The authors would like to acknowledge support from the CAMH Foundation, thanks to Michael and Sonja Koerner, the Kimel Family, and the Paul E. Garfinkel New Investigator Catalyst Award. This project was funded by the Fonds de Recherches Sant&#233; Qu&#233;bec, the Canadian Institutes of Health Research (CIHR), the Natural Sciences and Engineering Research Council of Canada, the Weston Brain Institute, the Alzheimer&#39;s Society of Canada, and the Micheal J. Fox Foundation for Parkinson&#39;s Research (MMC), as well as CIHR, the Ontario Mental Health Foundation, NARSAD, and the National Institute of Mental Health (R01MH099167) (ANV). The authors would also like to thank Anusha Ravichandran for assistance acquiring the images.

Study Participants
The protocol in this manuscript was developed for five representative high-resolution images collected from healthy volunteers (3F, 2M; age 29-57, avg. 37) who were free of neurological and neuropsychiatric disorders and cases of severe head trauma. All subjects were recruited at the Centre for Addiction and Mental Health (CAMH). The study was approved by the CAMH Research Ethics Board and was conducted in keeping with the Declaration of Helsinki. All subjects provided written, informed consent for data acquisition and sharing. For details about the acquisition sequence used to collect these images, please refer to Winterburn et al., 2013 and Park et al., 2014.26,34 Images for all five subjects were checked for quality and retained. The hippocampus spanned an average of 118 coronal slices in these images.
1. Software Set-up
<ol>
	<li>Open Display: From the terminal using the following command: Display image_name.mnc -label label_name.mnc. The program will open 3 windows: 3D visualization window, 3-orientation image viewing window, and a navigation window. The terminal will also be used to run the program. Enlarge the coronal view, as the segmentations will be performed coronally. Zoom in on the hippocampus. Select F (Segmenting) in the navigation window. Select F (XY Radius:0.1). The terminal window will prompt for the user to &#8220;Enter xy brush size: &#8221;. Set to 0.1. This will set the size of your paintbrush. The user can now begin drawing the hippocampus onto the MR image.</li>
</ol>
2. Whole Hippocampus Manual Segmentation
<ol>
	<li>Set-up: Using a T1-weighted image, scroll to the anterior-most coronal slice of the hippocampus. To advance slices in the anterior direction, use the &#39;+&#39; key; use the &#39;-&#39; key to move in the posterior direction.</li>
	<li>Slice A: Anterior-Most Slice: Using the right-click on the mouse, draw the outer-most border of the hippocampal grey matter where it meets the surrounding temporal lobe white matter and use the high-intensity white matter of the alveus to assist with the superior border, where the hippocampus meets the amygdala12,22. Use the E (Label Fill) key in the segmentation menu of the navigation window to fill in the label inside the border. Continue to apply these borders throughout the anterior hippocampal head.</li>
	<li>Slice B: Hippocampal Head 1 (Figure 1B):
	<ol>
		<li>Superior, inferior, lateral, medial borders: Continue to draw the borders as described in step 2.2, using the white matter of the temporal lobe and alveus as a guide.</li>
		<li>Supero-medial border: For this, using the axial view, draw a horizontal line from the anterior edge of the lateral hippocampus29, and include anything below this line as hippocampus. NOTE: The supero-medial border becomes more ambiguous in these slices, where the grey matter of the hippocampus blends with the grey matter of the amygdala.</li>
	</ol>
	</li>
	<li>Slice C: Hippocampal Head 2 with Dentations: Depending on the subject, the dentations of the hippocampus may be visible for 3-4 slices (typically, they are more visible on T2-weighted versus T1-weighted images). In these slices, continue to use the white matter of the alveus and temporal lobe to guide border segmentation12,22. For further details, follow steps 2.5.1-2.5.2.</li>
	<li>Slice D: Hippocampal Head 3:
	<ol>
		<li>Superior, inferior, lateral, medial borders: Draw the inferior border of the hippocampus at the white matter of the temporal lobe, the lateral border at the inferior horn of the lateral ventricle, the superior border, following the curve of the dentations, at the white matter of the alveus/fimbria, and the medial border at the hypointense region of the ambient cistern12,22.</li>
		<li>Supero-medial and infero-medial borders: Continue to define the supero-medial border as described in step 2.3.2. Draw the inferior portion of the medial border where the hippocampus thins slightly and extends into the mildly hyperintense grey matter of the entorhinal cortex12,22.</li>
	</ol>
	</li>
	<li>Slice E: Hippocampal Head 4 with Uncus: Continue to draw the inferior, lateral, and superior borders described in steps 2.5.1-2.5.2. Include the uncus (which is located medal to the main body of the hippocampus and is surrounded by low-intensity CSF) in the hippocampal segmentation12,22.</li>
	<li>Slice F: Hippocampal Body: Continue to draw the inferior, lateral, medial, and superior borders described in steps 2.5.1-2.5.2. Draw the infero-medial border at the point where the hippocampus thins as it transitions to entorhinal cortex/para-hippocampal gyrus12,22. Do not include the low-intensity CSF of the vestigial hippocampal sulcus in the segmentation.</li>
	<li>Slice G: Hippocampal Tail 1: Begin segmenting hippocampal tail-type slices when the crus of the fornix is first visible. Exclude the fascicular gyrus (a grey matter structure which blends with the hippocampus in parts of the hippocampal tail) from the segmentation by extrapolating the shape of the fascicular gyrus into the hippocampal tail from more anterior slices12,22. This extrapolation is only possible for 2-3 slices, after which the two structures cannot be accurately distinguished; at this point, treat all visible grey matter in this area as hippocampus .</li>
