14 results on '"Joseph E. Knox"'
Search Results
2. Endogenous pathology in tauopathy mice progresses via brain networks
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Denise M.O. Ramirez, Jennifer D. Whitesell, Nikhil Bhagwat, Talitha L. Thomas, Apoorva D. Ajay, Ariana Nawaby, Benoît Delatour, Sylvie Bay, Pierre LaFaye, Joseph E. Knox, Julie A. Harris, Julian P. Meeks, and Marc I. Diamond
- Subjects
tau, neurodegeneration, PS19, serial two-photon tomography, Allen Mouse Brain Common Coordinate Framework (CCFv3), brain networks, network diffusion models ,Article - Abstract
This archive contains the data and code used for the analyses in the manuscript: “Endogenous pathology in tauopathy mice progresses via brain networks”. One-Sentence Summary: Whole brain imaging of tau deposition reveals retrograde-dominant network-based propagation in a tauopathy mouse model. Data files File Description Ramirez_etal_ProbMaps_ccf_10um.zip Registered 8-bit probability maps in the same dimensions as the 10um resolution Allen Institute for Brain Science Common Coordinate Framework atlas. probability_per_structure_background_subtracted.csv Background-subtracted p-tau intensity values per brain region Code repositories File (GH release link) Description https://github.com/julianmeeks/utsw_wbmf_stpt_tau/releases/tag/v1.0.1 MATLAB scripts and associated files used to compile sample x region matrices of p-tau density. https://github.com/DrJigsaw/tau_network_analysis/releases/tag/v1.0.0 Code to quantify tau values per structure contrasted against background levels. https://github.com/nikhil153/tau_network_spread_analysis/releases/tag/v1.0.0 Python scripts and notebooks to 1) define a network based on directional anatomical connectome and 2) fit and compare diffusion models of tau spread. Note: Detailed documentation to reproduce analyses and figures in the manuscript is provided on the individual GitHub code repositories listed in the table (file hyperlinks).  
- Published
- 2023
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3. Behavioral Circuit-Specific Effects of Brain X-Chromosome Inactivation Determines X-Linked Phenotypic Penetrance
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Eric R. Szelenyi, Danielle Fisenne, Joseph E. Knox, Julie A. Harris, James A. Gornet, Ramesh Palaniswamy, Yongsoo Kim, Kannan Umadevi Venkataraju, and Pavel Osten
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History ,Polymers and Plastics ,Business and International Management ,Industrial and Manufacturing Engineering - Published
- 2022
4. Hierarchical organization of cortical and thalamic connectivity
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Jennifer D. Whitesell, Stephen McConoughey, Yaoyao Li, Yun Wang, Xiuli Kuang, Alex M. Henry, Ali Williford, Quanxin Wang, Amy Bernard, Karla E. Hirokawa, Stefan Mihalas, Robert Howard, Anh Ho, Wayne Wakeman, Maitham Naeemi, David Feng, Peter A. Groblewski, Seung Wook Oh, Leonard Kuan, Nile Graddis, Joseph E. Knox, Benjamin Ouellette, Andrew Cho, Jérôme Lecoq, Hongkui Zeng, Christof Koch, Jennifer Luviano, Hannah Choi, Marty Mortrud, Charles R. Gerfen, Julie A. Harris, Allan R. Jones, Phillip Bohn, Phil Lesnar, Linzy Casal, Shiella Caldejon, Staci A. Sorensen, Lydia Ng, John W. Phillips, Aaron Feiner, Nathalie Gaudreault, and Elise Shen
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Male ,0301 basic medicine ,Computer science ,Thalamus ,Cre recombinase ,Projection neuron ,Article ,Mice ,03 medical and health sciences ,0302 clinical medicine ,Neural Pathways ,Biological neural network ,medicine ,Animals ,Hierarchical organization ,Axon ,Cerebral Cortex ,Multidisciplinary ,Computational neuroscience ,Integrases ,Axons ,Mice, Inbred C57BL ,030104 developmental biology ,medicine.anatomical_structure ,Cortical network ,Female ,Neuroscience ,030217 neurology & neurosurgery - Abstract
