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 open SPM, open a command window and type &#39;spm&#39; into the command line.</li>
	<li>Press &#39;PET &#38; VBM&#39; to open the structural MRI toolbox.</li>
	<li>Press &#39;Batch&#39; to open the Batch Editor. This allows segmentation to be performed on multiple scans at a time.</li>
	<li>Select &#39;SPM | Spatial | Segment&#39;.</li>
	<li>Click &#39;Data | Select Files&#39;. Choose the T1-weighted scans as input. 
	NOTE: The files must be unzipped NifTi files, with the extension being &#39;.nii&#39;.</li>
	<li>Click on &#39;output files | Grey Matter&#39; and ensure that &#39;Native Space&#39; is selected, do the same for White Matter. If the CSF segmentation is not required leave this as &#39;None&#39;.</li>
	<li>If the scans have already been bias-corrected, change the &#39;Bias Corrected&#39; option to &#39;Don&#39;t Save Corrected&#39;. For the &#39;Clean up any partitions&#39; option, test the three different options and use visual quality control (QC, Section 8) to determine which works best for the data.</li>
	<li>Leave the other settings as their defaults. Then, click on the green flag to run the segmentation. 
	NOTE: This takes around 5 minutes per participant, and the command line will say, &#39;Running Segment&#39;. When finished, the command window will display &#39;Done&#39;.</li>
	<li>Perform visual QC on the GM (C1*.nii file) as described in Section 8.</li>
</ol>
2. Segmentation via SPM 8: New Segment
NOTE: This procedure is performed via the SPM8 GUI. The SPM8 guide provides further detail and can be found at: http://www.fil.ion.ucl.ac.uk/spm/doc/spm8_manual.pdf. Make sure that SPM8 is installed and set in the software path. Open the SPM software, typically performed by typing &#34;spm&#34; into a command line. This opens a graphical user interface (GUI) window with a range of options that can be selected to perform analysis.&#160;
<ol>
	<li>Press &#39;PET &#38; VBM&#39;.</li>
	<li>Press &#39;Batch&#39; to open the Batch Editor.</li>
	<li>Select &#39;SPM | Tools | New Segment&#39; in the Batch window. Select the T1 image files (with extension &#39;.nii&#39;).</li>
	<li>Set &#39;Native Tissue type&#39; to &#39;Native Space&#39;. As needed, turn off the different tissue classes (such as CSF) - if not required - by setting them to &#39;None&#39;. Set &#39;Warped Tissue&#39; to &#39;None&#39;. 
	NOTE: All other options can be left as the default setting.</li>
	<li>Click the green flag to run the segmentation. 
	NOTE: The command line will say, &#39;Running New Segment&#39;. Once it has finished running the MATLAB command line will say, &#39;Done New Segment&#39;.</li>
	<li>Perform visual QC on the GM (C1*.nii file) as described in Section 8.</li>
</ol>
3. Segmentation via SPM 12: Segment
NOTE: This procedure is performed via the SPM12 GUI. The SPM12 guide provides further detail and can be found at:http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf.
<ol>
	<li>Open the SPM software by typing &#39;spm&#39; into the command window. This opens a graphical user interface (GUI) window with a range of options that can be selected to perform analysis.</li>
	<li>Press &#39;PET &#38; VBM&#39;. Press &#39;Batch&#39; to open the Batch Editor.</li>
	<li>Click on &#39;SPM | Spatial | Segment&#39;. Then, click on &#39;Data | Volumes&#39;.</li>
	<li>Set &#39;Native Tissue type&#39; to &#39;Native Space&#39;. Turn off the tissue classes that are not required (such as CSF) by setting them to &#39;None&#39;. Set &#39;Warped Tissue&#39; to &#39;None&#39;. 
	NOTE: All other options can be left as default setting.</li>
	<li>Click the green flag to run the segmentation. 
	NOTE: The command window will display: &#39;Running Segment&#39;. Once the run is complete, it will display: &#39;Done Segment&#39;.</li>
