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Dec 2020
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Macroscopic Structural and Connectome Mapping of the Mouse Brain Using Diffusion Magnetic Resonance Imaging
利用弥散磁共振成像绘制小鼠大脑的宏观结构和连接组图   

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Abstract

Translational work in rodents elucidates basic mechanisms that drive complex behaviors relevant to psychiatric and neurological conditions. Nonetheless, numerous promising studies in rodents later fail in clinical trials, highlighting the need for improving the translational utility of preclinical studies in rodents. Imaging of small rodents provides an important strategy to address this challenge, as it enables a whole-brain unbiased search for structural and dynamic changes that can be directly compared to human imaging. The functional significance of structural changes identified using imaging can then be further investigated using molecular and genetic tools available for the mouse. Here, we describe a pipeline for unbiased search and characterization of structural changes and network properties, based on diffusion MRI data covering the entire mouse brain at an isotropic resolution of 100 µm. We first used unbiased whole-brain voxel-based analyses to identify volumetric and microstructural alterations in the brain of adult mice exposed to unpredictable postnatal stress (UPS), which is a mouse model of complex early life stress (ELS). Brain regions showing structural abnormalities were used as nodes to generate a grid for assessing structural connectivity and network properties based on graph theory. The technique described here can be broadly applied to understand brain connectivity in other mouse models of human disorders, as well as in genetically modified mouse strains.


Graphic abstract:


Pipeline for characterizing structural connectome in the mouse brain using diffusion magnetic resonance imaging. Scale bar = 1 mm.


Keywords: Diffusion MRI (弥散核磁共振), Fiber tractography (纤维束成像), Structural connectivity (结构连接), Brain network properties (大脑网络属性), Mouse brain (鼠脑)

Background

Diffusion magnetic resonance imaging (dMRI) is an imaging technique that uses the random diffusion of water molecules to probe tissue microstructure (Le Bihan, 2003; Mori and Zhang; 2006; Novikov, 2021). Recent advances in imaging and computational processing allowed dMRI images with 100 µm resolution or higher to be obtained from rodents (Aggarwal et al., 2010; Calabrese et al., 2015). These can then be used to assess local volumetric changes through microstructural alterations in dMRI parameters, such as fractional anisotropy (FA), and to determine structural connectivity between different brain regions (Wu et al., 2013; Lerch et al., 2017; Feo and Giove, 2019; Badea et al., 2019; White et al., 2020; Pallast et al., 2020).


High resolution dMRI studies in small rodents provide a novel and promising frontier for improving the translational utility of preclinical studies (Kaffman et al., 2019; Muller et al., 2020). This is primarily because of the direct comparison that can be drawn with parallel studies in humans. In addition, unbiased voxel-based screening can identify specific brain regions that show structural changes. In turn, these can be used as nodes to construct a network and to characterize structural connectivity between specific nodes, identify critical hubs, and quantify network properties, such as global efficiency and small-worldness (Feo and Giove, 2019; Pallast et al., 2020; White et al., 2020). This unbiased agnostic approach is conceptually different from the more traditional region of interest (ROI) approach, in which structural changes in specific brain regions or connectomes are examined (Helmstaedter et al., 2013; Takemura et al., 2013; Saleeba et al., 2019). Nonetheless, dMRI studies are highly complementary with traditional neuroscience approaches, as structural changes identified by dMRI can be further examined using microscopy and genomic/proteomic approaches, and their contribution to complex behavior can be rigorously tested using chemogenetic and optogenetic tools (Kaffman et al., 2019; Muller et al., 2020).


dMRI provides multi-level information about structural changes in the intact tissue, including volumetric changes, dMRI parameters related to microstructure, and structural connectivity. The last fifteen years have witnessed rapid development in dMRI-based tract reconstruction, or tractography (Tuch et al., 2002; Mori and van Zijl, 2002; Tournier et al., 2007; Wedeen et al., 2008), which serves as an important component of the Human Connectome Project (Toga et al., 2012; Van Essen et al., 2013). With the development of high-resolution dMRI acquisition and tractography methods, dMRI tractography can now quickly survey macroscopic structural connectivity in the entire brain without sectioning, which is time consuming and prone to distortions and tissue damage (Moldrich et al., 2010; Wu et al., 2013; Calabrese et al., 2015; Xiong et al., 2018). It also permits simultaneous examination of multiple white matter connections in the same specimen, further reducing the time and cost. With the latest tools for brain connectivity analysis, tractography results can be used to examine changes at both individual pathways and entire connectome levels (Edwards et al., 2020).


dMRI also has several drawbacks, including lower resolution in gray matter regions compared to T1/T2-weighted MRI (Dorr et al., 2008; White et al., 2020) and limited spatial resolution and specificity compared to light microscopy findings with chemical or viral tracers (Wu and Zhang, 2016; Edwards et al., 2020). The need to rigorously correct for multiple comparisons when conducting whole-brain voxel analysis further hinders the detection of subtle changes and is particularly challenging when looking for interaction between two variables, such as early life stress (ELS) and sex (White et al., 2020). High resolution dMRI usually requires perfusing the animal, which prevents longitudinal rescanning of the same animals. Although techniques for in vivo high resolution dMRI of rodent brains have emerged (Wu et al., 2013; Wu et al., 2014), the exposure to anesthesia during MRI (2-3 h per session) may introduce additional confounding factors. Therefore, portraying a standardized procedure for reliable and reproducible estimation of microstructural changes in the mouse brain is crucial.


The protocol described here covers image acquisition, whole brain voxel analyses for volumetric and FA changes, tractography, and analysis. Compared to similar methods described before (Calabrese et al., 2015; Edwards et al., 2020), this protocol is based on the structural labels in the Allen Mouse Brain Atlas, which makes it relatively straightforward to compare tractography results with viral tracer results in the Allen Mouse Brain Connectivity Atlas (Oh et al., 2014; White et al., 2020). Unbiased whole-brain voxel analyses were used to identify brain regions that show changes in volume and dMRI parameters (e.g., FA) induced by ELS, and to compare them with those reported in humans exposed to early adversity. Fourteen brain regions that showed structural changes were used as nodes to generate a 14 × 14 matrix in each hemisphere. The network properties of this grid were then characterized using graph theory and compared with findings in humans exposed to early adversity (White et al., 2020). Our protocol relies on precise image registration to transfer structural labels from the atlas to subject images and will not work when there are large tissue deformations, such as those caused by brain tumors or severe necrosis. The protocol also has a node-to-node analysis step for small connections (e.g., in the amygdala network) that may be obscured in a whole brain analysis. Altogether, the protocol is useful for characterizing whole brain structural connectivity in mouse models of diseases.

