of 45 um /46 angles {Wang N, 2020 #972}. At this spatial/angular resolution it is possible to map whole brain connectivity with high correspondence to retroviral tracers {Calabrese, 2015 #895}. But the MRH derived connectomes can be derived in less than a day where the retroviral tracer studies require months/years {Oh, 2014 #971}. The resolution index (angular samples/voxel volume) for this previous work was >500,000 {Johnson, 2018 #894}. Figure S2 shows a comparison between that previous work and the new atlas presented in this paper with a resolution index of 32 million. Light sheet microscopy (LSM) has undergone similar rapid evolution over the last 20 years. The invention of tissue clearing, advances in immuno histochemistry and development of selective plane illumination microscopy (SPIM) now make it possible to acquire whole mouse brain images at submicron spatial resolution with a vast array of cell specific markers{Ueda, 2020 #974}{Park, 2018 #953}{Murray, 2015 #952}{Gao, 2014 #973}. And these advantages can be realized in scan times of < 6hrs. The major limitation from these studies is the distortion in the tissue from dissection from the cranium, swelling from clearing and staining, and tissue damage from handling. We report here the merger of these two methods: 1. MRH with the brain in the skull to provide accurate geometry, cytoarchitectural measures using scalar imaging metrics and whole brain connectivity at 15 um isotropic spatial resolution with super resolution track density images @ 5 um isotropic resolution; 2. whole brain multichannel LSM @ 1.8x1.8x4.0 um; 3. a big image data infrastructure that enables label mapping from the atlas to the MR image, geometric correction to the light sheet data, label mapping to the light sheet volumes and quantitative extraction of regional cell density. These methods make it possible to generate a comprehensive collection of image derived phenotypes (IDP) of cells and circuits covering the whole mouse brain with throughput that can be scaled for quantitative neurogenetics.

High spatial resolution x-ray microanalysis of interfaces

1995 ◽  
Vol 53 ◽  
pp. 284-285
Author(s):  
J. R. Michael
Keyword(s):  
Spatial Resolution ◽  
Electron Probe ◽  
Solid Angle ◽  
Collection Efficiency ◽  
Spatial Scales ◽  
Energy Dispersive Spectrometer ◽  
X Rays ◽  
X Ray ◽  
Probe Size ◽  
Brightness Field

X-ray microanalysis in the analytical electron microscope (AEM) refers to a technique by which chemical composition can be determined on spatial scales of less than 10 nm. There are many factors that influence the quality of x-ray microanalysis. The minimum probe size with sufficient current for microanalysis that can be generated determines the ultimate spatial resolution of each individual microanalysis. However, it is also necessary to collect efficiently the x-rays generated. Modern high brightness field emission gun equipped AEMs can now generate probes that are less than 1 nm in diameter with high probe currents. Improving the x-ray collection solid angle of the solid state energy dispersive spectrometer (EDS) results in more efficient collection of x-ray generated by the interaction of the electron probe with the specimen, thus reducing the minimum detectability limit. The combination of decreased interaction volume due to smaller electron probe size and the increased collection efficiency due to larger solid angle of x-ray collection should enhance our ability to study interfacial segregation.

scholarly journals The Modular Organization of Pain Brain Networks: An fMRI Graph Analysis Informed by Intracranial EEG

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Camille Fauchon ◽  
David Meunier ◽  
Isabelle Faillenot ◽  
Florence B Pomares ◽  
Hélène Bastuji ◽  
...  
Keyword(s):  
Information Integration ◽  
Functional Organization ◽  
Brain Connectivity ◽  
Brain Networks ◽  
Modular Organization ◽  
Noxious Heat ◽  
Intracranial Eeg ◽  
Brain Connectome ◽  
Posterior Parietal ◽  
The Brain

Abstract Intracranial EEG (iEEG) studies have suggested that the conscious perception of pain builds up from successive contributions of brain networks in less than 1 s. However, the functional organization of cortico-subcortical connections at the multisecond time scale, and its accordance with iEEG models, remains unknown. Here, we used graph theory with modular analysis of fMRI data from 60 healthy participants experiencing noxious heat stimuli, of whom 36 also received audio stimulation. Brain connectivity during pain was organized in four modules matching those identified through iEEG, namely: 1) sensorimotor (SM), 2) medial fronto-cingulo-parietal (default mode-like), 3) posterior parietal-latero-frontal (central executive-like), and 4) amygdalo-hippocampal (limbic). Intrinsic overlaps existed between the pain and audio conditions in high-order areas, but also pain-specific higher small-worldness and connectivity within the sensorimotor module. Neocortical modules were interrelated via “connector hubs” in dorsolateral frontal, posterior parietal, and anterior insular cortices, the antero-insular connector being most predominant during pain. These findings provide a mechanistic picture of the brain networks architecture and support fractal-like similarities between the micro-and macrotemporal dynamics associated with pain. The anterior insula appears to play an essential role in information integration, possibly by determining priorities for the processing of information and subsequent entrance into other points of the brain connectome.

