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; personal identification information from the image files. 
		NOTE: MRICloud offers a DICOM-to-Analyze converter (https://braingps.mricloud.org/t1prep) for the file format conversion as well as the de-identification of personal health information.
		<ol>
			<li>Double-click to open Dcm2Analyze.exe. A popup window will open (Figure 1A).</li>
			<li>Specify the input DICOM data directory path as input and Analyzed image path and file name as output.</li>
			<li>Click &#34;Go&#34; to complete the conversion. 
			NOTE: The conversion needs to be performed for each individual patient, and it would be beneficial to output all converted images in one study folder for batch processing (1.2.2.1).</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Multi-atlas based T1 image segmentation using MRICloud19.
	<ol>
		<li>Log-in &#34;Brain GPS&#34; (register for first-time users) from https://mricloud.org. Select &#34;Segmentation&#34; tool from the main menu (Figure 1B). There are two application programming interface (API) options under &#34;Segmentation&#34;: &#34;T1-MultiAtlas&#34; for single T1 image segmentation, and &#34;T1-MultiAtlas Batch&#34; for batch processing. The change-point analysis requires large population data, and thereby, batch processing is often the choice.</li>
		<li>Submit jobs on &#34;T1-MultiAtlas Batch&#34; API.
		<ol>
			<li>Compress multiple Analyzed image files into a zip file. Click &#34;+.zip&#34; in Figure 1B to upload the zip file. 
			NOTE: The current cloud portal limits the number of images to 30 in each zip folder. Large datasets can be separated into multiple zip folders to be processed. Special request can be made to accommodate more images per zip file. In the future, we may potentially increase the limited number or even remove the limit when more remote computational resources become available.</li>
			<li>Fill in the required fields in Figure 1B.
			<ol>
				<li>Processing server: Choose &#34;Computational Anatomy Science Gateway&#34;.</li>
				<li>Slice Type: Choose from &#34;Sagittal&#34;, &#34;Axial&#34;, or &#34;Sagittal converted to Axial&#34;. 
				NOTE: &#34;Sagittal&#34; refers to images acquired with sagittal orientation with in-plane view in Anterior-Posterior and Head-Foot directions. &#34;Axial&#34; refers to images acquired axial orientation with in-plane view in Left-Right and Anterior-Posterior directions. &#34;Sagittal converted to Axial&#34; refers to images that were originally acquired in axial orientation but later converted to sagittal images (those are typically &#34;Axial&#34; images with long neck area).</li>
				<li>Multi-atlas library: Choose the atlas library with closest age range to the user data to optimize the segmentation accuracy. We provide prebuilt multi-atlas datasets18 with different age ranges, e.g., &#34;Pediatric 4-8yr&#34;, &#34;Pediatric 8-12yr&#34;, &#34;Adult 22-50&#34;, and &#34;Adult 50-90&#34;, as well as different atlas versions. Information about atlas versions can be found in https://braingps.mricloud.org/atlasrepo.</li>
			</ol>
			</li>
		</ol>
		</li>
		<li>Check the job status through &#34;My job status&#34; (Figure 1C). Once the jobs are finished, a &#34;Download Results&#34; button will appear that allows users to download the segmentation results as a .zip file.</li>
		<li>Visualize the results. For single subject, the volumes obtained from the segmentation result can be visualized online (not feasible for batch processing results).
		<ol>
			<li>Click &#34;View result&#34; button in Figure 1C. The webpage will turn to the visualization interface (Figure 1D). The axial, sagittal, and coronal views of the segmentation map are overlapped on the T1-weighted anatomical image. 3D 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.</li>
			<li>Adjust the visualization options, including overlay ON/OFF, opacity of the overlay, zoom in and out, and slice positions from the upper right panel in Figure 1D.</li>
			<li>Group finest segmentation parcels into different granularity. In our atlases, we defined five levels of granularity with two types ontology relations18,30. A tree-view of the hierarchical multi-level anatomical definitions is shown in the lower left panel (Figure 1D). Click the level numbers to expand the structures at the corresponding ontology level. The segmentation maps will simultaneously switch to the corresponding ontology level. 
			NOTE: Bi-monthly MRICloud workshops are help at Johns Hopkins University School of Medicine to offer hands-on tutorials of the online operations described above. Workshop Information can be found from https://braingps.mricloud.org/workshops.</li>
		</ol>
		</li>
		<li>Download the segmentation results for subsequent analysis. Unzip the results to a user study directory, e.g., put together the segmentation results from all the subjects in one study directory, each in their individual subject folders. 
