Name: whole-brain segmentation and change-point analysis of anatomical brain mri&#8212;application in premanifest huntington's disease
Uploaded: 2018-06-09T13:00:00
Description: Watch this Scientific Journal Video about Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRIâ€”Application in Premanifest Huntington's Disease at JoVE.com

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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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. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Inferring+changepoint+times+of+medial+temporal+lobe+morphometric+change+in+preclinical+Alzheimer's+disease.">Inferring changepoint times of medial temporal lobe morphometric change in preclinical Alzheimer's disease.</a> Neuroimage Clin. 5, 178-187 (2014).</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+critical+gestational+age+at+birth+that+alters+brain+development+in+preterm-born+infants+using+multi-modal+MRI.">Mapping the critical gestational age at birth that alters brain development in preterm-born infants using multi-modal MRI.</a> NeuroImage. 149, 33-43 (2017).</li><li>Zhang, Y., 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=Indexing+disease+progression+at+study+entry+with+individuals+at-risk+for+Huntington+disease.">Indexing disease progression at study entry with individuals at-risk for Huntington disease.</a> Am J Med Genet B Neuropsychiatr Genet. 156b (7), 751-763 (2011).</li><li>Vonsattel, J. 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">
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			<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 MRIâ€”Application in Premanifest Huntington's Disease at JoVE.com

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI&#8212;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.

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

The overall goal of this image analysis pipeline is to perform quantitative volumetric analysis of T1 weighted MR images and to identify critical change-points when brain atrophy begins in neurodegenerative diseases. This method can help answer key questions in quantitative analysis of brain anatomical changes during the course of neurodegenerative disease. For example, the order and the pattern of brain atrophy in the Huntington's disease.The main advantage of this technique is that the combination of Atlas-based segmentation and change-point analysis bring unique spatial temporal information about brain degeneration from large population of studies. The volumetric analysis of brain MRI was achieved by MRICloud, which provides fully automated image segmentation and quantification through remote computation and web-based user interface. Change-point analysis of the brain MRI provides us a systematic view of the disease progression across the whole brain.The applications of this technique extend toward a range of neural degenerative disease. To begin this procedure, convert three dimensional T1 weighted images from vendor-specific DICOM format to analyzed format. Double click to open Dcm2Analyze.exe.A pop up window will open. Specify the input DICOM data directory path as input and analyzed image file path and name as output. Then, click go to complete the conversion.For MultiAtlas-based T1 image segmentation using MRICloud, log in BrainGPS and select segmentation tool from the main menu. There are two options under segmentation, the T1-MultiAtlas for single T1 image segmentation, and T1-MultiAtlas Batch for batch processing. Submit jobs on T1-MultiAtlas Batch API by compressing multiple analyzed image files into a ZIP file, and then click zip to upload the ZIP file.Now, fill in the required fields. For processing server, choose Computational Anatomy Science Gateway. For slice type, choose from Sagittal, Axial, or Sagittal converted to Axial.For MultiAtlas library, choose the Atlas library with the closest age range to the user data to optimize the segmentation accuracy. Check the job status through my job status. Once the jobs are finished, a download results button will appear that allows users to download the segmentation results as a ZIP file.Results will be saved in individual folders. Visualize the results by clicking the view results button. The web page will turn to the visualization interface.The Axial, Sagittal, and Coronal views of the segmentation map are overlapped on the T1 weighted anatomical image. 3-D rendering of the segmented brain structures are shown in the upper-left window. Color of the overlaying segmentation map indicates the z-score of the structural volumes.Adjust the visualization options, including overlay on/off, opacity of the overlay, zoom in and out, and slice positions from the upper right panel. To perform batch processing to obtain brain volumes in a population, use an in-house MATLAB program to extract individual brain volumes and combine them in a spreadsheet. In MATLAB, run main.m, and a GUI will pop out. In the T1 volume extraction from MRICloud panel, specify the inputs, including the study directory where the downloaded segmentation results are saved, and the multi-level lookup table file path and file name. Specify the output directory of the spreadsheet file where the volume data will be written to.Click the extract volume button to run the analysis. A message will show after successful completion. Results can be checked in the spreadsheet, where the rows contain individual subject data and the columns contain the structural volumes of these individuals defined at multiple anatomical granularities.To calculate change-points for individual brain structures, use the same MATLAB GUI. In the change-point analysis panel, specify the spreadsheets that contain the multi-level volume data and the associated diagnostic information. Then, specify the directory of the output text file, which the change-point results will be written to.Afterward, choose the level of granularity and type of ontology definition in the drop down box, at which the change-point analysis will be performed. Also, choose the main repressor to be used in the change-point analysis, for example, CAP score for Huntington's disease. Click the calculate change-point button to perform the analysis and the resultant change-points will be saved in the user-defined output excel file.To perform statistical evaluations of the change-points in the MATLAB GUI, specify the parameters for statistical tests, including the number of permutation and number of bootstrap. Click the statistical test button to run the tests. After this step, the p-values from the permutation test as well as the mean and standard deviation from the bootstrap operation will be written to the output text file as extra columns.Optionally, to map the change-points into the brain and 3-D space, click map change-point. After this step, and change-point map will be generated and visualized by overlying on the T1 weighted image. Shown here are the representative results of the Atlas-based whole brain segmentation at multiple granularity levels.Axial and Coronal views of multi-level segmentation maps are overlaid on T1 weighted anatomical images. The hierarchical anatomical relations between the hemisphere the cerebral nuclei, the basal ganglia of the cerebral nuclei, the striatum and globus pallidus of the basal ganglia, and putamen and caudate of the striatum are illustrated in 3-D. Here, the scatter plots demonstrate the change-point analysis of these structures, where the blue dots denote the z-scores of volumetric data after correcting for age, sex, and intercranial volumes.The black curves are the fitted z-scores, with regression to the change-point dependent CAP scores, and the red lines indicate the positions of the change-points. Here are the whole-brain change-point maps at multiple granularity levels. The regions that show significant change-points are mapped onto a T1 weighed image, and the colors indicate the change-point values in unit of CAP score.Once mastered, this procedure can be accomplished in several hours for the image segmentation part, depending on the availability of the cloud resource. And, another few hours for the change-point analysis, depending on the user-defined level of anatomical granularity. While attempting this procedure, it is important to always check the quality of the original image and accuracy of image segmentation before proceeding to the change-point analysis.After its development, this technique paved the way for researchers in the field of medical image analysis to explore whole-brain anatomical change and its spatial temporal pattern in large MRI studies of neural degenerative diseases. After watching this video, you should have a good understanding of how to perform automated segmentation of T1 weighted images and how to use the MATLAB GUI to run change-point analysis of photometric data.

