The work presented in this manuscript was funded by an Australian National Health and Medical Research Council (NHMRC) Ideas Grant: APP1184403.

NOTE: The data used in this study were part of a project approved by the Monash University Human Research Ethics Committee (project 7810). Participants provided written informed consent. While the pipeline can be run on Mac, Windows, or Linux operating systems, ACAPULCO, SUIT, and the QC pipelines have explicitly been tested on Linux (Ubuntu) and Mac (Catalina, Big Sur v11.0.1) operating systems.
1. Module 1: ACAPULCO (anatomical parcellation)
<ol>
	<li>Data collection
	<ol>
		<li>Collect 3D T1-weighted MRI images of the whole brain at a resolution of 1 mm3 or less. Isotropic voxel dimensions (typically 1 mm x 1 mm x 1 mm), and a 3-Telsa (or greater) scanner are recommended. Consult with an imaging specialist at their radiography center to set up and acquire data that meet these specifications. 
		NOTE: T2-weighted images are sometimes useful for volumetric analyses; however, the pipeline presented here relies on T1-weighted data only, and some of the tools used are exclusive to this type of data. As such, T2-weighted images cannot be used.</li>
		<li>Undertake a visual quality assessment of images to exclude gross cerebellar malformations (e.g., large lesions) or substantial motion artifacts that prevent identification of major cerebellar landmarks (e.g., the major anatomical fissures). Do not automatically exclude atrophied cerebella, even if substantial.</li>
		<li>For group studies, also consider quantitative quality assessments using freely-available, standardized tools such as MRIQC46&#160;to further identify problematic data.</li>
		<li>Convert all data to NIFTI-GZ format using a tool such as dcm2niix47.</li>
	</ol>
	</li>
	<li>Recommended data organization
	<ol>
		<li>Obtain all necessary software as listed in the Table of Materials. Ensure Docker48&#160;or Singularity49, Matlab50, and SPM1251&#160;are installed prior to running the pipeline. 
		NOTE: Extensive written and video tutorials describing the pipeline are also available (see the Table of Materials).</li>
		<li>Once all necessary software is installed, create folders in the working directory and label them &#39;acapulco,&#39; &#39;suit,&#39; and &#39;freesurfer.&#39; Do this using the mkdir command from the command line.</li>
		<li>In the &#39;acapulco&#39; directory, create an output folder. In the output folder, create a directory for each subject in the study containing the T1-weighted image in NIFTI-GZ format. 
		NOTE: It is recommended to keep a copy of the original data elsewhere.</li>
	</ol>
	</li>
	<li>Anatomical cerebellar parcellation using ACAPULCO
	<ol>
		<li>Go to the Table of Materials and download the relevant scripts and containers required to run ACAPULCO (under acapulco pipeline files). In the &#39;acapulco&#39; directory, place the (i) ACAPULCO Docker OR Singularity container (&#39;acapulco_0.2.1.tar.gz&#39; or &#39;.sif&#39;, respectively), (ii) contents of the QC_scripts archive (3 files: &#39;QC_Master.R,&#39; &#39;QC_Plots.Rmd,&#39; and &#39;QC_Image_Merge.Rmd&#39;), and (iii) &#39;R.sif&#39; (singularity) OR&#160;&#39;calculate_icv.tar&#39; (docker) file.</li>
		<li>Open a terminal, and from the command line, run the ACAPULCO container on a single image (replace &#60;&#60;subject&#62;&#62; in the following). Wait for ~5 min for processing to complete.
		<ol>
			<li>Using Docker, type the command: 
			docker load --input acapulco_0.2.1.tar.gz 
			docker run -v $PWD:$PWD -w $PWD -t --user $(id -u):$(id -g) --rm acapulco:latest -i output/&#60;&#60;subject&#62;&#62;/&#60;&#60;subject&#62;&#62;.nii.gz -o output/&#60;&#60;subject&#62;&#62;</li>
			<li>Using Singularity, type the command: 
			singularity run --cleanenv -B $PWD:$PWD acapulco-0.2.1.sif -i output/&#60;&#60;subject&#62;&#62;/&#60;&#60;subject&#62;&#62;.nii.gz -o output/&#60;&#60;subject&#62;&#62;</li>
		</ol>
		</li>
		<li>Loop across all subjects/scans in the cohort. See the Table of Materials for a link to the ENIGMA Imaging Protocols website for downloading the pipeline (under ENIGMA Cerebellum Volumetrics Pipeline) and the tutorial manual containing examples of how to create a for-loop for processing multiple subjects serially.</li>
		<li>After processing, look for the following files generated in the subject-specific folders:
		<ol>
			<li>Identify &#34;&#60;subject&#62;_n4_mni_seg_post_inverse.nii.gz&#34;: parcellated cerebellum mask in original (subject space).</li>
			<li>Identify &#34;&#60;subject&#62;_n4_mni_seg_post_volumes.csv&#34;: volumes (in mm3) for each of the 28 subunits generated by acapulco;</li>
			<li>Identify representative images (in &#39;pics&#39; directory): sagittal, axial, and coronal.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Statistical outlier detection and quality control (QC)
	<ol>
		<li>From the terminal and in the &#39;acapulco&#39; directory, ensure that the contents of QC_scripts are in the &#39;acapulco&#39; directory. To run the QC scripts:
		<ol>
			<li>Using Docker, type the command: 
			docker load calculate_icv.tar 
			docker run -v $PWD:$PWD -w $PWD --rm -it luhancheng/calculate_icv:latest Rscript 
			QC_Master.R output/</li>
			<li>Using Singularity, type the command: 
			singularity exec -B $PWD:$PWD R.sif Rscript /path/to/QC_Master.R /path/to/acapulco/output</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Examining the QC images generated by ACAPULCO 
	NOTE: There is a 3-step process for quality checking the ACAPULCO parcellated images.
