Name: manual segmentation of the human choroid plexus using brain mri
Uploaded: 2023-12-15T12:40:00.000+00:00
Duration: 4 min 25 s
Description: Watch this Scientific Journal Video about Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies at JoVE.com

This work was supported by a National Institute of Mental Health Award R01 MH131586 (to P.L and M.R), R01 MH078113 (to M.K), and a Sydney R Baer Jr Foundation Grant (to P.L).

The present protocol was approved by the Institutional Review Board at Beth Israel Deaconess Medical Center. A healthy subject with a brain MRI scan that was free of artifacts or movement was used for this protocol demonstration, and written informed consent was obtained. A 3.0 T MRI scanner with a 32-channel head coil (see Table of Materials) was used to acquire 3D-T1 images with a 1 mm x 1 mm x 1.2 mm resolution. The MP-RAGE ASSET sequence with a 256 x 256 field of view, TR/TE/TI=7.38/3.06/400 ms, and an 11-degree flip angle was used.
1. Importing brain MRI to 3D Slicer
NOTE: 3D Slicer provides documentation related to its user interface.
<ol>
	<li>Prepare the brain MRI DICOM (Digital Imaging and Communications in Medicine) or NIFTI (Neuroimaging Informatics Technology Initiative) files for importing into 3D Slicer.</li>
	<li>Import DICOM data by clicking the DCM button in the upper left corner of the toolbar. Then click the Import DICOM files button to import DICOM format data.</li>
	<li>If the MRI data is in the NIFTI format, import it by clicking the DATA button in the upper left corner of the toolbar. In the pop-up dialog, select Choose directory to Add to batch import NIFTI data in a folder or select Choose File(s) to Add to import specific NIFTI files. Then, click the OK button to proceed with data upload to 3D Slicer.</li>
	<li>After importation, MRI data will appear in the window on the right displaying axial, sagittal, and coronal planes.</li>
	<li>Change the layout of the windows by navigating to Layouts and selecting a specific layout. This can be found by either clicking on the Layouts module image in 3D Slicer&#39;s toolbar itself or via View &#62; Layouts from the application menu.</li>
</ol>
2. Downloading DICOM from sample data in 3D Slicer
<ol>
	<li>Click the Download sample data button in the opening screen of Welcome to Slicer section. Then select the MRHead button, and it will start the download process, which may take a few minutes.</li>
	<li>Ensure that the brain MRI data with axial, sagittal, and coronal planes are shown on the right window.</li>
</ol>
3. Quality control and adjusting the MRI image
<ol>
	<li>Determine qualitatively the image quality and the presence of artifacts due to head motion or scanning issues by critically reviewing each MRI slice.</li>
	<li>Zoom the image slice by right-clicking and moving the mouse up or down to zoom out or in, respectively.</li>
	<li>To move the image slice around, left-click the image, press and hold the Shift key, and drag the mouse around.</li>
	<li>Adjusting the image brightness can help with viewing the choroid plexus. To do this, either click on Adjust window/level of volume in the toolbar or left-click the image and move the mouse up or down to raise or lower the brightness, respectively.</li>
	<li>Adjusting contrast can additionally help with finding the choroid plexus. Left-click on the image slice and move the mouse to the left or right to increase or decrease the contrast, respectively. To determine the appropriate contrast for the choroid plexus, use the deep gray matter nuclei (central masses of gray matter arrayed around the lateral and third ventricles) or the signal intensity shown in the contrast scale bar.</li>
	<li>Once the preferred contrast is selected, maintain the same contrast throughout segmentation and do not adjust for potential variations in supra- and infra-tentorial regions.</li>
</ol>
4. Creating the manual segments of the choroid plexus
<ol>
	<li>To begin segmentation of the lateral, third, and fourth ventricle choroid plexus, create the segmentation files in the Segment Editor module. To navigate there, click either on the Segment Editor in the toolbar or go to the Modules: drop-down menu and select Segment Editor.</li>
	<li>Click on the drop-down menu for segmentation to select different segmentations (if multiple segmentations were created) and rename the currently selected segmentation.</li>
	<li>Use the Master Volume drop-down to choose which NIFTI or DICOM sets need editing. Only when the volume file is selected, the user can start segmenting/editing.</li>
	<li>Click the Add button twice to add two segments for the lateral ventricle choroid plexus. To rename these, double-click on the name and change them to Right lateral ventricle choroid plexus and Left lateral ventricle choroid plexus.</li>
	<li>Click the Add button again to add segments for the third and fourth ventricle choroid plexus, and rename them to &#34;3rd ventricle choroid plexus&#34; and &#34;4th ventricle choroid plexus&#34;.</li>
</ol>
5. Viewing different slices and segmentations
<ol>
	<li>Before editing, perform a background study to know how to move between layouts in the viewing window and how to change the view or opacity of the segmentations.</li>
	<li>At the top of the viewing window and to the left of the slice slider, click on the pin icon. This will open a drop-down menu, which may vary depending on the specific layout the window is in. 
	&#8203;NOTE: Utilizing different layouts can be helpful when segmenting the choroid plexus since its structure can vary between individuals. For example, the &#39;Conventional&#39; layout allows the user to simultaneously view all three slices and a 3D view of the scene. Choosing &#39;Red/Yellow/Green slice only&#39; gives the user a close-up view of the 2D slice to allow for a more precise segmentation of the choroid plexus.</li>
</ol>
6. Delineating lateral ventricle choroid plexus ROIs
NOTE: Image registration to a template is not necessary for manual segmentation.
<ol>
	<li>For the lateral ventricle choroid plexus, begin in the axial plane ensuring that the images are positioned based on the bicommissural line. Then use the trigonum collaterale as a reference point for locating the lateral ventricle choroid plexus.
	<ol>
		<li>Once edits have been made in the axial plane, move to the remaining views (sagittal and coronal) to ensure that the manual segmentation of the lateral ventricle choroid plexus is not capturing the surrounding brain parenchyma or CSF.</li>
	</ol>
	</li>
	<li>To begin editing, click on the segment to work on, and the segment name will become highlighted.</li>
	<li>Click on the Paint or Draw tool in the Effects section of Segment Editor to begin manual segmentation. 
	NOTE: It is best to start segmenting in one plane (coronal, axial, or sagittal), and after segmentation has been completed in all of the slices, move to other planes to check and refine the manual segmentation. It is suggested that the user starts with the axial or coronal planes as the lateral ventricle choroid plexus is more easily seen in these views.</li>
	<li>When using the Draw tool, left-click and hold down to draw a contour at the boundary of the lateral ventricle choroid plexus. Once traced, right-click to fill in the drawn-in area.</li>
	<li>When using the Paint tool, first select the diameter of the brush to be used for painting. A 3% or 5% brush is suggested for a more precise delineation of the choroid plexus, while 10% can be useful for larger selections.</li>
	<li>For either tool, utilize Paint or Erase to rectify any erroneous delineations by adding or removing selections. 
	&#8203;NOTE: Referencing other planes of view can aid in identifying the lateral ventricle choroid plexus structure versus other brain structures, such as surrounding gray matter, the fornix, corpus callosum, or hippocampus. The user is encouraged to exclude brain scans that have choroid plexus cysts identified.</li>
	<li>Use the level of the red nucleus as a landmark for stopping the segmentation of the choroid plexus in the lateral ventricles.</li>
</ol>
7. Delineating third and fourth ventricle choroid plexus ROIs
NOTE: Higher resolution T1w images (such as 0.7 or 0.8 mm) and those obtained on a 7T MRI would provide a more accurate and reliable manual segmentation of the third and fourth ventricle choroid plexus. Segmenting the third and fourth ventricle choroid plexus is more difficult than the lateral ventricle choroid plexus as these regions can be much smaller and with fewer voxels to delineate.
<ol>
	<li>For the third ventricle choroid plexus, begin in the sagittal plane and use the foramen of Monro, fornix, corpus callosum, thalamus, and internal cerebral vein as reference points to pinpoint the choroid plexus in the 3rd ventricle. Moving between slices within the same plane can aid in determining whether a region is the fornix, thalamus, vein, or third ventricle choroid plexus.
	<ol>
		<li>Once edits have been made in the sagittal plane, navigate to the remaining views (axial and coronal) to ensure that the manual segmentation of the third ventricle choroid plexus is not selecting the surrounding brain parenchyma or CSF.</li>
	</ol>
	</li>
	<li>Similarly, for the fourth ventricle choroid plexus, begin in the sagittal plane and use the superior cerebellar peduncle, pons, and medulla as reference points to pinpoint the choroid plexus in the fourth ventricle. Moving between slices within the same plane can aid in determining whether a region is the cerebellum, cerebellar tonsil, inferior medullary velum or 4th ventricle choroid plexus.
	<ol>
		<li>Once edits have been completed in the sagittal plane, move to the remaining views (axial and coronal) to ensure that the manual segmentation of the fourth ventricle choroid plexus is not selecting surrounding brain parenchyma or CSF.</li>
	</ol>
	</li>
</ol>
8. Calculating the volumes of the choroid plexus
<ol>
	<li>From the Modules drop-down menu, navigate to Quantification and select Segment Statistics.</li>
	<li>Under Inputs, select the new segmentation map for quantifying under the Segmentation tool and choose the MRI volume from the Scalar volume. For Output table (under Output), choose the Table option. Once complete, press Apply, and a table containing the volume of the choroid plexus will appear in various units.</li>
</ol>
9. Saving the segments and volume results
<ol>
	<li>Click the Save button in the upper left corner of the toolbar to save generated files.</li>
	<li>Save the segmentation files as .nrrd (3D slicer file), .nii.gz (NIFTI file) or .tsv (table file).</li>
</ol>
10. Determining accuracy, performance, and agreement of the segmentation
NOTE: It is recommended to use the MONAI package (see Table of Materials), which describes the Dice Coefficient (DC) and the DeepMind average Surface Distance (avgSD). Details on DC and avgSD are described below. In order to compute these metrics, readers will need to know how to program (e.g., python, read images from disk, re-format the data to the appropriate input arrays for these functions). There is no user-friendly package that includes all these metrics.
<ol>
	<li>The DC score is a standard approach to quantify the overlap of two geometric domains. To compute the average DC score between two segmentations, provide two tensors y_pred and y, i.e., multi-frame images with one frame for each binarized label image. Tensors y_pred and y may contain segmentations of two different manual raters, repeated segmentations of the same rater, or automated prediction and manual ground truth.
	<ol>
		<li>Use the function monai.metrics.compute_meandice to calculate the mean DC score.</li>
		<li>Generate suitable binary label tensors with monai.transforms.post. 
		NOTE: The include_background parameter can be set to False to exclude the first category (channel index 0) from the DC computation, which is, by convention, assumed to be a background.</li>
	</ol>
	</li>
	<li>Consider the avgSD score less common, and note that the approach can differ as multiple definitions exist for surface distance. For example, use the max distance (also known as Hausdorff distance, highly sensitive to outliers), the mean distance (as described here), and the 95th percentile (highly robust) as frequently used measures.
	<ol>
		<li>Use the function compute_average_surface_distance to calculate the avgSD score.</li>
		<li>Ensure that this function computes the Average Surface Distance from y_pred to y under the default setting.</li>
		<li>In addition, if symmetric = True, ensure that the average symmetric surface distance between these two inputs is returned.</li>
	</ol>
	</li>
	<li>Perform the statistical analysis of the DC and avgSD score across multiple cases can be performed by using the robust Wilcoxon signed-rank test for paired analysis.</li>
	<li>Consider using the Intraclass correlation coefficient (ICC) as another commonly used method to determine whether multiple participants can be rated reliably by different raters. Remember that ICC operates on a set of paired measurements (e.g., the volume) of segmentations and not on the segmentation images directly. To compute ICC, use R software and R Studio (see Table of Materials), which makes the process straightforward.
	<ol>
		<li>Download the package using install.packages(&#34;psych&#34;) and load the library(psych).</li>
		<li>Enter the data frame, which includes the participants (rows) and a rater in each column, by using Data &#60;- data.frame(df). Then visualize the measurements using plot (Data).</li>
		<li>To run ICC, use ICC(Data), which generates a table of the different types of ICC, e.g., to obtain the inter- or intra-rater scores.</li>
	</ol>
	</li>
</ol>