		NOTE: The results include</li>
	</ol>
	<ul style="margin-left: 40px;">
		<li>example.img: original T1 image in axial orientation.</li>
		<li>example_MNI.img: T1 image in MNI space following the Talairach coordinates.</li>
		<li>example_7Labels.img and example_7Labels_MNI.img: Coarse segmentation with 7 parcels (gray matter, white matter, cerebrospinal fluid, lateral ventricle, skull, and background) in native and MNI space, respectively.</li>
		<li>example_283Labels_M2.img and example_283Labels_M2_MNI.img: fine segmentation with 283 parcels (atlas version V9B) in native and MNI space, respectively. The exact number of labels depends on the atlas version.</li>
		<li>example_corrected_MNI_stats.txt and example_MNI_stats.txt: statistics of volumes of each brain parcels in native and MNI space.</li>
		<li>multilevel_lookup_table.txt: Multilevel ontology definition of the brain parcels.</li>
	</ul>
	</li>
	<li>Perform batch processing to obtain brain volumes in a population. Use an in-house Matlab (www.mathworks.com) batch processing script to extract brain volumes from individual result folders and combine the volumetric data of all subjects at all granularity levels to a spreadsheet. A graphic user interface (GUI) is used to specify the input and output.
	<ol>
		<li>Open Matlab.exe, run Main.fig, and a GUI will pop out (Figure 2A).</li>
		<li>In the &#34;T1 Volume extraction from MRICloud&#34; panel (upper panel in Figure 2A), specify the inputs, including the study directory where the downloaded the segmentation results are saved (see 1.2.5); and the multi-level lookup table file path and file name.</li>
		<li>Specify the output spreadsheet file path and file name where the volume data will be write to.</li>
		<li>Click &#34;Extract volume&#34; button to run the analysis. Results can be checked in the user-defined spreadsheet. 
		NOTE: In addition, an R package is developed to process the MRICloud outputs and perform further statistical analysis, provided by Dr. Brian Caffo31. The package can be downloaded from https://github.com/bcaffo/MRIcloudT1volumetrics.</li>
	</ol>
	</li>
</ol>
2. Change-point Analysis of Volumetric Data
NOTE: Please refer the theory and mathematical details of the change-point model to our previous publications21,24,23. Briefly, for HD data, a regression model is established to define a critical change-point (in terms of CAP score), starting from which, the linear regression between CAP and volume becomes statistical significant, while correcting for the effects of patient&#39;s age, gender, and intracranial volume. In-house Matlab scripts, along with a GUI (Figure 2A, lower panel), was developed to compute the change-points for individual structures and perform statistical analysis. The scripts are available to users upon request.
<ol>
	<li>Calculate change-points for individual brain structures.
	<ol>
		<li>In the &#34;Change-point analysis&#34; panel in Figure 2A, specify the file path and name of the multilevel volume spreadsheet, which is generated according to 1.3.</li>
		<li>Specify the file path and name of the output text file, which the change-point results will be written to.</li>
		<li>Choose the level of granularity and type of ontology definition in the drop-down box (Level 1 - 5), at which the change-point analysis will be performed.</li>
		<li>Click &#34;Calculate change-point&#34; button to perform the change-point analysis, as described in reference23, and the resultant change-points will be saved in the output text file.</li>
	</ol>
	</li>
	<li>Statistical evaluations of the change-points.
	<ol>
		<li>In the Matlab GUI (Figure 2A, lower panel), specify parameters for statistical tests, including the number of permutation (default 10,000), number of bootstrap (default 10,000), and p-value threshold (after the FDR correction, default 0.05).</li>
		<li>Click &#34;Statistical test&#34; button to run the tests. After this step, the p-value (by permutation test), before and after false-discovery rate (FDR) correction, as well as the standard deviation and the 95% confidence interval (by bootstrap) of the change-points will be written to the output text file as extra columns. See details of statistical test procedures in23,24.</li>
	</ol>
	</li>
	<li>Generate change-point maps (optional). In the Matlab GUI (Figure 2A, lower panel), click &#34;Map change-point&#34; button to generate change-point maps. Statistically significant change-point values will be mapped onto the MNI-space anatomical image to visualize the spatial pattern. This mapping can be done at different granularity levels, depending on the anatomical level specified in 2.1. The change-point maps can be overlaid on T1-weighted images using MRIcro (Figure 2B) (http://www.cabi.gatech.edu/mricro/mricro/).</li>
</ol>