	<ol>
		<li>Open the &#39;QC_Images.html&#39; in a web browser and quickly (~10 s per subject) scroll through the images to identify obvious failures or systematic issues. Note the subject IDs of failed or suspect parcellated images for follow-up. 
		NOTE: See Figure 3 for a guide on the neuroanatomy of the cerebellar lobules and Figure 4, Figure 5, and Figure 6 in the representative results section below for examples of &#39;good&#39; parcellations, &#39;subtle mis-parcellations,&#39; and &#39;global failure&#39; parcellations.</li>
		<li>Open the &#39;Plots_for_Outliers.html&#39; to check the boxplots for quantitative statistical outliers. Look for outliers (2.698 s.d above or below the mean) above or below the whiskers of the box plots. Hover over the data points to display the Subject ID. Identify the outliers denoted by a &#39;1&#39; in the relevant column in the &#39;Outliers.csv&#39; file, and note the total number of segments identified as outliers for each subject in the final column in &#39;Outliers.csv.&#39;</li>
		<li>Manually inspect each image having one or more outliers. CRITICAL: Using a standard NIFTI image viewer (e.g., FSLEyes or MRICron), overlay the ACAPULCO mask onto the original T1w image to check the quality of the parcellation slice-by-slice.
		<ol>
			<li>To generate overlays for detailed QC from the command line using FSLEyes, i) change the directory to the &#39;acapulco&#39; directory, ii) specify the subject to view (replace &#60;subject&#62;): 
			subj=&#60;subject_name&#62;</li>
			<li>Copy/paste the following code to the terminal (without manually changing {subj} as this has been set by the previous line: 
			t1_image=output/${subj}/${subj}.nii.gz 
			acapulco_image=output/${subj}/${subj}_n4_mni_seg_post_inverse.nii.gz 
			fsleyes ${t1_image} ${acapulco_image} --overlayType label --lut random_big --outline --outlineWidth 3 ${acapulco_image} --overlayType volume --alpha 50 --cmap random 
			NOTE: A determination will need to be made whether to include the abnormal segment or not, i.e., is there a parcellation error, or is it just normal variability in the individual&#39;s anatomy? Each parcellated region is considered individually, so a few regions can be excluded for an image, while the remainder can be retained if correct.</li>
			<li>Do one or more parcellated regions need to be excluded from the final dataset? 
			If Yes (outlier is confirmed), exclude this parcellation(s) from the analysis by replacing the volume estimate with NA in the corresponding cell of the &#39;Cerebel_vols.csv&#39; file for that subject.</li>
			<li>Do parcellation errors result in some of the cerebellum being excluded from the mask? 
			If Yes, (for example, if particular cerebellar lobules are missing from the mask or appear &#39;cut off&#39;), immediately exclude the subject from further analyses (i.e., do not proceed to run the SUIT module on those subjects).</li>
		</ol>
		</li>
	</ol>
	</li>
</ol>
2. Module 2: SUIT cerebellum-optimized voxel-based morphometry
<ol>
	<li>Voxel-based morphometry analyses using SUIT 
	CRITICAL: This pipeline requires the ACAPULCO module to have already been run, as it relies on the generation of a subject-specific cerebellar mask for optimization of the registration and normalization of the cerebellum to the SUIT template. If the subject-specific mask generated by ACAPULCO does not include the whole cerebellum, this warrants exclusion from the SUIT module.&#160;For instructions on running SUIT standalone, see52.
	<ol>
		<li>Obtain all necessary software listed in the Table of Materials. Ensure the SPM12 folder and all subfolders are in the MATLAB path. Ensure enigma_suit scripts are saved in &#39;spm12/toolbox&#39; directory and added to the MATLAB path. To check the MATLAB path, type pathtool in the MATLAB command window, then click Add with subfolders to add the relevant folders.</li>
		<li>Run the SUIT pipeline for one or more subjects. Wait for ~15-20 min (if using the graphical user interface [GUI]) and ~5-7 min if running from the terminal (bash/shell) for processing to complete.
		<ol>
			<li>To use the GUI (subjects will be run in serial), from the MATLAB command window, type the command: 
			suit_enigma_all</li>
			<li>In the first pop-up window, select the subject folders from the &#39;acapulco/output&#39; directory to include in the analysis. Click on the individual folders on the right side of the window, or right-click and Select All. Press Done. In the second pop-up window, select the SUIT directory, where the analyses will be written.</li>
			<li>OR Call the function from the MATLAB command line for a single subject, type the command: 
			suit_enigma_all(&#39;/path/to/acapulco/output/subjdir&#39;,&#39;/path/to/suitoutputdir&#39;)</li>
			<li>OR Call the function from the terminal window, outside of MATLAB, for a single subject by typing the command: 
			matlab -nodisplay -nosplash -r &#34;suit_enigma_all(&#39;/path/to/acapulco/output/subjdir&#39;,&#39;/path/to/suitoutputdir&#39;), exit&#34;</li>
		</ol>
		</li>
		<li>See the Table of Materials for a link to the ENIGMA Imaging Protocols website for downloading the pipeline (under ENIGMA Cerebellum Volumetrics Pipeline) and the tutorial manual containing examples of how to create a for-loop for processing multiple subjects serially.</li>
		<li>Look for the following points regarding the script.
		<ol>
			<li>Ensure that the script copies the N4 bias-corrected, MNI-aligned (rigid-body) T1 image and the ACAPULCO cerebellum mask into the output directory.</li>