	<li>Perform visual QC on the GM (C1*.nii file) as described in Section 8.</li>
</ol>
4. Segmentation via FSL FAST
NOTE: This procedure is done in the command line. The FSL guide provides further detail and can be found at: https://fsl.fmrib.ox.ac.uk/fsl/fslwiki.
<ol>
	<li>Run BET brain extraction. This may need to be optimized for different datasets, but the basic command is: 
	bet T1_ID.nii bet_T1_ID.nii</li>
	<li>Run FSL FAST segmentation: 
	fast bet_T1_ID.nii 
	NOTE: This will output partial volume maps and binary regions for GM, CSF, and WM.</li>
	<li>Perform visual QC on the GM region (the file ending *_pve_1.nii.gz) as described in Section 8.</li>
</ol>
5. Segmentation via FreeSurfer
NOTE: This procedure is done in the command line. The FreeSurfer guide provides further detail and can be found at: https://surfer.nmr.mgh.harvard.edu/.
<ol>
	<li>Set the directory where the data is by typing: 
	export SUBJECTS_DIR=/path/to/nii/files</li>
	<li>Run the segmentation by running the commands: 
	recon-all -i T1_ID.nii -subjid T1_ID -autorecon1 -cw256 
	recon-all -subjid T1_ID -autorecon2 -autorecon3 
	NOTE: The commands take &#62; 10 h per participant. The -cw256 flag is needed to crop scans with fields of view larger than 256 down to this size for processing.</li>
	<li>Check that processing has completed correctly by looking at the script located in the &#39;output folder | scripts | recon-all.log&#39;. Check that the last line says &#39;recon-all -s T1_ID finished without error&#39;.</li>
	<li>Perform visual QC on the GM region as described in Section 8.</li>
</ol>
6. Segmentation via ANTs
NOTE: This procedure is done in the command line. ANTs is a more complex software than the other tools and it should be noted that the procedure explained here could be further optimised for each cohort to improve the results. ANTs documentation can be found at: http://stnava.github.io/ANTsDoc/. There are two ways to segment the images into tissue classes as described below.&#160;
<ol>
	<li>To use the first method, run the command &#39;antsAtropos.sh&#39; with default settings and without including tissue priors. 
	NOTE: This often performs well especially when only 3 tissue classes are required: GM, WM, other.
	<ol>
		<li>Set the path to ANTs software by typing the command: 
		export ANTSPATH=/path/to/ANTs/bin/</li>
		<li>Run the segmentation pipeline by typing the command: 
		antsAtroposN4.sh -d &#60;image_dimension&#62; -a &#60;t1.nii.gz&#62; -c &#60;number of tissue classes&#62; -o &#60;output&#62;
		<ol>
			<li>Optional arguments for this command are: 
			Brain mask: -x &#60;mask.nii.gz&#62;; 
			Tissue priors: -p &#60;segmentationPriors%d.nii.gz&#62;.</li>
		</ol>
		</li>
		<li>This will create a folder with the output including partial volume maps and an extracted brain. Perform visual QC on the GM region as described in Section 8.</li>
	</ol>
	</li>
	<li>To generate more tissue classes (GM, subcortical GM, WM, CSF, other, etc.) or perform the segmentation on a cohort showing neural pathology, use specific tissue priors. Download tissue priors from different websites. Alternatively, use a study-specific template to make priors - this is much more complex but can be beneficial, especially in cohorts with pathological brain changes.
	<ol>
		<li>To create a study-specific template/priors, first create a study-specific template: 
		antsMultivariateTemplateConstruction.sh -d &#60;image_dimension&#62; -o template &#60;other options&#62; &#60;images.nii.gz&#62;
		<ol>
			<li>Optional arguments for this command are: 
			-c: control for parallel computation. 
			If running in serial, use a 0; -j: number of cores; -r: do rigid-body registration of inputs before creating template (default 0) -- 0 == off 1 == on. This is only useful when an initial template is not available.</li>
		</ol>
		</li>
		<li>Download a brainmask and priors from the ANTs website. 
