Name: automated segmentation of cortical grey matter from t1-weighted mri images
Uploaded: 2019-01-07T14:00:00
Description: Watch this Scientific Journal Video about Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images at JoVE.com

We wish to thank all those at the CHDI/High Q Foundation responsible for the TRACK-HD study; in particular, Beth Borowsky, Allan Tobin, Daniel van Kammen, Ethan Signer, and Sherry Lifer. The authors also wish to extend their gratitude to the TRACK-HD study participants and their families. This work was undertaken at UCLH/UCL, which received a proportion of funding from the Department of Health&#39;s National Institute for Health Research Biomedical Research Centres funding scheme. S.J.T. acknowledges support of the National Institute for Health Research through the Dementias and Neurodegenerative Research Network, DeNDRoN.
TRACK-HD Investigators: 
C. Campbell, M. Campbell, I. Labuschagne, C. Milchman, J. Stout, Monash University, Melbourne, VIC, Australia; A. Coleman, R. Dar Santos, J. Decolongon, B. R. Leavitt, A. Sturrock, University of British Columbia, Vancouver, BC, Canada; A. Durr, C. Jauffret, D. Justo, S. Lehericy, C. Marelli, K. Nigaud, R. Valabr&#232;gue, ICM Institute, Paris, France; N. Bechtel, S. Bohlen, R. Reilmann, University of M&#252;nster, M&#252;nster, Germany; B. Landwehrmeyer, University of Ulm, Ulm, Germany; S. J. A. van den Bogaard, E. M. Dumas, J. van der Grond, E. P. &#39;t Hart, R. A. Roos, Leiden University Medical Center, Leiden, Netherlands; N. Arran, J. Callaghan, D. Craufurd, C. Stopford, University of Manchester, Manchester, United Kingdom; D. M. Cash, IXICO, London, United Kingdom; H. Crawford, N. C. Fox, S. Gregory, G. Owen, N. Z. Hobbs, N. Lahiri, I. Malone, J. Read, M. J. Say, D. Whitehead, E. Wild, University College London, London, United Kingdom; C. Frost, R. Jones, London School of Hygiene and Tropical Medicine, London, United Kingdom; E. Axelson, H. J. Johnson, D. Langbehn, University of Iowa, IA, United States; and S. Queller, C. Campbell, Indiana University, IN, United States.

Note: Ensure that all images are in NifTI format. Conversion to NifTI is not covered here.
1. Segmentation via SPM 8: Unified Segment
NOTE: This procedure is performed via the SPM8 GUI which operates within Matlab. The SPM8 guide provides further detail and can be found at: http://www.fil.ion.ucl.ac.uk/spm/doc/spm8_manual.pdf.
<ol>
	<li>Make sure that SPM8 is installed and set in the software path.</li>
	<li>SPM segmentation is performed using a GUI. To o...