Materials and Reagents

  1. 5 ml syringe (Sigma-Aldrich, catalog number: Z683582-100EA)

  2. Vacutainer safety-lock blood collection set (25 G × 3/4” × 12”, 0.5 × 19 × 305 mm, Becton Dickinson, catalog number: 367283)

  3. Nylon Zip ties (4” and 8” in length, LECO plastics, part# L-4-18, L-8-50)

  4. 50 ml conical tubes (Corning, catalog number: 352070)

  5. BALB/cByJ mice (Jackson Laboratories, catalog number: 001026, 8-10 weeks old, males and females)

  6. Chloral hydrate (Sigma, catalog number: 102425)

  7. Heparin (Sigma, catalog number: H3393-50KU)

  8. PBS (Corning, catalog number: 21-031)

  9. Gadodiamide (Omniscan, CAS# 131410-48-5)

  10. 10% Formalin solution (PolyScience, catalog number: 08279-20)

  11. Perfluoropolyether (Fomblin®, PerkinElmer LLC, CAS# 69991067-9, Sigma-Aldrich, catalog number: 317926)

Equipment

  1. Tools for routine transcardiac perfusion in mice (peristaltic pump and tubing, sharp small scissors, blunt tweezers, 21 G infusion butterfly (Becton Dickinson, catalog number: 367281), large container for blood collection, top of an insulated foam box to pin the mouse, and 23 G needles.

  2. Horizontal 7 Tesla (T) Magnetic Resonance (MR) system (Bruker Biospin, Billercia, MA, USA) or other high-field (7T or greater) MRI system

  3. 4-channel receive only cryogenic probe (Bruker Biospin, Billerica, MA, USA)

  4. 72 mm inner diameter volume transmit coil (Bruker Biospin, Billerica, MA, USA)

  5. Animal holder for the cryogenic probe (Bruker Biospin, Billerica, MA, USA)

  6. Vacuum and vacuum chamber (e.g., 1-gal)

Software

  1. Paravision (PV 6.0.1 or later)

  2. Matlab R2019b or later (www.mathworks.com)

  3. DTIStudio (www.mristudio.org)

  4. AMIRA (thermofisher.com, version 5.0 or later)

  5. DiffeoMap (www.mristudio.org) or ANTs (http://stnava.github.io/ANTs/)

  6. Mrtrix (www.mrtrix.org)

  7. Graph theoretical network analysis toolbox (GRETNA) (www.nitrc.org/projects/gretna)

  8. GraphPad Prism (Version 8.4.3 for Windows, GraphPad Software, La Jolla California USA) (www.graphpad.com)

Procedure

  1. Ex-vivo brain sample preparation

    1. Anesthetize the mouse with chloral hydrate (intraperitoneal injection 100 mg/kg in sterile PBS).

    2. Transcardially perfuse the mouse with 35 ml of cold PBS/heparin (50 units/ml) solution followed by 35 ml of 10% formalin. The perfusion rate is approximately 12 ml/min for PBS and formalin, with good perfusion assessed by the liver changing color from dark red to brownish/grey and the animal carcass becoming stiff.

    3. Decapitate the mouse at the mid-cervical line (around C3-C4), making sure not to damage the spinal cord, and place the head in 50 ml 10% Formalin solution at 4°C for 24 h in a 50 ml conical tube.

    4. After 24 h post-fixation, replace the formalin with PBS. Samples can be stored at 4°C at this point until ready to be scanned.

    5. Replace the PBS solution with 50 ml of 2 mM gadodiamide solution in PBS.

    6. Store the sample at 4°C for one week for the gadodiamide to diffuse into the tissue.

    7. Trim the skin and muscle tissues but keep the skull and eyeballs intact (Figure 1A). Remove the mandible bone and the tongue.

    8. Place one brain in the barrel of a 5 ml syringe, with the nose facing the hub of the syringe, then place 2-3 small pieces of bent zip-tie at the bottom and back of the brain to properly fix the specimen within the syringe barrel (Figure 1A).

    9. Replacing the cap of the syringe with a loosely tied vacutainer, fill the syringe with perfluoropolyether (Fomblin®), insert the plunger, flip the syringe so that the hub points upward, and remove the cap.

    10. Place the syringe with its hub pointing upward in a vacuum chamber for 30 min to remove air bubbles (Figure 1B).

    11. Remove the syringe from the vacuum chamber, push out the remaining air and PBS in the barrel, and seal the cap of the syringe by tightening the vacutainer (Figure 1C).


  2. MR data acquisition

    1. Place the syringe horizontally in the animal holder for the cryogenic probe and adjust the sample position so that the dorsal part of the brain is as close to the cryogenic coil as possible to maximize sensitivity. Use tape to fix the syringe to the animal holder (Figure 1D).

    2. Insert the animal holder into the magnet under the cryogenic probe (Figure 1E).

    3. Acquire a pilot scan using the Bruker Localizer protocol. For any MRI studies, Localizer is the very first scan that acquires reference images of the subject in three orthogonal planes. The images of the resulting scan appear in the ‘geometry editor,’ where the first three viewports show the reference brain slices in axial, sagittal, and coronal orientations. Therefore, the Localizer provides a quick view of the specimen in the magnet (Figure 1F). Check whether the sample is in the most sensitive region of the cryogenic probe with no apparent tilt toward the left or right sides. Adjust the position of the subject and re-run the Localizer protocol prior to proceeding to the next step.

    4. Adjust the tuning and match of the cryogenic probe and acquire a map of the main magnetic field (B0 field) over the entire sample.