		NOTE: This mask may need to be edited to make sure it is a good approximation of the template brain. The brainmask is one of the most important parts of the pipeline; if it is poor, then brain extraction/Atropos will run poorly. Some of the download options are: 
		https://figshare.com/articles/ANTs_ANTsR_Brain_Templates?915436. 
		The downloaded template should then be registered to the study template.</li>
		<li>Calculate the registration, which will output a series of warps that can then be applied to the downloaded template to transform it to study-specific template space. To calculate the registration, use the command: 
		antsRegistrationSyNQuick.sh -d 3 -f template.nii.gz -m downloaded_template.nii.gz -o downloaded_to_template -n 6
		<ol>
			<li>The options in this command are: 
			-d: dimension (i.e., 3D scans would be &#39;3&#39;); -f: fixed image (i.e., the space where the images need to end up); -m: moving image (i.e., the image that needs to be moved); -o: output name (no extension needed); -n: number of threads.</li>
		</ol>
		</li>
		<li>Apply the registration to the data: 
		antsApplyTransforms -d 3 -i downloaded_template.nii.gz -r template.nii.gz -o downloaded_to_template.nii.gz -t downloaded_to_template1Warp.nii.gz -t downloaded_to_template0GenericAffine.mat.
		<ol>
			<li>The options in this command are: 
			-d: dimension (i.e., 3D scans would be &#39;3&#39;); -i: input image (i.e., the image that needs to be moved); -r: reference image (i.e., the reference image defines the spacing, origin, size, and direction of the output warped image); -o output name, this is the downloaded template in the study-specific template space (extension needed in this case); -t transform file name, the output file from the registration calculation.</li>
		</ol>
		</li>
		<li>Visually check the registration for correspondence between the study-specific template and downloaded template (to do this, open the study-specific template on top of the downloaded template).</li>
		<li>If the registration has worked, apply the transformation to the downloaded priors and extracted template brain, repeating step 6.2.5. 
		NOTE: Following these steps, there will be a study-specific template, a downloaded template aligned with the study-specific template, along with a downloaded brain extraction mask and tissue priors also aligned with the study-specific template.</li>
		<li>Run the study specific template through antsCorticalThickness.sh; this provides GM, WM, and CSF regions that can be used for study-specific priors: 
		antsCorticalThickness.sh -d 3 -a template.nii.gz -e downloaded_to_template.nii.gz -m downloaded_binarised_template_extracted_brain_in_studyspace.nii.gz -p downloaded_labelsPriors%d.nii.gz -o CT_template
		<ol>
			<li>The options in this command are: 
			-d: dimension (i.e., 3D scans would be &#39;3&#39;); -a: image to be segmented (in this case, the study-specific template); -e: brain template (not skull stripped; in this case, the downloaded template that has been registered to the study-specific template); -m: downloaded brain extraction mask (in this case, the extracted brain from the downloaded template that has been registered to the study-specific template); -p: priors specified using c-style formatting (e.g., -p labelsPriors%02d.nii.gz). 
			NOTE: The command assumes that the first four priors are ordered as follows: 1: CSF, 2: cortical GM, 3: WM, and 4: subcortical GM (in this case, the priors from the downloaded template that has been registered to the study-specific template).</li>
		</ol>
		</li>
		<li>Running this command will result in generated priors for the template, but they need smoothing prior to use in Atropos segmentation. The smoothing command is part of ANTs software. Smooth all priors using the command: 