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The authors have nothing to disclose.

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><tbody><tr><td>MATLAB</td><td>Mathworks</td><td>N/A</td><td>Version 2015b and above</td></tr><tr><td>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...

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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 ...

whole-brain-segmentation-change-point-analysis-anatomical-brain

Research

JoVE Journal

Medicine

12.1K Views.  Johns Hopkins University School of Medicine. Dit witboek beschrijft een statistisch model voor volumetrische MRI data-analyse, waarmee de "verandering-point" wanneer de atrofie van de hersenen in premanifest de ziekte van Huntington begint. Geheel-brain mapping van de verandering-punten wordt bereikt op basis van hersenen volumes verkregen met behulp van een atlas gebaseerde segmentatie pijpleiding van T1-gewogen beelden.

Video: Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

Basics of Multivariate Analysis in Neuroimaging Data

Multivariate analysis techniques for neuroimaging data have recently received increasing attention as they have many attractive features that cannot be easily realized by the more commonly used univariate, voxel-wise, techniques1,5,6,7,8,9. Multivariate approaches evaluate correlation/covariance of activation across brain regions, rather than proceeding on a voxel-by-voxel basis. Thus, their results can be more easily interpreted as a signature of neural networks. Univariate approaches, on the other hand, cannot directly address interregional correlation in the brain. Multivariate approaches can also result in greater statistical power when compared with univariate techniques, which are forced to employ very stringent corrections for voxel-wise multiple comparisons. Further, multivariate techniques also lend themselves much better to prospective application of results from the analysis of one dataset to entirely new datasets. Multivariate techniques are thus well placed to provide information about mean differences and correlations with behavior, similarly to univariate approaches, with potentially greater statistical power and better reproducibility checks. In contrast to these advantages is the high barrier of entry to the use of multivariate approaches, preventing more widespread application in the community. To the neuroscientist becoming familiar with multivariate analysis techniques, an initial survey of the field might present a bewildering variety of approaches that, although algorithmically similar, are presented with different emphases, typically by people with mathematics backgrounds. We believe that multivariate analysis techniques have sufficient potential to warrant better dissemination. Researchers should be able to employ them in an informed and accessible manner. The current article is an attempt at a didactic introduction of multivariate techniques for the novice. A conceptual introduction is followed with a very simple application to a diagnostic data set from the Alzheimer s Disease Neuroimaging Initiative (ADNI), clearly demonstrating the superior performance of the multivariate approach.

The current article describes the basics of multivariate analysis and contrasts it to the more commonly used voxel-wise univariate analysis. Both types of analysis are applied to a clinical-neuroscience data set. Supplementary split-half simulations show better replication of the multivariate results in independent data sets.

Multivariate analysis techniques for neuroimaging data have recently received increasing attention as they have many attractive features that cannot be ...