    5. Use the Bruker MapShim procedure to adjust shimming currents to achieve a relatively homogeneous B0 field. In brief, select the specific scan to calculate the shim. Then choose Map_shim from the setup tab and define the target volume of interest in cubic, cylinder, or ellipsoid shapes. Shift, resize or rotate the target volume in the geometry editor such that the volume covers the entire specimen. Run the scan to compute the optimum shim values in the target volume based on the B0 map measured in the previous step.

    6. Acquire high angular resolution diffusion weighted imaging (HARDI) of the whole mouse brain using a modified 3D gradient and spin echo (GRASE) sequence (Wu et al., 2013) (an alternative is the 3D multi-shot diffusion weighted echo planar imaging (EPI) sequence provided by Bruker) and with the following imaging parameters:

      1. Echo time (TE)/repetition time (TR): 33/400 min.

      2. Number of non-diffusion weighted images (b0s): 2.

      3. Number of diffusion weighted images (DWIs): 60, auto-generated by the sequence.

      4. b-value: 5,000 s/mm2.

      5. Resolution: 100 µm isotropic.



    Figure 1. Preparation for MRI.

    A. Sample preparation: Remove the tissues outside of the skull carefully without damaging the eyeballs (top panel). Place the brain in a 5ml syringe with small pieces of zip-ties to fix its position (bottom panel). B-C. Remove air from the syringe: Connect the syringe to a loosely tied vacutainer filled with Fomblin® (shown in C) and place in the vacuum chamber (shown in B). Remove the vacutainer and turn on the vacuum for 30 min to remove air bubbles. Push out the remaining air after vacuum and seal the top by tightening the vacutainer. D. Place the sample in a manufacturer-made sample holder designed for the cryogenic probe. D’. A zoom-in view of the sample. E. Insert the sample holder into the magnet bore of the magnet. E’. A zoom-in view of the holder (indicated by the white arrow). F. Three orthogonal plane images acquired using the Localizer protocol on a 7 Tesla Bruker preclinical MRI system.

Data analysis

  1. Data pre-processing

    For each dataset, perform the following steps accordingly:

    1. Motion correction: Using DTIStudio (Jiang et al., 2006), align all DWIs to the average of b0s to remove small sample displacements due to vibrations and B0 field drift during the long scan (Figure 2A).

    2. Skull-stripping: Use AMIRA segmentation editor to remove non-brain tissues and define the subject specific whole brain mask (Figure 2B).

    3. Estimation of diffusion tensor: From the raw data, compute the tensor model (Mori and Zhang, 2006) within the respective brain mask using weighted linear least squares estimations as implemented in MRtrix (command: dwi2tensor) (Figure 2C) (Tournier et al., 2012).

    4. Computation of average DWIs and fractional anisotropy (FA): Compute the average DWI (aDWI) from 60 DWIs using Matlab, and calculate the FA map from the tensor using MRtrix (command: tensor2metric) (Basser et al., 1994; Tournier et al., 2012).

    5. Image registration and transfer of atlas labels into subject’s native space: Using DiffeoMap, normalize the aDWI and FA maps to an MRI-based atlas (Chuang et al., 2011; Arefin et al., 2019) via multi-channel (aDWI + FA) large deformation diffeomorphic metric mapping (LDDMM) (Ceritoglu et al., 2009) (Figure 3A). Next, transfer the structural labels (i.e., brain regions or nodes) to the subject’s-native space using the inverse mapping from LDDMM (Figure 3B, also see Note 1). If DiffeoMap is not available, ANTs (http://stnava.github.io/ANTs/) can be used instead (Figure 2D).



      Figure 2. Illustration of the data pre-processing steps.

      A. Motion correction using DTIStudio. Run Automatic Image Registration (circled) to align all diffusion weighted images (DWIs) to the non-diffusion-weighted image (b0). B. Use the AMIRA segmentation editor to generate a binary mask (purple) for the brain. C. Schematic diagram of the steps to compute the tensor and FA from the rawdata using Mrtrix. D. Use DiffeoMap for image registration and transformation of atlas labels into subject’s native image space.



      Figure 3. Image registration pipeline.

      A. Co-registration of the MR data (subject) into group averaged mouse brain atlas template using multi-channel LDDMM. B. Transformation of structural labels from MRI-based atlas to subject’s native space.


  2. Data post-processing

    Assessment of brain microstructural changes

    At first, compute the Jacobian determinant value for each voxel from the mapping between atlas and subject images generated by LDDMM, and conduct whole-brain voxel-based morphometric analysis in Matlab to identify local volumetric changes affected by rearing, sex, and their interaction (2 × 2 ANOVA, FDR corrected, α = 0.1, P < 0.0105, cluster size > 25 voxels). See the Matlab codes (source data 1) used to conduct 2 × 2 ANOVA (White et al., 2020). Then, similarly perform 2 × 2 ANOVA (FDR corrected, α = 0.1, P < 0.007, cluster size > 25 voxels) to examine the voxel-wise changes in FA (White et al., 2020). These analyses will provide unbiased overviews of morphometric changes due to rearing, sex, and rearing by sex interaction.


    Selection of brain regions (nodes) for structural connectivity assessment

    Identify nodes that show rearing-mediated volumetric and FA changes to investigate structural connectivity alterations between nodes, as well as modifications in the brain global and regional network properties (also see Note 2). These nodes will be identical for both left and right hemispheres.


    Assessment of brain structural connectivity using fiber tractography

    Upon pre-processing the data and selection of potential brain nodes, execute the following steps accordingly for each individual subject to map axonal projections between nodes using probabilistic fiber tractography in MRtrix:

    Step 1: From the pre-processed raw data, estimate the response function for spherical deconvolution (command: dwi2response) (Tournier et al., 2012, Tournier et al., 2013). Specify the algorithm name ‘tournier’ (other options: dhollander, manual, fa, msmt_5tt, tax), gradient table, brain mask, and the maximum harmonic degree (lmax = 6).

    Step 2: Estimate the whole brain fiber orientation distribution (FOD) map from the pre-processed raw data and respective response function (command: dwi2fod) (Tournier et al., 2007). Define the algorithm name ‘CSD,’ gradient table, and brain mask.

