High‐throughput light sheet tomography platform for automated fast imaging of whole mouse brain

Acquiring information of the neural structures in the whole‐brain level is vital for systematically exploring mechanisms and principles of brain function and dysfunction. Most methods for whole brain imaging, while capable of capturing the complete morphology of neurons, usually involve complex sample preparation and several days of image acquisition. The whole process including optical clearing or resin embedding is time consuming for a quick survey of the distribution of specific neural circuits in the whole brain. Here, we develop a high‐throughput light‐sheet tomography platform (HLTP), which requires minimum sample preparation. This method does not require optical clearing for block face light sheet imaging. After fixation using paraformaldehyde, an aligned 3 dimensional image dataset of a whole mouse brain can be obtained within 5 hours at a voxel size of 1.30 × 1.30 × 0.92 μm. HLTP could be a very efficient tool for quick exploration and visualization of brain‐wide distribution of specific neurons or neural circuits.      

Convolutional neural networks for reconstruction of undersampled optical projection tomography data applied to in vivo imaging of zebrafish

Optical projection tomography (OPT) is a 3D mesoscopic imaging modality that can utilize absorption or fluorescence contrast. 3D images can be rapidly reconstructed from tomographic data sets sampled with sufficient numbers of projection angles using the Radon transform, as is typically implemented with optically cleared samples of the mm‐to‐cm scale. For in vivo imaging, considerations of phototoxicity and the need to maintain animals under anesthesia typically preclude the acquisition of OPT data at a sufficient number of angles to avoid artifacts in the reconstructed images. For sparse samples, this can be addressed with iterative algorithms to reconstruct 3D images from undersampled OPT data, but the data processing times present a significant challenge for studies imaging multiple animals. We show here that convolutional neural networks (CNN) can be used in place of iterative algorithms to remove artifacts—reducing processing time for an undersampled in vivo zebrafish dataset from 77 to 15 minutes. We also show that using CNN produces reconstructions of equivalent quality to compressed sensing with 40% fewer projections. We further show that diverse training data classes, for example, ex vivo mouse tissue data, can be used for CNN‐based reconstructions of OPT data of other species including live zebrafish.      

Deep learning of diffraction image patterns for accurate classification of five cell types

Development of label‐free methods for accurate classification of cells with high throughput can yield powerful tools for biological research and clinical applications. We have developed a deep neural network of DINet for extracting features from cross‐polarized diffraction image (p‐DI) pairs on multiple pixel scales to accurately classify cells in five types. A total of 6185 cells were measured by a polarization diffraction imaging flow cytometry (p‐DIFC) method followed by cell classification with DINet on p‐DI data. The averaged value and SD of classification accuracy were found to be 98.9% ± 1.00% on test data sets for 5‐fold training and test. The invariance of DINet to image translation, rotation, and blurring has been verified with an expanded p‐DI data set. To study feature‐based classification by DINet, two sets of correctly and incorrectly classified cells were selected and compared for each of two prostate cell types. It has been found that the signature features of large dissimilarities between p‐DI data of correctly and incorrectly classified cell sets increase markedly from convolutional layers 1 and 2 to layers 3 and 4. These results clearly demonstrate the importance of high‐order correlations extracted at the deep layers for accurate cell classification.      

In Vivo Quantitative Imaging Provides Insights into Trunk Neural Crest Migration

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Calcium imaging of infrared-stimulated activity in rodent brain

Infrared neural stimulation (INS) is a promising neurostimulation technique that can activate neural tissue with high spatial precision and without the need for exogenous agents. However, little is understood about how infrared light interacts with neural tissue on a cellular level, particularly within the living brain. In this study, we use calcium sensitive dye imaging on macroscopic and microscopic scales to explore the spatiotemporal effects of INS on cortical calcium dynamics. The INS-evoked calcium signal that was observed exhibited a fast and slow component suggesting activation of multiple cellular mechanisms. The slow component of the evoked signal exhibited wave-like properties suggesting network activation, and was verified to originate from astrocytes through pharmacology and 2-photon imaging. We also provide evidence that the fast calcium signal may have been evoked through modulation of glutamate transients. This study demonstrates that pulsed infrared light can induce intracellular calcium modulations in both astrocytes and neurons, providing new insights into the mechanisms of action of INS in the brain.     