Enhancing Neuroimaging Analysis in Brain Disorders

The authors have no competing financial interests.

Critical steps of the protocol 
Three critical steps require special attention when implementing this protocol. First, checking the quality and contrast of MR images is key to ensuring accurate segmentation. If the quality of the image is too poor, or the contrast is too low or too high, it may lead to the inaccurate delineation of the choroid plexus. The contrast for the image can be adjusted by viewing the image&#39;s grayscale value or by calibrating the values to enhance the contrast between the gray matter nuclei and gray matter. Second, the raters need to be familiar with the anatomy of the choroid plexus and have specialized training. If raters are unfamiliar with the anatomy of the choroid plexus and adjacent brain regions, they may segment the choroid plexus incorrectly, making the choroid plexus&#39; volume inaccurate. Lastly, it is important to evaluate the intra- and inter-rater reproducibility to ensure that raters carrying out manual segmentation can reproduce their own, as well as other raters&#39; segmentation of the choroid plexus. These numbers are also highly relevant when validating automated segmentation tools at a later stage. Additionally, if the dataset is fixed, and if multiple raters will be used for manual segmentation, then it is recommended that the same window setting be used so that the raters are looking at the same image with the same contrast and brightness. If the window setting changes between raters looking at the same image, then the same image might be segmented differently.
Modifications and troubleshooting 
Users can make some modifications to this protocol. First, choroid plexus tissue located in the para septal area and the inferno-anterior-lateral part of the temporal horn, which is adjacent to the septum pellucidum, fornix, and hippocampus, can make the segmentation of the choroid plexus challenging. To address this difficulty, it would be suggested to conduct the segmentation of the choroid plexus in all three dimensions, and a reference (Figure 6) is provided for segmenting the choroid plexus in these complex regions. Second, it is also important to know when to stop the segmentation. For the choroid plexus in the lateral and third ventricles, the red nucleus can be used as a stopping landmark, while for the fourth ventricle choroid plexus, the foramen of Magendie can be used as a stopping point. Third, challenges may exist when distinguishing the boundary between the choroid plexus and CSF in the posterior-basal part of the lateral ventricles. To address this concern, signal intensity and anatomical considerations can be used to aid the rater in making appropriate segmentation decisions. Fourth, if a low-resolution image is being used, it would be recommended to be more conservative in the segmentation procedure and to prioritize using contrast-enhanced imaging to validate the segmentation of the choroid plexus in this temporal region. If contrast-enhanced imaging is not available, then it would be suggested to exclude this region from the segmentation process. However, if a high-resolution image is being used, then it would be recommended to be more liberal in the segmentation procedure. Also, if the demarcation between the choroid plexus and the brain parenchyma can be made on a high-resolution image of the temporal horn, then a contrast-enhanced image would not be necessary. Fifth, 3D Slicer can run on a touchscreen computer where a stylus pen instead of a mouse can be used to enhance the tracing of the choroid plexus. However, this software is not currently available on the iPad. Lastly, software crashing issues may be encountered on some computers when the choroid plexus of more than ten subjects have been delineated in succession. In this case, clicking the Save button frequently can prevent data loss caused by the software crash.
Limitations 
While manual segmentation of the choroid plexus is the gold standard for obtaining accurate volume data, there are several limitations related to the type and quality of the scan, as well as the experience of the rater21. For example, choroid plexus size can vary depending on age or disease state, which may impact the size of the ventricle and the choroid plexus. Thus, the choroid plexus may appear small in young, healthy individuals, making it difficult to manually segment. This issue may be compounded if the image is of poorer resolution (1.2 or 1.5 mm isovoxel) and/or captured using a 1.5 T MRI scanner. Manual segmentation of the choroid plexus could additionally be impacted by the brightness and contrast of the image, making it difficult to identify the boundaries, resulting in over or underestimation of the volume. Additionally, the third and fourth ventricle choroid plexus are small structures, which can be challenging to segment properly if a higher resolution image is not available (0.7 or 0.8 mm isovoxel). A limitation to using a 3D Slicer instead of other open-source manual segmentation software is the inability to perform image segmentation simultaneously in three dimensions, a feature offered through ITK-SNAP51 that can improve the speed of image segmentation of the choroid plexus. Additionally, manual segmentation is a time-consuming and tedious task, making the study of the choroid plexus in large cohorts with thousands or tens of thousands of individuals impractical, highlighting the need for accurate automatic choroid plexus segmentation tools. Lastly, simply counting choroid plexus voxels without accounting for the partial volume effects of CSF or white matter may introduce errors in the volume measurement.
Significance with respect to existing methods 
The dependence on FreeSurfer for choroid plexus segmentation, which has poor accuracy and doesn&#39;t segment the third and fourth ventricle choroid plexus, limits the foundational work that can be completed to better understand the role of the choroid plexus in health and disease. Additionally, a more accurate delineation of the choroid plexus may also be leveraged by the Alzheimer&#39;s neuroimaging community to reduce the contamination of medial temporal tau PET signal by off-target binding in the choroid plexus55. While initial adaptations of machine learning (GMM) and deep-learning techniques (3D U-Net, nnU-Net, Axial-MLP 8) to choroid plexus labels have improved the segmentation accuracy upon FreeSurfer-derived choroid plexus labels46,48,49, methods are unfortunately only trained and evaluated in small, homogeneous datasets, neither publicly available nor easy-to-use tools and incomplete only including the choroid plexus within the lateral ventricles. One caveat is that at the time of the resubmission of this protocol, there was an article published by Yazdan-Panah et al. where they conducted manual segmentation of the lateral ventricle choroid plexus using ITK-SNAP56. They used these manually segmented images to train a 2-step 3D U-Net and demonstrated an average DC of 0.72 with the ground truth, and it outperformed FreeSurfer and FastSurfer-based segmentations56. The generalizability to other resolutions, scanners, ages, and multiple diseases has not been established and is, in fact, unlikely given the challenge of the domain transfer.
Future applications 
Because of the limitations noted above, a protocol for accurately segmenting the choroid plexus is needed. Furthermore, in order to create an automated segmentation tool for the choroid plexus, which can be challenging to develop due to the nature of this structure, a comprehensive annotated dataset of the choroid plexus is needed spanning various parameters and combining it with a set of methodological innovations for the state-of-the-art open-source software, FastSurfer42,43, an advanced and scalable deep learning-based neuroimaging pipeline for automated cortical and subcortical segmentation. FastSurferCNN has been shown to outperform 3D U-Net, SDNet, and QuickNAT models for cortical and subcortical segmentation of close to 100 structures with an average DCs &#62; 8542. Thus, a large and comprehensive annotation of the choroid plexus can be used with FastSurfer to significantly extend to (1) a 3D architecture with improved internal augmentation techniques, (2) the capability to also predict - for the first time - partial volume estimates directly, as well as (3) output segmentations at higher resolutions (super-resolution) for data harmonization. The authors plan to further work on adapting and developing FastSurfer to create a highly accurate choroid plexus segmentation tool for the lateral, third, and fourth ventricles and share the same openly with the research community.