		SmoothImage 3 CT_template_BrainSegmentationPosteriors2.nii.gz CT_template_BrainSegmentationPosteriors_smoothed.nii.gz</li>
		<li>Prior to running Atropos, run brain extraction on all native space scans. The study-specific template can be used, and extracted brain generated from running antsCorticalThickness.sh on the template (step 6.2.1): 
		antsBrainExtraction.sh -d 3 -a T1.nii.gz -e template.nii.gz -m template_BrainExtractionBrain.nii.gz -o T1_brain.nii.gz
		<ol>
			<li>The options in this command are: 
			-d: dimensions; -a: anatomical image; -e: brain extraction template (i.e., template created, without skull stripping); -m: study specific brainmask used for brain extraction; -o: output prefix.</li>
		</ol>
		</li>
		<li>Then run Atropos: 
		antsAtroposN4.sh -d 3 -a T1.nii.gz -x T1_brain.nii.gz -c 3 -o Atropos_specific_template
		<ol>
			<li>The options in this command are: 
			-d = dimensions; -a: anatomical image; -x: brain extraction mask generated from the brain extraction; -c: number of tissue classes to segment; -o: output prefix; -p: study-specific segmentation priors &#60;segmentationPriors%d.nii.gz&#62;</li>
		</ol>
		</li>
		<li>Perform visual QC on the GM region as described in Section 8.</li>
	</ol>
	</li>
</ol>
7. Segmentation via MALP-EM
<ol>
	<li>To run MALP-EM, open a terminal window, change the directory into the MALP-EM install directory and type: 
	./malpem-proot -i T1_scan.nii -o ./ -m optional_brain_mask_final.nii.gz -f 3T -t 6 -c</li>
	<li>Once the command has been completed, check that there is an output folder with tissue classes and regional segmentations.</li>
	<li>Perform visual QC on the GM as described in Section 8.</li>
</ol>
8. Visual Quality Control
NOTE: Visual quality control should be performed on all segmented regions to be used in the analysis. Quality control ensures that the segmentations are of a high standard and represent reliable segmentation of the CGM. To perform quality control, each scan is opened and overlaid on the original T1 to compare the generated region to the CGM visible on the scan.
<ol>
	<li>SPM, FSL, ANTs, and MALP-EM Segmentations
	<ol>
		<li>Perform visual QC using FSLeyes: 
		https://users.fmrib.ox.ac.uk/~paulmc/fsleyes_userdoc/ 
		Note: FSLview (an older viewer) can also be used in the same way.</li>
		<li>Open a terminal window and open the T1 and the GM regions overlaid on the T1. To do this, type: 
		fsleyes T1.nii Region1.nii Region2.nii.</li>
		<li>Once FSLeyes opens, use the opacity toggle on the top pane to adjust/reduce the opacity and allow visualisation of the T1 image underneath the GM region. Change the color of the segmentation overlay via the &#39;color dropdown tab&#39; in the top pane.</li>
		<li>Scroll through every slice in the brain. 
		NOTE: Here this is done using the coronal view, but users should use the view that they have most experience with.</li>
		<li>Check every slice for regions of under- or over-estimation of the region being inspected. 
		NOTE: See the representative results section for examples of good and bad segmentations.</li>
	</ol>
	</li>
	<li>FreeSurfer QC
	<ol>
		<li>Peform visual QC using FreeView. 
		NOTE: Refer to the documentation here: 
		https://surfer.nmr.mgh.harvard.edu/fswiki/FreeviewGuide/FreeviewGeneralUsage/FreeviewQuickStart.</li>
		<li>Open a terminal window. To view the volumetric GM region overlaid on the T1, change directory to the subject folder and type: 
		freeview ./mri/T1.mgz ./mri/aparc+aseg.mgz:colormap=lut:opacity:.3</li>
		<li>Scroll through every slice in the brain. 
		NOTE: Here this is done using the coronal view, but users should use the view that they have most experience with.</li>
		<li>Check every slice for regions of under- or over-estimation of the region being inspected. 
		NOTE: See the representative results section for examples of the segmentations.</li>
	</ol>
	</li>
</ol>

