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1.  Clinical characteristics of patients with spinocerebellar ataxias 1, 2, 3 and 6 in the US; a prospective observational study 
Background
All spinocerebellar ataxias (SCAs) are rare diseases. SCA1, 2, 3 and 6 are the four most common SCAs, all caused by expanded polyglutamine-coding CAG repeats. Their pathomechanisms are becoming increasingly clear and well-designed clinical trials will be needed.
Methods
To characterize the clinical manifestations of spinocerebellar ataxia (SCA) 1, 2, 3 and 6 and their natural histories in the United States (US), we conducted a prospective multicenter study utilized a protocol identical to the European consortium study, using the Scale for the Assessment and Rating of Ataxia (SARA) score as the primary outcome, with follow-ups every 6 months up to 2 years.
Results
We enrolled 345 patients (60 SCA1, 75 SCA2, 138 SCA3 and 72 SCA6) at 12 US centers. SCA6 patients had a significantly later onset, and SCA2 patients showed greater upper-body ataxia than patients with the remaining SCAs. The annual increase of SARA score was greater in SCA1 patients (mean ± SE: 1.61 ± 0.41) than in SCA2 (0.71 ± 0.31), SCA3 (0.65 ± 0.24) and SCA6 (0.87 ± 0.28) patients (p = 0.049). The functional stage also worsened faster in SCA1 than in SCA2, 3 and 6 (p = 0.002).
Conclusions
The proportions of different SCA patients in US differ from those in the European consortium study, but as in the European patients, SCA1 progress faster than those with SCA2, 3 and 6. Later onset in SCA6 and greater upper body ataxia in SCA2 were noted. We conclude that progression rates of these SCAs were comparable between US and Europe cohorts, suggesting the feasibility of international collaborative clinical studies.
doi:10.1186/1750-1172-8-177
PMCID: PMC3843578  PMID: 24225362
Spinocerebellar ataxia; Natural history; SARA; Progression rate
2.  MRI shows a region-specific pattern of atrophy in spinocerebellar ataxia type 2 
Cerebellum (London, England)  2012;11(1):272-279.
In this study, we used manual delineation of high-resolution magnetic resonance imaging (MRI) to determine the spatial and temporal characteristics of the cerebellar atrophy in spinocerebellar ataxia type 2 (SCA2). Ten subjects with SCA2 were compared to ten controls. The volume of the pons, the total cerebellum, and the individual cerebellar lobules were calculated via manual delineation of structural MRI. SCA2 showed substantial global atrophy of the cerebellum. Furthermore, the degeneration was lobule-specific, selectively affecting the anterior lobe, VI, Crus I, Crus II, VIII, uvula, corpus medullare, and pons, while sparing VIIB, tonsil/paraflocculus, flocculus, declive, tuber/folium, pyramis, and nodulus. The temporal characteristics differed in each cerebellar subregion: 1) Duration of disease: Crus I, VIIB, VIII, uvula, corpus medullare, pons, and the total cerebellar volume correlated with the duration of disease; 2) Age: VI, Crus II, and flocculus correlated with age in control subjects; 3) Clinical scores: VI, Crus I, VIIB, VIII, corpus medullare, pons, and the total cerebellar volume correlated with clinical scores in SCA2. No correlations were found with the age of onset. Our extrapolated volumes at the onset of symptoms suggest that neurodegeneration may be present even during the presymptomatic stages of disease. The spatial and temporal characteristics of the cerebellar degeneration in SCA2 are region-specific. Furthermore, our findings suggest the presence of presymptomatic atrophy and a possible developmental component to the mechanisms of pathogenesis underlying SCA2. Our findings further suggest that volumetric analysis may aid in the development of a non-invasive, quantitative biomarker.
doi:10.1007/s12311-011-0308-8
PMCID: PMC3785794  PMID: 21850525
ataxia; spinocerebellar ataxia type 2 (SCA2); magnetic resonance imaging (MRI); biomarker
3.  Automated Segmentation of the Cerebellar Lobules using Boundary Specific Classification and Evolution 
The cerebellum is instrumental in coordinating many vital functions ranging from speech and balance to eye movement. The effect of cerebellar pathology on these functions is frequently examined using volumetric studies that depend on consistent and accurate delineation, however, no existing automated methods adequately delineate the cerebellar lobules. In this work, we describe a method we call the Automatic Classification of Cerebellar Lobules Algorithm using Implicit Multi-boundary evolution (ACCLAIM). A multiple object geometric deformable model (MGDM) enables each boundary surface of each individual lobule to be evolved under different level set speeds. An important innovation described in this work is that the speed for each lobule boundary is derived from a classifier trained specifically to identify that boundary. We compared our method to segmentations obtained using the atlas-based and multi-atlas fusion techniques, and demonstrate ACCLAIM’s superior performance.