<ol>
	<li>Katuwal, G. 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=Inter-Method+Discrepancies+in+Brain+Volume+Estimation+May+Drive+Inconsistent+Findings+in+Autism.">Inter-Method Discrepancies in Brain Volume Estimation May Drive Inconsistent Findings in Autism.</a> Frontiers in Neuroscience. 10, 439 (2016).</li><li>Johnson, E. B., 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=Recommendations+for+the+Use+of+Automated+Gray+Matter+Segmentation+Tools:+Evidence+from+Huntington's+disease.">Recommendations for the Use of Automated Gray Matter Segmentation Tools: Evidence from Huntington's disease.</a> Frontiers in Neurology. 8, 519 (2017).</li><li>Schwarz, C. G., 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+large-scale+comparison+of+cortical+thickness+and+volume+methods+for+measuring+Alzheimer's+disease+severity.">A large-scale comparison of cortical thickness and volume methods for measuring Alzheimer's disease severity.</a> NeuroImage: Clinical. 11, 802-812 (2016).</li><li>Clarkson, M. 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+comparison+of+voxel+and+surface+based+cortical+thickness+estimation+methods.">A comparison of voxel and surface based cortical thickness estimation methods.</a> NeuroImage. 57 (3), 856-865 (2011).</li><li>Eggert, L. D., Sommer, J., Jansen, A., Kircher, T., Konrad, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Accuracy+and+reliability+of+automated+gray+matter+segmentation+pathways+on+real+and+simulated+structural+magnetic+resonance+images+of+the+human+brain.">Accuracy and reliability of automated gray matter segmentation pathways on real and simulated structural magnetic resonance images of the human brain.</a> Public Library of Science One. 7 (9), 45081 (2012).</li><li>Fellhauer, I., 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=Comparison+of+automated+brain+segmentation+using+a+brain+phantom+and+patients+with+early+Alzheimer's+dementia+or+mild+cognitive+impairment.">Comparison of automated brain segmentation using a brain phantom and patients with early Alzheimer's dementia or mild cognitive impairment.</a> Psychiatry Research. 233 (3), 299-305 (2015).</li><li>Gronenschild, E. H. B. M., 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=The+effects+of+FreeSurfer+version,+workstation+type,+and+Macintosh+operating+system+version+on+anatomical+volume+and+cortical+thickness+measurements.">The effects of FreeSurfer version, workstation type, and Macintosh operating system version on anatomical volume and cortical thickness measurements.</a> Public Library of Science One. 7 (6), 38234 (2012).</li><li>Iscan, Z., 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=Test-retest+reliability+of+freesurfer+measurements+within+and+between+sites:+Effects+of+visual+approval+process.">Test-retest reliability of freesurfer measurements within and between sites: Effects of visual approval process.</a> Human Brain Mapping. 36 (9), 3472-3485 (2015).</li><li>Kazemi, K., Noorizadeh, N. <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+SPM,+FSL,+and+Brainsuite+for+Brain+MR+Image+Segmentation.">Quantitative Comparison of SPM, FSL, and Brainsuite for Brain MR Image Segmentation.</a> Journal of Biomedical Physics &amp; Engineering. 4 (1), 13-26 (2014).</li><li>Klauschen, F., Goldman, A., Barra, V., Meyer-Lindenberg, A., Lundervold, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Evaluation+of+automated+brain+MR+image+segmentation+and+volumetry+methods.">Evaluation of automated brain MR image segmentation and volumetry methods.</a> Human Brain Mapping. 30 (4), 1310-1327 (2009).</li><li>McCarthy, C. S., Ramprashad, A., Thompson, C., Botti, J. A., Coman, I. L., Kates, W. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+comparison+of+FreeSurfer-generated+data+with+and+without+manual+intervention.">A comparison of FreeSurfer-generated data with and without manual intervention.</a> Frontiers in Neuroscience. 9, (2015).</li><li>Tohka, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Partial+volume+effect+modeling+for+segmentation+and+tissue+classification+of+brain+magnetic+resonance+images:+A+review.">Partial volume effect modeling for segmentation and tissue classification of brain magnetic resonance images: A review.</a> World Journal of Radiology. 6 (11), 855-864 (2014).</li><li>Sled, J. G., Zijdenbos, A. P., Evans, A. 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I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cortical+surface-based+analysis.+I.+Segmentation+and+surface+reconstruction.">Cortical surface-based analysis. I. Segmentation and surface reconstruction.</a> NeuroImage. 9, 179-194 (1999).</li><li>Fischl, B., Sereno, M. I., Dale, A. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cortical+surface-based+analysis.+II:+Inflation,+flattening,+and+a+surface-based+coordinate+system.">Cortical surface-based analysis. II: Inflation, flattening, and a surface-based coordinate system.</a> NeuroImage. 9, 195-207 (1999).</li><li>Avants, B. B., Tustison, N. J., Wu, J., Cook, P. A., Gee, J. 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Recently, research has demonstrated that the use of different volumetric methods may have important implications for neuroimaging studies1,2. By publishing protocols that help guide novice users in how to apply different neuroimaging tools, as well as how to perform QC on the results output by these tools, researchers may select the best method to apply to their dataset.
While most steps in this SOP can be adjusted to suit the data and...