    Step 3. Generate the whole brain fiber tractogram from the FOD map (command: tckgen) (Tournier et al., 2009). Use the whole brain mask as the ‘seed region’ to enable tracking fibers throughout the brain for whole brain tractography (whole brain tractogram) (Figure 4A). Set the tractography method to probabilistic, the FOD amplitude cut-off to 0.05, the minimum length of the fiber to 3 mm, and the target number of the streamlines to be counted to 5 million.

    Step 4: For node-to-node tractography, the whole brain tractography in step 3 may not generate enough streamlines for small nodes (e.g., amygdala). Further increasing the total number of streamlines (> 5 million) may not resolve this issue but requires significant computational resources. In this case, extract the regions of interest (ROIs) from the atlas co-registered into the subject’s native space using Matlab. Next, define a specific node as ‘seed region’ to initiate the fiber tracking from and another node as ‘target’ to define the fiber termination point. Then use these two nodes to extract the streamlines connecting two nodes (seed and target) using the tckedit command (Figure 4B). Consider two nodes as ‘connected’ if there is at least one streamline terminating at the target node; otherwise, they are ‘not connected.’



    Figure 4. Fiber tractography pipeline.

    A. Estimation of mouse whole brain fiber tractogram from the fiber orientation distribution (FOD) map. Red, green, and blue colors represent the fiber projections in x, y, and z-axis, respectively. Five million fibers were generated from each subject; 100 K streamlines were extracted for better visualization of the brain structures. B. Extraction of fibers connecting two specific nodes (seed = amygdala and target = PFC).


    Generating brain structural connectome matrix

    Repeat step 4 to estimate the structural connections between all possible pairs of nodes (ignore intra-regional connectivity) for both hemispheres (Figure 5A). For example, for 14 nodes in one hemisphere, the total number of tractograms would be the number of nodes N = 14 multiplied by N-1, or 14 × 13 = 182. Finally, for M number of seed regions and N number of target regions, generate an M × N matrix individually for the left and the right hemispheres. Assign the seed and target regions in horizontal and vertical axis, respectively, so that each cell represents the number of streamlines connecting the corresponding seed and target nodes (Figure 5B). Consider the number of streamlines between nodes as a measure of the connection strength. Generate the connectome matrix for all subjects and name them according to the subject IDs.



    Figure 5. Generation of the mouse brain structural connectome.

    A. Extraction of fibers connecting seed and target nodes. B. Generation of structural connectome from the tractograms estimated from selected seed and target nodes. Blue cells correspond to the tractograms shown in A, and white cells indicate intra-regional connectivity (not counted). C. Use the GRETNA software to compute global and regional brain network properties. Panels on the left list all possible properties available for computation. Select the properties based on the study design and transfer them to the pipeline option on the right panel using the respective arrows. Load the connectome matrix for all subjects belonging to one group with specific group ID and then load for the next group with different ID. Specify the output folder to store the results and define the network configuration. Finally, hit the ‘Run’ button to start computation.


    Brain network properties analysis

    Use the Matlab based Graph theoretical network analysis toolbox (GRETNA) to compute the brain global and regional network properties (Wang et al., 2015). Perform the following steps accordingly for brain network-based analysis (Figure 5C):

    1. Create an individual data folder containing two sub-folders for left and right hemispheres for each group.

    2. Save the connectome matrices as ‘.mat’ files in the respective folders.

    3. Open GRETNA in Matlab and select ‘Network Analysis’ (GRETNA >> Network Analysis).

    4. In the ‘Brain Connectivity Matrix’ tab, load all connectivity matrices of one hemisphere from one group and assign the group ID. Do the same for the other group.

    5. Locate a directory for saving the results in the ‘output directory’ tab.

    6. Next, select network properties to be computed from the Global Network Metrics and Nodal and Modular Network Metrics tabs.

    7. For global brain network analysis, select ‘Global – Small-World (SW)’ and ‘Global – Efficiency (Geff).’ For regional network properties, select ‘Nodal – Clustering Coefficient (NCp),’ ‘Nodal – Efficiency (Neff),’ and ‘Nodal – Degree Centrality (Dcent).’ Other properties can be selected as per the study design or requirements.

    8. Configure the brain network in the ‘Network Analysis’ tab as follows:

      Parameters Value
      Sign of matrix Absolute
      Thresholding method Network sparsity
      Threshold sequence 0.05, 0.1, 0.15 (or as per the study design)
      Network type Weighted
      Random network number 1,000


    9. Recheck the loaded data and the network configuration. Hit the ‘Run’ button if everything looks alright. Computation time depends on the number of subjects, size of the connectome matrices, random network number, and the threshold sequence.

    10. Once the computation is done, results can be retrieved from the output directory. For further assistance, please refer to the following manual from Neuroimaging Tools and Resources Collaboratory (NITRC): https://www.nitrc.org/docman/view.php/668/2262/manual_v2.0.0.pdf.


    Statistical analysis of the estimated structural connectivity and brain network properties

    To investigate the effect of rearing and sex on brain structural connectivity and brain network properties, perform a two-way ANOVA with rearing condition (CTL or UPS) and sex as fixed factors, followed by post-hoc comparisons using Tukey’s HSD or Sidak’s test using GraphPad Prism.

Notes

  1. It is very important to check whether structural labels were correctly transferred and show good agreement with the corresponding structures. We recommend refining the segmentation manually, slice by slice, along the axial orientation, forfeiting attention to the other two orientations as well as to the slices preceding and following if necessary.

  2. Selection of nodes for brain network analysis is crucial. Using unbiased voxel-based analyses, identify only those nodes which show UPS-mediated volumetric and FA alterations and are highly connected based on the Allen Mouse Brain Connectivity Atlas (Oh et al., 2014). Furthermore, selected nodes should be non-overlapping, having a unique set of connections to other nodes, and well delineated using a standard parcellation scheme that is comparable across species (Kaiser, 2011).

Acknowledgments

This work was supported by NIMH R01MH119164 (AK and JZ), NIMH R01MH118332 (AK and JZ), and R01NS102904 (JZ). For the original research paper where this protocol has been used, see White et al. (2020).

Competing interests

The authors declare no conflict of interest.