<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. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+nonparametric+method+for+automatic+correction+of+intensity+nonuniformity+in+MRI+data.">A nonparametric method for automatic correction of intensity nonuniformity in MRI data.</a> IEEE Transactions on Medical Imaging. 17, 87-97 (1998).</li><li>Ashburner, J., Friston, K. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Unified+segmentation.">Unified segmentation.</a> NeuroImage. 26 (3), 839-851 (2005).</li><li>Jenkinson, M., Beckmann, C., Behrens, T. E., Woolrich, M. W., Smith, S. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=FSL.">FSL.</a> NeuroImage. 62, 782-790 (2012).</li><li>Dale, A. M., Fischl, B., Sereno, M. 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. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+open+source+multivariate+framework+for+n-tissue+segmentation+with+evaluation+on+public+data.">An open source multivariate framework for n-tissue segmentation with evaluation on public data.</a> Neuroinformatics. 9 (4), 381-400 (2011).</li><li>Ledig, C., 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=Robust+whole-brain+segmentation:+application+to+traumatic+brain+injury.">Robust whole-brain segmentation: application to traumatic brain injury.</a> Medical Image Analysis. 21 (1), 40-58 (2015).</li></ol>

The authors have nothing to disclose.

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.
<p class="jove_content" ...

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 ...

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

Universidad de Cartagena UDEC

Research

JoVE Journal

Neuroscience

8.8K Views.  UCL Institute of Neurology. 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. 

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

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

In this paper we present an automated system based mainly on the computed tomography (CT) images consisting of two main components: the midline shift estimation and intracranial pressure (ICP) pre-screening system. To estimate the midline shift, first an estimation of the ideal midline is performed based on the symmetry of the skull and anatomical features in the brain CT scan. Then, segmentation of the ventricles from the CT scan is performed and used as a guide for the identification of the actual midline through shape matching. These processes mimic the measuring process by physicians and have shown promising results in the evaluation. In the second component, more features are extracted related to ICP, such as the texture information, blood amount from CT scans and other recorded features, such as age, injury severity score to estimate the ICP are also incorporated. Machine learning techniques including feature selection and classification, such as Support Vector Machines (SVMs), are employed to build the prediction model using RapidMiner. The evaluation of the prediction shows potential usefulness of the model. The estimated ideal midline shift and predicted ICP levels may be used as a fast pre-screening step for physicians to make decisions, so as to recommend for or against invasive ICP monitoring.

An automated midline shift estimation and intracranial pressure (ICP) pre-screening system based on computed tomography (CT) images for patients with traumatic brain injury (TBI) is proposed using image processing and machine learning techniques.

In this paper we present an automated system based mainly on the computed tomography (CT) images consisting of two main components: the midline shift ...

Medicine

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 ...

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Obtaining in vivo human brain tissue volumetrics from MRI is often complicated by various technical and biological issues. These challenges are exacerbated when significant brain atrophy and age-related white matter changes (e.g. Leukoaraiosis) are present. Lesion Explorer (LE) is an accurate and reliable neuroimaging pipeline specifically developed to address such issues commonly observed on MRI of Alzheimer's disease and normal elderly. The pipeline is a complex set of semi-automatic procedures which has been previously validated in a series of internal and external reliability tests1,2. However, LE's accuracy and reliability is highly dependent on properly trained manual operators to execute commands, identify distinct anatomical landmarks, and manually edit/verify various computer-generated segmentation outputs.
LE can be divided into 3 main components, each requiring a set of commands and manual operations: 1) Brain-Sizer, 2) SABRE, and 3) Lesion-Seg. Brain-Sizer's manual operations involve editing of the automatic skull-stripped total intracranial vault (TIV) extraction mask, designation of ventricular cerebrospinal fluid (vCSF), and removal of subtentorial structures. The SABRE component requires checking of image alignment along the anterior and posterior commissure (ACPC) plane, and identification of several anatomical landmarks required for regional parcellation. Finally, the Lesion-Seg component involves manual checking of the automatic lesion segmentation of subcortical hyperintensities (SH) for false positive errors.
While on-site training of the LE pipeline is preferable, readily available visual teaching tools with interactive training images are a viable alternative. Developed to ensure a high degree of accuracy and reliability, the following is a step-by-step, video-guided, standardized protocol for LE's manual procedures.