The mammalian cortex is a laminar structure containing many areas and cell types that are densely interconnected in complex ways, and for which generalizable principles of organization remain mostly unknown. Here we describe a major expansion of the Allen Mouse Brain Connectivity Atlas resource1, involving around a thousand new tracer experiments in the cortex and its main satellite structure, the thalamus. We used Cre driver lines (mice expressing Cre recombinase) to comprehensively and selectively label brain-wide connections by layer and class of projection neuron. Through observations of axon termination patterns, we have derived a set of generalized anatomical rules to describe corticocortical, thalamocortical and corticothalamic projections. We have built a model to assign connection patterns between areas as either feedforward or feedback, and generated testable predictions of hierarchical positions for individual cortical and thalamic areas and for cortical network modules. Our results show that cell-class-specific connections are organized in a shallow hierarchy within the mouse corticothalamic network. Using mouse lines in which subsets of neurons are genetically labelled, the authors provide generalized anatomical rules for connections within and between the cortex and thalamus.
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- 2019
5. High-resolution data-driven model of the mouse connectome
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Jennifer D. Whitesell, Kameron Decker Harris, Nile Graddis, Joseph E. Knox, Stefan Mihalas, Hongkui Zeng, Eric Shea-Brown, and Julie A. Harris
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0303 health sciences ,Mesoscopic physics ,Mouse ,Computer science ,Applied Mathematics ,General Neuroscience ,Information processing ,High resolution ,computer.software_genre ,Computer Science Applications ,Data-driven ,03 medical and health sciences ,0302 clinical medicine ,Artificial Intelligence ,Connectome ,Data mining ,computer ,030217 neurology & neurosurgery ,Research Articles ,Whole-brain ,030304 developmental biology - Abstract
Knowledge of mesoscopic brain connectivity is important for understanding inter- and intraregion information processing. Models of structural connectivity are typically constructed and analyzed with the assumption that regions are homogeneous. We instead use the Allen Mouse Brain Connectivity Atlas to construct a model of whole-brain connectivity at the scale of 100 μm voxels. The data consist of 428 anterograde tracing experiments in wild type C57BL/6J mice, mapping fluorescently labeled neuronal projections brain-wide. Inferring spatial connectivity with this dataset is underdetermined, since the approximately 2 × 105 source voxels outnumber the number of experiments. To address this issue, we assume that connection patterns and strengths vary smoothly across major brain divisions. We model the connectivity at each voxel as a radial basis kernel-weighted average of the projection patterns of nearby injections. The voxel model outperforms a previous regional model in predicting held-out experiments and compared with a human-curated dataset. This voxel-scale model of the mouse connectome permits researchers to extend their previous analyses of structural connectivity to much higher levels of resolution, and it allows for comparison with functional imaging and other datasets., Author Summary Anatomical tracing experiments can provide a wealth of information regarding connectivities originating from the injection sites. However, it is difficult to integrate all this information into a comprehensive connectivity model. In this study we construct a high-resolution model of the mouse brain connectome using the assumption that connectivity patterns vary smoothly within brain regions, and we present several extensions of this model. We believe that this higher resolution connectome will be of great use to the community, enabling comparisons with other data modalities, such as functional imaging and gene expression, as well as for theoretical studies.