			<li>Ensure that the script segments the grey and white matter of the cerebellum.</li>
			<li>Ensure that the script corrects for overinclusion errors in the parcellation using the ACAPULCO mask.</li>
			<li>Ensure that the script DARTEL normalizes and reslices the data into SUIT space with Jacobian modulation so that the value of each voxel is proportional to its original volume.</li>
			<li>Check each subject&#39;s folder for the following final outputs: &#39;wd&#60;subject&#62;_seg1.nii&#39; (grey matter) and &#39;wd&#60;subject&#62;_seg2.nii&#39; (white matter).</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Statistical outlier detection and quality control
	<ol>
		<li>Visually inspect the normalized, modulated images (wd*) for major failures. In MATLAB, type the command: 
		spm_display_4D</li>
		<li>Manually select the &#39;wd*seg1&#39; images from the suit subfolders, or navigate to the &#39;suit&#39; directory; insert &#39;^wd.*seg1&#39; in the Filter box (no quotations) and press Rec button. Press Done.</li>
		<li>Scroll through the images to ensure they are all well-aligned. See Figure 7 for correctly normalized images from&#160;healthy controls (A,B) and an individual with a heavily atrophic cerebellum (D). 
		NOTE: At this stage, the between-subject anatomy is very similar (as they have been registered to the same template), and volume differences are instead encoded by differing voxel intensities. Major failures will be obvious, e.g., blank images, large areas of missing tissue, unusual intensity gradients (i.e., bright voxels all at the top, dark voxels all at the bottom). These images should be excluded from subsequent steps.</li>
		<li>Check spatial covariance for outliers. In MATLAB, type the command: 
		check_spatial_cov
		<ol>
			<li>Select the &#39;wd*seg1&#39; images as per the previous step. When prompted, select the following options: Prop scaling: Yes; Variable to covary out: No; Slice (mm): - 48 , Gap: 1.</li>
			<li>Look at the boxplot displaying the mean spatial covariance of each image relative to all others in the sample. Identify data points that are &#62;2s.d. below the mean in the MATLAB command window. For these, inspect the &#34;&#60;subj&#62;_n4_mni.nii.gz&#34; image in the SUIT folder for artifacts (motion, anatomical abnormalities), image quality issues, or preprocessing errors.</li>
			<li>If the image quality and preprocessing are acceptable and visual inspection of the modulated images in the previous step does not indicate an issue with segmentation and normalization, retain these data in the sample. Otherwise, exclude these data.</li>
		</ol>
		</li>
	</ol>
	</li>
</ol>
3. MODULE 3 (optional): Intracranial Volume (ICV) estimation using FreeSurfer
NOTE: This module will use the FreeSurfer pipeline to calculate ICV. It does not need to be re-run if there are existing Freesurfer outputs for the cohort (any version).
<ol>
	<li>Setting up FreeSurfer
	<ol>
		<li>Ensure FreeSurfer is downloaded and installed53. Go to the Table of Materials and download the relevant scripts to run this Module (under ICV pipeline files). When working with FreeSurfer, set the following variables: 
		export FREESURFER_HOME=&#60;freesurfer_installation_ directory&#62; 
		source $FREESURFER_HOME/SetUpFreeSurfer.sh</li>
		<li>Replace &#60;path&#62; in the following: 
		export SUBJECTS_DIR=&#60;path&#62;/enigma/Freesurfer</li>
	</ol>
	</li>
	<li>Running Freesurfer autorecon1
	<ol>
		<li>For a single subject, from inside the &#39;freesurfer&#39; directory (processing time ~20 min), type the command: 
		cd &#60;path&#62;/enigma/freesurfer 
		recon-all -i ../input/&#60;subject&#62;.nii.gz -s &#60;subject&#62; -autorecon1</li>
		<li>See the tutorial manual for examples of how to create a for-loop for processing multiple subjects serially.</li>
	</ol>
	</li>
	<li>Calculation of ICV
	<ol>
		<li>Data organization
		<ol>
			<li>In the &#39;freesurfer&#39; directory, place the (i) Docker OR Singularity container used in Module 1 (&#39;calculate_icv.tar&#39; or &#39;R.sif,&#39; respectively) and (ii) xfm2det script (see the Table of Materials). Then, do a git clone to clone the required ICV script: 
			git clone&#160;https://github.com/Characterisation-Virtual-Laboratory/calculate_icv</li>
		</ol>
		</li>
		<li>Running ICV extraction (processing time ~5 min)
		<ol>
			<li>From &#39;freesurfer&#39; directory, with singularity (&#39;R.sif&#39;) container, type: 
			singularity exec --cleanenv -B $PWD:$PWD R.sif calculate_icv/calculate_icv.py --freesurfer_dir=/path/to/freesurfer --acapulco_dir=/path/to/acapulco/QC/Cerebelvolsfile --output_csv_name=Cerebel_vols.csv calculate_icv</li>
			<li>From &#39;freesurfer&#39; directory, with docker container, type: 
			docker run -v $PWD:$PWD -w $PWD -rm -it luhancheng/calculate_icv:latest 
			calculate_icv/calculate_icv.py --freesurfer_dir=/path/to/Freesurfer -- 
			acapulco_dir=/path/to/acapulco/QC/Cerebelvolsfile --output_csv_name=Cerebel_vols.csv 
			calculate_icv</li>
			<li>Running script without container-see the Table of Materials for additional required software and dependencies. From the &#39;freesurfer&#39; directory, type: 
			./calculate_icv/ calculate_icv.py ---freesurfer_dir=/path/to/freesurfer -- 
			acapulco_dir=/path/to/acapulco/QC/Cerebelvolsfile -- 
			output_csv_name=Cerebel_vols.csv calculate_icv 
			NOTE: This will calculate the ICV for each subject and append a column with ICV to the end of the &#39;Cerebel_vols.csv&#39; file.</li>
		</ol>
		</li>
	</ol>
	</li>
</ol>