Ethics

Animal experimentation: All studies were approved by the Institutional Animal Care and Use Committee (IACUC) at Yale University, protocol #2020-10981, and were conducted in accordance with the recommendations of the NIH Guide for the Care and the Use of Laboratory Animals.

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简介

[摘要]啮齿类动物的转化工作阐明了驱动复杂行为的基本机制与精神和神经疾病有关。尽管如此,许多有希望的啮齿动物研究后来在临床试验中失败,突出表明需要提高临床前研究的转化效用在啮齿动物中。小型啮齿动物的成像提供了应对这一挑战的重要策略,因为它使全脑无偏搜索结构和动态变化,可以直接与人体成像相比。使用成像识别的结构变化的功能意义然后可以使用可用于小鼠的分子和遗传工具进一步研究。在这里,我们描述用于结构变化和网络的无偏搜索和表征的管道属性,基于以 100 的各向同性分辨率覆盖整个小鼠大脑的扩散 MRI 数据
微米。我们首先使用基于全脑体素的无偏分析来识别体积和微观结构暴露于不可预测的产后压力 (UPS) 的成年小鼠大脑的变化,这是一种小鼠复杂的早期生活压力 (ELS) 模型。显示结构异常的大脑区域被用作节点生成网格,用于基于图评估结构连通性和网络属性理论。这里描述的技术可以广泛应用于理解其他领域的大脑连接。人类疾病的小鼠模型,以及转基因小鼠品系。

[背景] 扩散磁共振成像 (dMRI) 是一种利用水分子的随机扩散来探测组织微观结构的成像技术 (Le Bihan, 2003; Mori and Zhang; 2006; Novikov, 2021)。成像和计算处理的最新进展允许从啮齿动物中获得分辨率为 100 µm 或更高的 dMRI 图像(Aggarwal等人,2010 年;Calabrese等人,2015 年)。然后,这些可用于通过 dMRI 参数(例如分数各向异性 (FA))的微观结构改变来评估局部体积变化,并确定不同大脑区域之间的结构连通性(Wu等人,2013 年;Lerch等人,2017 年;Feo和 Giove,2019 年;Badea等人,2019 年;White等人,2020 年;Pallast等人,2020 年)。
小型啮齿动物的高分辨率 dMRI 研究为提高临床前研究的转化效用提供了一个新的、有前景的前沿(Kaffman等人,2019 年;Muller等人,2020 年)。这主要是因为可以与人类的平行研究进行直接比较。此外,基于体素的无偏筛选可以识别显示结构变化的特定大脑区域。反过来,这些可以用作节点来构建网络并表征特定节点之间的结构连通性、识别关键枢纽并量化网络属性,例如全局效率和小世界性(Feo 和 Giove,2019 年;Pallast等人,2019 年)。 ,2020 年;怀特等人,2020 年)。此无偏无关的方法是从感兴趣(ROI)的方法更传统的区域,其中,在特定脑区域或connectomes结构变化进行检查(Helmstaedter概念上不同等人,2013;竹村等人。,2013; Saleeba等人。 , 2019)。尽管如此,DMRI研究是与传统的神经高度互补的方法,如通过DMRI识别的结构变化可以利用显微镜和基因组/蛋白质组学方法进一步检查,他们的复杂行为的贡献可以用chemogenetic和光遗传学工具进行严格测试(Kaffman等。 ,2019 年;穆勒等人,2020 年)。
dMRI 提供有关完整组织结构变化的多级信息,包括体积变化、与微观结构相关的 dMRI 参数和结构连接。过去十五年来基于DMRI-道重建,或跟踪技术发展迅速(Tuch等,2002;森和面包车Zijl,2002;图尼埃等,2007; Wedeen等,2008),作为人类连接组计划的一个重要组成部分(Toga等人,2012 年;Van Essen等人,2013 年)。随着高分辨率 dMRI 采集和纤维束成像方法的发展,dMRI 纤维束成像现在可以快速测量整个大脑的宏观结构连通性,无需切片,这既耗时又容易变形和组织损伤(Moldrich et al ., 2010; Wu et al ., 2010; Wu et al . al ., 2013; Calabrese et al ., 2015; Xiong et al ., 2018)。它还允许同时检查同一标本中的多个白质连接,进一步减少时间和成本。借助最新的大脑连接分析工具,牵引成像结果可用于检查单个通路和整个连接组水平的变化(Edwards等,2020)。
dMRI 也有几个缺点,包括与 T1/T2 加权 MRI 相比,灰质区域的分辨率较低(Dorr等人,2008 年;White等人,2020 年)以及与使用化学或病毒示踪剂(Wu 和 Zhang,2016 年;Edwards等人,2020 年)。在进行全脑体素分析时需要严格校正多重比较进一步阻碍了细微变化的检测,并且在寻找两个变量之间的相互作用时尤其具有挑战性,例如早期生活压力 (ELS) 和性别(White等人, 2020)。高分辨率 dMRI 通常需要对动物进行灌注,从而防止对同一动物进行纵向重新扫描。尽管啮齿动物大脑的体内高分辨率 dMRI技术已经出现(Wu等人,2013 年;Wu等人,2014 年),但 MRI 期间的麻醉暴露(每次 2-3 小时)可能会引入额外的混杂因素。因此,描绘一个标准化的程序,以可靠和可重复地估计小鼠大脑的微观结构变化是至关重要的。
此处描述的协议涵盖图像采集、体积和 FA 变化的全脑体素分析、牵引成像和分析。与之前描述的类似方法(Calabrese等人,2015 年;Edwards等人,2020 年)相比,该协议基于 Allen Mouse Brain Atlas 中的结构标签,这使得将牵引成像结果与病毒示踪结果进行比较变得相对简单在 Allen Mouse Brain Connectivity Atlas(Oh等人,2014 年;White等人,2020 年)中。使用无偏全脑体素分析来识别显示由 ELS 引起的体积和 dMRI 参数(例如,FA)变化的大脑区域,并将它们与暴露于早期逆境的人类报告的那些区域进行比较。显示结构变化的 14 个大脑区域被用作节点,以在每个半球生成一个 14 × 14 的矩阵。然后使用图论表征该网格的网络特性,并与暴露于早期逆境的人类的发现进行比较(White等,2020)。我们的协议依赖于精确的图像配准来将结构标签从图谱转移到主题图像,并且当有大的组织变形时将不起作用,例如由脑肿瘤或严重坏死引起的变形。该协议还有一个节点到节点的分析步骤,用于在全脑分析中可能被掩盖的小连接(例如,在杏仁核网络中)。总之,该协议可用于表征疾病小鼠模型中的全脑结构连接。