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- 2018
6. Regional, layer, and cell-class specific connectivity of the mouse default mode network
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Ali Williford, Nile Graddis, Karla E. Hirokawa, Wayne Wakeman, Stefan Mihalas, Philip R. Nicovich, Thuc Nghi Nguyen, Olivia Fong, Adam Liska, Phillip Bohn, Anh Ho, Lydia Ng, Emma Garren, Boaz P. Levi, Kimberly A. Smith, Nick Dee, Julie A. Harris, David Feng, Alex M. Henry, Cindy T. J. van Velthoven, Peter A. Groblewski, Alessandro Gozzi, Jennifer D. Whitesell, Hongkui Zeng, Bosiljka Tasic, Maitham Naeemi, Joseph E. Knox, Leonard Kuan, and Ludovico Coletta
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Resting state functional magnetic resonance imaging ,Cell type ,Then test ,medicine.anatomical_structure ,Retrosplenial cortex ,Cell ,medicine ,Neuron ,Biology ,Neuroscience ,Multiple disorders ,human activities ,Default mode network - Abstract
The evolutionarily conserved default mode network (DMN) is characterized by temporally correlated activity between brain regions during resting states. The DMN has emerged as a selectively vulnerable network in multiple disorders, so understanding its anatomical composition will provide fundamental insight into how its function is impacted by disease. Reproducible rodent analogs of the human DMN offer an opportunity to investigate the underlying brain regions and structural connectivity (SC) with high spatial and cell type resolution. Here, we performed systematic analyses using mouse resting state functional magnetic resonance imaging to identify the DMN and whole brain axonal tracing data, co-registered to the 3D Allen Mouse Common Coordinate Framework reference atlas. We identified the specific, predominantly cortical, brain regions comprising the mouse DMN and report preferential SC between these regions. Next, at the cell class level, we report that cortical layer (L) 2/3 neurons in DMN regions project almost exclusively to other DMN regions, whereas L5 neurons project to targets both in and out of the DMN. We then test the hypothesis that in- and out-DMN projection patterns originate from distinct L5 neuron sub-classes using an intersectional viral tracing strategy to label all the axons from neurons defined by a single target. In the ventral retrosplenial cortex, a core DMN region, we found two L5 projection types related to the DMN and mapped them to unique transcriptomically-defined cell types. Together, our results provide a multi-scale description of the anatomical correlates of the mouse DMN.
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- 2020
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7. Regional, Layer, and Cell-Type-Specific Connectivity of the Mouse Default Mode Network
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Phillip Bohn, Lydia Ng, Maitham Naeemi, Thuc Nghi Nguyen, Karla E. Hirokawa, Stefan Mihalas, Ali Williford, Kimberly A. Smith, Leonard Kuan, Joseph E. Knox, Nick Dee, Hongkui Zeng, Julie A. Harris, Ludovico Coletta, Alex M. Henry, Peter A. Groblewski, Olivia Fong, Adam Liska, Nile Graddis, Anh Ho, David Feng, Cindy T. J. van Velthoven, Wayne Wakeman, Jennifer D. Whitesell, Bosiljka Tasic, Boaz P. Levi, Alessandro Gozzi, Philip R. Nicovich, and Emma Garren
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0301 basic medicine ,retrosplenial cortex ,Single cell transcriptomics ,Cell type specific ,Population ,Biology ,single cell transcriptomics ,Axonal tracing ,Article ,projection neuron types ,Mice ,03 medical and health sciences ,0302 clinical medicine ,Retrosplenial cortex ,Connectome ,medicine ,Animals ,DMN ,Layer (object-oriented design) ,education ,Default mode network ,axonal projections ,Neurons ,education.field_of_study ,medicine.diagnostic_test ,General Neuroscience ,Brain ,Default Mode Network ,Magnetic Resonance Imaging ,030104 developmental biology ,connectivity ,cortical connectome ,Nerve Net ,Functional magnetic resonance imaging ,viral tracer ,human activities ,Neuroscience ,030217 neurology & neurosurgery - Abstract