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Medicine

Isolation of Soluble and Insoluble PrP Oligomers in the Normal Human Brain

The central event in the pathogenesis of prion diseases involves a conversion of the host-encoded cellular prion protein PrPC into its pathogenic isoform ...

Identification and Isolation of Slow-Dividing Cells in Human Glioblastoma Using Carboxy Fluorescein Succinimidyl Ester (CFSE)

Identification and Isolation of Slow-Dividing Cells in Human Glioblastoma Using Carboxy Fluorescein Succinimidyl Ester CFSE

Tumor heterogeneity represents a fundamental feature supporting tumor robustness and presents a central obstacle to the development of therapeutic ...

The Use of Primary Human Fibroblasts for Monitoring Mitochondrial Phenotypes in the Field of Parkinson's Disease

Parkinson's disease (PD) is the second most  common movement disorder and affects 1% of people over the age of 60 1. Because ageing is  the most important ...

Quantitative Analysis of Chromatin Proteomes in Disease

In the nucleus reside the proteomes whose functions are most intimately linked with gene regulation. Adult mammalian cardiomyocyte nuclei are unique due ...

Utilizing a Cranial Window to Visualize the Middle Cerebral Artery During Endothelin-1 Induced Middle Cerebral Artery Occlusion

Creation of a cranial window is a method that allows direct visualization of structures on the cortical surface of the brain1-3. This technique can be ...

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 ...

Controlling Parkinson's Disease With Adaptive Deep Brain Stimulation

Adaptive deep brain stimulation (aDBS) has the potential to improve the treatment of Parkinson&#39;s disease by optimizing stimulation in real time ...

Detection of Disease-associated &#945;-synuclein by Enhanced ELISA in the Brain of Transgenic Mice Overexpressing Human A53T Mutated &#945;-synuclein

In addition to established methods like Western blot, new methods are needed to quickly and easily quantify disease-associated &#945;-synuclein (&#945;SD) ...

A Tissue Displacement-based Contusive Spinal Cord Injury Model in Mice

Producing a consistent and reproducible contusive spinal cord injury (SCI) is critical to minimizing behavioral and histological variabilities between ...

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Impaired functional connectivity in the Default Mode Network (DMN) may be involved in the progression of Alzheimer&#39;s Disease (AD). The Posterior ...