We thank the PREDICT-HD investigators, particularly, Dr. Hans Johnson and Dr. Jane S. Pauslen from University of Iowa, for their generosity in sharing the MRI data and constructive discussion on the data analysis and results.
This work is supported by NIH grants R21 NS098018, P50 NS16375, NS40068, R01 NS086888, R01 NS084957, P41 EB015909, P41 EB015909, R01 EB000975, R01 EB008171, and U01 NS082085.

1. Atlas-based Whole Brain Segmentation
<ol>
	<li>Data preparation
	<ol>
		<li>Convert three-dimensional (3D) T1-weighted images, typically acquired with MPRAGE (magnetization-prepared rapid gradient-echo) sequence, from vendor-specific DICOM (Digital Imaging and Communication) format to Analyzed format. Note that the cloud computation requires users&#39; data to be transferred to remote clusters. According to the Health Insurance Portability and Accountability Act (HIPPA), remove the patients&#39; per...

<ol>
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P., 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=Hippocampal+volumes+in+Alzheimer's+disease,+Parkinson's+disease+with+and+without+dementia,+and+in+vascular+dementia:+An+MRI+study.">Hippocampal volumes in Alzheimer's disease, Parkinson's disease with and without dementia, and in vascular dementia: An MRI study.</a> Neurology. 46 (3), 678-681 (1996).</li><li>Miller, M. I., Faria, A. V., Oishi, K., Mori, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High-throughput+neuro-imaging+informatics.">High-throughput neuro-imaging informatics.</a> Front Neuroinform. 7, 31 (2013).</li><li>Mori, S., Oishi, K., Faria, A. V., Miller, 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=Atlas-based+neuroinformatics+via+MRI:+harnessing+information+from+past+clinical+cases+and+quantitative+image+analysis+for+patient+care.">Atlas-based neuroinformatics via MRI: harnessing information from past clinical cases and quantitative image analysis for patient care.</a> Annu Rev Biomed Eng. 15, 71-92 (2013).</li><li>Klein, A., Mensh, B., Ghosh, S., Tourville, J., Hirsch, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mindboggle:+automated+brain+labeling+with+multiple+atlases.">Mindboggle: automated brain labeling with multiple atlases.</a> BMC Med Imaging. 5, 7 (2005).</li><li>Heckemann, R. A., Hajnal, J. 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I., Trouve, A., Younes, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Diffeomorphometry+and+geodesic+positioning+systems+for+human+anatomy.">Diffeomorphometry and geodesic positioning systems for human anatomy.</a> Technology. 2, (2013).</li><li>Tang, X., 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=Bayesian+Parameter+Estimation+and+Segmentation+in+the+Multi-Atlas+Random+Orbit+Model.">Bayesian Parameter Estimation and Segmentation in the Multi-Atlas Random Orbit Model.</a> PLoS One. 8 (6), e65591 (2013).</li><li>Wang, H., 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=Multi-Atlas+Segmentation+with+Joint+Label+Fusion.">Multi-Atlas Segmentation with Joint Label Fusion.</a> IEEE Trans Pattern Anal Mach Intell. 35 (3), 611-623 (2013).</li><li>Wu, D., 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=Resource+atlases+for+multi-atlas+brain+segmentations+with+multiple+ontology+levels+based+on+T1-weighted+MRI.">Resource atlases for multi-atlas brain segmentations with multiple ontology levels based on T1-weighted MRI.</a> Neuroimage. 125, 120-130 (2015).</li><li>Mori, S., 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=MRICloud:+Delivering+High-Throughput+MRI+Neuroinformatics+as+Cloud-Based+Software+as+a+Service.">MRICloud: Delivering High-Throughput MRI Neuroinformatics as Cloud-Based Software as a Service.</a> Computing in Science &amp; Engineering. 18 (5), 21-35 (2016).</li><li>Wu, D., Ceritoglu, C., Miller, M. I., Mori, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Direct+estimation+of+patient+attributes+from+anatomical+MRI+based+on+multi-atlas+voting.">Direct estimation of patient attributes from anatomical MRI based on multi-atlas voting.</a> Neuroimage-Clinical. 12, 570-581 (2016).</li><li>Miller, M. 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=Network+Neurodegeneration+in+Alzheimer's+Disease+via+MRI+Based+Shape+Diffeomorphometry+and+High-Field+Atlasing.">Network Neurodegeneration in Alzheimer's Disease via MRI Based Shape Diffeomorphometry and High-Field Atlasing.</a> Front Bioeng Biotechnol. 3, 54 (2015).</li><li>Faria, A. V., 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=Content-based+image+retrieval+for+brain+MRI:+an+image-searching+engine+and+population-based+analysis+to+utilize+past+clinical+data+for+future+diagnosis.">Content-based image retrieval for brain MRI: an image-searching engine and population-based analysis to utilize past clinical data for future diagnosis.</a> Neuroimage Clin. 7, 367-376 (2015).</li><li>Wu, D., 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=Mapping+the+order+and+pattern+of+brain+structural+MRI+changes+using+change-point+analysis+in+premanifest+Huntington's+disease.">Mapping the order and pattern of brain structural MRI changes using change-point analysis in premanifest Huntington's disease.</a> Hum Brain Mapp. , (2017).</li><li>Younes, L., Albert, M., Miller, M. I., Team, B. 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P., 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=Neuropathological+classification+of+Huntington's+disease.">Neuropathological classification of Huntington's disease.</a> J Neuropathol Exp Neurol. 44 (6), 559-577 (1985).</li><li>Paulsen, J. S., 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=Detection+of+Huntington's+disease+decades+before+diagnosis:+the+Predict-HD+study.">Detection of Huntington's disease decades before diagnosis: the Predict-HD study.</a> J Neurol Neurosurg Psychiatry. 79 (8), 874-880 (2008).</li><li>Paulsen, J. S., 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=Prediction+of+manifest+Huntington's+disease+with+clinical+and+imaging+measures:+a+prospective+observational+study.">Prediction of manifest Huntington's disease with clinical and imaging measures: a prospective observational study.</a> Lancet Neurol. 13 (12), 1193-1201 (2014).</li><li>Djamanakova, 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=Tools+for+multiple+granularity+analysis+of+brain+MRI+data+for+individualized+image+analysis.">Tools for multiple granularity analysis of brain MRI data for individualized image analysis.</a> Neuroimage. 101, 168-176 (2014).</li><li>. A package for T1 volumetric analysis of MRIcloud output. R package version 0.0.1 Available from: <a target="_blank" href="https://github.com/bcaffo/MRIcloudT1volumetrics">https://github.com/bcaffo/MRIcloudT1volumetrics</a> (2017)</li><li>Aljabar, P., Heckemann, R. A., Hammers, A., Hajnal, J. 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As demonstrated in this paper, whole-brain segmentation of brain MRI can be conveniently achieved using our online platform MRICloud. T1-weighted MRI based volumetric marker has shown to be robust and sensitive to a range of neurodegenerative diseases1,2,3. The volumetric measures are used for various downstream analysis, such as mathematical modeling, and feature-selection and classification analysis to assist clinical diagnosi...