Summary The evolutionarily conserved default mode network (DMN) is a distributed set of brain regions coactivated during resting states that is vulnerable to brain disorders. How disease affects the DMN is unknown, but detailed anatomical descriptions could provide clues. Mice offer an opportunity to investigate structural connectivity of the DMN across spatial scales with cell-type resolution. We co-registered maps from functional magnetic resonance imaging and axonal tracing experiments into the 3D Allen mouse brain reference atlas. We find that the mouse DMN consists of preferentially interconnected cortical regions. As a population, DMN layer 2/3 (L2/3) neurons project almost exclusively to other DMN regions, whereas L5 neurons project in and out of the DMN. In the retrosplenial cortex, a core DMN region, we identify two L5 projection types differentiated by in- or out-DMN targets, laminar position, and gene expression. These results provide a multi-scale description of the anatomical correlates of the mouse DMN., Graphical Abstract, Highlights • Mouse resting-state default mode network anatomy described at high resolution in 3D • Systematic axon tracing shows cortical DMN regions are preferentially interconnected • Layer 2/3 DMN neurons project mostly in the DMN; layer 5 neurons project in and out • Retrosplenial cortex contains distinct types of in- and out-DMN projection neurons, The default mode network is vulnerable to brain disorders, but details of its anatomy and connectivity are coarse. Whitesell et al. use modern neuroanatomical tools in the mouse, including whole-brain imaging and viral tracing, to provide high-resolution anatomical descriptions and identify cell type correlates of this conserved brain network.
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- 2020
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8. Brain X chromosome inactivation is not random and can protect from paternally inherited neurodevelopmental disease
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James Gornet, Pavel Osten, Eric R. Szelenyi, Yongsoo Kim, Danielle Fisenne, Joseph E. Knox, Ramesh Palaniswamy, Julie A. Harris, and Kannan Umadevi Venkataraju
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Genetics ,0303 health sciences ,Mechanism (biology) ,Disease ,Biology ,Penetrance ,Phenotype ,X-inactivation ,Lesion ,03 medical and health sciences ,0302 clinical medicine ,medicine ,medicine.symptom ,Allele ,030217 neurology & neurosurgery ,De novo mutations ,030304 developmental biology - Abstract
Non-random (skewed) X chromosome inactivation (XCI) in the female brain can ameliorate X-linked phenotypes, though clinical studies typically consider 80-90% skewing favoring the healthy allele as necessary for this effect1–10. Here we quantify for the first time whole-brain XCI at single-cell resolution and discover a preferential inactivation of paternal to maternal X at ∼60:40 ratio, which surprisingly impacts disease penetrance. In Fragile-X-syndrome mouse model, Fmr1-KO allele transmitted maternally in ∼60% brain cells causes phenotypes, but paternal transmission in ∼40% cells is unexpectedly tolerated. In the affected maternal Fmr1-KO(m)/+ mice, local XCI variability within distinct brain networks further determines sensory versus social manifestations, revealing a stochastic source of X-linked phenotypic diversity. Taken together, our data show that a modest ∼60% bias favoring the healthy allele is sufficient to ameliorate X-linked phenotypic penetrance, suggesting that conclusions of many clinical XCI studies using the 80-90% threshold should be re-evaluated. Furthermore, the paternal origin of the XCI bias points to a novel evolutionary mechanism acting to counter the higher rate of de novo mutations in male germiline11–16. Finally, the brain capacity to tolerate a major genetic lesion in ∼40% cells is also relevant for interpreting other neurodevelopmental genetic conditions, such as brain somatic mosaicism.