Use-dependent plasticity in barrel cortex: Intrinsic signal imaging reveals functional expansion of spared whisker representation into adjacent deprived columns

We used optical imaging of intrinsic cortical signals, elicited by whisker stimulation, to define areas of activation in primary sensory cortex of normal hamsters and hamsters subjected to neonatal follicle ablation at postnatal day seven (P7). Follicle ablations were unilateral, and spared either C-row whiskers or the second whisker arc. This study was done to determine if the intrinsic cortical connectivity pattern of the barrel cortex, established during the critical period, affects the process of representational plasticity that follows whisker follicle ablation. Additionally, we tested the ability to monitor such changes in individual cortical whisker representations using intrinsic signal imaging. Stimulation of a single whisker yielded peak activation of a barrel-sized patch in the somatotopically appropriate location in normal cortex. In both row and arc-spared animals, functional representations corresponding to spared follicles were significantly stronger and more oblong than normal. The pattern of activation differed in the row-sparing and arc-sparing groups, in that the expansion was preferentially into deprived, not spared areas. Single whisker stimulation in row-spared cases preferentially activated the corresponding barrel arc, while stimulation of one whisker in arc-spared cases produced elongated activation down the barrel row. Since whisker deflection normally has a net inhibitory effect on neighboring barrels, our data suggest that intracortical inhibition fails to develop normally in deprived cortical columns. Because thalamocortical projections are not affected by follicle ablation after P7, we suggest that the effects we observed are largely cortical, not thalamocortical.     

有无反馈对欺骗过程的神经机制的调控作用

欺骗行为会导致欺骗结果的产生,欺骗结果又会直接影响欺骗行为的发生及其内在机制.虽然有研究表明,欺骗结果会对相应的欺骗过程产生调控作用,但对这一调控作用的机制并不清楚.本研究采用功能核磁共振技术,对两组被试分别使用有、无反馈(欺骗结果)的GKT范式并记录两组被试在进行诚实反应和欺骗反应时的大脑激活模式.结果发现,有反馈组与无反馈组相比,有反馈组的诚实反应和欺骗反应都导致了左侧顶叶皮层、左背部前扣带皮层、左侧脑岛、双侧视皮层和右侧小脑的更大激活;对两组而言,欺骗反应和诚实反应都导致了右腹外侧前额区域、双侧缘上回、左侧脑岛、右后内侧额叶、右侧颞中回和右侧纹状体的更大激活;此外,与无反馈组相比,有反馈组的欺骗反应与诚实反应在双侧纹状体和左侧脑岛上的激活差异更加明显.这些结果表明,有无欺骗结果对欺骗过程的神经机制具有调控作用,当需要面临欺骗结果时,欺骗过程将更大程度地涉及到奖赏预期和风险厌恶过程的参与.     

The search for a hippocampal engram

Understanding the molecular and cellular changes that underlie memory, the engram, requires the identification, isolation and manipulation of the neurons involved. This presents a major difficulty for complex forms of memory, for example hippocampus-dependent declarative memory, where the participating neurons are likely to be sparse, anatomically distributed and unique to each individual brain and learning event. In this paper, I discuss several new approaches to this problem. In vivo calcium imaging techniques provide a means of assessing the activity patterns of large numbers of neurons over long periods of time with precise anatomical identification. This provides important insight into how the brain represents complex information and how this is altered with learning. The development of techniques for the genetic modification of neural ensembles based on their natural, sensory-evoked, activity along with optogenetics allows direct tests of the coding function of these ensembles. These approaches provide a new methodological framework in which to examine the mechanisms of complex forms of learning at the level of the neurons involved in a specific memory.     

基于卷积神经网络的超声影像肝癌自动分类

目的 针对从原发性肝癌中检测肝细胞癌（HCC）的灵敏度不高和诊断结果高度依赖放射科医生的专业性和临床经验，本文利用深度卷积神经网络（CNN）的方法自动学习B超和超声造影（CEUS）图像中的特征信息，并实现对肝癌的分类。方法 建立并验证基于CNN的多个二维（2D）和三维（3D）分类模型，分别对116例患者（其中100例HCC和16例非HCC）的B超和CEUS影像进行定量分析，并对比分析各个模型的分类性能。结果 实验结果表明，3D-CNN模型的各方面性能指标都优于2D-CNN模型，验证了3D-CNN模型能同时提取肿瘤区域的2D影像特征及血流时间动态变化特征，比2D-CNN模型更适用于HCC与非HCC分类。其中3D-CNN模型的AUC、准确率和敏感度值最高，分别达到了85%、85%和80%。此外，由于HCC和非HCC样本不均衡，通过扩充非HCC样本的数量可以提升网络的分类性能。结论 本文提出的3D-CNN模型能够实现快速、准确的肝癌分类，有望应用于辅助临床医师诊断与治疗肝癌。     

Ultrafast,accurate, and robust localization of anisotropic dipoles

The resolution of single molecule localization imaging techniques largely depends on the precision of localization algorithms. However, the commonly used Gaussian function is not appropriate for anisotropic dipoles because it is not the true point spread function. We derived the theoretical point spread function of tilted dipoles with restricted mobility and developed an algorithm based on an artificial neural network for estimating the localization, orientation and mobility of individual dipoles. Compared with fitting-based methods, our algorithm demonstrated ultrafast speed and higher accuracy, reduced sensitivity to defocusing, strong robustness and adaptability, making it an optimal choice for both two-dimensional and threedimensional super-resolution imaging analysis.     