The authors have no conflicts of interest to disclose.

The cerebellum is critical to a wide range of human motor3, cognitive58, affective10, and language7,59&#160;functions and is implicated in many neurological and psychiatric diseases. The availability of a standardized and easily implementable approach for the quantification of regional cerebellar volumes will contribute to increasingly detailed &#39;whole-brain&#39; structure-function mapping...

The cerebellum is a part of the brain historically associated with motor control1,2,3&#160;and is thought to be integrally involved in only a small set of rare diseases, such as inherited ataxias4. However, converging lines of research from anatomical tracing studies in nonhuman primates, as well as human lesion and neuroimaging studies, provide compelling evidence for a role of the cerebellum in a wide array of cognitive5,6,7, affective8,9,10,11, and other nonmotor functions7,12&#160;(see6&#160;for review). Furthermore, abnormalities of the cerebellum are increasingly implicated in a broad range of neurological and psychiatric disorders, including Parkinson&#39;s disease13, Alzheimer&#39;s disease14,15, epilepsy16,17, schizophrenia18, and autism spectrum disorder19. Therefore, it has become essential to incorporate the cerebellum into functional and structural models of human brain diseases and normative behavioral variability.
Anatomically, the cerebellum can be divided along its superior to inferior axis into three lobes: anterior, posterior, and flocculonodular. The lobes are further subdivided into 10 lobules denoted by roman numerals I-X20,21&#160;(Figure 1). The cerebellum can also be grouped into midline (vermis) and lateral (hemisphere) zones, which respectively receive inputs from the spinal cord and cerebral cortex. The anterior lobe, comprising lobules I-V, has traditionally been associated with motor processes and has reciprocal connections with cerebral motor cortices22. The posterior lobe, comprising lobules VI-IX, is primarily associated with nonmotor processes11&#160;and has reciprocal connections with the prefrontal cortex, posterior parietal, and superior temporal cerebral cortices8,23. Lastly, the flocculonodular lobe, comprising lobule X, has reciprocal connections with vestibular nuclei that govern eye movements and body equilibrium during stance and gait21.
A growing body of recent work using functional neuroimaging has further refined understanding of the functional neuroanatomy of the cerebellum beyond its anatomical divisions. For example, resting-state functional magnetic resonance imaging (fMRI) techniques have been used to map the pattern of functional interactions between the cerebellum and cerebrum24. Additionally, using a task-based parcellation approach, King and colleagues7&#160;demonstrated that the cerebellum shows a rich and complex pattern of functional specialization across its breadth, evidenced by distinct functional boundaries associated with a variety of motor, affective, social, and cognitive tasks. Collectively, these studies highlight the importance of examining individual cerebellar subunits to develop complete biological characterizations of cerebellum involvement in both healthy variability and neurological diseases characterized by alterations in cerebellar structure and/or function.
The present work focuses on methods for quantifying local changes in cerebellar volume using structural MRI in humans. In general, there are two fundamental approaches to the quantification of regional brain volume using MRI data: feature-based segmentation and voxel-based registration. Feature-based segmentation approaches use anatomical landmarks and standardized atlases to automatically identify boundaries between subregions. Mainstream software packages for segmentation include FreeSurfer25, BrainSuite26, and FSL-FIRST27. However, these packages provide only coarse parcellations of the cerebellum (e.g., labeling the whole grey matter and whole white matter in each hemisphere), thus overlooking the individual cerebellar lobules. These approaches are also prone to mis-segmentation, particularly overinclusion of the surrounding vasculature.
New machine-learning and multi-atlas labeling algorithms have been developed, which provide more accurate and finer-grained parcellation of the cerebellum, including Automatic Classification of Cerebellar Lobules Algorithm using Implicit Multi-boundary evolution (ACCLAIM28,29), Cerebellar Analysis Toolkit (CATK30), Multiple Automatically Generated Templates (MAGeT31), Rapid automatic segmentation of the human cerebellum and its lobules (RASCAL32), graph-cut segmentation33, and CEREbellum Segmentation (CERES34). In a recent paper comparing state-of-the-art fully automated cerebellum parcellation approaches, CERES2 was found to outperform other approaches relative to gold-standard manual segmentation of the cerebellar lobules35. More recently, Han and colleagues36&#160;developed a deep-learning algorithm called ACAPULCO (Automatic Cerebellum Anatomical Parcellation using U-Net with locally constrained optimization), which performs on par with CERES2, has broad applicability to both healthy and atrophied cerebellums, is available in open-source Docker and Singularity container format for &#39;off-the-shelf&#39; implementation, and is more time-efficient than other approaches. ACAPULCO automatically parcellates the cerebellum into 28 anatomical regions.
In contrast to feature-based segmentation, voxel-based registration approaches operate by precisely mapping an MRI to a template image. To achieve this mapping, the voxels in the original image must be distorted in size and shape. The magnitude of this distortion effectively provides a measure of volume at each voxel relative to the gold-standard template. This form of volumetric assessment is known as &#39;voxel-based morphometry&#39;37. Whole-brain voxel-based registration approaches, such as FSL-FLIRT38/FNIRT39, SPM unified segmentation40, and CAT1241, are commonly used for voxel-based morphometry. However, these approaches do not account well for the cerebellum, resulting in poor reliability and validity in infratentorial regions (cerebellum, brainstem42). To account for these limitations, the SUIT (Spatially Unbiased Infra-tentorial Template) algorithm was developed to optimize cerebellum registration and improve the accuracy of voxel-based morphometry42,43.
Feature-based segmentation and voxel-based registration approaches for the estimation of regional cerebellar volume have fundamental strengths and weaknesses. Segmentation approaches are substantially more accurate for quantifying the volume of anatomically defined areas (e.g., lobules35). However, boundaries between distinct functional modules of the cerebellum do not map onto its anatomical folia and fissures (equivalent to gyri and sulci of the cerebrum7). As registration-based approaches are not constrained by anatomical landmarks, finer-grained spatial inference and high dimensional structure-function mapping of the cerebellum is possible44. Taken together, segmentation and registration approaches are complementary to one another and can be used to answer different research questions.
Here, a new standardized pipeline is presented, which integrates these existing, validated approaches to provide optimized and automated parcellation (ACAPULCO) and voxel-based registration of the cerebellum (SUIT) for volumetric quantification (Figure 2). The pipeline builds upon the established approaches to include quality control protocols, using qualitative visualization and quantitative outlier detection, and a rapid method for obtaining an estimation of intracranial volume (ICV) using Freesurfer. The pipeline is fully automated, with manual intervention only required for checking the quality control outputs, and can be run on Mac, Windows, and Linux operating systems. The pipeline is freely available with no restrictions of its use for noncommercial purposes and can be accessed from the ENIGMA Consortium Imaging Protocols webpage (under &#34;ENIGMA Cerebellum Volumetrics Pipeline&#34;), following the completion of a brief registration form45.
All required software is listed in the Table of Materials, and detailed tutorials, including a live demonstration, are available upon download of the pipeline, in addition to the protocol described below. Finally, representative results are provided, from the implementation of the pipeline in a cohort of people with Friedreich ataxia (FRDA) and age-and sex-matched healthy controls, alongside recommendations for group-level statistical inferential analyses.