Choroid plexus function 
The choroid plexus is a highly vascularized structure in the brain consisting of fenestrated capillaries and a monolayer of choroid plexus epithelial cells1. The choroid plexus projects into the lateral, third, and fourth cerebral ventricles and produces cerebrospinal fluid (CSF), which plays an important role in neural patterning2 and brain physiology3,4. The choroid plexus secretes neurovascular substances, encompasses a stem-cell like repository, and acts as a physical barrier to impede the entrance of toxic metabolites, an enzymatic barrier to remove moieties that circumvent the physical barrier, and an immunological barrier to protect against foreign invaders5. The choroid plexus modulates neurogenesis6, synaptic plasticity7, inflammation8, circadian rhythm9,10, gut brain-axis11, and cognition12. Moreover, peripheral cytokines, stress, and infection (including SARS-CoV-2) can disrupt the blood-CSF barrier13,14,15,16. Thus, the choroid plexus-CSF system is integral for neurodevelopment, neurocircuit maturation, brain homeostasis, and repair17. Since immune, inflammatory, metabolic, and enzymatic alterations impact the brain, researchers are using neuroimaging tools to assess the role of the choroid plexus across the lifespan and in brain disorders18,19,20. However, limitations exist in commonly used automated tools for choroid plexus segmentation, such as FreeSurfer, which result in the choroid plexus being poorly segmented. Thus, there is a critical need for ground truth manual segmentation of the choroid plexus that can be used to develop an accurate automated tool for choroid plexus segmentation.
Choroid plexus in neurodevelopment and brain disorders 
The role of the choroid plexus in brain disorders has long been neglected, mainly because it was regarded as a supporting player whose role was to cushion the brain and maintain a proper salt balance2,21. However, the choroid plexus has gained attention as a structure linked to brain disorders such as pain syndromes22, SARS-CoV-216,23,24, neurodevelopmental2, and brain disorders19, suggesting a transdiagnostic effect in the development of behavioral disorders. In neurodevelopmental disorders, choroid plexus cysts were associated with an increased risk of developmental delay, attention-deficit/hyperactivity disorder (ADHD), or autism spectrum disorder (ASD)25,26. Additionally, lateral ventricle choroid plexus volume was found to be increased in patients with ASD27. In brain disorders,choroid plexus abnormalities have been described since 1921 in psychotic disorders28,29. Previous studies have identified choroid plexus enlargement using FreeSurfer segmentation in a large sample of patients with psychotic disorders compared to both their first-degree relatives and controls19. These findings were replicated using manually segmented choroid plexus volume in a large sample of clinical high-risk for psychosis population and found that these patients had larger choroid plexus volume compared to healthy controls30. There are a growing number of studies demonstrating choroid plexus enlargement in complex regional pain syndrome22, stroke31, multiple sclerosis20,32, Alzheimer&#39;s33,34, and depression35, with some demonstrating a link between peripheral and brain immune/inflammatory activity. These neuroimaging studies are promising; however, poor lateral ventricle choroid plexus segmentation by FreeSurfer21 limits the trustworthiness of automated choroid plexus volume estimation. As a result, studies in multiple sclerosis20,32, depression35, Alzheimer&#39;s34, and early psychosis36 have begun manually segmenting the lateral ventricle choroid plexus, but there are no current guidelines for how to do this, nor is their guidance on segmenting the third and fourth ventricle choroid plexus.
Common segmentation tools exclude the choroid plexus 
Brain segmentation pipelines such as FreeSurfer37,38,39, FMRIB Software Library (FSL)40, SLANT41, and FastSurfer (developed by the co-author Martin Reuter)42,43, accurately and reliably segment cortical and subcortical structures employing atlas-based (FSL), atlas- and surface-based (FreeSurfer), and deep learning segmentation paradigms (SLANT and FastSurfer). Weaknesses of some of these approaches include processing speed, limited generalization to different scanners, field strengths and voxel sizes37,44, and forced alignment of the label map in a standard atlas space. However, the capability to segment the choroid plexus and the compatibility with high-resolution MRI is only addressed by FreeSurfer and FastSurfer. The neural networks behind FastSurfer, are trained on FreeSurfer choroid plexus labels, so they inherit FreeSurfer&#39;s previously discussed reliability and coverage limitations, with the third and fourth ventricles being ignored21. Current limitations for high-resolution MRI also exist, but FreeSurfer&#39;s high-resolution stream45 and FastSurferVINN43 can be used to handle this issue.
Current choroid plexus segmentation tools 
There is only one freely available segmentation tool for the choroid plexus, but segmentation accuracy is limited. Accurate choroid plexus segmentation can be impacted by a variety of factors, including (1) variability in choroid plexus location (spatially non-stationary) due to its location within the ventricles, (2) differences in voxel intensity, contrast, resolution (within-structure heterogeneity) due to cellular heterogeneity, dynamic choroid plexus function, pathological changes, or partial volume effects, (3) age- or pathology-related ventricular size differences impacting choroid plexus size, and (4) proximity to adjacent subcortical structures (hippocampus, amygdala, caudate, and cerebellum), which are also difficult to segment. Given these challenges, FreeSurfer segmentations often under or over-estimate, mislabel or ignore the choroid plexus.
Three recent publications addressed the gap of reliable choroid plexus segmentation with a Gaussian Mixture Model (GMM)46, an Axial-MLP47, and U-Net-based deep learning approaches48. Each model was trained and evaluated using private, manually labeled datasets of at most 150 subjects with a limited diversity of scanners, sites, demographics, and disorders. While these publications46,48,49 achieved significant improvements over FreeSurfer&#39;s choroid plexus segmentation - sometimes doubling the intersection of prediction and ground truth, neither method is (1) validated in high-resolution MRI, (2) has dedicated generalization and reliability analyses, (3) features large representative training and testing datasets, (4) specifically addresses or analyzes choroid plexus segmentation challenges such as partial volume effects, or (5) is publicly available as a ready-to-use tool. Thus, the current &#34;gold standard&#34; for choroid plexus segmentation is manual tracing, e.g., using 3D Slicer50 or ITK-SNAP51, which has not been previously described and has been a major challenge for researchers wishing to examine the role of the choroid plexus in their studies. 3D Slicer was chosen for manual segmentation due to the author&#39;s familiarity with the software and because it provides the user with various tools based on different approaches that can be combined to obtain the desired result. Other tools can be used, such as ITK-SNAP, which is primarily oriented on image segmentation, and once the tool is mastered, good results can be obtained by the user. Additionally, the authors have conducted a case-control study demonstrating the high accuracy and reliability of their manual segmentation technique using 3D Slicer30, and that specific methodology is described herein.