	<li>Slice H: Hippocampal Tail 2: Segment the low-intensity grey matter of the posterior hippocampal tail from the surrounding high-intensity white matter.</li>
	<li>Slice I: Posterior-Most Slice: Segment the small remaining area of hippocampal grey matter from the surrounding white matter of the temporal lobe.</li>
</ol>
3. Hippocampal Subfield Manual Segmentation
<ol>
	<li>Set-up: Using a T2-weighted image, scroll to the anterior-most coronal slice of the hippocampus (as in step 2.1). To change the color of the paintbrush, select D (Set Paint Lbl:) on the segmenting menu in the navigation window. The command terminal will prompt: &#8220;Enter current paint label:&#8221;. Enter a number between 1 and 255. Each number corresponds to a different label color.</li>
	<li>Slice A: Anterior-Most Slice: Since subfield divisions are not yet visible in the anterior-most slice, draw a line dividing the visible hippocampal grey matter along its longest visible axis (which is not necessarily parallel to any of the cardinal axes) into two equal sections to approximate the true anatomy12,22. Label the superior of these two sections as CA1 and the inferior section as subiculum by choosing a different colored label for each subfield23,35.</li>
	<li>Slice B: Hippocampal Head 1: Label the low-intensity area in the middle of the hippocampal formation as SR/SL/SM13,37. When the bend along the inferior edge of the hippocampus becomes clear, use this landmark as the lateral border separating the subiculum from the CA112,22. Continue to follow the longest axis of the hippocampus to draw the CA1-subiculum border on the supero-medial tip37.</li>
	<li>Slice C: Hippocampal Head 2 with Dentations:
	<ol>
		<li>SR/SL/SM, CA4/DG, and subiculum: Label the SR/SL/SM, CA4/DG, and subiculum as described for slice D (step 3.5.1).</li>
		<li>CA2/CA3 and CA1: Define the border between CA1 and CA2/CA3 as a 45&#186; angle line extending in the supero-lateral direction from the most supero-lateral edge of the SR/SL/SM12,22. Extend the CA2/CA3 medially along the superior edge to the trough between the dentations12,22. Label the rest of the superior edge as CA112,22.</li>
	</ol>
	</li>
	<li>Slice D: Hippocampal Head 3
	<ol>
		<li>SR/SL/SM, CA4/DG, and subiculum: Label the dark SR/SL/SM band first, which will follow the curve of the CA137. Label any high-intensity grey matter inside of the SR/SL/SM as CA4/DG12,22,23,35,37. This may not be a continuous region, as in Figure 2C. Continue to define the subiculum-CA1 border using the bend in the inferior hippocampus12,22.</li>
		<li>CA2/CA3 and CA1: Continue to define the CA1 and CA2/CA3 border as in step 3.4.2. Extend the CA2/CA3 medially halfway along the superior edge of the hippocampus12,22 and label the other half of the superior edge as CA112,22.</li>
		<li>Supero-medial hippocampal head: In this slice, divide the supero-medial hippocampal head vertically in half. Label the medial half as SR/SL/SM12. Divide the lateral half in half again, this time horizontally. Label the superior portion as CA4/DG and the inferior portion as CA2/CA312.</li>
	</ol>
	</li>
	<li>Slice E: Hippocampal Head 4 with Uncus
	<ol>
		<li>Lateral hippocampal head (subiculum): In the lateral portion of these slices, define the subiculum-CA1 border as a vertical line extending in the inferior direction from the most medial edge of the CA4/DG12,22.</li>
		<li>Lateral hippocampal head (CA1, CA2/CA3, CA4/DG, SR/SL/SM.): Define the CA1-CA2/CA3 border in the same way as in step 3.4.2. Continue to label the SR/SL/SM as the low intensity region following the curve of the CA regions. Label the CA4/DG as the center cavity inside the SR/SL/SM, as in step 3.5.1.</li>
		<li>Uncal hippocampal head (SR/SL/SM): Label the uncus of the hippocampus for approximately 10 slices as the hippocampal head transitions into the hippocampal body. In the uncus, label the low intensity region in the center as SR/SL/SM (when this is difficult to see, approximate the anatomy by segmenting a line 2-3 voxels wide up the center of the uncus)12.</li>
		<li>Uncal hippocampal head (CA2/CA3, CA4/DG): Draw a line at the superior edge of the SR/SL/SM section along infero-lateral/supero-medial axis of the uncus. Label all grey matter above this line as CA2/CA312. Label any unlabeled grey matter below this line (on either side of the SR/SL/SM) as CA4/DG12.</li>
	</ol>
	</li>
	<li>Slice F: Hippocampal Body: Continue to apply the borders described in step 3.6.1-3.6.2.</li>
	<li>Slice G: Hippocampal Tail 1: Continue to apply the rules described in step 3.6.1-3.6.2. The subiculum-CA1 border becomes a 45&#186; angle line extending in the infero-medial direction from the medial edge of the CA4/DG12,22.</li>
	<li>Slice H: Hippocampal Tail 2: Once the fascicular gyrus can no longer be distinguished from the hippocampal formation, label the entire outer layer as CA1, the low-intensity area inside of this as SR/SL/SM (as in previous slices), and any remaining grey matter in the middle as CA4/DG12,22.</li>
	<li>Slice I: Posterior-Most Slice: Once the dark SR/SL/SM is no longer visible in the center of the hippocampal formation, label the entire structure as CA112,22.</li>
</ol>
4. Protocol Reliability
<ol>
	<li>Resegment either the right or left hippocampus of each subject after waiting approximately one month from performing the original segmentation. Segment all of the subfields along the entire anterior-posterior length of the hippocampus, trying to follow the protocol rules as consistently as is possible.</li>
	<li>Calculate the Dice&#8217;s kappa between the original and resegmented volumes: 
	<img alt="Equation 1" src="/files/ftp_upload/51861/51861eq1.jpg" /> 
	where k=Dice&#8217;s kappa and A and B are label volumes.</li>
</ol>