<table fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>ACAPULCO pipeline files&#160;</td>
			<td>0.2.1</td>
			<td>http://enigma.ini.usc.edu/protocols/imaging-protocols/</td>
			<td>Please make sure to use acapulco version 0.2.1</td>
		</tr>
		<tr>
			<td>Docker for Mac</td>
			<td></td>
			<td>https://docs.docker.com/desktop/mac/install/</td>
			<td>macOS must be version 10.14 or newer 
			Docker requires sudo priviledges 
			Docker imposes a memory (RAM) constraint on Mac OS. To increase the RAM, open Docker Desktop, go to Preferences and click on resources. Increase the Memory to the maximum</td>
		</tr>
		<tr>
			<td>Docker for Windows</td>
			<td></td>
			<td>https://docs.docker.com/docker-for-windows/install/</td>
			<td></td>
		</tr>
		<tr>
			<td>ENIGMA SUIT scripts</td>
			<td></td>
			<td>http://enigma.ini.usc.edu/protocols/imaging-protocols/</td>
			<td></td>
		</tr>
		<tr>
			<td>FreeSurfer</td>
			<td>7</td>
			<td>https://surfer.nmr.mgh.harvard.edu/fswiki/DownloadAndInstall</td>
			<td>Following variables need to be set everytime you work with Freesurfer: 
			export FREESURFER_HOME=<img alt="equation" src="/files/ftp_upload/63340/lesser.png" />freesurfer _installation_directory<img alt="equation" src="/files/ftp_upload/63340/greater.png" /> 
			source $FREESURFER_HOME/SetUpFreeSurfer.sh</td>
		</tr>
		<tr>
			<td></td>
			<td></td>
			<td></td>
			<td>export SUBJECTS_DIR=<img alt="equation" src="/files/ftp_upload/63340/lesser.png" />path<img alt="equation" src="/files/ftp_upload/63340/greater.png" />/enigma/Freesurfer</td>
		</tr>
		<tr>
			<td>FSL (for FSLeyes). Optional</td>
			<td>6</td>
			<td>https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FslInstallation</td>
			<td></td>
		</tr>
		<tr>
			<td>ICV pipeline files</td>
			<td></td>
			<td>http://enigma.ini.usc.edu/protocols/imaging-protocols/</td>
			<td>ICV pipeline can be run in two ways: 1) with docker/singularity. You will not require additionl software; 2) without docker/singularity- this involves running the ICV script (calculate_icv.py) manually. You will require the following additional software: 
			Python version <img alt="equation" src="/files/ftp_upload/63340/greater.png" />=3.5 
			Python module pandas 
			Python module fire 
			Python module tabulate 
			Python module Colorama</td>
		</tr>
		<tr>
			<td></td>
			<td></td>
			<td>https://github.com/Characterisation-Virtual-Laboratory/calculate_icv</td>
			<td></td>
		</tr>
		<tr>
			<td>MATLAB*</td>
			<td>2019 or newer</td>
			<td>https://au.mathworks.com/</td>
			<td>An academic license is required</td>
		</tr>
		<tr>
			<td>Singularity</td>
			<td>3.7 or newer</td>
			<td>https://www.sylabs.io/docs/</td>
			<td>Prefered for high performance computing (HPC) clusters</td>
		</tr>
		<tr>
			<td>SPM</td>
			<td>12</td>
			<td>http://www.fil.ion.ucl.ac.uk/spm/software/spm12/</td>
			<td>Make sure spm12 and all subfolders are in your MATLAB path</td>
		</tr>
		<tr>
			<td>SUIT Toolbox</td>
			<td>3.4</td>
			<td>http://www.diedrichsenlab.org/imaging/suit_download.htm</td>
			<td>Make sure you place SUIT toolbox in spm12/toolbox directory</td>
		</tr>
		<tr>
			<td>Troubleshooting manual and segmentation output examples</td>
			<td></td>
			<td>http://enigma.ini.usc.edu/protocols/imaging-protocols/</td>
			<td></td>
		</tr>
		<tr>
			<td>Tutorial manual and video</td>
			<td></td>
			<td>http://enigma.ini.usc.edu/protocols/imaging-protocols/</td>
			<td>Manual and accompanying live demonstration provide detailed step-by-step instructions on how to run the pipeline from start to finish.</td>
		</tr>
		<tr>
			<td>*Not freely available; an academic license is required</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
	</tbody>
</table>