PMCID: PMC3979931  PMID: 24683958
4.  Principal Component Analysis of Cerebellar Shape on MRI Separates SCA Types 2 and 6 into Two Archetypal Modes of Degeneration 
Cerebellum (London, England)  2012;11(4):887-895.
Although “cerebellar ataxia” is often used in reference to a disease process, presumably there are different underlying pathogenetic mechanisms for different subtypes. Indeed, spinocerebellar ataxia (SCA) types 2 and 6 demonstrate complementary phenotypes, thus predicting a different anatomic pattern of degeneration. Here, we show that an unsupervised classification method, based on principal component analysis (PCA) of cerebellar shape characteristics, can be used to separate SCA2 and SCA6 into two classes, which may represent disease-specific archetypes. Patients with SCA2 (n=11) and SCA6 (n=7) were compared against controls (n=15) using PCA to classify cerebellar anatomic shape characteristics. Within the first three principal components, SCA2 and SCA6 differed from controls and from each other. In a secondary analysis, we studied five additional subjects and found that these patients were consistent with the previously defined archetypal clusters of clinical and anatomical characteristics. Secondary analysis of five subjects with related diagnoses showed that disease groups that were clinically and pathophysiologically similar also shared similar anatomic characteristics. Specifically, Archetype #1 consisted of SCA3 (n=1) and SCA2, suggesting that cerebellar syndromes accompanied by atrophy of the pons may be associated with a characteristic pattern of cerebellar neurodegeneration. In comparison, Archetype #2 was comprised of disease groups with pure cerebellar atrophy (episodic ataxia type 2 (n=1), idiopathic late-onset cerebellar ataxias (n=3), and SCA6). This suggests that cerebellar shape analysis could aid in discriminating between different pathologies. Our findings further suggest that magnetic resonance imaging is a promising imaging biomarker that could aid in the diagnosis and therapeutic management in patients with cerebellar syndromes.
doi:10.1007/s12311-011-0334-6
PMCID: PMC3932524  PMID: 22258915
Ataxia; Magnetic resonance imaging (MRI); Principal component analysis (PCA); Cerebellum; Biomarker
5.  SEGMENTATION OF THE COMPLETE SUPERIOR CEREBELLAR PEDUNCLES USING A MULTI-OBJECT GEOMETRIC DEFORMABLE MODEL 
The superior cerebellar peduncles (SCPs) are white matter tracts that serve as the major efferent pathways from the cerebellum to the thalamus. With diffusion tensor images (DTI), tractography algorithms or volumetric segmentation methods have been able to reconstruct part of the SCPs. However, when the fibers cross, the primary eigenvector (PEV) no longer represents the primary diffusion direction. Therefore, at the crossing of the left and right SCP, known as the decussation of the SCPs (dSCP), fiber tracts propagate incorrectly. To our knowledge, previous methods have not been able to segment the SCPs correctly. In this work, we explore the diffusion properties and seek to volumetrically segment the complete SCPs. The non-crossing SCPs and dSCP are modeled as different objects. A multi-object geometric deformable model is employed to define the boundaries of each piece of the SCPs, with the forces derived from diffusion properties as well as the PEV. We tested our method on a software phantom and real subjects. Results indicate that our method is able to the resolve the crossing and segment the complete SCPs with repeatability.
doi:10.1109/ISBI.2013.6556409
PMCID: PMC3892703  PMID: 24443683
SCP; MGDM; GGVF; Westin index; Fiber crossing
6.  Approaching Expert Results Using a Hierarchical Cerebellum Parcellation Protocol for Multiple Inexpert Human Raters 
NeuroImage  2012;64:616-629.
Volumetric measurements obtained from image parcellation have been instrumental in uncovering structure-function relationships. However, anatomical study of the cerebellum is a challenging task. Because of its complex structure, expert human raters have been necessary for reliable and accurate segmentation and parcellation. Such delineations are time-consuming and prohibitively expensive for large studies. Therefore, we present a three-part cerebellar parcellation system that utilizes multiple inexpert human raters that can efficiently and expediently produce results nearly on par with those of experts. This system includes a hierarchical delineation protocol, a rapid verification and evaluation process, and statistical fusion of the inexpert rater parcellations. The quality of the raters’ and fused parcellations was established by examining their Dice similarity coefficient, region of interest (ROI) volumes, and the intraclass correlation coefficient of region volume. The intra-rater ICC was found to be 0.93 at the finest level of parcellation.
doi:10.1016/j.neuroimage.2012.08.075
PMCID: PMC3590024  PMID: 22975160
Human Cerebellum; Manual labeling; Delineation; Parcellation; STAPLE; STAPLER; Label fusion
7.  Parcellation of the Thalamus Using Diffusion Tensor Images and a Multi-object Geometric Deformable Model 
Proceedings of SPIE  2013;8669:10.1117/12.2006119.