关键字:弥散核磁共振, 纤维束成像, 结构连接, 大脑网络属性, 鼠脑

材料和试剂

 

1.     5 ml注射器(Sigma-Aldrich目录号:Z683582-100EA

2.     Vacutainer 安全锁采血套装(25 G × 3/4” × 12”0.5 × 19 × 305 mmBecton Dickinson目录号:367283

3.     尼龙扎带(4 英寸和 8 英寸长,LECO 塑料,部件号 L-4-18L-8-50

4.     50 ml锥形管(Corning,目录号:352070

5.     BALB/cByJ 小鼠(Jackson Laboratories,目录号:0010268-10 周龄,雄性和雌性)

6.     水合氯醛(Sigma,目录号:102425

7.     肝素(Sigma,目录号:H3393-50KU

8.     PBS(康宁,目录号:21-031

9.     GadodiamideOmniscanCAS# 131410-48-5

10.  10% 福尔马林溶液(PolyScience,目录号:08279-20

11.  全氟聚醚(Fomblin ® PerkinElmer LLCCAS# 69991067-9Sigma-Aldrich,目录号:317926

 

设备

 

1.     小鼠常规经心灌注工具(蠕动泵和管道、锋利的小剪刀、钝镊子、21 G 输液蝴蝶(Becton Dickinson,目录号:367281)、用于采血的大容器、用于固定鼠标的绝缘泡沫盒顶部, 23 G 针。

2.     水平 7 特斯拉 (T) 磁共振 (MR) 系统(Bruker BiospinBillerciaMAUSA)或其他高场(7T 或更大)MRI 系统

3.     4 通道仅接收低温探针(Bruker BiospinBillericaMAUSA

4.     72 mm 内径体积发射线圈(Bruker BiospinBillericaMA,美国)

5.     低温探针的动物支架(Bruker BiospinBillericaMAUSA

6.     真空和真空室(例如1-gal

 

软件

 

1.     ParavisionPV 6.0.1 或更高版本)

2.     Matlab R2019b 或更高版本www.mathworks.com )

3.     DTIStudio ( www.mristudio.org )

4.     AMIRAthermofisher.com,版本 5.0 或更高版本)

5.     DiffeoMap ( www.mristudio.org ) ANTs ( http://stnava.github.io/ANTs/ )

6.     Mrtrix ( www.mrtrix.org )

7.     图论网络分析工具箱(GRETNA)(www.nitrc.org/projects/gretna

8.     GraphPad PrismWindows 8.4.3 版,GraphPad 软件,美国加州拉霍亚)(www.graphpad.com


程序

 

A.    离体大脑样品制备

1.     用水合氯醛麻醉小鼠(在无菌 PBS 中腹腔注射100 毫克/千克)。

2.     35 ml PBS/肝素(50 单位/ml)溶液经心灌注小鼠,然后加入 35 ml 10% 福尔马林。PBS 和福尔马林的灌注速率约为 12 毫升/分钟,良好的灌注通过肝脏颜色从深红色变为棕/灰色和动物尸体变得僵硬来评估。

3.     在颈中线(C3-C4 左右)处斩首鼠标,确保不会损伤脊髓,并将头部置于50 毫升锥形管中的 50 毫升 10% 福尔马林溶液中,在°C 放置 24 小时。

4.     固定后 24 小时后,用 PBS 替换福尔马林。此时样品可以储存在°C ,直到准备好进行扫描。

5.     50 毫升 2 mM钆二胺 PBS 溶液代替 PBS 溶液。

6.     将样品在°C 储存一周,以便gadodiamide扩散到组织中。

7.     修剪皮肤和肌肉组织,但保持头骨和眼球完好无损(图 1A)。去除下颌骨和舌头。

8.     将一个大脑放入 5 毫升注射器的桶中,鼻子朝向注射器的中心,然后在大脑的底部和背面放置 2-3 小块弯曲的扎带,以将标本正确固定在注射器内桶 ( 1A)

9.     用松散的真空容器更换注射器的盖子,用全氟聚醚 (Fomblin ® )填充注射器,插入柱塞,翻转注射器使针头朝上,然后取下盖子。

10.  将注射器的中心朝上放置在真空室中 30 分钟以去除气泡(图 1B)。

11.  从真空室中取出注射器,推出桶中剩余的空气和 PBS,并通过拧紧真空容器密封注射器的盖子(图 1C)。

B.    磁共振数据采集

1.     将注射器水平放置在低温探针的动物支架中,并调整样品位置,使大脑的背部尽可能靠近低温线圈,以最大限度地提高灵敏度。使用胶带将注射器固定到动物支架上(图 1D)。

2.     将动物支架插入低温探针下的磁铁中(图 1E)。

3.     使用 Bruker Localizer 协议获取试点扫描。对于任何 MRI 研究,Localizer 是第一个在三个正交平面上获取对象参考图像的扫描。结果扫描的图像出现在几何编辑器中,其中前三个视口显示轴向、矢状和冠状方向的参考脑切片。因此,定位器可以快速查看磁铁中的样品(图 1F)。检查样品是否在低温探头最敏感的区域,没有明显的向左或向右倾斜。在继续下一步之前,调整主体的位置并重新运行定位器协议。

4.     调整低温探头的调谐和匹配,并获取整个样品的主磁场(0场)图。

5.     使用布鲁克 MapShim 程序调整匀场电流以实现相对均匀的0场。简而言之,选择特定的扫描来计算垫片。然后从设置选项卡中选择 Map_shim 并以立方体、圆柱体或椭圆体形状定义感兴趣的目标体积。在几何编辑器中移动、调整大小或旋转目标体积,使体积覆盖整个样本。运行扫描以根据上一步中测量的0图计算目标体积中的最佳垫片值。

