Name: a standardized pipeline for examining human cerebellar grey matter morphometry using structural magnetic resonance imaging
Uploaded: 2022-02-04T14:00:00
Description: Watch this Scientific Journal Video about A Standardized Pipeline for Examining Human Cerebellar Grey Matter Morphometry using Structural Magnetic Resonance Imaging at JoVE.com

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

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 ...

Diffusion Tensor Magnetic Resonance Imaging in Chronic Spinal Cord Compression

Chronic spinal cord compression is the most common cause of spinal cord impairment in patients with nontraumatic spinal cord damage. Conventional magnetic resonance imaging (MRI) plays an important role in both confirming the diagnosis and evaluating the degree of compression. However, the anatomical detail provided by conventional MRI is not sufficient to accurately estimate neuronal damage and/or assess the possibility of neuronal recovery in chronic spinal cord compression patients. In contrast, diffusion tensor imaging (DTI) can provide quantitative results according to the detection of water molecule diffusion in tissues. In the present study, we develop a methodological framework to illustrate the application of DTI in chronic spinal cord compression disease. DTI fractional anisotropy (FA), apparent diffusion coefficients (ADCs), and eigenvector values are useful for visualizing microstructural pathological changes in the spinal cord. Decreased FA and increases in ADCs and eigenvector values were observed in chronic spinal cord compression patients compared to healthy controls. DTI could help surgeons understand spinal cord injury severity and provide important information regarding prognosis and neural functional recovery. In conclusion, this protocol provides a sensitive, detailed, and noninvasive tool to evaluate spinal cord compression.

Here, we present a protocol for the application of diffusion tensor imaging parameters to evaluate spinal cord compression.

Chronic spinal cord compression is the most common cause of spinal cord impairment in patients with nontraumatic spinal cord damage. Conventional magnetic ...

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.