Magnetic resonance imaging (MRI) has substantially enhanced our ability to examine the brain anatomy and functions in neurodegenerative diseases1,2,3. T1-weighted structural MRI is one of most widely adopted imaging tools in routine clinical practice to assess the brain anatomy and related pathology. Quantitative analysis of the high-resolution T1-weighted images provides useful markers to measure anatomical changes during brain degeneration. In particular, segmentation based quantification approaches effectively reduces the image dimensionality from voxel level (on the order of (106)) to anatomical structural level ((102)) for high-throughput neuroinformatics4,5. Automated brain segmentation can be achieved using atlas-based methods6,7,8,9 that map the pre-defined anatomical labels from an atlas onto the patient images. Among the atlas-based methods, multi-atlas algorithms10,11,12,13,14 have yielded superior segmentation accuracy and robustness. Our group has developed a fully automated T1 multi-atlas segmentation pipeline, with advanced diffeomorphic image registration algorithms15, multi-atlas fusion methods16,17, and rich multi-atlas libraries18. The pipeline has been distributed on a cloud-computing platform, MRICloud19, since 2015, and it has been used to study neurodegenerative diseases, such as Alzheimer&#39;s disease (AD)20,21, Primary Progressive Aphasia22, and Huntington&#39;s Disease23.
Once the high-resolution images are segmented into brain structures, regional features, such as volumes, can be used to establish mathematical models to characterize the neuroanatomical changes. A change-point analysis method was recently established by our group to analyze the temporal order, in which statistically significant brain morphometric changes occur, based on longitudinal and/or cross-sectional MRI data. This statistical model was first developed to quantify shape-based diffeomorphometry over age in AD patients21,24; and it was later adapted to investigate brain structural changes in Huntington&#39;s disease (HD), as well as to describe brain developmental changes in neonatal brains25. In HD patients, the change-point was defined in with respect to the CAG-age product (CAP) score, as an indicator of the extent of exposure to the CAG expansion in HTT 26. It is well-known that striatal atrophy is one of the earliest markers in HD, followed by the globus pallidus27. Yet, the changes in striatum in relation to other gray and white matter structures across the brain remains unclear. Such relation is crucial for us to understand the disease progression. Change-point analysis of volumetric changes in all brain structures will likely provide systematic information of brain atrophy in premanifest phase of HD.
Here we demonstrate the procedures to perform whole-brain segmentation using MRICloud (www.mricloud.org), and steps to perform change-point analysis of volumetric data in premanifest HD subjects. The MRI data were collected from a large population multicenter PREDICT-HD study28,29 with approximately 400 controls and premanifest HD subjects. The combination of atlas-based segmentation and change-point analysis brings unique information about the spatiotemporal order of the brain structural changes and the disease progression pattern across the brain. The techniques are potentially applicable to a range of neurodegenerative diseases with various biomarkers to map the brain degeneration.

<table border="0" cellpadding="0" cellspacing="0" width="849">
	<tbody>
		<tr height="40">
			<td height="40" style="height:40px;width:216px;">MATLAB</td>
			<td style="width:123px;">Mathworks</td>
			<td style="width:344px;">N/A</td>
			<td style="width:167px;">Version 2015b and above</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">Dell Workstation</td>
			<td>Dell</td>
			<td></td>
			<td>Dell Precision T5500 (Intel Xeon CPU)</td>
		</tr>
	</tbody>
</table>

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.

Using the procedures described in 1.1-1.3, whole brain segmentation maps can be obtained from MRICloud. In the current version of atlas (V9B), 283 parcels are segmented at the finest granularity (level 5), which can be grouped to different levels of granularity, e.g., from hemisphere to lobules and parcels, according to specific ontology definitions. Figure 3 shows two types of multi-level segmentations at five levels, in axial and coronal views. For...

Watch this Scientific Journal Video about Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRIÃ¢â‚¬â€Application in Premanifest Huntington's Disease at JoVE.com

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRIÃƒÆ’Ã‚Â¢ÃƒÂ¢Ã¢â‚¬Å¡Ã‚Â¬ÃƒÂ¢Ã¢â€šÂ¬Ã‚ÂApplication in Premanifest Huntington's Disease

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.

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 ...

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