<table><tbody><tr><td>3D Slicer</td><td>3D Slicer</td><td>https://www.slicer.org/</td><td>A free, open source software for visualization, processing, segmentation, registration, and analysis of medical, biomedical, and other 3D images and meshes; and planning and navigating image-guided procedures.</td></tr><tr><td>FreeSurfer</td><td>FreeSurfer</td><td>https://surfer.nmr.mgh.harvard.edu/</td><td>An open source neuroimaging toolkit for processing, analyzing, and visualizing human brain MR images</td></tr><tr><td>ITK-SNAP</td><td>ITK-SNAP</td><td>http://www.itksnap.org/pmwiki/pmwiki.php</td><td>A free, open-source, multi-platform software application used to segment structures in 3D and 4D biomedical images.&amp;nbsp;</td></tr><tr><td>Monai Package</td><td>Monai Consortium</td><td>https://docs.monai.io/en/stable/metrics.html</td><td>Use for Dice Coefficient and DeepMind average Surface Distance.&amp;nbsp;</td></tr><tr><td>MRI scanner</td><td>GE</td><td>Discovery MR750&amp;nbsp;</td><td></td></tr><tr><td>Psych Package</td><td>R-Project</td><td>https://cran.r-project.org/web/packages/psych/index.html</td><td>A general purpose toolbox developed originally for personality, psychometric theory and experimental psychology.</td></tr><tr><td>R Software</td><td>R-Project</td><td>https://www.r-project.org/</td><td>R is a free software environment for statistical computing and graphics.&amp;nbsp;</td></tr><tr><td>RStudio</td><td>Posit</td><td>https://posit.co/</td><td>An RStudio integrated development environment (IDE) is a set of tools built to help you be more productive with R and Python.&amp;nbsp;</td></tr><tr><td>Windows or Apple OS Desktop or Laptop</td><td>Any company</td><td>n/a</td><td>Needed for running the software used in this protocol.&amp;nbsp;</td></tr></tbody></table>

manual segmentation of the human choroid plexus using brain mri

The choroid plexus has been implicated in neurodevelopment and a range of brain disorders. Evidence demonstrates that the choroid plexus is critical for brain maturation, immune/ inflammatory regulation, and behavioral/cognitive functioning. However, current automated neuroimaging segmentation tools are poor at accurately and reliably segmenting the lateral ventricle choroid plexus. Furthermore, there is no existing tool that segments the choroid plexus located in the third and fourth ventricles of the brain. Thus, a protocol delineating how to segment the choroid plexus in the lateral, third, and fourth ventricle is needed to increase the reliability and replicability of studies examining the choroid plexus in neurodevelopmental and brain disorders. This protocol provides detailed steps to create separately labeled files in 3D Slicer for the choroid plexus based on DICOM or NIFTI images. The choroid plexus will be manually segmented using the axial, sagittal, and coronal planes of T1w images making sure to exclude voxels from gray or white matter structures bordering the ventricles. Windowing will be adjusted to assist in the localization of the choroid plexus and its anatomical boundaries. Methods for assessing accuracy and reliability will be demonstrated as part of this protocol. Gold standard segmentation of the choroid plexus using manual delineations can be used to develop better and more reliable automated segmentation tools that can be openly shared to elucidate changes in the choroid plexus across the lifespan and within various brain disorders.