Neuroimaging is widely used in both clinical and research settings. There is a current move to improve the reproducibility of studies that quantify brain volume from magnetic resonance imaging (MRI) scans; thus, it is important that investigators share experiences of using available MRI tools for segmenting MRI scans into regional volumes, to improve the standardization and optimization of methods1. This protocol provides a step-by-step guide to using seven different tools to segment the cortical grey matter (CGM; grey matter which excludes subcortical regions) from T1-weighted MRI scans. These tools were previously used in a methodological comparison of segmentation methods2, which demonstrated variable performance between tools on an Huntington&#39;s disease cohort. Since performance of these tools is thought to vary among different datasets, it is important for researchers to test a number of tools before selecting only one to apply to their dataset.
Grey matter (GM) volume is regularly used as a measure of brain morphology. Volumetric measures are generally reliable and able to discriminate between healthy controls and clinical groups3. The volume of different tissue types of brain regions is most often calculated using automated software tools that identify these tissue types. Thus, to create high quality delineations (segmentations) of the GM, accurate delineation of the white matter (WM) and cerebrospinal fluid (CSF) is critical in achieving accuracy of the GM region. There are a number of automated tools that may be used for performing GM segmentation, and each requires different processing steps and results in a different output. A number of studies have applied the tools to different datasets to compare them with one other, and some have optimized specific tools1,4,5,6,7,8,9,10,11. Previous work has demonstrated that variability between volumetric tools can result in inconsistencies within the literature when studying brain volume, and these differences have been suggested as driving factors for false conclusions being drawn about neurological conditions1.
Recently, a comparison of different segmentation tools in a cohort that included both healthy control participants and participants with Huntington&#39;s disease was performed. Huntington&#39;s disease is a genetic neurodegenerative disease with a typical onset in adulthood. Gradual atrophy of subcortical and CGM is a prominent and well-studied neuropathological feature of the disease. The results demonstrated variable performance of seven segmentation tools that were applied to the cohort, supporting previous work that demonstrated variability in findings depending on the software used to calculate brain volumes from MRI scans. This protocol provides information on the processing used in Johnson et al. (2017)2 that encourages careful methodological selection of the most appropriate tools for use in neuroimaging. This manual covers the segmentation of GM volume but does not cover the segmentation of lesions, such as those seen in multiple sclerosis.

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.

Average brain volumes for 20 control participants, along with demographic information, is shown in Table 1. This acts as a guide for expected values when using these tools. Results should be viewed in the context of the original T1.nii image. All GM regions should be inspected as per the steps described in section 8. When performing visual QC, it is important to directly compare the GM regions to the T1 scan by viewing them overlaid on the T1.
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Watch this Scientific Journal Video about Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images at JoVE.com

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

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 ...