The thalamus is a sub-cortical gray matter structure that relays signals between the cerebral cortex and midbrain. It can be parcellated into the thalamic nuclei which project to different cortical regions. The ability to automatically parcellate the thalamic nuclei could lead to enhanced diagnosis or prognosis in patients with some brain disease. Previous works have used diffusion tensor images (DTI) to parcellate the thalamus, using either tensor similarity or cortical connectivity as information driving the parcellation. In this paper, we propose a method that uses the diffusion tensors in a different way than previous works to guide a multiple object geometric deformable model (MGDM) for parcellation. The primary eigenvector (PEV) is used to indicate the homogeneity of fiber orientations. To remove the ambiguity due to the fact that the PEV is an orientation, we map the PEV into a 5D space known as the Knutsson space. An edge map is then generated from the 5D vector to show divisions between regions of aligned PEV’s. The generalized gradient vector flow (GGVF) calculated from the edge map drives the evolution of the boundary of each nucleus. Region based force, balloon force, and curvature force are also employed to refine the boundaries. Experiments have been carried out on five real subjects. Quantitative measures show that the automated parcellation agrees with the manual delineation of an expert under a published protocol.
doi:10.1117/12.2006119
PMCID: PMC3875234  PMID: 24382992
thalamic parcellation; DTI; 5D Knutsson space; multiple object geometric deformable model
8.  THALAMIC PARCELLATION FROM MULTI-MODAL DATA USING RANDOM FOREST LEARNING 
The thalamus sub-cortical gray matter structure consists of contiguous nuclei, each individually responsible for communication between various cerebral cortex and midbrain regions. These nuclei are differentially affected in neurodegenerative diseases such as multiple sclerosis and Alzheimer’s. However thalamic parcellation of the nuclei, manual or automatic, is difficult given the limited contrast in any particular magnetic resonance (MR) modality. Several groups have had qualitative success differentiating nuclei based on spatial location and fiber orientation information in diffusion tensor imaging (DTI). In this paper, we extend these principles by combining these discriminating dimensions with structural MR and derived information, and by building random forest learners on the resultant multi-modal features. In training, we form a multi-dimensional feature per voxel, which we associate with a nucleus classification from a manual rater. Learners are trained to differentiate thalamus from background and thalamic nuclei from other nuclei. These learners inform the external forces of a multiple object level set model. Our cross-validated quantitative results on a set of twenty subjects show the efficacy and reproducibility of our results.
doi:10.1109/ISBI.2013.6556609
PMCID: PMC3799867  PMID: 24145869
Diffusion tensor imaging; machine learning; deformable models; object segmentation; random forests
9.  Gross feature recognition of Anatomical Images based on Atlas grid (GAIA): Incorporating the local discrepancy between an atlas and a target image to capture the features of anatomic brain MRI☆ 
NeuroImage : Clinical  2013;3:202-211.
We aimed to develop a new method to convert T1-weighted brain MRIs to feature vectors, which could be used for content-based image retrieval (CBIR). To overcome the wide range of anatomical variability in clinical cases and the inconsistency of imaging protocols, we introduced the Gross feature recognition of Anatomical Images based on Atlas grid (GAIA), in which the local intensity alteration, caused by pathological (e.g., ischemia) or physiological (development and aging) intensity changes, as well as by atlas–image misregistration, is used to capture the anatomical features of target images.
As a proof-of-concept, the GAIA was applied for pattern recognition of the neuroanatomical features of multiple stages of Alzheimer's disease, Huntington's disease, spinocerebellar ataxia type 6, and four subtypes of primary progressive aphasia. For each of these diseases, feature vectors based on a training dataset were applied to a test dataset to evaluate the accuracy of pattern recognition. The feature vectors extracted from the training dataset agreed well with the known pathological hallmarks of the selected neurodegenerative diseases. Overall, discriminant scores of the test images accurately categorized these test images to the correct disease categories. Images without typical disease-related anatomical features were misclassified. The proposed method is a promising method for image feature extraction based on disease-related anatomical features, which should enable users to submit a patient image and search past clinical cases with similar anatomical phenotypes.
Highlights
•A novel method to convert anatomical brain MRIs to feature vectors is introduced.•Degree of local atlas–image disagreement is used to capture the anatomical features.•The method was applied for pattern recognition of various neurodegenerative diseases.•The feature vectors agreed well with the known pathological hallmarks of diseases.•The method accurately categorized test images to the correct disease categories.
doi:10.1016/j.nicl.2013.08.006
PMCID: PMC3791278  PMID: 24179864
Atlas; Feature recognition; Alzheimer's disease; Huntington's disease; Primary progressive aphasia; Spinocerebellar ataxia
10.  Superficially Located White Matter Structures Commonly Seen in the Human and the Macaque Brain with Diffusion Tensor Imaging 
Brain connectivity  2011;1(1):37-47.