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.

Cerebellum parcellation (ACAPULCO)
Quality control of cerebellum parcellated masks: 
The following examples demonstrate the ACAPULCO parcellated outputs and guide decision-making about a) the quality of the parcellated mask at the individual level and b) subsequent inclusion or exclusion of a particular lobule(s) from the statistical analyses. Ultimately, the decision to include or exclude a subject is subjective; examples of &#39;good parcellations,&#39;...

Watch this Scientific Journal Video about A Standardized Pipeline for Examining Human Cerebellar Grey Matter Morphometry using Structural Magnetic Resonance Imaging at JoVE.com

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

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 ...

a-standardized-pipeline-for-examining-human-cerebellar-grey-matter

Research

JoVE Journal

Neuroscience

3.8K Views.  Monash University. 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.

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

Magnetic Resonance Spectroscopy of live Drosophila melanogaster using Magic Angle Spinning

High-Resolution Magic Angle Spinning (HRMAS) proton magnetic resonance spectroscopy (1H-MRS) is a novel non-destructive technique that improves spectral line-widths and allows high-resolution spectra to be obtained from extracts, intact cells, cell cultures, and more importantly intact tissue to investigate relationships between metabolites and cellular processes. In vivo HRMAS 1H-MRS studies have yet to be reported in the live fruit fly Drosophila melanogaster. Drosophila, as a simpler genetic organism, allows the multiple biological functions and various evolutionarily conserved signaling pathways to be examined at the whole organism level and it is a useful model for investigating genetics and physiology. To this end, we developed and implemented an in vivo HRMAS 1H-MRS method to investigate live Drosophila at 14.1 T. Here, we outline an HRMAS 1H-MRS protocol for the molecular characterization of Drosophila with a conventional MR spectrometer equipped with an HRMAS probe. This technique is a novel, in vivo, non-destructive Drosophila metabolite measurement approach, which enables the identification of disease biomarkers and thus may contribute to novel therapeutic development.

Magnetic Resonance Spectroscopy of live Drosophila melanogaster using Magic Angle Spinning

This technique enables the use of high-resolution magic angle spinning proton MR spectroscopy (HRMAS 1H-MRS) for molecular characterization of live Drosophila melanogaster with a conventional 14.1 tesla spectrometer equipped with an HRMAS probe.

High-Resolution Magic Angle Spinning (HRMAS) proton magnetic resonance spectroscopy (1H-MRS) is a novel non-destructive technique that improves spectral ...

Multiple-mouse Neuroanatomical Magnetic Resonance Imaging

The field of mouse phenotyping with magnetic resonance imaging (MRI) is rapidly growing, motivated by the need for improved tools for characterizing and evaluating mouse models of human disease. MRI is an excellent modality for investigating genetically altered animals. It is capable of whole brain coverage, can be used in vivo, and provides multiple contrast mechanisms for investigating different aspects of neuranatomy and physiology. The advent of high-field scanners along with the ability to scan multiple mice simultaneously allows for rapid phenotyping of novel mutations.
Effective mouse MRI studies require attention to many aspects of experiment design. In this article, we will describe general methods to acquire quality images for mouse phenotyping using a system that images mice concurrently in shielded transmit/receive radio frequency (RF) coils in a common magnet (Bock et al., 2003). We focus particularly on anatomical phenotyping, an important and accessible application that has shown a high potential for impact in many mouse models at our imaging centre. Before we can provide the detailed steps to acquire such images, there are important practical considerations for both in vivo brain imaging (Dazai et al., 2004) and ex vivo brain imaging (Spring et al., 2007) that should be noted. These are discussed below.

Magnetic resonance imaging (MRI) has become an increasingly popular tool for examining the phenotype of genetically altered mice. This article illustrates the methods necessary to achieve high-throughput phenotyping of genetically altered mice using multiple-mouse MRI.