This method can answer key questions in the neuroimaging and neurology fields. Such as, whether there are group differences in cortical volume for non-clinical versus clinical populations. The main advantage to this technique is that it enables researchers to use the best tool for their data.To begin, first open the SPM software by opening a MATLAB command window and typing SPM into the command line. Then, to perform unified segmentation, select PET VBM to open the structural MRI toolbox. Now, open the batch editor to perform segmentation on multiple scans at once.Select SPM, Spatial, and Segment, then Data, select Files, and choose the T1-Weighted scans as Input. Next, click on Output Files, Grey Matter, and ensure that Native Space is selected, and repeat this for White Matter. If the CSF segmentation is not required, leave this set to None.If the scans have already been Bias Corrected, change this option to Don't Save Corrected. Then use Clean up any partitions and test all three options prior to running the full analysis. Now, leave the other settings set to defaults and click on the green flag to run the segmentation.The MATLAB window will say Done when segmentation is finished. Finally, perform Visual Quality Control on the resulting Grey Matter NIfTI file. To perform the New Segment option in SPM 8, first select PET VBM before opening the batch editor.Then select SPM, Tools, New Segment, and select the NIfTI format T1 image files to be used. Set the Native Tissue Type option to Native Space, and turn off Tissue Classes that are not required. Also turn off Warped Tissue, then click on the green flag to run the segmentation and perform Visual Quality Control when completed.To perform segmentation in SPM 12 again press PET VBM and open the batch editor. Then select SPM, Spatial Segment, and Data Volumes. Then select Native Space Tissue Type and turn off unneeded Tissue Classes.Set Warped Tissue to None, and click the green flag. Once the segmentation has completed, again be sure to perform Visual Quality Control as detailed in the next section of this protocol. Visual Quality Control can be performed using FSLeyes.Begin by opening a terminal window, then open FSLeyes by typing FSLeyes in the terminal. Then select File, Add from File, and select the original T1 and the segmented regions to view them. Once FSLeyes opens, use the opacity toggle to allow visualization of the underlying T1 image.Also change the color of the segmentation overlay as needed via the color dropdown tab in the top pane. Now, scroll through every slice in the brain and check each one for regions of under or overestimation in the region being inspected. Visual Quality Control is an essential step for this procedure.By comparing your segmented regions to the original T1 scan, you can ensure that your regions are of a high quality, and that your conclusions are biologically accurate. To perform Visual Quality Control of FreeSurfer data using Freeview, open a terminal window and change the directory to the subject folder that contains the processed FreeSurfer output. Then type the command seen on on the screen here to view the volumetric grey matter region overlaid on the T1.Again, scroll through every slice in the brain and check for regions of under or overestimation for the brain region being inspected.Here, we see an example of a failed segmentation displayed on a T1 scan. This segmentation should be reprocessed and excluded from analysis if it cannot be improved. This figure shows examples of the performance of different tools on the temporal lobe on a T1 scan.Examples of a good regional delineation are seen here, while examples of poor regional delineation, showing spillage in the left and right temporal lobes, are shown here. This figure shows examples of the performance of different tools on the occipital lobe on a T1 scan. Here we see the T1 scan with an example of a good regional delineation, while here is an example of a poor regional delineation, showing spillage into the medial dura.Here we see an example of a grey matter region spilled into the dura, highlighted by the blue region. This figure shows an example of a grey matter region that has excluded regions of the cortex from segmentation, best shown in the axial view. While attempting this procedure, it's important to remember to test different tools in your data, and to perform Visual Quality Control on the process scans.After it's development, this technique paved the way for neuroimaging researchers to study changes in brain volume over time without requiring invasive tests.

automated-segmentation-cortical-grey-matter-from-t1-weighted-mri

Research

JoVE Journal

Neuroscience

Generation of Neural Stem Cells from Discarded Human Fetal Cortical Tissue

Neural stem cells (NSCs) reside along the ventricular zone neuroepithelium during the development of the cortical plate. These early progenitors ultimately give rise to intermediate progenitors and later, the various neuronal and glial cell subtypes that form the cerebral cortex. The capacity to generate and expand human NSCs (so called neurospheres) from discarded normal fetal tissue provides a means with which to directly study the functional aspects of normal human NSC development 1-5. This approach can also be directed toward the generation of NSCs from known neurological disorders, thereby affording the opportunity to identify disease processes that alter progenitor proliferation, migration and differentiation 6-9. We have focused on identifying pathological mechanisms in human Down syndrome NSCs that might contribute to the accelerated Alzheimer's disease phenotype 10,11. Neither in vivo nor in vitro mouse models can replicate the identical repertoire of genes located on human chromosome 21. 
Here we use a simple and reliable method to isolate Down syndrome NSCs from aborted human fetal cortices and grow them in culture. The methodology provides specific aspects of harvesting the tissue, dissection with limited anatomical landmarks, cell sorting, plating and passaging of human NSCs. We also provide some basic protocols for inducing differentiation of human NSCs into more selective cell subtypes.

A simple and reliable method on isolation and culture of neural stem cells from discarded human fetal cortical tissue is described. Cultures derived from known human neurological disorders can be used for characterization of pathological cellular and molecular processes, as well as provide a platform to assess pharmacological efficacy.

Neural stem cells (NSCs) reside along the ventricular zone neuroepithelium during the development of the cortical plate. These early progenitors ...

