小鼠全视野光学成像数据处理与统计分析工具箱及其在神经影像中的应用

版权申诉

生物医学工程

功能磁共振成像

5星 · 超过95%的资源 90 浏览量 2025-03-01 18:45:44 上传评论收藏 1.43MB PDF 举报

资源推荐

资源详情

资源评论

Open-source statistical and data processing tools for

wide-field optical imaging data in mice

Lindsey M. Brier

and Joseph P. Culver

a,b,c,d,

Washington University School of Medicine, Department of Radiology, St. Louis, Missouri,

United States

Washington University School of Arts and Science, Department of Physics, St. Louis, Missouri,

United States

Washington University School of Engineering, Department of Biomedical Engineering,

St. Louis, Missouri, United States

Washington University School of Engineering, Department of Electrical and

Systems Engineering, St. Louis, Missouri, United States

Abstract

Significance: Wide-field optical imaging (WOI) can produce concurrent hemodynamic and

cell-specific calcium recordings across the entire cerebral cortex in animal models. There have

been multiple studies using WOI to image mouse models with various environmental or genetic

manipulations to understand various diseases. Despite the utility of pursuing mouse WOI along-

side human functional magnetic resonance imaging (fMRI), and the multitude of analysis tool-

boxes in the fMRI literat ure, there is not an available open-source, user-friendly data processing

and statistical analysis toolbox for WOI data.

Aim: To assemble a MATLAB toolbox for processing WOI data, as described and adapted to

combine techniques from multiple WOI groups and fMRI.

Approach: We outline our MATLAB toolbox on GitHub with multiple data analysis packages

and translate a commonly used statistical approach from the fMRI literature to the WOI data. To

illustrate the utility of our MATLAB toolbox, we demonstrate the ability of the proces sing and

analysis framework to detect a well-established deficit in a mouse model of stroke and plot

activation areas during an electrical paw stimulus experiment.

Results: Our processing toolbox and statistical methods isolate a somatosensory-based deficit 3

days following photothrombotic stroke and cleanly localize sensory stimulus activations.

Conclusions: The toolbo x presented here details an open-source, user-friendly compilation of

WOI processing tools with statistical methods to apply to any biological question investigated

with WOI techniques.

Distribution or reproduction of this work in whole or in part requires full attribution of the original

publication, including its DOI. [DOI: 10.1117/1.NPh.10.1.016601]

Keywords: calcium imaging; wide-field imaging; optical imaging; data processing.

Paper 22078TNR received Aug. 16, 2022; accepted for publication Feb. 2, 2023; published

online Mar. 1, 2023.

1 Introduction

Functional neuroimaging has enhanced our study of systems neuroscience and understandi ng of

neural networks.

1,2

Mainly, this has been accomplished with blood oxygen level-dependent

(BOLD) fluctuations in functional magnetic resonance (fMRI) data in human subjects.

In order

to better understand human conditions, there has been an increase in functional neuroimaging in

animal models, also performed using fMRI.

4–6

However, the relatively small size of the mouse

brain offers multiple techni cal and logistical challenges with fMRI. Therefore, there has been a

*Address all correspondence to Joseph P. Culver, [email protected]

Neurophotonics 016601-1 Jan–Mar 2023

•

Vol. 10(1)

parallel development of wide-field optical imaging (WOI) techniques in the mouse, yielding

similar blood-based surrogates of neural activity at a similar spatial scale with various logistical

tradeoffs versus fMRI.

The advent of genetically encoded calcium indicators (GECIs) enables

cell-specific labeling and led to increased temporal resolution for WOI compared to traditionally

measured hemodynamics.

8–10

Combined hemoglobin and fluorophore imaging is readily avail-

able with optical imaging systems and harnesses the advantages of GECIs as well as maintains

a translatable blood-based recording directly comparable to human fMRI. WOI analysis faces

many of the same procedural steps and therefore difficulties as those experienced with fMRI

analysis, such as data processing, visualization, and statistical testing. However, the relative nov-

elty of WOI compared to fMRI means that there is a need for many of the solutions within the

fMRI community to be translated into the WOI data analysis communities. We have developed

a toolbox (MATLAB) that addresses a number of fundamental concerns.

One of the biggest statistical challenges within the functional neuroimaging community is

the problem of correcting for multiple statistical tests. Many solutions have been proposed within

the fMRI community,

11,12

however, in general these have not been translated into an easy-to-

use WOI toolbox. Historically, functional connectivity (FC) is examined using a seed-based

approach.

For seed-based maps, common practice includes performing a pixel or voxel-wise

statistical test (e.g., Student’s t ) resulting in thousands of tests being performed withi n the field-

of-view (FOV). The most stringent correction (i.e., Bonferroni) assumes each statistical test is

independent.