- Published
- 2018
9. Whole brain imaging reveals distinct spatial patterns of amyloid beta deposition in three mouse models of Alzheimer’s disease
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Wayne Wakeman, Andrew Pelos, Jennifer D. Whitesell, Phillip Bohn, Karla E. Hirokawa, Joseph E. Knox, Anh Ho, Alice Mukora, Julie A. Harris, Alex R. Buckley, Leonard Kuan, and Nile Graddis
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0303 health sciences ,Amyloid pathology ,biology ,Amyloid beta ,Hippocampal formation ,03 medical and health sciences ,0302 clinical medicine ,Neuroimaging ,Retrosplenial cortex ,In vivo ,mental disorders ,biology.protein ,Amyloid precursor protein ,Cortical subplate ,Neuroscience ,030217 neurology & neurosurgery ,030304 developmental biology - Abstract
A variety of Alzheimer’s disease (AD) mouse models overexpress mutant forms of human amyloid precursor protein (APP), producing high levels of amyloid β (Aβ) and forming plaques However, the degree to which these models mimic spatiotemporal patterns of Aβ deposition in brains of AD patients is unknown. Here, we mapped the spatial distribution of Aβ plaques across ages in three APP-overexpression mouse lines (APP/PS1, Tg2576, hAPP-J20) using in vivo labeling with methoxy-X04, high throughput whole brain imaging, and an automated informatics pipeline. Images were acquired with high resolution serial 2-photon tomography and labeled plaques were detected using custom-built segmentation algorithms. Image series were registered to the Allen Mouse Brain Common Coordinate Framework, a 3D reference atlas, enabling automated brain-wide quantification of plaque density, number, and location. In both APP/PS1 and Tg2576 mice, plaques were identified first in isocortex, followed by olfactory, hippocampal, and cortical subplate areas. In hAPP-J20 mice, plaque density was highest in hippocampal areas, followed by isocortex, with little to no involvement of olfactory or cortical subplate areas. Within the major brain divisions, distinct regions were identified with high (or low) plaque accumulation; e.g., the lateral visual area within the isocortex of APP/PS1 mice had relatively higher plaque density compared with other cortical areas, while in hAPP-J20 mice, plaques were densest in the ventral retrosplenial cortex. In summary, we show how whole brain imaging of amyloid pathology in mice reveals the extent to which a given model recapitulates the regional Aβ deposition patterns described in AD.
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- 2018
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10. Whole brain imaging reveals distinct spatial patterns of amyloid beta deposition in three mouse models of Alzheimer's disease
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Jennifer D. Whitesell, Andrew Pelos, Julie A. Harris, Wayne Wakeman, Phillip Bohn, Anh Ho, Alice Mukora, Alex R. Buckley, Leonard Kuan, Karla E. Hirokawa, Nile Graddis, and Joseph E. Knox
- Subjects
0301 basic medicine ,Amyloid pathology ,Amyloid beta ,Mice, Transgenic ,Neuroimaging ,Hippocampal formation ,Article ,03 medical and health sciences ,Mice ,0302 clinical medicine ,Retrosplenial cortex ,In vivo ,Alzheimer Disease ,mental disorders ,Amyloid precursor protein ,Image Processing, Computer-Assisted ,Cortical subplate ,Animals ,Amyloid beta-Peptides ,biology ,General Neuroscience ,Brain ,Disease Models, Animal ,030104 developmental biology ,biology.protein ,Neuroscience ,030217 neurology & neurosurgery - Abstract
A variety of Alzheimer's disease (AD) mouse models overexpress mutant forms of human amyloid precursor protein (APP), producing high levels of amyloid β (Aβ) and forming plaques. However, the degree to which these models mimic spatiotemporal patterns of Aβ deposition in brains of AD patients is unknown. Here, we mapped the spatial distribution of Aβ plaques across age in three APP-overexpression mouse lines (APP/PS1, Tg2576, and hAPP-J20) using in vivo labeling with methoxy-X04, high throughput whole brain imaging, and an automated informatics pipeline. Images were acquired with high resolution serial two-photon tomography and labeled plaques were detected using custom-built segmentation algorithms. Image series were registered to the Allen Mouse Brain Common Coordinate Framework, a 3D reference atlas, enabling automated brain-wide quantification of plaque density, number, and location. In both APP/PS1 and Tg2576 mice, plaques were identified first in isocortex, followed by olfactory, hippocampal, and cortical subplate areas. In hAPP-J20 mice, plaque density was highest in hippocampal areas, followed by isocortex, with little to no involvement of olfactory or cortical subplate areas. Within the major brain divisions, distinct regions were identified with high (or low) plaque accumulation; for example, the lateral visual area within the isocortex of APP/PS1 mice had relatively higher plaque density compared with other cortical areas, while in hAPP-J20 mice, plaques were densest in the ventral retrosplenial cortex. In summary, we show how whole brain imaging of amyloid pathology in mice reveals the extent to which a given model recapitulates the regional Aβ deposition patterns described in AD.