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Neurosci. 7 (12), 952-966 (2006).</li><li>van Leemput, K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Automated+segmentation+of+hippocampal+subfields+from+ultra-high+resolution+in+vivo.">Automated segmentation of hippocampal subfields from ultra-high resolution in vivo.</a> MRI. Hippocampus. 19 (6), 549-557 (2009).</li><li>Winterburn, J. L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+novel+in+vivo+atlas+of+human+hippocampal+subfields+using+high-resolution+3+T+magnetic+resonance+imaging.">A novel in vivo atlas of human hippocampal subfields using high-resolution 3 T magnetic resonance imaging.</a> NeuroImage. 74, 254-265 (2013).</li><li>Wisse, L. E. M., Gerritsen, L., Zwanenburg, J. J. M., Kuijf, H. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Subfields+of+the+hippocampal+formation+at+7+T+MRI:+in+vivo.+volumetric+assessment.">Subfields of the hippocampal formation at 7 T MRI: in vivo. volumetric assessment.</a> NeuroImage. 61 (4), 1043-1049 (2012).</li><li>Yelnik, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+three-dimensional,+histological+and+deformable+atlas+of+the+human+basal+ganglia.+I.+Atlas+construction+based+on+immunohistochemical+and+MRI+data.">A three-dimensional, histological and deformable atlas of the human basal ganglia. I. Atlas construction based on immunohistochemical and MRI data.</a> NeuroImage. 34 (2), 618-638 (2007).</li><li>Yushkevich, P. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+high-resolution+computational+atlas+of+the+human+hippocampus+from+postmortem+magnetic+resonance+imaging+at+9.4+T.">A high-resolution computational atlas of the human hippocampus from postmortem magnetic resonance imaging at 9.4 T.</a> NeuroImage. 44 (2), 385-398 (2009).</li><li>Yushkevich, P. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantitative+Comparison+of+21+Protocols+for+Labeling+Hippocampal+Subfields+and+Parahippocampal+Subregions+in+In+Vivo+MRI:+Towards+a+Harmonized+Segmentation+Protocol.">Quantitative Comparison of 21 Protocols for Labeling Hippocampal Subfields and Parahippocampal Subregions in In Vivo MRI: Towards a Harmonized Segmentation Protocol.</a> NeuroImage. , (2015).</li></ol>