This is certainly not the case when examining neighboring pixels within a brain

region for multip le reasons. For most mesoscopic WOI instruments, blurring by tissue light

scattering brings the effective full-width half-maximum (FWHM) to a size that spans multiple

pixels thus rendering each pixel not independent from an instrumentation point of view.

Additionally, from a biological point of view, it is reasonable to assume an amount of depend-

ence between neighboring pixels within the same brain region. A more plausible approach to

handle the multiple comparisons problem that has become fairly standard for fMRI is the use of a

clustering analysis, coupled with random field theory, to weight larger regions of interest (ROIs;

i.e., large clusters) of contiguous neighboring significant pixels as more likely to be a statistically

significant finding than small ROIs.

15,16

In this pap er, we translate the clustering approach to

WOI pixel space application.

Here, we provide a mouse optical data processing toolbox to streamline and make processing

steps transparent and user-friendly. Within it, we adopt the fMRI cluster size-based approach to

determining statistical significance and apply it to wide-field optical FC mapping. We demon-

strate the utility of this toolbox and various analytical packages within the context of photo-

thrombotic stroke and sensory stimulus activations.

2 Methods

2.1 Animals

Four 3- to 4-month-old mice (two male, two female) were imaged at baseline (Day 0) and on

Day 3 post photothrombotic stroke to left somatosensory forepaw cortex. All mice were

Thy1-GCaMP6f [Jackson Laboratories Strain: C57BL/6J-Tg(Thy1-GCaMP6f)GP5.5Dkim;

stock: 024276]. These mice express the protein GCaMP6f in excitatory neurons, primarily

in cortical layers ii, iii, v, and vi.

All studies were approved by the Washington University

School of Medicine Animals Studies Committee and follow the guidelines of the National

Institutes of Health’s Guide for the Care and Use of Laboratory Animals.

2.2 Surgical Preparations

Prior to imaging, typical surgical preparations wer e implemented.

9,17

Briefly, isoflurane anes-

thesia (3% induction, 1% maintenance, 0.5 L∕ min) was used for sedation and an optically

transparent 14 × 18 mm plexiglass window was implanted with translucent dental cement

(C&B-Metabond, Parkell Inc., Edgewood, New York) following a midline incision and

clearing of skin and periosteal membranes. The window covered the majo rity of the dorsal

Brier and Culver: Open-source statistical and data processing tools for wide-field optical imaging data in mice

Neurophotonics 016601-2 Jan–Mar 2023

•

Vol. 10(1)

cortical surface and provided an anchor for head fixation and allowed for chronic, repeatable

imaging.

2.3 Photothrombosis

Mice were secured in a stereotaxic frame under isoflurane anesthesia. 200 μ L of Rose Bengal

(Sigma Aldrich) dissolved in saline (10 g∕L) was injected intrape ritoneally. After 4 min,

a 532-nm diode-pumped solid-state laser (Shanghai Laser & Optics Century) was focused to

2.2 mm left and 0.5 mm anterior to bregma with a 0.5 mm spot size and at 23 mW for 10 min.

Mice were imaged at baseline [i.e., prior to photothrombosis (Day 0)], and 72 h post (Day 3). The

dataset used in the following analyses consists of two five-minute imaging runs from each

mouse. The stroke data were processed and analyzed as described below. Calcium data were

filtered with a 0.4 to 4.0 Hz Butterworth bandpass filter and hemoglobin data with a 0.009

to 0.08 Hz Butterworth bandpass filter. These frequency bands wer e selected as they correspond

to delta (0.4 to 4.0 Hz) and infraslow (0.009 to 0.08 Hz) ranges. The canonical FC frequency

band (infraslow, 0.009 to 0.08 Hz) was used for hemoglobin-based analysis similar to the blood

oxygen level dependent (BOLD) analysis used in the fMRI community.

2.4 Fluorescence and Optical Intrinsic Signal (OIS) Imaging

Mice were head-fixed in a stereotaxic frame and body secured in a black felt pouch for imaging.

Sequentially firing LEDs (Mightex Systems, Pleasanton, California) passed through a series of

dichroic lenses (Semrock, Rochester, New York) into a liquid light guide (Mightex Systems,

Pleasanton, California) that terminated in a 75 mm f∕1.8 lens (Navitar, Rochester, New York)

to focus the light onto the dorsal cortical surface. LEDs consisted of 470 nm (GCaMP6f exci-

tation), 530, 590, and 625 nm light. An sCMOS camera (Zyla 5.5, Andor Technologies, Belfast,

Northern Ireland, United Kingdom) coupled with an 85 mm f∕1.4 camera lens (Rokinon, New

York, New York) was used to captur e fluorescence/reflectance produced at 16.8 Hz per wave-

length of LED. A 515 nm longpass filter (Semrock, Rochester, New York) was used to discard

GCaMP6f excitation light. Cross polarization (Adorama, New York, New York) between the

illumination lens and collection lens discarded artifacts due to specular refl ection. The field-

of-view (FOV) recorded covered the majority of the convexity of the cerebral cortex

(∼1.1 cm

), extending from the olfactory bulb to the superior colliculus. All imaging data were

binned in 156 × 156 pixel

images at ∼100 μm

per pixel.