The proposed method has undergone iterative refinement for the lateral ventricle choroid plexus, involving extensive testing on a cohort of 169 healthy controls and 340 patients with clinically high risk for psychosis30. Using the technique described above, the authors obtained high intra-rater accuracy and reliability with a DC = 0.89, avgHD = 3.27 mm3, and single-rater ICC = 0.9730, demonstrating the strength of the protocol described herein.
Handling quality control issues and 3D Slicer settings 
Before starting the segmentation process, it is necessary to check the quality of the brain scan to ensure that there is no head motion or artifacts that interfere with manual segmentation (Figure 1A). Next, brightness and contrast may be adjusted to assist with better visualization of the choroid plexus. Some brain scans may have head motion, and it is important to determine whether the artifact would adversely impact the delineation of the choroid plexus (Figure 1B). Additionally, images with brightness and contrast artifacts make it difficult to distinguish the borders of the choroid plexus (Figure 1C,D). In this case, try adjusting the brightness and contrast until it is suitable for manual segmentation. Ensure that brain scans that cannot be easily segmented for the choroid plexus are excluded.
Lateral ventricle choroid plexus segmentation 
As shown in Figure 2, five main parts are used to load and display the images (part 1), select different 3D slicer functions (part 2), tools for segmenting the lateral choroid plexus (part 3), visualizing the axial, coronal, and sagittal images (part 4), calculating the volume of the lateral ventricle choroid plexus (part 5), and saving the results from the manual segmentation. The T1w brain scan can be uploaded using the Welcome to Slicer interface by downloading sample data from MRHead dataset in 3D Slicer (Figure 3) or importing the NIFTI or DICOM file from an existing dataset (Figure 4A,B). There is also an option in this panel to edit the brightness and contrast of the image (Figure 4C). After loading the T1w brain scan, it will be displayed in the slice view interface and prepared for lateral ventricle choroid plexus segmentation. Manual segmentation is created using the Segment Editor module (Figure 5A), and the master volume name can be confirmed in Figure 5B. In Figure 5C, the labels for the right and left lateral ventricle choroid plexus can be added and labeled in different colors (Figure 5C), and the region of interest itself can be delineated by using the Draw or Paint Tool (Figure 5D). Figure 6 labels the lateral ventricle choroid plexus and its surrounding brain structures, such as the caudate nucleus, hippocampus, fornix, and the third ventricle, which provides landmarks for the segmentation of the lateral ventricle choroid plexus in some of the more complex regions. To generate and extract choroid plexus volume data from the manual segmentations, select the Segment Statistics module (Figure 7A). There are a few options to select from for outputting the data (Figure 7B). The new files containing the calculated lateral ventricle choroid plexus volume can now be saved by pressing the Save button (Figure 7C).
Third and fourth ventricle choroid plexus segmentation 
As seen in Figure 8, the 3rd ventricle choroid plexus can be easily viewed in the lower left panel depicting the sagittal plane. Notably, the Foramen of Monro can be observed arching below the corpus callosum, with the choroid plexus highlighted within the third ventricle in green. The third ventricle and the third ventricle choroid plexus can also be viewed in the axial and coronal planes (upper left and lower right panels of Figure 8, respectively). Finally, a 3D rendering of the third ventricle choroid plexus is shown in the upper right panel of Figure 8. Figure 9 labels the third ventricle choroid plexus and its surrounding brain structures, including the corpus callosum, fornix, thalamus, internal cerebral vein, and third ventricle, which provides landmarks for the segmentation of the third ventricle choroid plexus in some of the more complex regions.
The fourth ventricle choroid plexus is harder to view and can be seen in Figure 10. The sagittal and coronal planes (lower left and lower right panels of Figure 10) allow for the best viewing of its structure. Care must be taken to ensure that parts of the cerebellum or the fourth ventricle itself are not delineated as choroid plexus. Figure 11 labels the fourth ventricle choroid plexus and its surrounding brain structures, including the medulla, pons, superior cerebella peduncle, inferior medullary velum, and fourth ventricle, which provides landmarks for the segmentation of the 4th ventricle choroid plexus in some of the more complex regions.
Segmentation accuracy, similarity, and agreement 
Segmentation of neuroanatomical structures can be directly compared in an image viewer, but the similarity is sometimes difficult to be assessed visually. Therefore, quantitative measures such as the DC52, measuring percent overlap, and the avgSD53, measuring distances between the boundary surfaces of the delineated structures, are used to compare predictions with ground truth or manual segmentations across or within raters to assess reliability. As depicted in Figure 12A, the DC for two 3D segmentations G and P is simply the volume of the overlap (intersection) divided by the average volume53:
<img alt="Equation 1" src="/files/ftp_upload/65341/65341eq01.jpg" style="margin: auto;" />
where | . | represents volume. It measures overlap on a scale between 0 and 1, where a value of 1 indicates exact agreement and 0 disjoint segmentations and is often multiplied by 100 to represent a percent overlap. The average surface distance (ASD) measures the average distance (in mm) between all points x on the boundary of G ( bd(G) ) to the boundary of P and vice-versa (Figure 12B). It is defined as
<img alt="Equation 2" src="/files/ftp_upload/65341/65341eq02.jpg" style="margin: auto;" />
with distance&#160;&#160;<img alt="Equation 3" src="/files/ftp_upload/65341/65341eq03.jpg" style="margin: auto;" />&#160; representing the minimum of the Euclidean norm53. In contrast to the DC, a smaller ASD indicates better capture of the segmentation boundaries, with a value of zero being the minimum (perfect match). Note that sometimes also, the maximum distance or the 95th percentile is used instead of the average, where the maximum is highly sensitive to single outliers, while the 95th percentile is robust but may miss small but relevant segmentation errors.
The agreement of volume estimates (not of the segmentations directly) between a set of paired segmentations can be measured using ICC54. This can be accomplished by having multiple participants rated by multiple raters (interclass ICC) or by the same rater (intraclass ICC) (Figure 12C). ICC scores range from 0 (poor reliability) to 1 (excellent reliability). For inter-rater reliability, it is suggested to use ICC1 (one-way fixed-effects model) for datasets where each segmentation is done by a different rater selected at random. Additionally, for datasets where multiple raters, chosen at random, work on the same segmentation, it is recommended to use ICC2 (two-way random-effects model) to test for absolute agreement in the segmentations. Finally, for intra-rater reliability, it is recommended to use ICC3 (two-way mixed effects model) (Figure 12C).
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/65341/65341fig01v2.jpg" /> 
Figure 1: Brain scan quality control. (A) Brain scan with good contrast and brightness, no evidence of artifacts, and no head motion. (B) Brain scan showing head motion (red arrow). (C) Brain scan with high brightness and low contrast or (D) low brightness and high contrast. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65341/65341fig01largev2.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/65341/65341fig02.jpg" /> 
Figure 2: The segmentation of the lateral ventricle choroid plexus in 3D Slicer. (1) is used to load the DICOM or NIFTI images and to save the results. (2) consists of a drop-down menu that can be used to enter the Segment Editor module (yellow arrow), which is used to segment the choroid plexus. The Quantification module (blue arrow) can also be selected here to calculate the volume of the choroid plexus. (3) shows the segment toolbar, which includes the draw, paint, and erase tools. (4) demonstrates the choroid plexus in axial, sagittal, and coronal views of the T1w image. The &#65279;3D rendering of the choroid plexus is also shown in the upper right corner. (5) displays the volume results from the manual choroid plexus segmentation, calculated using the Segment Statistics module. The final results can be saved using the save button mentioned in (1). <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65341/65341fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/65341/65341fig03.jpg" /> 
Figure 3: Loading 3D Slicer sample data. This figure demonstrates how to download the sample data from the 3DSlicer interface. First, &#34;Download Sample Data&#34; must be selected, and then &#34;MRHead&#34; must be chosen, which displays the axial, sagittal, and coronal views of the brain scan on the right side of the screen. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65341/65341fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/65341/65341fig04.jpg" /> 
Figure 4: Loading the T1w brain scan. This figure demonstrates how to upload the T1w brain scan using either NIFTI (left panel) or DICOM (right panel) files. (A) For NIFTI files, either the &#34;Choose Directory to Add&#34; or &#34;Choose File(s) to Add&#34; must be selected, followed by selecting &#34;OK&#34;. (B) For DICOM files, selecting &#34;Add DICOM Data&#34;, followed by &#34;Import DICOM files&#34; and then pressing &#34;OK&#34; is needed. These two approaches will display the axial, sagittal, and coronal views of the brain scan on the right side of the screen. (C) To adjust the brightness and contrast of the images, the red button must be selected. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65341/65341fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/65341/65341fig05.jpg" /> 
Figure 5: Lateral ventricle choroid plexus segmentation. After the T1w brain scan has been loaded into the 3D Slicer. (A) Selecting the &#34;Segmentation Editor&#34; module. (B) Confirming the module and the master volume for manual segmentation of the lateral ventricle choroid plexus. (C) Creating labels for the right and left lateral ventricle choroid plexus. (D) Using the &#34;draw&#34; and &#34;paint&#34; tools to manually delineate the lateral ventricle choroid plexus. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65341/65341fig05large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/65341/65341fig06.jpg" /> 
Figure 6: Adjacent structures to the lateral ventricle choroid plexus. Adjacent brain structures include the fornix, caudate nucleus, hippocampus, and the third ventricle. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65341/65341fig06large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 7" class="xfigimg" src="/files/ftp_upload/65341/65341fig07.jpg" /> 
Figure 7: Volume calculation. Calculating the volume of the choroid plexus and saving the segments and volume results. (A) Selecting the Segment Statistics module. (B) Selecting for outputting the data. (C) Pressing the Save button to save the new files containing the calculated lateral ventricle choroid plexus volume. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65341/65341fig07large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 8" class="xfigimg" src="/files/ftp_upload/65341/65341fig08.jpg" /> 
Figure 8: Third ventricle choroid plexus segmentation. Depicted here are the axial, coronal, and sagittal views of the third ventricle choroid plexus that has been manually segmented using the 3D Slicer. The top right corner shows a 3D rendering of the third ventricle choroid plexus. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65341/65341fig08large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 9" class="xfigimg" src="/files/ftp_upload/65341/65341fig09.jpg" /> 
Figure 9: Adjacent structures to the third ventricle choroid plexus. Adjacent brain structures include the fornix, internal cerebral vein, thalamus, corpus callosum, and 3rd ventricle. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65341/65341fig09large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 10" class="xfigimg" src="/files/ftp_upload/65341/65341fig10.jpg" /> 
Figure 10: Fourth ventricle choroid plexus segmentation. Depicted here are the axial, coronal, and sagittal views of the fourth ventricle choroid plexus that has been manually segmented using the 3D Slicer. The top right corner shows a 3D rendering of the fourth ventricle choroid plexus. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65341/65341fig10large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 11" class="xfigimg" src="/files/ftp_upload/65341/65341fig11.jpg" /> 
Figure 11: Adjacent structures to the fourth ventricle choroid plexus. Adjacent brain structures include the medulla oblongata, pons, cerebellum, cerebellar vermis, and cerebellar tonsils. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65341/65341fig11large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 12" class="xfigimg" src="/files/ftp_upload/65341/65341fig12.jpg" /> 
Figure 12: Determining segmentation accuracy, performance, and agreement. (A) Depicting how the percent overlap is calculated using the Dice Coefficient (DC) score. (B) The average surface distance (avgSD) measures the distances between the boundary surfaces of the delineated structures in order to compare predictions with ground truth, or manual segmentations across or within raters to assess reliability. (C) The Intraclass Correlation Coefficient (ICC) can be used for inter-rater (repeated measurements of the same subject) or intra-rater (multiple measurements from the same raters) reliability analysis. A representative example and output are provided. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65341/65341fig12large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies at JoVE.com