6.     使用修改后的 3D 梯度和自旋回波 (GRASE) 序列(Wu 等人,2013 年)获取整个小鼠大脑的高角分辨率扩散加权成像(HARDI)(另一种方法是 3D 多脉冲扩散加权回波平面成像( EPI) 序列由布鲁克提供) 并具有以下成像参数:

a.     回波时间 (TE)/重复时间 (TR)33/400 分钟。

b.     非扩散加权图像的数量(s):2

c.     扩散加权图像 (DWI) 的数量:60,由序列自动生成。

d.     b 值:5,000 s/mm 

e.     分辨率:100 µm 各向同性。

 

1. MRI 的准备工作。

A. 样品制备:小心取出头骨外的组织,不要损坏眼球(顶部面板)。将大脑放入 5ml 注射器中,并用小块拉链固定其位置(底部面板)。公元前。排除注射器中的空气:将注射器连接到一个松散捆绑的装有 Fomblin ®的真空容器(如 C 所示)并放入真空室(如 B 所示)。取下真空吸尘器并打开真空 30 分钟以去除气泡。抽真空后排出剩余的空气,并通过拧紧真空吸尘器密封顶部。D. 将样品放置在制造商为低温探针设计的样品架中。D'。样本的放大视图。E. 将样品架插入磁铁的磁铁孔中。E'。支架的放大视图(由白色箭头指示)。F. 7 Tesla Bruker 临床前 MRI 系统上使用 Localizer 协议获取的三个正交平面图像。

数据分析

 

A.    数据预处理

对于每个数据集,相应地执行以下步骤:

1.     运动校正:使用 DTIStudioJiang等人2006 年),将所有 DWI s的平均值对齐,以消除长扫描期间由于振动和0场漂移引起的小样本位移(图 2A)。

2.     头骨剥离:使用 AMIRA 分割编辑器去除非脑组织并定义主题特定的全脑面具(图 2B)。

3.     扩散张量的估计:根据原始数据,使用 MRtrix(命令:dwi2tensor)(图 2C)(Tournier等人. , 2012)

4.     计算平均 DWI 和分数各向异性 (FA):使用 Matlab 60 DWI 计算平均 DWI (aDWI),并使用 MRtrix(命令:tensor2metric)从张量计算 FA 图(Basser等人1994 年;Tournier等人)., 2012)

5.     图谱标签的图像配准和转移到受试者的原生空间:使用 DiffeoMap,通过多通道 (aDWI + FA) aDWI FA 图归一化为基于 MRI 的图谱 (Chuang et al ., 2011; Arefin et al. , 2019) ) 大变形微分形态度量映射 (LDDMM)Ceritoglu等人2009 年)(图 3A)。接下来,使用来自 LDDMM 的逆映射将结构标签(大脑区域或节点)转移到主体的本机空间(图 3B,另见注 1)。如果 DiffeoMap 不可用,则可以使用ANT ( http://stnava.github.io/ANTs/ ) 代替(图 2D)。

 

 

2. 数据预处理步骤的图示。

A.使用 DTIStudio 进行运动校正。运行自动图像配准(圆圈)以将所有扩散加权图像 (DWI) 与非扩散加权图像 (b )对齐。B. 使用 AMIRA 分割编辑器为大脑生成二进制掩码(紫色)。C. 使用 Mrtrix 从原始数据计算张量和 FA 的步骤示意图。D. 使用 DiffeoMap 进行图像配准并将图集标签转换为主体的本机图像空间。

 

 

3. 图像配准管道。

A. 使用多通道 LDDMM MR 数据(受试者)共同注册到组平均小鼠脑图谱模板中。B. 结构标签从基于 MRI 的地图集到受试者的原生空间的转换。

 

B.    数据后处理

评估大脑微观结构变化

首先,从 LDDMM 生成的图谱和对象图像之间的映射中计算每个体素的雅可比行列式值,并在 Matlab 中进行基于全脑体素的形态测量分析,以识别受饲养、性别及其相互作用影响的局部体积变化( 2 × 2 方差分析,FDR 校正,α = 0.1< 0.0105,簇大小 > 25 个体素)。请参阅用于进行 2 × 2 方差分析的 Matlab 代码(源数据 1)(White等人2020 年)。然后,类似地执行 2 × 2 方差分析(FDR 校正,α = 0.1< 0.007,簇大小 > 25 体素)以检查 FA 的体素变化(White2020)。这些分析将提供由于饲养、性别和按性别相互作用饲养的形态测量变化的公正概述。

 

用于结构连通性评估的大脑区域(节点)的选择

识别显示饲养介导的体积和 FA 变化的节点,以研究节点之间的结构连接变化,以及大脑全局和区域网络属性的修改(另见注 2)。这些节点对于左半球和右半球都是相同的。

 

使用纤维束成像评估大脑结构连接

在预处理数据和选择潜在的大脑节点后,对每个个体执行以下步骤,以使用 MRtrix 中的概率纤维束成像映射节点之间的轴突投影:

步骤 1:根据预处理的原始数据,估计球形反卷积的响应函数(命令:dwi2response )(Tournier等人,2012Tournier 等人,2013 年)。指定算法名称“tournier”(其他选项:dhollandermanualfamsmt_5tttax)、梯度表、脑膜和最大谐波度(max = 6)。

步骤 2:根据预处理的原始数据和相应的响应函数(命令:dwi2fod )估计全脑纤维取向分布 (FOD) 图(Tournier等人,2007 年)。定义算法名称“CSD”、梯度表和大脑掩码。

步骤 3. FOD 图(命令:tckgen )生成全脑纤维束图(Tournier等人,2009 年)。使用全脑面罩作为种子区域,以便跟踪整个大脑的纤维以进行全脑束成像(全脑束图)(图 4A)。将牵引方法设置为概率, FOD 振幅截止为 0.05, 光纤的最小长度为 3 mm, 目标流线数为 500 万。