Neuroscience

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

The scaled subprofile model (SSM)1-4 is a multivariate PCA-based algorithm that identifies major sources of variation in patient and control group brain image data while rejecting lesser components (Figure 1). Applied directly to voxel-by-voxel covariance data of steady-state multimodality images, an entire group image set can be reduced to a few significant linearly independent covariance patterns and corresponding subject scores. Each pattern, termed a group invariant subprofile (GIS), is an orthogonal principal component that represents a spatially distributed network of functionally interrelated brain regions. Large global mean scalar effects that can obscure smaller network-specific contributions are removed by the inherent logarithmic conversion and mean centering of the data2,5,6. Subjects express each of these patterns to a variable degree represented by a simple scalar score that can correlate with independent clinical or psychometric descriptors7,8. Using logistic regression analysis of subject scores (i.e. pattern expression values), linear coefficients can be derived to combine multiple principal components into single disease-related spatial covariance patterns, i.e. composite networks with improved discrimination of patients from healthy control subjects5,6. Cross-validation within the derivation set can be performed using bootstrap resampling techniques9. Forward validation is easily confirmed by direct score evaluation of the derived patterns in prospective datasets10. Once validated, disease-related patterns can be used to score individual patients with respect to a fixed reference sample, often the set of healthy subjects that was used (with the disease group) in the original pattern derivation11. These standardized values can in turn be used to assist in differential diagnosis12,13 and to assess disease progression and treatment effects at the network level7,14-16. We present an example of the application of this methodology to FDG PET data of Parkinson's Disease patients and normal controls using our in-house software to derive a characteristic covariance pattern biomarker of disease.

Multivariate techniques including principal component analysis (PCA) have been used to identify signature patterns of regional change in functional brain images. We have developed an algorithm to identify reproducible network biomarkers for the diagnosis of neurodegenerative disorders, assessment of disease progression, and objective evaluation of treatment effects in patient populations.

The scaled subprofile model (SSM)1-4 is a multivariate PCA-based algorithm that identifies major sources of variation in patient and control group brain ...

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Diffusion tensor imaging (DTI) techniques provide information on the microstructural processes of the cerebral white matter (WM) in vivo. The present applications are designed to investigate differences of WM involvement patterns in different brain diseases, especially neurodegenerative disorders, by use of different DTI analyses in comparison with matched controls. 	
DTI data analysis is performed in a variate fashion, i.e. voxelwise comparison of regional diffusion direction-based metrics such as fractional anisotropy (FA), together with fiber tracking (FT) accompanied by tractwise fractional anisotropy statistics (TFAS) at the group level in order to identify differences in FA along WM structures, aiming at the definition of regional patterns of WM alterations at the group level. Transformation into a stereotaxic standard space is a prerequisite for group studies and requires thorough data processing to preserve directional inter-dependencies. The present applications show optimized technical approaches for this preservation of quantitative and directional information during spatial normalization in data analyses at the group level. On this basis, FT techniques can be applied to group averaged data in order to quantify metrics information as defined by FT. Additionally, application of DTI methods, i.e. differences in FA-maps after stereotaxic alignment, in a longitudinal analysis at an individual subject basis reveal information about the progression of neurological disorders. Further quality improvement of DTI based results can be obtained during preprocessing by application of a controlled elimination of gradient directions with high noise levels.	
In summary, DTI is used to define a distinct WM pathoanatomy of different brain diseases by the combination of whole brain-based and tract-based DTI analysis.

Diffusion tensor imaging (DTI) basically serves as an MRI-based tool to identify in vivo the microstructure of the brain and pathological processes due to neurological disorders within the cerebral white matter. DTI-based analyses allow for application to brain diseases both at the group level and in single subject data.

Diffusion tensor imaging (DTI) techniques provide information on the microstructural processes of the cerebral white matter (WM) in vivo. The present ...

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

High-resolution In Vivo Manual Segmentation Protocol for Human Hippocampal Subfields Using 3T Magnetic Resonance Imaging