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies 

Despite the crucial role of the choroid plexus in the brain, neuroimaging studies of this structure are scarce due to the lack of reliable automated segmentation tools. The present protocol aims to ensure gold-standard manual segmentation of the choroid plexus that can inform future neuroimaging studies.

Manual Segmentation of the Human Choroid Plexus Using Brain MRI

The choroid plexus has been implicated in neurodevelopment and a range of brain disorders. Evidence demonstrates that the choroid plexus is critical for ...

manual-segmentation-of-the-human-choroid-plexus-using-brain-mri

Nanyang Technological University

Research

JoVE Journal

Neuroscience

2.2K Views.  Beth Israel Deaconess Medical Center. We streven ernaar een toegankelijk en gestandaardiseerd protocol op te stellen voor het handmatig segmenteren van de plexus choroideus in de laterale derde en vierde ventrikels van de hersenen. We hopen een antwoord te bieden op alle veelgestelde vragen of problemen die zich kunnen voordoen bij het handmatig segmenteren van de plexus choroideus, vooral gezien de complexe aard van de locatie in de hersenen. Het ontwikkelen van dit protocol zal leiden tot de creatie van een dataset die groot ge...

Video: Auteur in de schijnwerpers: hiaten in de anatomie overbruggen en een basis leggen voor algoritmische studies 

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

In this paper we present an automated system based mainly on the computed tomography (CT) images consisting of two main components: the midline shift estimation and intracranial pressure (ICP) pre-screening system. To estimate the midline shift, first an estimation of the ideal midline is performed based on the symmetry of the skull and anatomical features in the brain CT scan. Then, segmentation of the ventricles from the CT scan is performed and used as a guide for the identification of the actual midline through shape matching. These processes mimic the measuring process by physicians and have shown promising results in the evaluation. In the second component, more features are extracted related to ICP, such as the texture information, blood amount from CT scans and other recorded features, such as age, injury severity score to estimate the ICP are also incorporated. Machine learning techniques including feature selection and classification, such as Support Vector Machines (SVMs), are employed to build the prediction model using RapidMiner. The evaluation of the prediction shows potential usefulness of the model. The estimated ideal midline shift and predicted ICP levels may be used as a fast pre-screening step for physicians to make decisions, so as to recommend for or against invasive ICP monitoring.

An automated midline shift estimation and intracranial pressure (ICP) pre-screening system based on computed tomography (CT) images for patients with traumatic brain injury (TBI) is proposed using image processing and machine learning techniques.

In this paper we present an automated system based mainly on the computed tomography (CT) images consisting of two main components: the midline shift ...

Medicine

Behavioral Assessment of Manual Dexterity in Non-Human Primates

The corticospinal (CS) tract is the anatomical support of the exquisite motor ability to skillfully manipulate small objects, a prerogative mainly of primates1. In case of lesion affecting the CS projection system at its origin (lesion of motor cortical areas) or along its trajectory (cervical cord lesion), there is a dramatic loss of manual dexterity (hand paralysis), as seen in some tetraplegic or hemiplegic patients. Although there is some spontaneous functional recovery after such lesion, it remains very limited in the adult. Various therapeutic strategies are presently proposed (e.g. cell therapy, neutralization of inhibitory axonal growth molecules, application of growth factors, etc), which are mostly developed in rodents. However, before clinical application, it is often recommended to test the feasibility, efficacy, and security of the treatment in non-human primates. This is especially true when the goal is to restore manual dexterity after a lesion of the central nervous system, as the organization of the motor system of rodents is different from that of primates1,2. Macaque monkeys are illustrated here as a suitable behavioral model to quantify manual dexterity in primates, to reflect the deficits resulting from lesion of the motor cortex or cervical cord for instance, measure the extent of spontaneous functional recovery and, when a treatment is applied, evaluate how much it can enhance the functional recovery.
The behavioral assessment of manual dexterity is based on four distinct, complementary, reach and grasp manual tasks (use of precision grip to grasp pellets), requiring an initial training of adult macaque monkeys. The preparation of the animals is demonstrated, as well as the positioning with respect to the behavioral set-up. The performance of a typical monkey is illustrated for each task. The collection and analysis of relevant parameters reflecting precise hand manipulation, as well as the control of force, are explained and demonstrated with representative results. These data are placed then in a broader context, showing how the behavioral data can be exploited to investigate the impact of a spinal cord lesion or of a lesion of the motor cortex and to what extent a treatment may enhance the spontaneous functional recovery, by comparing different groups of monkeys (treated versus sham treated for instance). Advantages and limitations of the behavioral tests are discussed. The present behavioral approach is in line with previous reports emphasizing the pertinence of the non-human primate model in the context of nervous system diseases2,3.

As manual dexterity is a prerogative mainly of primates, behavioral tasks have been developed in macaque monkeys. Four reach and grasp prehension tasks, measuring hand manipulation ability and force, allow to establish functional recovery after a lesion of the central nervous system and to test the effect of a treatment.