A method for generating natural and user-defined sniffing patterns in anesthetized or reduced preparations

Sniffing has long been thought to play a critical role in shapingneural responses to odorants at multiple levels of the nervoussystem. However, it has been difficult to systematically examinehow particular parameters of sniffing behavior shape odorant-evokedactivity, in large part because of the complexity of sniffingbehavior and the difficulty in reproducing this behavior inan anesthetized or reduced preparation. Here we present a methodfor generating naturalistic sniffing patterns in such preparations.The method involves a nasal ventilator whose movement is controlledby an analog command voltage. The command signal may consistof intranasal pressure transients recorded from awake rats andmice or user-defined waveforms. This "sniff playback" devicegenerates intranasal pressure and airflow transients in anesthetizedanimals that approximate those recorded from the awake animaland are reproducible across trials and across preparations.The device accurately reproduces command waveforms over an amplituderange of approximately 1 log unit and up to frequencies of approximately12 Hz. Further, odorant-evoked neural activity imaged duringsniff playback appears similar to that seen in awake animals.This method should prove useful in investigating how the parametersof odorant sampling shape neural responses in a variety of experimentalsettings.     

Epidermal Growth Factor Signaling Promotes Sleep through a Combined Series and Parallel Neural Circuit

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Neural network organization for courtship-song feature detection in Drosophila

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High-Accuracy Detection of Neuronal Ensemble Activity in Two-Photon Functional Microscopy Using Smart Line Scanning

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Infrared neural stimulation induces intracellular Ca2+ release mediated by phospholipase C

The influence of infrared laser pulses on intracellular Ca²⁺ signaling was investigated in neural cell lines with fluorescent live cell imaging. The probe Fluo‐4 was used to measure Ca²⁺ in HT22 mouse hippocampal neurons and nonelectrically excitable U87 human glioblastoma cells exposed to 50 to 500 ms infrared pulses at 1470 nm. Fluorescence recordings of Fluo‐4 demonstrated that infrared stimulation induced an instantaneous intracellular Ca²⁺ transient with similar dose‐response characteristics in hippocampal neurons and glioblastoma cells (half‐maximal effective energy density EC₅₀ of around 58 J.cm^?2). For both type of cells, the source of the infrared‐induced Ca²⁺ transients was found to originate from intracellular stores and to be mediated by phospholipase C and IP₃‐induced Ca²⁺ release from the endoplasmic reticulum. The activation of phosphoinositide signaling by IR light is a new mechanism of interaction relevant to infrared neural stimulation that will also be widely applicable to nonexcitable cell types. The prospect of infrared optostimulation of the PLC/IP₃ cell signaling cascade has many potential applications including the development of optoceutical therapeutics.      

High-resolution single-shot phase-shifting interference microscopy using deep neural network for quantitative phase imaging of biological samples

White light phase-shifting interference microscopy (WL-PSIM) is a prominent technique for high-resolution quantitative phase imaging (QPI) of industrial and biological specimens. However, multiple interferograms with accurate phase-shifts are essentially required in WL-PSIM for measuring the accurate phase of the object. Here, we present single-shot phase-shifting interferometric techniques for accurate phase measurement using filtered white light (520±36 nm) phase-shifting interference microscopy (F-WL-PSIM) and deep neural network (DNN). The methods are incorporated by training the DNN to generate (a) four phase-shifted frames and (b) direct phase from a single interferogram. The training of network is performed on two different samples i.e., optical waveguide and MG63 osteosarcoma cells. Further, performance of F-WL-PSIM+DNN framework is validated by comparing the phase map extracted from network generated and experimentally recorded interferograms. The current approach can further strengthen QPI techniques for high-resolution phase recovery using a single frame for different biomedical applications.     

Diagnosing benign and malignant lesions in breast tissue sections by using IR-microspectroscopy

The collection of IR spectra through microscope optics and the visualization of the IR data by IR imaging represent a visualization approach, which uses infrared spectral features as a native intrinsic contrast mechanism. To illustrate the potential of this spectroscopic methodology in breast cancer research, we have acquired IR-microspectroscopic data from benign and malignant lesions in breast tissue sections by point microscopy with spot sizes of 30-40 μm. Four classes of distinct breast tissue spectra were defined and stored in the data base: fibroadenoma (a total of 1175 spectra from 14 patients), ductal carcinoma in situ (a total of 1349 spectra from 8 patients), connective tissue (a total of 464 spectra), and adipose tissue (a total of 146 spectra). Artifical neural network analysis, a supervised pattern recognition method, was used to develop an automated classifier to separate the four classes. After training the artifical neural network classifier, infrared spectra of independent external validation data sets (“unknown spectra”) were analyzed. In this way, all spectra (a total of 386) taken from micro areas inside the epithelium of fibroadenomas from 4 patients were correctly classified. Out of the 421 spectra taken from micro areas of the in situ component of invasive ductal carcinomas of 3 patients, 93% were correctly identified. Based on these results, the potential of the IR-microspectroscopic approach for diagnosing breast tissue lesions is discussed.