scholarly journals Uncovering Statistical Links Between Gene Expression and Structural Connectivity Patterns in the Mouse Brain

2021 ◽  
Author(s):  
Nestor Timonidis ◽  
Alberto Llera ◽  
Paul H. E. Tiesinga
Keyword(s):  
Gene Expression ◽  
Mouse Brain ◽  
Structural Connectivity ◽  
Enrichment Analysis ◽  
Underlying Mechanism ◽  
Synaptic Function ◽  
Reticular Nucleus ◽  
Sources Of Information ◽  
Independent Components ◽  
Brain Connectome

AbstractFinding links between genes and structural connectivity is of the utmost importance for unravelling the underlying mechanism of the brain connectome. In this study we identify links between the gene expression and the axonal projection density in the mouse brain, by applying a modified version of the Linked ICA method to volumetric data from the Allen Institute for Brain Science for identifying independent sources of information that link both modalities at the voxel level. We performed separate analyses on sets of projections from the visual cortex, the caudoputamen and the midbrain reticular nucleus, and we determined those brain areas, injections and genes that were most involved in independent components that link both gene expression and projection density data, while we validated their biological context through enrichment analysis. We identified representative and literature-validated cortico-midbrain and cortico-striatal projections, whose gene subsets were enriched with annotations for neuronal and synaptic function and related developmental and metabolic processes. The results were highly reproducible when including all available projections, as well as consistent with factorisations obtained using the Dictionary Learning and Sparse Coding technique. Hence, Linked ICA yielded reproducible independent components that were preserved under increasing data variance. Taken together, we have developed and validated a novel paradigm for linking gene expression and structural projection patterns in the mouse mesoconnectome, which can power future studies aiming to relate genes to brain function.

Implementation of an Urban Parameterization Scheme into the Regional Climate Model COSMO-CLM

2013 ◽  
Vol 52 (10) ◽  
pp. 2296-2311 ◽  
Author(s):  
Kristina Trusilova ◽  
Barbara Früh ◽  
Susanne Brienen ◽  
Andreas Walter ◽  
Valéry Masson ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Climate Model ◽  
Spatial Scales ◽  
Regional Climate ◽  
Coupled Model ◽  
Urban Land ◽  
Urban Land Use ◽  
Small Scale ◽  
Model Simulations ◽  
Scale Modelling

AbstractAs the nonhydrostatic regional model of the Consortium for Small-Scale Modelling in Climate Mode (COSMO-CLM) is increasingly employed for studying the effects of urbanization on the environment, the authors extend its surface-layer parameterization by the Town Energy Budget (TEB) parameterization using the “tile approach” for a single urban class. The new implementation COSMO-CLM+TEB is used for a 1-yr reanalysis-driven simulation over Europe at a spatial resolution of 0.11° (~12 km) and over the area of Berlin at a spatial resolution of 0.025° (~2.8 km) for evaluating the new coupled model. The results on the coarse spatial resolution of 0.11° show that the standard and the new models provide 2-m temperature and daily precipitation fields that differ only slightly by from −0.1 to +0.2 K per season and ±0.1 mm day−1, respectively, with very similar statistical distributions. This indicates only a negligibly small effect of the urban parameterization on the model's climatology. Therefore, it is suggested that an urban parameterization may be omitted in model simulations on this scale. On the spatial resolution of 0.025° the model COSMO-CLM+TEB is able to better represent the magnitude of the urban heat island in Berlin than the standard model COSMO-CLM. This finding shows the importance of using the parameterization for urban land in the model simulations on fine spatial scales. It is also suggested that models could benefit from resolving multiple urban land use classes to better simulate the spatial variability of urban temperatures for large metropolitan areas on spatial scales below ~3 km.