Isolation of Cerebrospinal Fluid from Rodent Embryos for use with Dissected Cerebral Cortical Explants

The CSF is a complex fluid with a dynamically varying proteome throughout development and in adulthood. During embryonic development, the nascent CSF differentiates from the amniotic fluid upon closure of the anterior neural tube. CSF volume then increases over subsequent days as the neuroepithelial progenitor cells lining the ventricles and the choroid plexus generate CSF. The embryonic CSF contacts the apical, ventricular surface of the neural stem cells of the developing brain and spinal cord. CSF provides crucial fluid pressure for the expansion of the developing brain and distributes important growth promoting factors to neural progenitor cells in a temporally-specific manner. To investigate the function of the CSF, it is important to isolate pure samples of embryonic CSF without contamination from blood or the developing telencephalic tissue. Here, we describe a technique to isolate relatively pure samples of ventricular embryonic CSF that can be used for a wide range of experimental assays including mass spectrometry, protein electrophoresis, and cell and primary explant culture. We demonstrate how to dissect and culture cortical explants on porous polycarbonate membranes in order to grow developing cortical tissue with reduced volumes of media or CSF. With this method, experiments can be performed using CSF from varying ages or conditions to investigate the biological activity of the CSF proteome on target cells.

The ventricular cerebrospinal fluid (CSF) bathes the neuroepithelial and cerebral cortical progenitor cells during early brain development in the embryo. Here we describe the method developed to isolate ventricular CSF from rodent embryos of different ages in order to investigate its biological function. In addition, we demonstrate our cerebral cortical explant dissection and culture technique that allows for explant growth with minimal volumes of culture medium or CSF.

The CSF is a complex fluid with a dynamically varying proteome throughout development and in adulthood. During embryonic development, the nascent CSF ...

Subtype-selective Electroporation of Cortical Interneurons

The study of central nervous system (CNS) maturation relies on genetic targeting of neuronal populations. However, the task of restricting the expression of genes of interest to specific neuronal subtypes has proven remarkably challenging due to the relative scarcity of specific promoter elements. GABAergic interneurons constitute a neuronal population with extensive genetic and morphological diversity. Indeed, more than 11 different subtypes of GABAergic interneurons have been characterized in the mouse cortex1. Here we present an adapted protocol for selective targeting of GABAergic populations. We achieved subtype selective targeting of GABAergic interneurons by using the enhancer element of the homeobox transcription factors Dlx5 and Dlx6, homologues of the Drosophila distal-less (Dll) gene2,3, to drive the expression of specific genes through in utero electroporation.

This procedure shows how to target interneurons in the developing mouse forebrain by means of in utero electroporation. This technique was particularly efficient to achieve selective gene expression in interneuron subtypes destined to the superficial layers of the cortex.

The study of central nervous system (CNS) maturation relies on genetic targeting of neuronal populations. However, the task of restricting the expression ...

A Guide to In vivo Single-unit Recording from Optogenetically Identified Cortical Inhibitory Interneurons

A major challenge in neurophysiology has been to characterize the response properties and function of the numerous inhibitory cell types in the cerebral cortex. We here share our strategy for obtaining stable, well-isolated single-unit recordings from identified inhibitory interneurons in the anesthetized mouse cortex using a method developed by Lima and colleagues1. Recordings are performed in mice expressing Channelrhodopsin-2 (ChR2) in specific neuronal subpopulations. Members of the population are identified by their response to a brief flash of blue light. This technique &#8211; termed &#8220;PINP&#8221;, or Photostimulation-assisted Identification of Neuronal Populations &#8211; can be implemented with standard extracellular recording equipment. It can serve as an inexpensive and accessible alternative to calcium imaging or visually-guided patching, for the purpose of targeting extracellular recordings to genetically-identified cells. Here we provide a set of guidelines for optimizing the method in everyday practice. We refined our strategy specifically for targeting parvalbumin-positive (PV+) cells, but have found that it works for other interneuron types as well, such as somatostatin-expressing (SOM+) and calretinin-expressing (CR+) interneurons.

A Guide to In vivo Single-unit Recording from Optogenetically Identified Cortical Inhibitory Interneurons

Here we describe our strategy for obtaining stable, well-isolated single-unit recordings from identified inhibitory interneurons in the anesthetized mouse cortex. Neurons expressing ChR2 are identified by their response to blue light. The method uses standard extracellular recording equipment, and serves as an inexpensive alternative to calcium imaging or visually-guided patching.