The human hippocampus has been broadly studied in the context of memory and normal brain function and its role in different neuropsychiatric disorders has been heavily studied. While many imaging studies treat the hippocampus as a single unitary neuroanatomical structure, it is, in fact, composed of several subfields that have a complex three-dimensional geometry. As such, it is known that these subfields perform specialized functions and are differentially affected through the course of different disease states. Magnetic resonance (MR) imaging can be used as a powerful tool to interrogate the morphology of the hippocampus and its subfields. Many groups use advanced imaging software and hardware (&#62;3T) to image the subfields; however this type of technology may not be readily available in most research and clinical imaging centers. To address this need, this manuscript provides a detailed step-by-step protocol for segmenting the full anterior-posterior length of the hippocampus and its subfields: cornu ammonis (CA) 1, CA2/CA3, CA4/dentate gyrus (DG), strata radiatum/lacunosum/moleculare (SR/SL/SM), and subiculum. This protocol has been applied to five subjects (3F, 2M; age 29-57, avg. 37). Protocol reliability is assessed by resegmenting either the right or left hippocampus of each subject and computing the overlap using the Dice&#39;s kappa metric. Mean Dice&#39;s kappa (range) across the five subjects are: whole hippocampus, 0.91 (0.90-0.92); CA1, 0.78 (0.77-0.79); CA2/CA3, 0.64 (0.56-0.73); CA4/dentate gyrus, 0.83 (0.81-0.85); strata radiatum/lacunosum/moleculare, 0.71 (0.68-0.73); and subiculum 0.75 (0.72-0.78). The segmentation protocol presented here provides other laboratories with a reliable method to study the hippocampus and hippocampal subfields in vivo using commonly available MR tools.

High-resolution In Vivo Manual Segmentation Protocol for Human Hippocampal Subfields Using 3T Magnetic Resonance Imaging

The goal of this manuscript is to study the hippocampus and hippocampal subfields using MRI. The manuscript describes a protocol for segmenting the hippocampus and five hippocampal substructures: cornu ammonis (CA) 1, CA2/CA3, CA4/dentate gyrus, strata radiatum/lacunosum/moleculare, and subiculum.

The human hippocampus has been broadly studied in the context of memory and normal brain function and its role in different neuropsychiatric disorders has ...

3D Modeling of the Lateral Ventricles and Histological Characterization of Periventricular Tissue in Humans and Mouse

The ventricular system carries and circulates cerebral spinal fluid (CSF) and facilitates clearance of solutes and toxins from the brain. The functional units of the ventricles are ciliated epithelial cells termed ependymal cells, which line the ventricles and through ciliary action are capable of generating laminar flow of CSF at the ventricle surface. This monolayer of ependymal cells also provides barrier and filtration functions that promote exchange between brain interstitial fluids (ISF) and circulating CSF. Biochemical changes in the brain are thereby reflected in the composition of the CSF and destruction of the ependyma can disrupt the delicate balance of CSF and ISF exchange. In humans there is a strong correlation between lateral ventricle expansion and aging. Age-associated ventriculomegaly can occur even in the absence of dementia or obstruction of CSF flow. The exact cause and progression of ventriculomegaly is often unknown; however, enlarged ventricles can show regional and, often, extensive loss of ependymal cell coverage with ventricle surface astrogliosis and associated periventricular edema replacing the functional ependymal cell monolayer. Using MRI scans together with postmortem human brain tissue, we describe how to prepare, image and compile 3D renderings of lateral ventricle volumes, calculate lateral ventricle volumes, and characterize periventricular tissue through immunohistochemical analysis of en face lateral ventricle wall tissue preparations. Corresponding analyses of mouse brain tissue are also presented supporting the use of mouse models as a means to evaluate changes to the lateral ventricles and periventricular tissue found in human aging and disease. Together, these protocols allow investigations into the cause and effect of ventriculomegaly and highlight techniques to study ventricular system health and its important barrier and filtration functions within the brain.

Using MRI scans (human), 3D imaging software, and immunohistological analysis, we document changes to the brain&#8217;s lateral ventricles. Longitudinal 3D mapping of lateral ventricle volume changes and characterization of periventricular cellular changes that occur in the human brain due to aging or disease are then modeled in mice.

The ventricular system carries and circulates cerebral spinal fluid (CSF) and facilitates clearance of solutes and toxins from the brain. The functional ...

