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>...

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 ...

Our pipeline uses state-of-the-art approaches for quantifying the volume of cerebellar subunits using human structural magnetic resonance images. The process includes anatomical parcellation, voxel-based morphometry, and quality control processes. Our standardized pipeline is mostly automated, available in Docker and Singularity format, and has broad applicability to a range of neurological diseases.To begin, ensure that Docker or Singularity, MATLAB, and SPM12 are installed. Then, using the make directory command in the command line, create folders in the working directory and label them acapulco, suit, and freesurfer. Next, in the acapulco directory, create an output folder.In the output folder, create a directory for each subject in the study containing the t1-weighted image in the NIFTI gz format. For anatomical cerebellar parcellation, download the acapulco container, then download the relevant scripts and containers required to run acapulco. Then place the acapulco Docker or Singularity container, contents of the QCs_scripts archive, and the RCIF container or calculate_dicv.tar file in the acapulco directory. Next, open a terminal and using Singularity, type the indicated command to run the acapulco container on a single image. Wait for five minutes for processing to complete.Then, loop across all subjects or scans in the cohort. After processing, look for the files generated in the subject-specific folders. Identify the parcellated cerebellum mask in original and volumes for each of the 28 subunits generated by acapulco.Then, from the pics directory, identify representative sagittal, axial, and coronal images. For statistical outlier detection and quality control, ensure that the contents of QC scripts are in the acapulco directory. Then, using Singularity, type the indicated command.For examining the QC images generated by acapulco, open QC_Images. html in a web browser and quickly scroll through the images to identify obvious failures or systematic issues. Note the subject IDs of failed or suspect parcellated images for follow up.Next, open Plots_for_Outliers. html to check the box plots for quantitative statistical outliers. Identify the outliers denoted by a 1 in the relevant column in the Outliers.csv file. and note the total number of segments identified as outliers for each subject in the final column in Outliers.csv. Manually inspect each image having one or more outliers.If using a standard NIFTI image viewer, overlay the acapulco mask onto the original t1-weighted image to check the quality of the parcellation slice by slice. If one or more parcellated regions need to be excluded from the final data set, exclude this parcellation from the analysis by replacing the volume estimate with NA in the corresponding cell of that subject. If parcellation errors result in some of the cerebellum being excluded from the mask, immediately exclude the subject from further analyses.For voxel-based morphometry analyses using SUIT, ensure that the SPM12 folder and all sub-folders are in the MATLAB path. Additionally, ensure that the enigma_suit scripts are saved in the SPM12 toolbox directory and added to the MATLAB path. To check the MATLAB path, type pathtool in the MATLAB command window and click Add with Subfolders to add the relevant folders.Next, using the graphical user interface, type suit_enigma_all in the MATLAB command window to run the SUIT pipeline for one or more subjects. In the first popup window, select the subject folders from the acapulco output directory to include in the analysis. Click on the individual folders on the right side of the window or right-click and select all.Then, press Done. In the second popup window, select the SUIT directory. To call the function from the MATLAB command line for a single subject, type the indicated command.When running SUIT from the command line, if the SPM12 or enigma_suit directories are not permanently saved to the MATLAB path, that step must be added into the command line. To call the function from the terminal window outside of MATLAB for a single subject, type the shown command. Check each subject's folder for the final outputs.To visually inspect the normalized modulated images for major failures, type spm_display_4d in MATLAB. Next, to select the required images, navigate to the SUIT directory and type the indicated command in the filter box. Then, press the Recursive button, followed by Done.Scroll through the images to ensure they are all well aligned. Next, to check spatial covariance for outliers, type check_spatial_cov in MATLAB. Then, select the modulated images from earlier and when prompted, set Prop.scaling to yes, Variable to covariance out to no, Slice to 48, and Gap to 1. Finally, look at the box plot displaying the mean spatial covariance of each image relative to all others in the sample. Identify the data points that are more than two standard deviations below the mean in the MATLAB command window.For these data points, inspect the appropriate images in the SUIT folder for artifacts, image quality issues. or pre-processing errors. Shown here is a heavily atrophied cerebellum from a spino-cerebellar ataxia 2 patient.Despite the tissue loss seen around the edges, SUIT has warped this image to the template quite well. This would not warrant an exclusion. Here, there is a clear gradient from top to bottom of the cerebellum and the image is quite scrappy, warranting an exclusion.Finally, in this example, the masking has not worked well. The edges are not clean and the image is smoother than those typically coming out of the SUIT pipeline. This would warrant an exclusion.Examples of good parcellations, including healthy and heavily atrophied cerebella, are shown here, while examples of misparcellations with subtle over-and under-inclusions of individual cerebellum lobules can be seen here. These types of errors generally require the exclusion of the individual lobules that are affected, while the remainder of the parcellated cerebellum can be retained. In contrast, global failures require a complete exclusion of the subject.When acapulco was run on a sample of 31 people with Friedreich ataxia and 37 healthy controls, the left lobule IX and right lobule Crus I had the highest exclusion rates. Comparison of volumes of 28 cerebellar anatomical lobules in Friedreich ataxia and healthy control individuals showed significantly reduced white matter in the corpus medullare in Friedreich ataxia. There were no other significant between-group differences.Examples of well-aligned images from both healthy controls and Friedreich ataxia are shown here, while examples of exclusions can be seen here. After testing the spatial covariance of all normalized images, two scans were detected as statistical outliers based on their mean spatial covariance with the rest of the sample. Non-parametric permutation tests were carried out in SNPM to test for significant between-group differences in cerebellum gray matter volumes.Results from the SUIT analysis showed that Friedreich ataxia patients had significantly reduced gray matter volume in bilateral anterior lobule I to V, and in medial posterior lobe regions including Vermis VI and Vermis IX.It is crucial to manually check the cerebellar masks to ensure that full cerebellar coverage has been achieved, and to also inspect the masks for over-and under-inclusions of cerebellar lobules. This pipeline facilitates multi-site group-level statistical analyses to be performed that are interested in looking at cerebellum gray matter structure. Other techniques, such as functional connectivity, can also be used to explore connectivity between individual cerebellar lobules and the cerebrum.

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Neuroscience

3.7K visualizações.  Monash University. Nosso oleoduto usa abordagens de última geração para quantificar o volume de subunidades cerebelares usando imagens de ressonância magnética estrutural humana. O processo inclui parcelamento anatômico, morfometria baseada em voxel e processos de controle de qualidade. Nosso pipeline padronizado é principalmente automatizado, disponível no formato Docker e Singularity, e tem ampla aplicabilidade a uma série de doenças neurológicas.Para começar, certifique-se de que Docker ou...