The white matter of the brain consists of fiber tracts that connect different regions of the brain. Among these tracts, the intrahemispheric cortico-cortical connections are called association fibers. The U-fibers are short association fibers that connect adjacent gyri. These fibers were thought to work as part of the cortico-cortical networks to execute associative brain functions. However, their anatomy and functions have not been documented in detail for the human brain. In past studies, U-fibers have been characterized in the human brain with diffusion tensor imaging (DTI). However, the validity of such findings remains unclear. In this study, DTI of the macaque brain was performed, and the anatomy of U-fibers was compared with that of the human brain reported in a previous study. The macaque brain was chosen because it is the most commonly used animal model for exploring cognitive functions and the U-fibers of the macaque brain have been already identified by axonal tracing studies, which makes it an ideal system for confirming the DTI findings. Ten U-fibers found in the macaque brain were also identified in the human brain, with a similar organization and topology. The delineation of these species-conserved white matter structures may provide new options for understanding brain anatomy and function.
doi:10.1089/brain.2011.0005
PMCID: PMC3569096  PMID: 22432953
association fiber; blade; diffusion tensor imaging; macaque, U-fiber; white matter
11.  Do brainstem omnipause neurons terminate saccades? 
Saccade-generating burst neurons (BN) are inhibited by omnipause neurons (OPN), except during saccades. OPN activity pauses before saccade onset and resumes at the saccade end. Microstimulation of OPN stops saccades in mid-flight, which shows that OPN can end saccades. However, OPN pause duration does not correlate well with saccade duration, and saccades are normometric after OPN lesions. We tested whether OPN were responsible for stopping saccades both in late-onset Tay–Sachs, which causes premature saccadic termination, and in individuals with cerebellar hypermetria. We studied gaze shifts between two targets at different distances aligned on one eye, which consist of a disjunctive saccade followed by vergence. High-frequency conjugate oscillations during the vergence movements that followed saccades were present in all subjects studied, indicating OPN silence. Thus, mechanisms other than OPN discharge (e.g., cerebellar caudal fastigial nucleus–promoting inhibitory BN discharge) must contribute to saccade termination.
doi:10.1111/j.1749-6632.2011.06170.x
PMCID: PMC3438674  PMID: 21950975
Tay–Sachs disease; saccades; omnipause neurons; fastigial nucleus; Müller paradigm
12.  Superficially Located White Matter Structures Commonly Seen in the Human and the Macaque Brain with Diffusion Tensor Imaging 
Brain Connectivity  2011;1(1):37-47.
Abstract
The white matter of the brain consists of fiber tracts that connect different regions of the brain. Among these tracts, the intrahemispheric cortico-cortical connections are called association fibers. The U-fibers are short association fibers that connect adjacent gyri. These fibers were thought to work as part of the cortico-cortical networks to execute associative brain functions. However, their anatomy and functions have not been documented in detail for the human brain. In past studies, U-fibers have been characterized in the human brain with diffusion tensor imaging (DTI). However, the validity of such findings remains unclear. In this study, DTI of the macaque brain was performed, and the anatomy of U-fibers was compared with that of the human brain reported in a previous study. The macaque brain was chosen because it is the most commonly used animal model for exploring cognitive functions and the U-fibers of the macaque brain have been already identified by axonal tracing studies, which makes it an ideal system for confirming the DTI findings. Ten U-fibers found in the macaque brain were also identified in the human brain, with a similar organization and topology. The delineation of these species-conserved white matter structures may provide new options for understanding brain anatomy and function.
doi:10.1089/brain.2011.0005
PMCID: PMC3569096  PMID: 22432953
association fiber; blade; diffusion tensor imaging; macaque, U-fiber; white matter
14.  Orthogonal diffusion-weighted MRI measures distinguish region-specific degeneration in cerebellar ataxia subtypes 
Journal of neurology  2009;256(11):1939-1942.
The cerebellar peduncles are excellent candidates for composite indicators of regional degeneration in posterior fossa structures, as the peduncles show histopathological changes in degenerative ataxia. We postulate that magnetic resonance imaging will reveal evidence of disease specific peduncle degeneration through macro-structural (cross-sectional area) and microstructural (fractional anisotropy, mean diffusivity) measures. This study presents a “proof of principle” using orthogonal diffusion tensor imaging cross-sections of the cerebellar peduncles to distinguish categories of cerebellar disease.
doi:10.1007/s00415-009-5269-1
PMCID: PMC2789274  PMID: 19653028

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