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- 2018
11. P4‐225: WHOLE BRAIN IMAGING REVEALS DISTINCT SPATIAL PATTERNS OF AMYLOID BETA DEPOSITION AND ATROPHY IN MOUSE MODELS OF ALZHEIMER'S DISEASE
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Phillip Bohn, Joseph E. Knox, Nile Graddis, Julie A. Harris, Alice Mukora, Karla A. Hirokawa, Jennifer D. Whitesell, Maitham Naeemi, Alex R. Buckley, and Leonard Kuan
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Pathology ,medicine.medical_specialty ,biology ,Epidemiology ,Chemistry ,Amyloid beta ,Health Policy ,medicine.disease ,Psychiatry and Mental health ,Cellular and Molecular Neuroscience ,Atrophy ,Developmental Neuroscience ,Neuroimaging ,medicine ,biology.protein ,Neurology (clinical) ,Geriatrics and Gerontology ,Deposition (chemistry) - Published
- 2018
12. The organization of intracortical connections by layer and cell class in the mouse brain
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Julie A. Harris, Stefan Mihalas, Karla E. Hirokawa, Jennifer D. Whitesell, Joseph E. Knox, Amy Bernard, Phillip Bohn, Shiella Caldejon, Linzy Casal, Andrew Cho, David Feng, Nathalie Gaudreault, Charles R. Gerfen, Nile Graddis, Peter A. Groblewski, Alex Henry, Anh Ho, Robert Howard, Leonard Kuan, Jerome Lecoq, Jennifer Luviano, Stephen McConoghy, Marty T. Mortrud, Maitham Naeemi, Lydia Ng, Seung W. Oh, Benjamin Ouellette, Staci A. Sorensen, Wayne Wakeman, Quanxin Wang, Ali Williford, John W. Phillips, Allan Jones, Christof Koch, and Hongkui Zeng
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0303 health sciences ,Cell type ,Computer science ,Projection neuron ,Cortex (botany) ,03 medical and health sciences ,0302 clinical medicine ,medicine.anatomical_structure ,medicine ,Connectome ,Axon ,Neuroscience ,030217 neurology & neurosurgery ,030304 developmental biology - Abstract
The mammalian cortex is a laminar structure composed of many cell types densely interconnected in complex ways. Recent systematic efforts to map the mouse mesoscale connectome provide comprehensive projection data on inter-areal connections, but not at the level of specific cell classes or layers within cortical areas. We present here a significant expansion of the Allen Mouse Brain Connectivity Atlas, with ~1000 new axonal projection mapping experiments across nearly all isocortical areas in 50 Cre driver lines. Using 13 lines most selective for cortical layer and/or projection neuron class we identify the differential contribution of each layer/class to the overall intracortical connectivity patterns. We find that layer 5 (L5) projection neurons account for essentially all intracortical outputs. L2/3, L4, and L6 neurons contact a subset of the L5 cortical targets. We describe the most common axon lamination patterns in target regions, and their relationships to source layer/class. Most patterns were consistent with previous anatomical rules used to determine hierarchical position between cortical areas (feedforward, feedback), with notable exceptions. We observe a diversity of target patterns arising from every source layer/class, but supragranular (L2/3 and upper L4) neurons are most associated with feedforward type patterns, whereas infragranular (L5 and L6) neurons have both feedforward and feedback. Network analyses revealed a modular organization of the intracortical connectome. Using the cell class-based target lamination patterns, we labeled all connections and intermodule connections as feed-forward or -back, and finally present an integrated view of the intracortical connectome as a hierarchical network.