A major challenge in neurophysiology has been to characterize the response properties and function of the numerous inhibitory cell types in the cerebral ...

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 ...

Optogenetic Functional MRI

The investigation of the functional connectivity of precise neural circuits across the entire intact brain can be achieved through optogenetic functional magnetic resonance imaging (ofMRI), which is a novel technique that combines the relatively high spatial resolution of high-field fMRI with the precision of optogenetic stimulation. Fiber optics that enable delivery of specific wavelengths of light deep into the brain in vivo are implanted into regions of interest in order to specifically stimulate targeted cell types that have been genetically induced to express light-sensitive trans-membrane conductance channels, called opsins. fMRI is used to provide a non-invasive method of determining the brain's global dynamic response to optogenetic stimulation of specific neural circuits through measurement of the blood-oxygen-level-dependent (BOLD) signal, which provides an indirect measurement of neuronal activity. This protocol describes the construction of fiber optic implants, the implantation surgeries, the imaging with photostimulation and the data analysis required to successfully perform ofMRI. In summary, the precise stimulation and whole-brain monitoring ability of ofMRI are crucial factors in making ofMRI a powerful tool for the study of the connectomics of the brain in both healthy and diseased states.

This protocol describes the steps and data analysis required to successfully perform optogenetic functional magnetic resonance imaging (ofMRI). ofMRI is a novel technique that combines high-field fMRI readout with optogenetic stimulation, allowing for cell type-specific mapping of functional neural circuits and their dynamics across the whole living brain.

The investigation of the functional connectivity of precise neural circuits across the entire intact brain can be achieved through optogenetic functional ...

Simultaneous Recordings of Cortical Local Field Potentials, Electrocardiogram, Electromyogram, and Breathing Rhythm from a Freely Moving Rat

Monitoring the physiological dynamics of the brain and peripheral tissues is necessary for addressing a number of questions about how the brain controls body functions and internal organ rhythms when animals are exposed to emotional challenges and changes in their living environments. In general experiments, signals from different organs, such as the brain and the heart, are recorded by independent recording systems that require multiple recording devices and different procedures for processing the data files. This study describes a new method that can simultaneously monitor electrical biosignals, including tens of local field potentials in multiple brain regions, electrocardiograms that represent the cardiac rhythm, electromyograms that represent awake/sleep-related muscle contraction, and breathing signals, in a freely moving rat. The recording configuration of this method is based on a conventional micro-drive array for cortical local field potential recordings in which tens of electrodes are accommodated, and the signals obtained from these electrodes are integrated into a single electrical board mounted on the animal's head. Here, this recording system was improved so that signals from the peripheral organs are also transferred to an electrical interface board. In a single surgery, electrodes are first separately implanted into the appropriate body parts and the target brain areas. The open ends of all of these electrodes are then soldered to individual channels of the electrical board above the animal's head so that all of the signals can be integrated into the single electrical board. Connecting this board to a recording device allows for the collection of all of the signals into a single device, which reduces experimental costs and simplifies data processing, because all data can be handled in the same data file. This technique will aid the understanding of the neurophysiological correlates of the associations between central and peripheral organs.

This study introduces a method for the simultaneous recording of local field potentials in the brain, electrocardiograms, electromyograms, and breathing signals of a freely moving rat. This technique, which reduces experimental costs and simplifies data analysis, will contribute to the understanding of the interactions between the brain and peripheral organs.

Monitoring the physiological dynamics of the brain and peripheral tissues is necessary for addressing a number of questions about how the brain controls ...