2.5 Toolbox Capabilities and Workflow: Data and User Input

In order to initiate use of the toolbox, data has to be loaded into MATLAB (we recommend

version 2022a or newer). Data should be in the form of pixels by pixels by frames. Example

data used in the following analyses were acquired using the aforementioned mesoscopic calcium

imaging modality, however, usage can be expanded to incorporate any data configured into this

data stack (e.g., voltage-sensitive dye imaging, data from other animal models). However,

one caveat to be noted is the following data processing pipeline was optimized on mesoscopic

WOI mouse data. All inputs and outputs to specific scripts mentioned and highlighted in

Fig. 1 are specified in the header of each script. The explanation that follows will walkthrough

each processing step, which are all in separate subroutines. Subroutines will be referenced, and

scripts that should be edited to run by the user are highlighted in Fig. 1 and Table 1.

Within the “START” folder at https://github.com/brierl/Mouse_WOI/tree/main/START, the

folder “Proc” contains the first script that should be used to load data in [following the flowchart

in Fig. 1(a)]. The script load_data.m allows the user to load the image stack and normalize one

frame (variable “frame5”) that will be used in the next script make_mask_landmarks.m. Within

make_mask_landmarks.m, the built-in MATLAB function roipoly.m is used to prompt the user

to create a binary mask representing brain regions (i.e., 1) or nonbrain regions (i.e., 0) saved to

the variable “isbrain.” The user is to click along the perimeter of the brain displayed in the frame

and double click upon closing the loop to create the binary file [example in Fig. 2(a)]. This file

will be used later and multiplied through the image stack so only pixels corresponding to brain is

Brier and Culver: Open-source statistical and data processing tools for wide-field optical imaging data in mice

Neurophotonics 016601-3 Jan–Mar 2023

•

Vol. 10(1)

剩余13页未读，继续阅读

评论收藏

内容反馈

版权申诉

duoduohenuonuo

2025-03-17

资源中能够借鉴的内容很多，值得学习的地方也很多，大家一起进步！

普通网友

粉丝: 4166

小鼠全视野光学成像数据处理与统计分析工具箱及其在神经影像中的应用

小鼠广泛场光学成像的体素分析方法与MATLAB实现（含可运行代码及解释）

dpabi小鼠数据处理.pdf

miRNA-146a干预中晚期APP_PS1双转基因小鼠的神经影像对照研究.pdf

小鼠行为及ephys记录数据分析代码合集

轴突标记的光学成像揭示了小鼠上丘囊功能层间通路的发展。

从小鼠视网膜多种成像方式探讨眼科光学成像技术进展

电针百会穴对APP_PS1双转基因痴呆模型小鼠18F-FDGPET_CT成像及学习记忆的影响.pdf

rodent-whole-brain-preprocessing-recipe:适用于小鼠和大鼠的全脑预处理工具箱

从荧光显微镜上获得283张小鼠脑片高分辨率图像数据集

小鼠胚胎期与成熟期肝脏表达谱芯片数据挖掘及生物信息学分析.pdf

浙贝乙素及其胆酸衍生物对小鼠脑部单胺类神经递质的影响

基于Python和C/C++的neuronVis神经细胞分析工具Jupyter Notebook源码

锰致小鼠黑质-纹状体神经功能损伤的研究

角度扫描型SPR传感器在免疫反应检测中的数据处理研究.pdf

工具与艾伦研究所的CCF数据在matlab中工作.zip

基因表达谱芯片用于小鼠膝关节炎组织的检测与分析.pdf

灵芝孢子油与深海鱼油联合应用对小鼠学习记忆及其大脑海马神经元表达NOS的影响

Python金融量化的高级库：TA-Lib-0.4.24（包含python3.7、3.8、3.9、3.10的32位和64位版本）

正大杯比赛国一获奖作品

xthreg2命令安装包

Stata18软件+新序列号

双色球2013001-2024133开奖数据.xlsx

FPGA技术中Verilog实现AD7606与AD7616并行读取模式的驱动代码设计及板级验证

2022泰迪杯数据分析技能赛B题代码 Jupyter Notebook

统计数模历年优秀论文-全国大学生统计建模大赛

分享BigemapPro地图配置文件

身份证归属地及代码表2025年全（免费下载）

TF_IDF，TF-IWF

数字后端dcg流畅

最新资源