The authors have no conflicts of interest to declare.

Hippocampal subfield segmentation in MR images is well-represented in the literature. However, existing protocols exclude portions of the hippocampus20,23,33,35, apply only to fixed images37, or require ultra-high field scanners for image acquisition35,37. This manuscript offers a segmentation protocol that includes five major subdivisions (CA1, CA2/CA3, CA4/dentate gyrus, SR/SL/SM, and subiculum) of the hippocampus and spans the entire anterior-posterior length of the structure. The comp...

The hippocampus is a widely studied medial temporal lobe structure that is associated with episodic memory, spatial navigation, and other cognitive functions10,31. Its role in neurodegenerative and neuropsychiatric disorders such as Alzheimer&#8217;s disease, schizophrenia, and bipolar disorder is well-documented4,5,18,24,30. The goal of this manuscript is to provide additional detail to the manual segmentation protocol published previously34 for human hippocampal subfields on high-resolution magnetic resonance (MR) images acquired at 3T. Additionally, the video component accompanying this manuscript will provide further assistance to researchers who wish to implement the protocol on their own datasets.
The hippocampus can be divided into subfields based on cytoarchitectonic differences observed in histologically-prepared post-mortem specimens12,22. Such post-mortem specimens define the ground truth for the identification and study of hippocampal subfields; however preparations of this nature require specialized skills and equipment for staining, and are limited by the availability of fixed tissue, especially in diseased populations. In vivo imaging has the advantage of a much larger pool of subjects, and also presents the opportunity for follow-up studies and observing changes in populations. Although it has been shown that signal intensities in T2-weighted ex vivo MR images reflect cellular density13, it is still difficult to identify undisputed borders between subfields using solely MR signal intensities. As such, a number of different approaches for identifying histology-level detail on MR images have been developed.
Some groups have made efforts to reconstruct and digitize histological datasets and then use these reconstructions along with image registration techniques to localize hippocampal subfield neuroanatomy on in vivo MR1,2,8,9,14,15,17,32. Although this is an effective technique for mapping a version of the histological ground truth directly onto MR images, reconstructions of this nature are difficult to complete. Projects such as these are limited by the availability of intact medial temporal lobe specimens, histological techniques, data loss during histological processing, and the fundamental morphological inconsistencies between fixed and in vivo brains. Other groups have used high-field scanners (7T or 9.4T) in an effort to acquire in vivo or ex vivo images with a small enough (0.20-0.35 mm isotropic) voxel size to visualize spatially localized differences in image contrast that are used to infer boundaries between subfields35,37. Even at 7T-9.4T and with such a small voxel size, the cytoarchitectonic characteristics of hippocampal subfields are not visible. As such, manual segmentation protocols have been developed that approximate the known histological boundaries on MR images. These protocols determine subfield boundaries by interpreting local image contrast differences and defining geometric rules (such as straight lines and angles) relative to visible structures. Although images taken at a high field strength are able to offer detailed insight into hippocampal subfields, high-field scanners are not yet common in clinical or research settings, so 7T and 9.4T protocols currently have limited applicability. Similar protocols have been developed for images collected on 3T and 4T scanners11,20,21,23,24,25,28,33. Many of these protocols are based on images with sub-1mm voxels voxel dimensions in the coronal plane, but have large slice thicknesses (0.8-3 mm)11,20,21,23,25,28,33 or large inter-slice distances20,28, both of which result in a significant measurement bias in the estimation of volumes of the individual subfields. Additionally, many of the existing 3T protocols exclude subfields in all or part of the hippocampal head or tail20,23,25,33 or do not provide detailed segmentations of important substructures (i.e., combine the DG with CA2/CA3 or do not include the strata radiatum/lacunosum/moleculare of the CA)11,20,21,23,24,25,28,33. There is therefore a need in the field for a detailed description of a protocol that can reliably identify relevant subfields throughout the head, body, and tail of the hippocampus that is based on a scanner commonly available in clinical and research settings. Efforts are currently underway by the Hippocampal Subfields Group (www.hippocampalsubfields.com) to harmonize the hippocampal subfield segmentation process between laboratories, similar to an existing harmonization effort for whole hippocampal segmentation6, and an initial paper comparing 21 existing protocols was recently published38. The work from this group will further elucidate optimal segmentation procedures.
This manuscript provides detailed written and video instructions for reliably implementing the hippocampal subfield segmentation protocol described previously by Winterburn and colleagues34 on high-resolution 3T MR images. The protocol has been implemented on five images of healthy controls for the whole hippocampus and five hippocampal subfields (CA1, CA2/CA3, CA4/dentate gyrus, strata radiatum/lacunosum/moleculare, and subiculum). These segmented images are available to the public online (cobralab.ca/atlases/Hippocampus). The protocol and the segmented images will be useful for groups who wish to study detailed hippocampal neuroanatomy in MR images.