Lesion Explorer (LE) is a semi-automatic, image-processing pipeline developed to obtain regional brain tissue and subcortical hyperintensity lesion volumetrics from structural MRI of Alzheimer's disease and normal elderly. To ensure a high level of accuracy and reliability, the following is a video-guided, standardized protocol for LE's manual procedures.

Obtaining in vivo human brain tissue volumetrics from MRI is often complicated by various technical and biological issues. These challenges are ...

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

In lesion-symptom mapping (LSM), brain function is inferred by relating the location of acquired brain lesions to behavioral or cognitive symptoms in a group of patients. With recent advances in brain imaging and image processing, LSM has become a popular tool in cognitive neuroscience. LSM can provide fundamental insights into the functional architecture of the human brain for a variety of cognitive and non-cognitive functions. A crucial step in performing LSM studies is the segmentation of lesions on brains scans of a large group of patients and registration of each scan to a common stereotaxic space (also called standard space or a standardized brain template). Described here is an open-access, standardized method for infarct segmentation and registration for the purpose of LSM, as well as a detailed and hands-on walkthrough based on exemplary cases. A comprehensive tutorial for the manual segmentation of brain infarcts on CT scans and DWI or FLAIR MRI sequences is provided, including criteria for infarct identification and pitfalls for different scan types. The registration software provides multiple registration schemes that can be used for processing of CT and MRI data with heterogeneous acquisition parameters. A tutorial on using this registration software and performing visual quality checks and manual corrections (which are needed in some cases) is provided. This approach provides researchers with a framework for the entire process of brain image processing required to perform an LSM study, from gathering of the data to final quality checks of the results.

Provided here is a practical tutorial for an open-access, standardized image processing pipeline for the purpose of lesion-symptom mapping. A step-by-step walkthrough is provided for each processing step, from manual infarct segmentation on CT/MRI to subsequent registration to standard space, along with practical recommendations and illustrations with exemplary cases.

In lesion-symptom mapping (LSM), brain function is inferred by relating the location of acquired brain lesions to behavioral or cognitive symptoms in a ...

Behavior

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, ...

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI&#8212;Application in Premanifest Huntington's Disease

Recent advances in MRI offer a variety of useful markers to identify neurodegenerative diseases. In Huntington&#39;s disease (HD), regional brain atrophy begins many years prior to the motor onset (during the &#34;premanifest&#34; period), but the spatiotemporal pattern of regional atrophy across the brain has not been fully characterized. Here we demonstrate an online cloud-computing platform, &#34;MRICloud&#34;, which provides atlas-based whole-brain segmentation of T1-weighted images at multiple granularity levels, and thereby, enables us to access the regional features of brain anatomy. We then describe a regression model that detects statistically significant inflection points, at which regional brain atrophy starts to be noticeable, i.e. the &#34;change-point&#34;, with respect to a disease progression index. We used the CAG-age product (CAP) score to index the disease progression in HD patients. Change-point analysis of the volumetric measurements from the segmentation pipeline, therefore, provides important information of the order and pattern of structural atrophy across the brain. The paper illustrates the use of these techniques on T1-weighted MRI data of premanifest HD subjects from a large multicenter PREDICT-HD study. This design potentially has wide applications in a range of neurodegenerative diseases to investigate the dynamic changes of brain anatomy.

This paper describes a statistical model for volumetric MRI data analysis, which identifies the &#34;change-point&#34; when brain atrophy begins in premanifest Huntington&#39;s disease. Whole-brain mapping of the change-points is achieved based on brain volumes obtained using an atlas-based segmentation pipeline of T1-weighted images.

Recent advances in MRI offer a variety of useful markers to identify neurodegenerative diseases. In Huntington&#39;s disease (HD), regional brain atrophy ...

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Statistical shape analysis of brain structures has been used to investigate the association between their structural changes and pathological processes. We have developed a software package for accurate and robust shape modeling and group-wise analysis. Here, we introduce an pipeline for the shape analysis, from individual 3D shape modeling to quantitative group shape analysis. We also describe the pre-processing and segmentation steps using open software packages. This practical guide would help researchers save time and effort in 3D shape analysis on brain structures.