Visualization of Cortical Modules in Flattened Mammalian Cortices

The cortex of mammalian brains is parcellated into distinct substructures or modules. Cortical modules typically lie parallel to the cortical sheet, and can be delineated by certain histochemical and immunohistochemical methods. In this study, we highlight a method to isolate the cortex from mammalian brains and flatten them to obtain sections parallel to the cortical sheet. We further highlight selected histochemical and immunohistochemical methods to process these flattened tangential sections to visualize cortical modules. In the somatosensory cortex of various mammals, we perform cytochrome oxidase histochemistry to reveal body maps or cortical modules representing different parts of the body of the animal. In the medial entorhinal cortex, an area where grid cells are generated, we utilize immunohistochemical methods to highlight modules of genetically determined neurons which are arranged in a grid-pattern in the cortical sheet across several species. Overall, we provide a framework to isolate and prepare layer-wise flattened cortical sections, and visualize cortical modules using histochemical and immunohistochemical methods in a wide variety of mammalian brains.

This article describes a detailed methodology to obtain flattened tangential sections from mammalian cortices and visualize cortical modules using histochemical and immunohistochemical methods.

The cortex of mammalian brains is parcellated into distinct substructures or modules. Cortical modules typically lie parallel to the cortical sheet, and ...

Convection Enhanced Delivery of Optogenetic Adeno-associated Viral Vector to the Cortex of Rhesus Macaque Under Guidance of Online MRI Images

In non-human primate (NHP) optogenetics, infecting large cortical areas with viral vectors is often a difficult and time-consuming task. Here, we demonstrate the use of magnetic resonance (MR)-guided convection enhanced delivery (CED) of optogenetic viral vectors into primary somatosensory (S1) and motor (M1) cortices of macaques to obtain efficient, widespread cortical expression of light-sensitive ion channels. Adeno-associated viral (AAV) vectors encoding the red-shifted opsin C1V1 fused to yellow fluorescent protein (EYFP) were injected into the cortex of rhesus macaques under MR-guided CED. Three months post-infusion, epifluorescent imaging confirmed large regions of optogenetic expression (&#62;130 mm2) in M1 and S1 in two macaques. Furthermore, we were able to record reliable light-evoked electrophysiology responses from the expressing areas using micro-electrocorticographic arrays. Later histological analysis and immunostaining against the reporter revealed widespread and dense optogenetic expression in M1 and S1 corresponding to the distribution indicated by epifluorescent imaging. This technique enables us to obtain expression across large areas of the cortex within a shorter period of time with minimal damage compared to the traditional techniques and can be an optimal approach for optogenetic viral delivery in large animals such as NHPs. This approach demonstrates great potential for network-level manipulation of neural circuits with cell-type specificity in animal models evolutionarily close to humans.&#160;

Here, we demonstrate magnetic resonance (MR)-guided convection enhanced delivery (CED) of viral vectors into the cortex as an efficient and simplified approach for achieving optogenetic expression across large cortical areas in the macaque brain.

In non-human primate (NHP) optogenetics, infecting large cortical areas with viral vectors is often a difficult and time-consuming task. Here, we ...

Functional MRI in Conjunction with a Novel MRI-compatible Hand-induced Robotic Device to Evaluate Rehabilitation of Individuals Recovering from Hand Grip Deficits

Functional magnetic resonance imaging (fMRI) is a non-invasive magnetic resonance imaging technique that images brain activation in vivo, using endogenous deoxyhemoglobin as an endogenous contrast agent to detect changes in blood-level-dependent oxygenation (BOLD effect). We combined fMRI with a novel robotic device (MR-compatible hand-induced robotic device [MR_CHIROD]) so that a person in the scanner can execute a controlled motor task, hand-squeezing, which is a very important hand movement to study in neurological motor disease. We employed parallel imaging (generalized auto-calibrating partially parallel acquisitions [GRAPPA]), which allowed higher spatial resolution resulting in increased sensitivity to BOLD. The combination of fMRI with the hand-induced robotic device allowed precise control and monitoring of the task that was executed while a participant was in the scanner; this may prove to be of utility in rehabilitation of hand motor function in patients recovering from neurological deficits (e.g., stroke). Here we outline the protocol for using the current prototype of the MR_CHIROD during an fMRI scan.

We performed functional MRI using a novel MRI-compatible hand-induced robotic device to evaluate its utility for monitoring hand motor function in individuals recovering from neurological deficits.

Functional magnetic resonance imaging (fMRI) is a non-invasive magnetic resonance imaging technique that images brain activation in vivo, using endogenous ...