<table border="0">
	<tbody>
		<tr>
			<td>Discovery MR750 3T</td>
			<td>GE</td>
			<td>NA</td>
			<td>Or equivalent 3T scanner</td>
		</tr>
		<tr>
			<td>Minc Tool Kit</td>
			<td>McConnell Brain Imaging Center, Montreal Neurological Institute</td>
			<td>NA</td>
			<td>Open source: http://www.bic.mni.mcgill.ca/ServicesSoftware/ServicesSoftwareMincToolKit</td>
		</tr>
	</tbody>
</table>

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.

Results from the protocol reliability test are summarized in Table 2. For the whole bilateral hippocampus, mean spatial overlap as measured by Dice&#8217;s kappa is 0.91 and ranges from 0.90 - 0.92. Subfield kappa values range from 0.64 (CA2/CA3) to 0.83 (CA4/dentate gyrus). Mean volumes for all subfields and the whole hippocampus are reported in Table 3. Volumes for the whole hippocampus range from 2456.72-3325.02 mm3. The CA2/CA3 is the smallest subfield at 208.33 mm3</...

Watch this Scientific Journal Video about High-resolution In Vivo Manual Segmentation Protocol for Human Hippocampal Subfields Using 3T Magnetic Resonance Imaging at JoVE.com

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.

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 ...

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9.2K vues.  University of Toronto. 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. 

Vidéo: High-resolution In Vivo Manual Segmentation Protocol for Human Hippocampal Subfields Using 3T 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 ...

A Rapid Approach to High-Resolution Fluorescence Imaging in Semi-Thick Brain Slices