We introduce a semi-automatic protocol for shape analysis on brain structures, including image segmentation using open software, and further group-wise shape analysis using an automated modeling package. Here, we demonstrate each step of the 3D shape analysis protocol with hippocampal segmentation from brain MR images.

Statistical shape analysis of brain structures has been used to investigate the association between their structural changes and pathological processes. ...

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.

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 ...

Enhancing Neuroimaging Analysis in Brain Disorders

Manual Segmentation of the Human Choroid Plexus Using Brain MRI

The choroid plexus has been implicated in neurodevelopment and a range of brain disorders. Evidence demonstrates that the choroid plexus is critical for brain maturation, immune/ inflammatory regulation, and behavioral/cognitive functioning. However, current automated neuroimaging segmentation tools are poor at accurately and reliably segmenting the lateral ventricle choroid plexus. Furthermore, there is no existing tool that segments the choroid plexus located in the third and fourth ventricles of the brain. Thus, a protocol delineating how to segment the choroid plexus in the lateral, third, and fourth ventricle is needed to increase the reliability and replicability of studies examining the choroid plexus in neurodevelopmental and brain disorders. This protocol provides detailed steps to create separately labeled files in 3D Slicer for the choroid plexus based on DICOM or NIFTI images. The choroid plexus will be manually segmented using the axial, sagittal, and coronal planes of T1w images making sure to exclude voxels from gray or white matter structures bordering the ventricles. Windowing will be adjusted to assist in the localization of the choroid plexus and its anatomical boundaries. Methods for assessing accuracy and reliability will be demonstrated as part of this protocol. Gold standard segmentation of the choroid plexus using manual delineations can be used to develop better and more reliable automated segmentation tools that can be openly shared to elucidate changes in the choroid plexus across the lifespan and within various brain disorders.

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies 

Despite the crucial role of the choroid plexus in the brain, neuroimaging studies of this structure are scarce due to the lack of reliable automated segmentation tools. The present protocol aims to ensure gold-standard manual segmentation of the choroid plexus that can inform future neuroimaging studies.

The choroid plexus has been implicated in neurodevelopment and a range of brain disorders. Evidence demonstrates that the choroid plexus is critical for ...

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are two of the fundamental noninvasive methods for identifying brain activity. Multimodal methods have sought to combine the high temporal resolution of EEG with the spatial precision of fMRI, but the complexity of this approach is currently in need of improvement. The protocol presented here describes the recently developed spatiotemporal fMRI-constrained EEG source imaging method, which seeks to rectify source biases and improve EEG-fMRI source localization through the dynamic recruitment of fMRI sub-regions. The process begins with the collection of multimodal data from concurrent EEG and fMRI scans, the generation of 3D cortical models, and independent EEG and fMRI processing. The processed fMRI activation maps are then split into multiple priors, according to their location and surrounding area. These are taken as priors in a two-level hierarchical Bayesian algorithm for EEG source localization. For each window of interest (defined by the operator), specific segments of the fMRI activation map will be identified as active to optimize a parameter known as model evidence. These will be used as soft constraints on the identified cortical activity, increasing the specificity of the multimodal imaging method by reducing cross-talk and avoiding erroneous activity in other conditionally active fMRI regions. The method generates cortical maps of activity and time-courses, which may be taken as final results, or used as a basis for further analyses (analyses of correlation, causation, etc.) While the method is somewhat limited by its modalities (it will not find EEG-invisible sources), it is broadly compatible with most major processing software, and is suitable for most neuroimaging studies.

An EEG-fMRI multimodal imaging method, known as the spatiotemporal fMRI-constrained EEG source imaging method, is described here. The presented method employs conditionally-active fMRI sub-maps, or priors, to guide EEG source localization in a manner that improves spatial specificity and limits erroneous results.