Direct Causal Networks for the Study of Transcranial Magnetic Stimulation Effects on Focal Epileptiform Discharges

2015 ◽  
Vol 25 (05) ◽  
pp. 1550006 ◽  
Author(s):  
Dimitris Kugiumtzis ◽  
Vasilios K. Kimiskidis
Keyword(s):  
Transcranial Magnetic Stimulation ◽  
Network Structure ◽  
Magnetic Stimulation ◽  
Brain Connectivity ◽  
Effective Connectivity ◽  
Epileptic Focus ◽  
Sham Stimulation ◽  
Focal Seizures ◽  
Epileptiform Discharges ◽  
The Brain

Background: Transcranial magnetic stimulation (TMS) can have inhibitory effects on epileptiform discharges (EDs) of patients with focal seizures. However, the brain connectivity before, during and after EDs, with or without the administration of TMS, has not been extensively explored. Objective: To investigate the brain network of effective connectivity during ED with and without TMS in patients with focal seizures. Methods: For the effective connectivity a direct causality measure is applied termed partial mutual information from mixed embedding (PMIME). TMS-EEG data from two patients with focal seizures were analyzed. Each EEG record contained a number of EDs in the majority of which TMS was administered over the epileptic focus. As a control condition, sham stimulation over the epileptogenic zone or real TMS at a distance from the epileptic focus was also performed. The change in brain connectivity structure was investigated from the causal networks formed at each sliding window. Conclusion: The PMIME could detect distinct changes in the network structure before, within, and after ED. The administration of real TMS over the epileptic focus, in contrast to sham stimulation, terminated the ED prematurely in a node-specific manner and regained the network structure as if it would have terminated spontaneously.

scholarly journals Global downscaling of remotely sensed soil moisture using neural networks

2018 ◽  
Vol 22 (10) ◽  
pp. 5341-5356 ◽  
Author(s):  
Seyed Hamed Alemohammad ◽  
Jana Kolassa ◽  
Catherine Prigent ◽  
Filipe Aires ◽  
Pierre Gentine
Keyword(s):  
Soil Moisture ◽  
Spatial Resolution ◽  
Land Surface ◽  
Vegetation Index ◽  
Spatial Scales ◽  
Ancillary Data ◽  
Repeat Time ◽  
Spatiotemporal Scales ◽  
Better Than

Abstract. Characterizing soil moisture at spatiotemporal scales relevant to land surface processes (i.e., of the order of 1 km) is necessary in order to quantify its role in regional feedbacks between the land surface and the atmospheric boundary layer. Moreover, several applications such as agricultural management can benefit from soil moisture information at fine spatial scales. Soil moisture estimates from current satellite missions have a reasonably good temporal revisit over the globe (2–3-day repeat time); however, their finest spatial resolution is 9 km. NASA's Soil Moisture Active Passive (SMAP) satellite has estimated soil moisture at two different spatial scales of 36 and 9 km since April 2015. In this study, we develop a neural-network-based downscaling algorithm using SMAP observations and disaggregate soil moisture to 2.25 km spatial resolution. Our approach uses the mean monthly Normalized Differenced Vegetation Index (NDVI) as ancillary data to quantify the subpixel heterogeneity of soil moisture. Evaluation of the downscaled soil moisture estimates against in situ observations shows that their accuracy is better than or equal to the SMAP 9 km soil moisture estimates.

scholarly journals Assessment of the Impacts of Image Signal-to-Noise Ratios in Impervious Surface Mapping

2019 ◽  
Vol 11 (22) ◽  
pp. 2603
Author(s):  
George Xian ◽  
Hua Shi ◽  
Cody Anderson ◽  
Zhuoting Wu
Keyword(s):  
Spatial Resolution ◽  
Signal To Noise Ratio ◽  
Spatial Scales ◽  
Regression Tree ◽  
Remote Sensing Data ◽  
Impervious Surface ◽  
Landsat 8 ◽  
Signal To Noise ◽  
Study Results ◽  
The Impact

Medium spatial resolution satellite images are frequently used to characterize thematic land cover and a continuous field at both regional and global scales. However, high spatial resolution remote sensing data can provide details in landscape structures, especially in the urban environment. With upgrades to spatial resolution and spectral coverage for many satellite sensors, the impact of the signal-to-noise ratio (SNR) in characterizing a landscape with highly heterogeneous features at the sub-pixel level is still uncertain. This study used WorldView-3 (WV3) images as a basis to evaluate the impacts of SNR on mapping a fractional developed impervious surface area (ISA). The point spread function (PSF) from the Landsat 8 Operational Land Imager (OLI) was used to resample the WV3 images to three different resolutions: 10 m, 20 m, and 30 m. Noise was then added to the resampled WV3 images to simulate different fractional levels of OLI SNRs. Furthermore, regression tree algorithms were incorporated into these images to estimate the ISA at different spatial scales. The study results showed that the total areal estimate could be improved by about 1% and 0.4% at 10-m spatial resolutions in our two study areas when the SNR changes from half to twice that of the Landsat OLI SNR level. Such improvement is more obvious in the high imperviousness ranges. The root-mean-square-error of ISA estimates using images that have twice and two-thirds the SNRs of OLI varied consistently from high to low when spatial resolutions changed from 10 m to 20 m. The increase of SNR, however, did not improve the overall performance of ISA estimates at 30 m.

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