The corticospinal (CS) tract is the anatomical support of the exquisite motor ability to skillfully manipulate small objects, a prerogative mainly of ...

Functional Neuroimaging Using Ultrasonic Blood-brain Barrier Disruption and Manganese-enhanced MRI

Although mice are the dominant model system for studying the genetic and molecular underpinnings of neuroscience, functional neuroimaging in mice remains technically challenging. One approach, Activation-Induced Manganese-enhanced MRI (AIM MRI), has been used successfully to map neuronal activity in rodents 1-5. In AIM MRI, Mn2+ acts a calcium analog and accumulates in depolarized neurons 6,7. Because Mn2+ shortens the T1 tissue property, regions of elevated neuronal activity will enhance in MRI. Furthermore, Mn2+ clears slowly from the activated regions; therefore, stimulation can be performed outside the magnet prior to imaging, enabling greater experimental flexibility. However, because Mn2+ does not readily cross the blood-brain barrier (BBB), the need to open the BBB has limited the use of AIM MRI, especially in mice.
One tool for opening the BBB is ultrasound. Though potentially damaging, if ultrasound is administered in combination with gas-filled microbubbles (i.e., ultrasound contrast agents), the acoustic pressure required for BBB opening is considerably lower. This combination of ultrasound and microbubbles can be used to reliably open the BBB without causing tissue damage 8-11.
Here, a method is presented for performing AIM MRI by using microbubbles and ultrasound to open the BBB. After an intravenous injection of perflutren microbubbles, an unfocused pulsed ultrasound beam is applied to the shaved mouse head for 3 minutes. For simplicity, we refer to this technique of BBB Opening with Microbubbles and UltraSound as BOMUS 12. Using BOMUS to open the BBB throughout both cerebral hemispheres, manganese is administered to the whole mouse brain. After experimental stimulation of the lightly sedated mice, AIM MRI is used to map the neuronal response.
To demonstrate this approach, herein BOMUS and AIM MRI are used to map unilateral mechanical stimulation of the vibrissae in lightly sedated mice 13. Because BOMUS can open the BBB throughout both hemispheres, the unstimulated side of the brain is used to control for nonspecific background stimulation. The resultant 3D activation map agrees well with published representations of the vibrissae regions of the barrel field cortex 14. The ultrasonic opening of the BBB is fast, noninvasive, and reversible; and thus this approach is suitable for high-throughput and/or longitudinal studies in awake mice.

A technique is described for broadly opening the blood-brain barrier in the mouse using microbubbles and ultrasound. Using this technique, manganese can be administered to the mouse brain. Because manganese is an MRI contrast agent that accumulates in depolarized neurons, this approach enables imaging of neuronal activity.

Although mice are the dominant model system for studying the genetic and molecular underpinnings of neuroscience, functional neuroimaging in mice remains ...

Manual Drainage of the Zebrafish Embryonic Brain Ventricles

Cerebrospinal fluid (CSF) is a protein rich fluid contained within the brain ventricles. It is present during early vertebrate embryonic development and persists throughout life. Adult CSF is thought to cushion the brain, remove waste, and carry secreted molecules1,2. In the adult and older embryo, the majority of CSF is made by the choroid plexus, a series of highly vascularized secretory regions located adjacent to the brain ventricles3-5. In zebrafish, the choroid plexus is fully formed at 144 hours post fertilization (hpf)6. Prior to this, in both zebrafish and other vertebrate embryos including mouse, a significant amount of embryonic CSF (eCSF) is present . These data and studies in chick suggest that the neuroepithelium is secretory early in development and may be the major source of eCSF prior to choroid plexus development7.
eCSF contains about three times more protein than adult CSF, suggesting that it may have an important role during development8,9. Studies in chick and mouse demonstrate that secreted factors in the eCSF, fluid pressure, or a combination of these, are important for neurogenesis, gene expression, cell proliferation, and cell survival in the neuroepithelium10-20. Proteomic analyses of human, rat, mouse, and chick eCSF have identified many proteins that may be necessary for CSF function. These include extracellular matrix components, apolipoproteins, osmotic pressure regulating proteins, and proteins involved in cell death and proliferation21-24. However, the complex functions of the eCSF are largely unknown.
We have developed a method for removing eCSF from zebrafish brain ventricles, thus allowing for identification of eCSF components and for analysis of the eCSF requirement during development. Although more eCSF can be collected from other vertebrate systems with larger embryos, eCSF can be collected from the earliest stages of zebrafish development, and under genetic or environmental conditions that lead to abnormal brain ventricle volume or morphology. Removal and collection of eCSF allows for mass spectrometric analysis, investigation of eCSF function, and reintroduction of select factors into the ventricles to assay their function. Thus the accessibility of the early zebrafish embryo allows for detailed analysis of eCSF function during development.

We present a method to collect cerebrospinal fluid (CSF) and to create a system which lacks CSF within the embryonic zebrafish brain ventricular system. This allows for further examination of CSF composition and its requirement during embryonic brain development.

Cerebrospinal fluid  (CSF) is a protein rich fluid contained within the brain ventricles. It is  present during early vertebrate embryonic development and ...

A Visual Description of the Dissection of the Cerebral Surface Vasculature and Associated Meninges and the Choroid Plexus from Rat Brain

This video presentation was created to show a method of harvesting the two most important highly vascular structures, not residing within the brain proper, that support forebrain function. They are the cerebral surface (superficial) vasculature along with associated meninges (MAV) and the choroid plexus which are necessary for cerebral blood flow and cerebrospinal fluid (CSF) homeostasis. The tissue harvested is suitable for biochemical and physiological analysis, and the MAV has been shown to be sensitive to damage produced by amphetamine and hyperthermia 1,2. As well, the major and minor cerebral vasculatures harvested in MAV are of potentially high interest when investigating concussive types of head trauma. The MAV dissected in this presentation consists of the pial and some of the arachnoid membrane (less dura) of the meninges and the major and minor cerebral surface vasculature. The choroid plexus dissected is the structure that resides in the lateral ventricles as described by Oldfield and McKinley3,4,5,6. The methods used for harvesting these two tissues also facilitate the harvesting of regional cortical tissue devoid of meninges and larger cerebral surface vasculature, and is compatible with harvesting other brain tissues such as striatum, hypothalamus, hippocampus, etc. The dissection of the two tissues takes from 5 to 10 min total. The gene expression levels for the dissected MAV and choroid plexus, as shown and described in this presentation can be found at GSE23093 (MAV) and GSE29733 (choroid plexus) at the NCBI GEO repository. This data has been, and is being, used to help further understand the functioning of the MAV and choroid plexus and how neurotoxic events such as severe hyperthermia and AMPH adversely affect their function.

This video presentation shows a method of harvesting the two most important highly vascular structures that support forebrain function. They are the cerebral surface (superficial) vasculature along with associated meninges (MAV) and the choroid plexus which are necessary for cerebral blood flow and cerebrospinal fluid (CSF) homeostasis.

This video presentation was created to show a method of harvesting the two most important highly vascular structures, not residing within the brain ...

A Comprehensive Protocol for Manual Segmentation of the Medial Temporal Lobe Structures

The present paper describes a comprehensive protocol for manual tracing of the set of brain regions comprising the medial temporal lobe (MTL): amygdala, hippocampus, and the associated parahippocampal regions (perirhinal, entorhinal,&#160;and parahippocampal proper). Unlike most other tracing protocols available, typically focusing on certain MTL areas (e.g., amygdala and/or hippocampus), the integrative perspective adopted by the present tracing guidelines allows for clear localization of all MTL subregions. By integrating information from a variety of sources, including extant tracing protocols separately targeting various MTL structures, histological reports, and brain atlases, and with the complement of illustrative visual materials, the present protocol provides an accurate, intuitive, and convenient guide for understanding the MTL anatomy. The need for such tracing guidelines is also emphasized by illustrating possible differences between automatic and manual segmentation protocols. This knowledge can be applied toward research involving not only structural MRI investigations but also structural-functional colocalization and fMRI signal extraction from anatomically defined ROIs, in healthy and clinical groups alike.