A fundamental goal to both basic and clinical neuroscience is to better understand the identities, molecular makeup, and patterns of connectivity that are characteristic to neurons in both normal and diseased brain. Towards this, a great deal of effort has been placed on building high-resolution neuroanatomical maps1-3. With the expansion of molecular genetics and advances in light microscopy has come the ability to query not only neuronal morphologies, but also the molecular and cellular makeup of individual neurons and their associated networks4. Major advances in the ability to mark and manipulate neurons through transgenic and gene targeting technologies in the rodent now allow investigators to 'program' neuronal subsets at will5-6. Arguably, one of the most influential contributions to contemporary neuroscience has been the discovery and cloning of genes encoding fluorescent proteins (FPs) in marine invertebrates7-8, alongside their subsequent engineering to yield an ever-expanding toolbox of vital reporters9. Exploiting cell type-specific promoter activity to drive targeted FP expression in discrete neuronal populations now affords neuroanatomical investigation with genetic precision.
Engineering FP expression in neurons has vastly improved our understanding of brain structure and function. However, imaging individual neurons and their associated networks in deep brain tissues, or in three dimensions, has remained a challenge. Due to high lipid content, nervous tissue is rather opaque and exhibits auto fluorescence. These inherent biophysical properties make it difficult to visualize and image fluorescently labelled neurons at high resolution using standard epifluorescent or confocal microscopy beyond depths of tens of microns. To circumvent this challenge investigators often employ serial thin-section imaging and reconstruction methods10, or 2-photon laser scanning microscopy11. Current drawbacks to these approaches are the associated labor-intensive tissue preparation, or cost-prohibitive instrumentation respectively.
Here, we present a relatively rapid and simple method to visualize fluorescently labelled cells in fixed semi-thick mouse brain slices by optical clearing and imaging. In the attached protocol we describe the methods of: 1) fixing brain tissue in situ via intracardial perfusion, 2) dissection and removal of whole brain, 3) stationary brain embedding in agarose, 4) precision semi-thick slice preparation using new vibratome instrumentation, 5) clearing brain tissue through a glycerol gradient, and 6) mounting on glass slides for light microscopy and z-stack reconstruction (Figure 1).
For preparing brain slices we implemented a relatively new piece of instrumentation called the 'Compresstome' VF-200 (http://www.precisionary.com/products_vf200.html). This instrument is a semi-automated microtome equipped with a motorized advance and blade vibration system with features similar in function to other vibratomes. Unlike other vibratomes, the tissue to be sliced is mounted in an agarose plug within a stainless steel cylinder. The tissue is extruded at desired thicknesses from the cylinder, and cut by the forward advancing vibrating blade. The agarose plug/cylinder system allows for reproducible tissue mounting, alignment, and precision cutting. In our hands, the 'Compresstome' yields high quality tissue slices for electrophysiology, immunohistochemistry, and direct fixed-tissue mounting and imaging. Combined with optical clearing, here we demonstrate the preparation of semi-thick fixed brain slices for high-resolution fluorescent imaging.

Here we describe a rapid and simple method to image fluorescently labeled cells in semi-thick brain slices. By fixing, slicing, and optically clearing brain tissue we describe how standard epifluorescent or confocal imaging can be used to visualize individual cells and neuronal networks within intact nervous tissue.

A fundamental goal to both basic and clinical neuroscience is to better understand the identities, molecular makeup, and patterns of connectivity that are ...

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 ...

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

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

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

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

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 ...

Metabolomic Analysis of Rat Brain by High Resolution Nuclear Magnetic Resonance Spectroscopy of Tissue Extracts

Studies of gene expression on the RNA and protein levels have long been used to explore biological processes underlying disease. More recently, genomics and proteomics have been complemented by comprehensive quantitative analysis of the metabolite pool present in biological systems. This strategy, termed metabolomics, strives to provide a global characterization of the small-molecule complement involved in metabolism. While the genome and the proteome define the tasks cells can perform, the metabolome is part of the actual phenotype. Among the methods currently used in metabolomics, spectroscopic techniques are of special interest because they allow one to simultaneously analyze a large number of metabolites without prior selection for specific biochemical pathways, thus enabling a broad unbiased approach. Here, an optimized experimental protocol for metabolomic analysis by high-resolution NMR spectroscopy is presented, which is the method of choice for efficient quantification of tissue metabolites. Important strengths of this method are (i) the use of crude extracts, without the need to purify the sample and/or separate metabolites; (ii) the intrinsically quantitative nature of NMR, permitting quantitation of all metabolites represented by an NMR spectrum with one reference compound only; and (iii) the nondestructive nature of NMR enabling repeated use of the same sample for multiple measurements. The dynamic range of metabolite concentrations that can be covered is considerable due to the linear response of NMR signals, although metabolites occurring at extremely low concentrations may be difficult to detect. For the least abundant compounds, the highly sensitive mass spectrometry method may be advantageous although this technique requires more intricate sample preparation and quantification procedures than NMR spectroscopy. We present here an NMR protocol adjusted to rat brain analysis; however, the same protocol can be applied to other tissues with minor modifications.

The neurochemistry of mammalian brain is changed in many neurological and systemic diseases. Characteristic profiles of cerebral metabolites can be efficiently obtained based on crude extracts of brain tissue. To this end, high-resolution NMR spectroscopy is employed, enabling detailed quantitative analysis of metabolite concentrations (metabolomics).

Studies of gene expression on the RNA and protein levels have long been used to explore biological processes underlying disease. More recently, genomics ...

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 ...

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 ...