The field of mouse phenotyping with magnetic resonance imaging (MRI) is rapidly growing, motivated by the need for improved tools for characterizing and ...

High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain

Functional MRI (fMRI) is a widely used tool for non-invasively measuring correlates of human brain activity. However, its use has mostly been focused upon measuring activity on the surface of cerebral cortex rather than in subcortical regions such as midbrain and brainstem. Subcortical fMRI must overcome two challenges: spatial resolution and physiological noise. Here we describe an optimized set of techniques developed to perform high-resolution fMRI in human SC, a structure on the dorsal surface of the midbrain; the methods can also be used to image other brainstem and subcortical structures.
High-resolution (1.2 mm voxels) fMRI of the SC requires a non-conventional approach. The desired spatial sampling is obtained using a multi-shot (interleaved) spiral acquisition1. Since, T2* of SC tissue is longer than in cortex, a correspondingly longer echo time (TE ~ 40 msec) is used to maximize functional contrast. To cover the full extent of the SC, 8-10 slices are obtained. For each session a structural anatomy with the same slice prescription as the fMRI is also obtained, which is used to align the functional data to a high-resolution reference volume.
In a separate session, for each subject, we create a high-resolution (0.7 mm sampling) reference volume using a T1-weighted sequence that gives good tissue contrast. In the reference volume, the midbrain region is segmented using the ITK-SNAP software application2. This segmentation is used to create a 3D surface representation of the midbrain that is both smooth and accurate3. The surface vertices and normals are used to create a map of depth from the midbrain surface within the tissue4.
Functional data is transformed into the coordinate system of the segmented reference volume. Depth associations of the voxels enable the averaging of fMRI time series data within specified depth ranges to improve signal quality. Data is rendered on the 3D surface for visualization.
In our lab we use this technique for measuring topographic maps of visual stimulation and covert and overt visual attention within the SC1. As an example, we demonstrate the topographic representation of polar angle to visual stimulation in SC.

This article describes techniques to perform high-resolution functional magnetic resonance imaging with 1.2 mm sampling in human midbrain and subcortical structures using a 3T scanner. Use of these techniques to resolve topographic maps of visual stimulation in the human superior colliculus (SC) is given as an example.

Functional MRI (fMRI) is a widely used tool for non-invasively measuring correlates of human brain activity. However, its use has mostly been focused upon ...

The Use of Magnetic Resonance Spectroscopy as a Tool for the Measurement of Bi-hemispheric Transcranial Electric Stimulation Effects on Primary Motor Cortex Metabolism

Transcranial direct current stimulation (tDCS) is a neuromodulation technique that has been increasingly used over the past decade in the treatment of neurological and psychiatric disorders such as stroke and depression. Yet, the mechanisms underlying its ability to modulate brain excitability to improve clinical symptoms remains poorly understood 33. To help improve this understanding, proton magnetic resonance spectroscopy (1H-MRS) can be used as it allows the in vivo quantification of brain metabolites such as &#947;-aminobutyric acid (GABA) and glutamate in a region-specific manner 41. In fact, a recent study demonstrated that 1H-MRS is indeed a powerful means to better understand the effects of tDCS on neurotransmitter concentration 34. This article aims to describe the complete protocol for combining tDCS (NeuroConn MR compatible stimulator) with 1H-MRS at 3 T using a MEGA-PRESS sequence. We will describe the impact of a protocol that has shown great promise for the treatment of motor dysfunctions after stroke, which consists of bilateral stimulation of primary motor cortices 27,30,31. Methodological factors to consider and possible modifications to the protocol are also discussed.

This article aims to describe a basic protocol for combining transcranial direct current stimulation (tDCS) with proton magnetic resonance spectroscopy (1H-MRS) measurements to investigate the effects of bilateral stimulation on primary motor cortex metabolism.

Transcranial direct current stimulation (tDCS) is a neuromodulation technique that has been increasingly used over the past decade in the treatment of ...

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 ...

High-resolution Structural Magnetic Resonance Imaging of the Human Subcortex In Vivo and Postmortem

The focus of this study was to test the resolution limits of structural MRI of a postmortem brain compared to living human brains. The resolution of structural MRI in vivo is ultimately limited by physiological noise, including pulsation, respiration and head movement. Although imaging hardware continues to improve, it is still difficult to resolve structures on the millimeter scale. For example, the primary visual sensory pathways synapse at the lateral geniculate nucleus (LGN), a visual relay and control nucleus in the thalamus that normally is organized into six interleaved monocular layers. Neuroimaging studies have not been able to reliably distinguish these layers due their small size that are less than 1 mm thick.
The resolving limit of structural MRI, in a postmortem brain was tested using multiple images averaged over a long duration (~24 h). The purpose was to test whether it was possible to resolve the individual layers of the LGN in the absence of physiological noise. A proton density (PD)1 weighted pulse sequence was used with varying resolution and other parameters to determine the minimum number of images necessary to be registered and averaged to reliably distinguish the LGN and other subcortical regions. The results were also compared to images acquired in living human brains. In vivo subjects were scanned in order to determine the additional effects of physiological noise on the minimum number of PD scans needed to differentiate subcortical structures, useful in clinical applications.

High-resolution Structural Magnetic Resonance Imaging of the Human Subcortex In Vivo and Postmortem

Here we present a protocol to determine the minimum number images that needed to be registered and averaged to resolve subcortical structures and test whether the individual layers of the LGN could be resolved in the absence of physiological noise.