The present work provides a comprehensive set of guidelines for manually tracing the medial temporal lobe (MTL) structures. This protocol can be applied to research involving structural and/or combined structural-functional magnetic resonance imaging (MRI) investigations of the MTL, in both healthy and clinical groups.

The present paper describes a comprehensive protocol for manual tracing of the set of brain regions comprising the medial temporal lobe (MTL): amygdala, ...

High-resolution In Vivo Manual Segmentation Protocol for Human Hippocampal Subfields Using 3T Magnetic Resonance Imaging

The human hippocampus has been broadly studied in the context of memory and normal brain function and its role in different neuropsychiatric disorders has been heavily studied. While many imaging studies treat the hippocampus as a single unitary neuroanatomical structure, it is, in fact, composed of several subfields that have a complex three-dimensional geometry. As such, it is known that these subfields perform specialized functions and are differentially affected through the course of different disease states. Magnetic resonance (MR) imaging can be used as a powerful tool to interrogate the morphology of the hippocampus and its subfields. Many groups use advanced imaging software and hardware (&#62;3T) to image the subfields; however this type of technology may not be readily available in most research and clinical imaging centers. To address this need, this manuscript provides a detailed step-by-step protocol for segmenting the full anterior-posterior length of the hippocampus and its subfields: cornu ammonis (CA) 1, CA2/CA3, CA4/dentate gyrus (DG), strata radiatum/lacunosum/moleculare (SR/SL/SM), and subiculum. This protocol has been applied to five subjects (3F, 2M; age 29-57, avg. 37). Protocol reliability is assessed by resegmenting either the right or left hippocampus of each subject and computing the overlap using the Dice&#39;s kappa metric. Mean Dice&#39;s kappa (range) across the five subjects are: whole hippocampus, 0.91 (0.90-0.92); CA1, 0.78 (0.77-0.79); CA2/CA3, 0.64 (0.56-0.73); CA4/dentate gyrus, 0.83 (0.81-0.85); strata radiatum/lacunosum/moleculare, 0.71 (0.68-0.73); and subiculum 0.75 (0.72-0.78). The segmentation protocol presented here provides other laboratories with a reliable method to study the hippocampus and hippocampal subfields in vivo using commonly available MR tools.

High-resolution In Vivo Manual Segmentation Protocol for Human Hippocampal Subfields Using 3T Magnetic Resonance Imaging

The goal of this manuscript is to study the hippocampus and hippocampal subfields using MRI. The manuscript describes a protocol for segmenting the hippocampus and five hippocampal substructures: cornu ammonis (CA) 1, CA2/CA3, CA4/dentate gyrus, strata radiatum/lacunosum/moleculare, and subiculum.

The human hippocampus has been broadly studied in the context of memory and normal brain function and its role in different neuropsychiatric disorders has ...

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Within neuroimaging research, a number of recent studies have discussed the impact of between-study differences in volumetric findings that are thought to result from the use of different segmentation tools to generate brain volumes. Here, processing pipelines for seven automated tools that can be used to segment grey matter within the brain are presented. The protocol provides an initial step for researchers aiming to find the most accurate method for generating grey matter volumes from T1-weighted MRI scans. Steps to undertake detailed visual quality control are also included in the manuscript. This protocol covers a range of potential segmentation tools and encourages users to compare the performance of these tools within a subset of their data before selecting one to apply to a full cohort. Furthermore, the protocol may be further generalized to the segmentation of other brain regions.

This protocol describes the process of applying seven different automated segmentation tools to structural T1-weighted MRI scans to delineate grey matter regions that can be used for the quantification of grey matter volume.

Within neuroimaging research, a number of recent studies have discussed the impact of between-study differences in volumetric findings that are thought to ...

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI&#8212;Application in Premanifest Huntington's Disease

Recent advances in MRI offer a variety of useful markers to identify neurodegenerative diseases. In Huntington&#39;s disease (HD), regional brain atrophy begins many years prior to the motor onset (during the &#34;premanifest&#34; period), but the spatiotemporal pattern of regional atrophy across the brain has not been fully characterized. Here we demonstrate an online cloud-computing platform, &#34;MRICloud&#34;, which provides atlas-based whole-brain segmentation of T1-weighted images at multiple granularity levels, and thereby, enables us to access the regional features of brain anatomy. We then describe a regression model that detects statistically significant inflection points, at which regional brain atrophy starts to be noticeable, i.e. the &#34;change-point&#34;, with respect to a disease progression index. We used the CAG-age product (CAP) score to index the disease progression in HD patients. Change-point analysis of the volumetric measurements from the segmentation pipeline, therefore, provides important information of the order and pattern of structural atrophy across the brain. The paper illustrates the use of these techniques on T1-weighted MRI data of premanifest HD subjects from a large multicenter PREDICT-HD study. This design potentially has wide applications in a range of neurodegenerative diseases to investigate the dynamic changes of brain anatomy.

This paper describes a statistical model for volumetric MRI data analysis, which identifies the &#34;change-point&#34; when brain atrophy begins in premanifest Huntington&#39;s disease. Whole-brain mapping of the change-points is achieved based on brain volumes obtained using an atlas-based segmentation pipeline of T1-weighted images.

Recent advances in MRI offer a variety of useful markers to identify neurodegenerative diseases. In Huntington&#39;s disease (HD), regional brain atrophy ...

A Standardized Pipeline for Examining Human Cerebellar Grey Matter Morphometry using Structural Magnetic Resonance Imaging

Multiple lines of research provide compelling evidence for a role of the cerebellum in a wide array of cognitive and affective functions, going far beyond its historical association with motor control. Structural and functional neuroimaging studies have further refined understanding of the functional neuroanatomy of the cerebellum beyond its anatomical divisions, highlighting the need for the examination of individual cerebellar subunits in healthy variability and neurological diseases. This paper presents a standardized pipeline for examining cerebellum grey matter morphometry that combines high-resolution, state-of-the-art approaches for optimized and automated cerebellum parcellation (Automatic Cerebellum Anatomical Parcellation using U-Net Locally Constrained Optimization; ACAPULCO) and voxel-based registration of the cerebellum (Spatially Unbiased Infra-tentorial Template; SUIT) for volumetric quantification. 
The pipeline has broad applicability to a range of neurological diseases and is fully automated, with manual intervention only required for quality control of the outputs. The pipeline is freely available, with substantial accompanying documentation, and can be run on Mac, Windows, and Linux operating systems. The pipeline is applied in a cohort of individuals with Friedreich ataxia (FRDA), and representative results, as well as recommendations on group-level inferential statistical analyses, are provided. This pipeline could facilitate reliability and reproducibility across the field, ultimately providing a powerful methodological approach for characterizing and tracking cerebellar structural changes in neurological diseases.

A standardized pipeline is presented for examining cerebellum grey matter morphometry. The pipeline combines high-resolution, state-of-the-art approaches for optimized and automated cerebellum parcellation and voxel-based registration of the cerebellum for volumetric quantification.

Multiple lines of research provide compelling evidence for a role of the cerebellum in a wide array of cognitive and affective functions, going far beyond ...

Manual Segmentation of the Human Choroid Plexus Using Brain MRI

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