Symmetric Bihemispheric Postmortem Brain Cutting to Study Healthy and Pathological Brain Conditions in Humans

Neuropathologists, at times, feel intimidated by the amount of knowledge needed to generate definitive diagnoses for complex neuropsychiatric phenomena described in those patients for whom a brain autopsy has been requested. Although the advancements of biomedical sciences and neuroimaging have revolutionized the neuropsychiatric field, they have also generated the misleading idea that brain autopsies have only a confirmatory value. This false idea created a drastic reduction of autopsy rates and, consequently, a reduced possibility to perform more detailed and extensive neuropathological investigations, which are necessary to comprehend numerous normal and pathological aspects yet unknown of the human brain. The traditional inferential method of correlation between observed neuropsychiatric phenomena and corresponding localization/characterization of their possible neurohistological correlates continues to have an undeniable value. In the context of neuropsychiatric diseases, the traditional clinicopathological method is still the best possible methodology (and often the only available) to link unique neuropsychiatric features to their corresponding neuropathological substrates, since it relies specifically upon the direct physical assessment of brain tissues. The assessment of postmortem brains is based on brain cutting procedures that vary across different neuropathology centers. Brain cuttings are performed in a relatively extensive and systematic way based on the various clinical and academic contingencies present in each institution. A more anatomically inclusive and symmetric bi-hemispheric brain cutting methodology should at least be used for research purposes in human neuropathology to coherently investigate, in depth, normal and pathological conditions with the peculiarities of the human brain (i.e., hemispheric specialization and lateralization for specific functions). Such a method would provide a more comprehensive collection of neuropathologically well-characterized brains available for current and future biotechnological and neuroimaging techniques. We describe a symmetric bi-hemispheric brain cutting procedure for the investigation of hemispheric differences in human brain pathologies and for use with current as well as future biomolecular/neuroimaging techniques.

Organized brain cutting procedures are necessary to correlate specific neuropsychiatric phenomena with definitive neuropathologic diagnoses. Brain cuttings are performed differently based on various clinico-academic contingencies. This protocol describes a symmetric bihemispheric brain cutting procedure to investigate hemispheric differences in human brain pathologies and to maximize current and future biomolecular/neuroimaging techniques.

Neuropathologists, at times, feel intimidated by the amount of knowledge needed to generate definitive diagnoses for complex neuropsychiatric phenomena ...

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.

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 ...

Brain Morphology of Cannabis Users With or Without Psychosis: A Pilot MRI Study

Cannabis is the illicit drug most commonly used worldwide, and its consumption can both induce psychiatric symptoms in otherwise healthy subjects and unmask a florid psychotic picture in patients with a prior psychotic risk. Previous studies suggest that chronic and long-term cannabis exposure may exert significant negative effects in brain areas enriched with cannabinoid receptors. However, whether brain alterations determined by cannabis dependency will lead to a clinically significant phenotype or to a psychotic outbreak at some point of an abuser&#8217;s life remains unclear. The aim of this study was to investigate morphological brain differences between chronic cannabis users with cannabis-induced psychosis (CIP) and non-psychotic cannabis users (NPCU) without any psychiatric conditions and correlate brain deficits with selective socio-demographic, clinical and psychosocial variables.
3T magnetic resonance imaging (MRI) scans of 10 CIP patients and 12 NPCU were acquired. The type of drug, the frequency, and the duration, as well socio-demographic, clinical and psychosocial parameters of dependency were measured. CIP patients had extensive grey matter (GM) decreases in right superior frontal gyrus, right precentral, right superior temporal gyrus, insula bilaterally, right precuneus, right medial occipital gyrus, right fusiform gyrus, and left hippocampus in comparison to chronic cannabis users without psychosis. Finally, in CIP patients, the results showed a negative correlation between a domain of the Brief Psychiatric Rating Scale (BPRS), BPRS-Activity, and selective GM volumes. Overall, the results suggest that cannabis-induced psychosis is characterized by selective brain reductions that are not present in NPCU. Therefore, neuroimaging studies may provide a potential ground for identifying putative biomarkers associated with the risk of developing psychosis in cannabis users.

This is a 3T magnetic resonance imaging study aiming to investigate grey matter volume differences between cannabis-induced psychosis patients and non-psychotic chronic cannabis users.

Cannabis is the illicit drug most commonly used worldwide, and its consumption can both induce psychiatric symptoms in otherwise healthy subjects and ...

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

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