步骤 4:对于节点到节点的纤维束成像,步骤 3 中的全脑纤维束成像可能无法为小节点(例如,杏仁核)生成足够的流线。进一步增加流线总数(> 500 万)可能无法解决此问题,但需要大量计算资源。在这种情况下,使用 Matlab 从共同注册到主题的本地空间的地图集中提取感兴趣的区域 (ROI)。接下来,将特定节点定义为种子区域以启动光纤跟踪,将另一个节点定义为目标以定义光纤终端点。然后使用这两个节点使用tckedit命令提取连接两个节点(种子和目标)的流线(图 4B)。如果至少有一条流线终止于目标节点,则将两个节点视为已连接;否则,它们未连接

 

 

4. 纤维束成像管道。

A. 纤维取向分布 (FOD) 图中估计小鼠全脑纤维束图。红色、绿色和蓝色分别代表 xy z 轴上的纤维投影。每个受试者产生了 500 万根纤维;提取了 100 K 流线以更好地可视化大脑结构。B. 提取连接两个特定节点的纤维(种子 = 杏仁核,目标 = PFC)。

 

生成大脑结构连接组矩阵

重复步骤 4 以估计两个半球的所有可能节点对(忽略区域内连接)之间的结构连接(图 5A)。例如,对于一个半球中的 14 个节点,tractograms 的总数将是节点数 N = 14 乘以 N-1,或 14 × 13 = 182。最后,对于M个种子区域和N个目标区域,分别为左半球和右半球生成一个× N矩阵。分别在水平轴和垂直轴上分配种子和目标区域,以便每个单元格代表连接相应种子和目标节点的流线数(图 5B)。考虑节点之间的流线数作为连接强度的度量。为所有主题生成连接组矩阵并根据主题 ID 为它们命名。

 

 

5. 小鼠大脑结构连接组的生成。

A. 连接种子和目标节点的纤维的提取。B. 从选定的种子和目标节点估计的牵引图生成结构连接组。蓝色单元格对应于 A 中显示的牵引图,白色单元格表示区域内连接(未计算)。C. 使用 GRETNA 软件计算全局和区域大脑网络属性。左侧的面板列出了所有可用于计算的可能属性。根据研究设计选择属性,并使用相应的箭头将它们传输到右侧面板上的管道选项。加载属于具有特定组 ID 的一组的所有受试者的连接组矩阵,然后加载具有不同 ID 的下一组。指定输出文件夹以存储结果并定义网络配置。最后,点击运行按钮开始计算。

 

脑网络特性分析

使用基于 Matlab 的图理论网络分析工具箱 (GRETNA) 计算大脑全局和区域网络属性(Wang等人2015 年)。对基于大脑网络的分析相应地执行以下步骤(图 5C):

1.     创建一个单独的数据文件夹,其中包含每个组的左右半球的两个子文件夹。

2.     在各自的文件夹中将连接组矩阵保存为“ .mat 文件。

3.     Matlab 中打开 GRETNA 并选择网络分析GRETNA >> 网络分析)。

4.     大脑连接矩阵选项卡中,从一组加载一个半球的所有连接矩阵并分配组 ID。对另一组做同样的事情。

5.     输出目录选项卡中找到用于保存结果的目录

6.     接下来,从 Global Network Metrics Nodal and Modular Network Metrics 选项卡中选择要计算的网络属性。

7.     对于全球大脑网络分析,请选择全球小世界 (SW)”全球效率 (Geff)”对于区域网络属性,选择节点聚类系数 (NCp) 节点效率 (Neff) 节点度中心性 (Dcent)” 。可以根据研究设计或要求选择其他属性。

8.     网络分析选项卡中配置大脑网络,如下所示:

参数 价值

矩阵的符号 绝对

阈值法 网络稀疏

阈值序列 0.050.10.15(或根据研究设计)

网络类型 加权

随机网络号 1,000

9.     重新检查加载的数据和网络配置。如果一切正常,请点击运行按钮。计算时间取决于受试者的数量、连接组矩阵的大小、随机网络数和阈值序列。

10.  计算完成后,可以从输出目录中检索结果。如需进一步帮助,请参阅以下神经影像工具和资源协作实验室 (NITRC) 的手册:https ://www.nitrc.org/docman/view.php/668/2262/manual_v2.0.0.pdf 

 

估计的结构连通性和大脑网络特性的统计分析

为了研究饲养和性别对大脑结构连接和大脑网络特性的影响,使用饲养条件(CTL UPS)和性别作为固定因素进行双向方差分析,然后使用 Tukey HSD Sidak 的检验进行事后比较GraphPad棱镜。

 

笔记

 

1.     检查结构标签是否正确转移并与相应结构表现出良好的一致性非常重要。我们建议手动细化分割,逐个切片,沿着轴向方向,在必要时不注意其他两个方向以及前后的切片。

2.     选择用于脑网络分析的节点至关重要。使用基于体素的无偏分析,仅识别那些显示 UPS 介导的体积和 FA 改变并且基于 Allen Mouse Brain Connectivity AtlasOh2014)高度连接的节点。此外,选定的节点应该是非重叠的,与其他节点有一组独特的连接,并使用跨物种可比的标准分割方案很好地描绘(Kaiser2011)。

 

致谢

 

这项工作得到了 NIMH R01MH119164AK JZ)、NIMH R01MH118332AK JZ)和 R01NS102904JZ)的支持。有关使用该协议的原始研究论文,请参阅 White等人(2020)

 

利益争夺

 

作者宣称没有利益冲突。

 

伦理

 

动物实验:所有研究均经耶鲁大学机构动物护理和使用委员会 (IACUC) 批准,协议 #2020-10981,并按照 NIH 实验动物护理和使用指南的建议进行。

 

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引用: Readers should cite both the Bio-protocol article and the original research article where this protocol was used:
  1. Arefin, T. M., Lee, C. H., White, J. D., Zhang, J. and Kaffman, A. (2021). Macroscopic Structural and Connectome Mapping of the Mouse Brain Using Diffusion Magnetic Resonance Imaging. Bio-protocol 11(22): e4221. DOI: 10.21769/BioProtoc.4221.
  2. White, J. D., Arefin, T. M., Pugliese, A., Lee, C. H., Gassen, J., Zhang, J. and Kaffman, A. (2020). Early life stress causes sex-specific changes in adult fronto-limbic connectivity that differentially drive learning. Elife 9: e58301.
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