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- 2018
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13. High resolution data-driven model of the mouse connectome
- Author
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Joseph E. Knox, Hongkui Zeng, Kameron Decker Harris, Nile Graddis, Stefan Mihalas, Julie A. Harris, Eric Shea-Brown, and Jennifer D. Whitesell
- Subjects
0303 health sciences ,Basis (linear algebra) ,Scale (ratio) ,Computer science ,business.industry ,Atlas (topology) ,Pattern recognition ,computer.software_genre ,Data-driven ,Functional imaging ,03 medical and health sciences ,Anterograde tracing ,0302 clinical medicine ,Voxel ,Connectome ,Artificial intelligence ,Projection (set theory) ,business ,computer ,030217 neurology & neurosurgery ,030304 developmental biology - Abstract
Knowledge of mesoscopic brain connectivity is important for understanding inter- and intra-region information processing. Models of structural connectivity are typically constructed and analyzed with the assumption that regions are homogeneous. We instead use the Allen Mouse Brain Connectivity Atlas to construct a model of whole brain connectivity at the scale of 100 µm voxels. The dataset used consists of 366 anterograde tracing experiments in wild type C7BL/6 mice, mapping fluorescently-labeled neuronal projections brain-wide. Inferring spatial connectivity with this dataset remains underdetermined, since the approximately 2 × 105 source voxels outnumber the number of experiments. To address this, we assume that connection patterns and strengths vary smoothly across major brain divisions. We model the connectivity at each voxel as a radial basis kernel-weighted average of the projection patterns of nearby injections. The voxel model outperforms a previous regional model in predicting held-out experiments and compared to a human-curated dataset. This voxel-scale model of the mouse connectome permits researchers to extend their previous analyses of structural connectivity to unprecedented levels of resolution, and allows for comparison with functional imaging and other datasets.
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- 2018
- Full Text
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14. High-Angle Backscatter from Snow on the Ground
- Author
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Joseph E. Knox
- Subjects
Orthomode transducer ,Azimuth ,Transducer ,law ,High angle ,Astrophysics::Earth and Planetary Astrophysics ,Radar ,Snow ,Polarization (waves) ,Geology ,law.invention ,Remote sensing ,Freezing point - Abstract
In January and February of 1981 and 1982, the Ballistic Research Laboratory (BRL) participated in SNOW-ONE at Camp Ethan Allen, VT, sponsored by the US Army Cold Regions Research and Engineering Laboratory (CRREL). The BRL conducted high-angle radar measurements there, measuring backscatter at 35 GHz. from various areas of undisturbed snow, acquiring time series data under varied weather conditions. A dual-polarized, 35 GHz radar was used to make the snow backscatter measurements. Polarity of the transmitted signal, vertical or horizontal, was controlled by means of an R.F. switch. The received signal was passed through an orthomode transducer to two receivers, allowing both parallel and cross polarized components to be recorded simultaneously. The sensor was supported 15 meters above the ground on an elevation-over-azimuth antenna mount. Elevation angle was adjusted so that radar beam angle was 30-degrees from vertical. Azimuth angle was varied so as to scan areas of undisturbed snow around the base of the support. The sensor was enclosed in an insulated box and heated to a fixed temperature. Signal returned to the sensor by the snow was, in large part, dependent upon the condition of the surface snow as it was affected by air temperature. When the temperature was well below freezing, sigma-zero was around -7dB with parallel polarization (-13dB, cross polarization). When the temperature increased to the freezing point, the snow became wet, packed easily, and had a lower sigma-zero (-17dB, parallel, and -22dB, cross polarization). The condition of the snow surface had a secondary effect upon the value of sigma-zero. A smoothed surface reflected less energy back to the sensor than a roughened surface. The combined effect of these two variables produced the changes that were measured in sigma-zero.
- Published
- 1983
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