The focus of this study was to test the resolution limits of structural MRI of a postmortem brain compared to living human brains. The resolution of ...

A Magnetic Resonance Imaging Protocol for Stroke Onset Time Estimation in Permanent Cerebral Ischemia

MRI provides a sensitive and specific imaging tool to detect acute ischemic stroke by means of a reduced diffusion coefficient of brain water. In a rat model of ischemic stroke, differences in quantitative T1 and T2 MRI relaxation times (qT1 and qT2) between the ischemic lesion (delineated by low diffusion) and the contralateral non-ischemic hemisphere increase with time from stroke onset. The time dependency of MRI relaxation time differences is heuristically described by a linear function and thus provides a simple estimate of stroke onset time. Additionally, the volumes of abnormal qT1 and qT2 within the ischemic lesion increase linearly with time providing a complementary method for stroke timing. A (semi)automated computer routine based on the quantified diffusion coefficient is presented to delineate acute ischemic stroke tissue in rat ischemia. This routine also determines hemispheric differences in qT1 and qT2 relaxation times and the location and volume of abnormal qT1 and qT2 voxels within the lesion. Uncertainties associated with onset time estimates of qT1 and qT2 MRI data vary from &plusmn; 25 min to &plusmn; 47 min for the first 5 hours of stroke. The most accurate onset time estimates can be obtained by quantifying the volume of overlapping abnormal qT1 and qT2 lesion volumes, termed 'Voverlap' (&plusmn; 25 min) or by quantifying hemispheric differences in qT2 relaxation times only (&plusmn; 28 min). Overall, qT2 derived parameters outperform those from qT1. The current MRI protocol is tested in the hyperacute phase of a permanent focal ischemia model, which may not be applicable to transient focal brain ischemia.

A protocol for stroke onset time estimation in a rat model of stroke exploiting quantitative magnetic resonance imaging (qMRI) parameters is described. The procedure exploits diffusion MRI for delineation of the acute stroke lesion and quantitative T1 and T2 (qT1 and qT2) relaxation times for timing of stroke.

MRI provides a sensitive and specific imaging tool to detect acute ischemic stroke by means of a reduced diffusion coefficient of brain water. In a rat ...

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 ...

Standardized Data Acquisition for Neuromelanin-Sensitive Magnetic Resonance Imaging of the Substantia Nigra

The dopaminergic system plays a crucial role in healthy cognition (e.g., reward learning and uncertainty) and neuropsychiatric disorders (e.g., Parkinson's disease and schizophrenia). Neuromelanin is a byproduct of dopamine synthesis that accumulates in dopaminergic neurons of the substantia nigra. Neuromelanin-sensitive magnetic resonance imaging (NM-MRI) is a noninvasive method for measuring neuromelanin in those dopaminergic neurons, providing a direct measure of dopaminergic cell loss in the substantia nigra and a proxy measure of dopamine function. Although NM-MRI has been shown to be useful for studying various neuropsychiatric disorders, it is challenged by a limited field-of-view in the inferior-superior direction resulting in the potential loss of data from the accidental exclusion of part of the substantia nigra. In addition, the field is lacking a standardized protocol for the acquisition of NM-MRI data, a critical step in facilitating large-scale multisite studies and translation into the clinic. This protocol describes a step-by-step NM-MRI volume placement procedure and online quality control checks to ensure the acquisition of good-quality data covering the entire substantia nigra.

This protocol shows how to acquire neuromelanin-sensitive magnetic resonance imaging data of the substantia nigra.

The dopaminergic system plays a crucial role in healthy cognition (e.g., reward learning and uncertainty) and neuropsychiatric disorders (e.g., ...

Endovascular Perforation Model for Subarachnoid Hemorrhage Combined with Magnetic Resonance Imaging (MRI)

The endovascular filament perforation model to mimic subarachnoid hemorrhage (SAH) is a commonly used model - however, the technique can cause a high mortality rate as well as an uncontrollable volume of SAH and other intracranial complications such as stroke or intracranial hemorrhage. In this protocol, a standardized SAH mouse model is presented, induced by endovascular filament perforation, combined with magnetic resonance imaging (MRI) 24 h after operation to ensure the correct bleeding site and exclude other relevant intracranial pathologies. Briefly, C57BL/6J mice are anesthetized with an intraperitoneal ketamine/xylazine (70 mg/16 mg/kg body weight) injection and placed in a supine position. After midline neck incision, the common carotid artery (CCA) and carotid bifurcation are exposed, and a 5-0 non-absorbable monofilament polypropylene suture is inserted in a retrograde fashion into the external carotid artery (ECA) and advanced into the common carotid artery. Then, the filament is invaginated into the internal carotid artery (ICA) and pushed forward to perforate the anterior cerebral artery (ACA). After recovery from surgery, mice undergo a 7.0 T MRI 24 h later. The volume of bleeding can be quantified and graded via postoperative MRI, enabling a robust experimental SAH group with the option to perform further subgroup analyses based on blood quantity.

Endovascular Perforation Model for Subarachnoid Hemorrhage Combined with Magnetic Resonance Imaging MRI

Here we present a standardized SAH mouse model, induced by endovascular filament perforation, combined with magnetic resonance imaging (MRI) 24 h after operation to ensure the correct bleeding site and exclude other relevant intracranial pathologies.

The endovascular filament perforation model to mimic subarachnoid hemorrhage (SAH) is a commonly used model - however, the technique can cause a high ...