Dataset Information

AAV-Mediated Anterograde Transsynaptic Tagging: Mapping Corticocollicular Input-Defined Neural Pathways for Defense Behaviors.

ABSTRACT: To decipher neural circuits underlying brain functions, viral tracers are widely applied to map input and output connectivity of neuronal populations. Despite the successful application of retrograde transsynaptic viruses for identifying presynaptic neurons of transduced neurons, analogous anterograde transsynaptic tools for tagging postsynaptically targeted neurons remain under development. Here, we discovered that adeno-associated viruses (AAV1 and AAV9) exhibit anterograde transsynaptic spread properties. AAV1-Cre from transduced presynaptic neurons effectively and specifically drives Cre-dependent transgene expression in selected postsynaptic neuronal targets, thus allowing axonal tracing and functional manipulations of the latter input-defined neuronal population. Its application in superior colliculus (SC) reveals that SC neuron subpopulations receiving corticocollicular projections from auditory and visual cortex specifically drive flight and freezing, two different types of defense behavior, respectively. Together with an intersectional approach, AAV-mediated anterograde transsynaptic tagging can categorize neurons by their inputs and molecular identity, and allow forward screening of distinct functional neural pathways embedded in complex brain circuits.

SUBMITTER: Zingg B

PROVIDER: S-EPMC5538794 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature

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Publications

AAV-Mediated Anterograde Transsynaptic Tagging: Mapping Corticocollicular Input-Defined Neural Pathways for Defense Behaviors.

Zingg Brian B Chou Xiao-Lin XL Zhang Zheng-Gang ZG Mesik Lukas L Liang Feixue F Tao Huizhong Whit HW Zhang Li I LI

Neuron 20161215 1

To decipher neural circuits underlying brain functions, viral tracers are widely applied to map input and output connectivity of neuronal populations. Despite the successful application of retrograde transsynaptic viruses for identifying presynaptic neurons of transduced neurons, analogous anterograde transsynaptic tools for tagging postsynaptically targeted neurons remain under development. Here, we discovered that adeno-associated viruses (AAV1 and AAV9) exhibit anterograde transsynaptic sprea ...[more]

PMID: 27989459

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Project description:Regional soft tissue mechanical strain offers crucial insights into tissue's mechanical function and vital indicators for different related disorders. Tagging magnetic resonance imaging (tMRI) has been the standard method for assessing the mechanical characteristics of organs such as the heart, the liver, and the brain. However, constructing accurate artifact-free pixelwise strain maps at the native resolution of the tagged images has for decades been a challenging unsolved task. In this work, we developed an end-to-end deep-learning framework for pixel-to-pixel mapping of the two-dimensional Eulerian principal strains [Formula: see text] and [Formula: see text] directly from 1-1 spatial modulation of magnetization (SPAMM) tMRI at native image resolution using convolutional neural network (CNN). Four different deep learning conditional generative adversarial network (cGAN) approaches were examined. Validations were performed using Monte Carlo computational model simulations, and in-vivo datasets, and compared to the harmonic phase (HARP) method, a conventional and validated method for tMRI analysis, with six different filter settings. Principal strain maps of Monte Carlo tMRI simulations with various anatomical, functional, and imaging parameters demonstrate artifact-free solid agreements with the corresponding ground-truth maps. Correlations with the ground-truth strain maps were R = 0.90 and 0.92 for the best-proposed cGAN approach compared to R = 0.12 and 0.73 for the best HARP method for [Formula: see text] and [Formula: see text], respectively. The proposed cGAN approach's error was substantially lower than the error in the best HARP method at all strain ranges. In-vivo results are presented for both healthy subjects and patients with cardiac conditions (Pulmonary Hypertension). Strain maps, obtained directly from their corresponding tagged MR images, depict for the first time anatomical, functional, and temporal details at pixelwise native high resolution with unprecedented clarity. This work demonstrates the feasibility of using the deep learning cGAN for direct myocardial and liver Eulerian strain mapping from tMRI at native image resolution with minimal artifacts.

Dataset Information

AAV-Mediated Anterograde Transsynaptic Tagging: Mapping Corticocollicular Input-Defined Neural Pathways for Defense Behaviors.

Publications

AAV-Mediated Anterograde Transsynaptic Tagging: Mapping Corticocollicular Input-Defined Neural Pathways for Defense Behaviors.

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