Principal component analysis of cerebellar shape on MRI separates SCA types 2 and 6 into two archetypal modes of degeneration

Integration of graph network with kernel SVM and logistic regression for identification of biomarkers in SCA12 and its diagnosis

Spinocerebellar ataxia type 12 is a hereditary and neurodegenerative illness commonly found in India. However, there is no established noninvasive automatic diagnostic system for its diagnosis and identification of imaging biomarkers. This work proposes a novel four-phase machine learning-based diagnostic framework to find spinocerebellar ataxia type 12 disease-specific atrophic-brain regions and distinguish spinocerebellar ataxia type 12 from healthy using a real structural magnetic resonance imaging dataset. Firstly, each brain region is represented in terms of statistics of coefficients obtained using 3D-discrete wavelet transform. Secondly, a set of relevant regions are selected using a graph network-based method. Thirdly, a kernel support vector machine is used to capture nonlinear relationships among the voxels of a brain region. Finally, the linear relationship among the brain regions is captured to build a decision model to distinguish spinocerebellar ataxia type 12 from healthy by using the regularized logistic regression method. A classification accuracy of 95% and a harmonic mean of precision and recall, i.e. F1-score of 94.92%, is achieved. The proposed framework provides relevant regions responsible for the atrophy. The importance of each region is captured using Shapley Additive exPlanations values. We also performed a statistical analysis to find volumetric changes in spinocerebellar ataxia type 12 group compared to healthy. The promising result of the proposed framework shows that clinicians can use it for early and timely diagnosis of spinocerebellar ataxia type 12.

Age-related differences of cerebellar cortex and nuclei: MRI findings in healthy controls and its application to spinocerebellar ataxia (SCA6) patients

Understanding cerebellar alterations due to healthy aging provides a reference point against which pathological findings in late-onset disease, for example spinocerebellar ataxia type 6 (SCA6), can be contrasted. In the present study, we investigated the impact of aging on the cerebellar nuclei and cerebellar cortex in 109 healthy controls (age range: 16 - 78 years) using 3 Tesla magnetic resonance imaging (MRI). Findings were compared with 25 SCA6 patients (age range: 38 - 78 years). A subset of 16 SCA6 (included: 14) patients and 50 controls (included: 45) received an additional MRI scan at 7 Tesla and were re-scanned after one year. MRI included T1-weighted, T2-weighted FLAIR, and multi-echo T2*-weighted imaging. The T2*-weighted phase images were converted to quantitative susceptibility maps (QSM). Since the cerebellar nuclei are characterized by elevated iron content with respect to their surroundings, two independent raters manually outlined them on the susceptibility maps. T1-weighted images acquired at 3T were utilized to automatically identify the cerebellar gray matter (GM) volume. Linear correlations revealed significant atrophy of the cerebellum due to tissue loss of cerebellar cortical GM in healthy controls with increasing age. Reduction of the cerebellar GM was substantially stronger in SCA6 patients. The volume of the dentate nuclei did not exhibit a significant relationship with age, at least in the age range between 18 and 78 years, whereas mean susceptibilities of the dentate nuclei increased with age. As previously shown, the dentate nuclei volumes were smaller and magnetic susceptibilities were lower in SCA6 patients compared to age- and sex-matched controls. The significant dentate volume loss in SCA6 patients could also be confirmed with 7T MRI. Linear mixed effects models and individual paired t-tests accounting for multiple comparisons revealed no statistical significant change in volume and susceptibility of the dentate nuclei after one year in neither patients nor controls. Importantly, dentate volumes were more sensitive to differentiate between SCA6 (Cohen's d = 3.02) and matched controls than the cerebellar cortex volume (d = 2.04). In addition to age-related decline of the cerebellar cortex and atrophy in SCA6 patients, age-related increase of susceptibility of the dentate nuclei was found in controls, whereas dentate volume and susceptibility was significantly decreased in SCA6 patients. Because no significant changes of any of these parameters was found at follow-up, these measures do not allow to monitor disease progression at short intervals.

Altered Capicua expression drives regional Purkinje neuron vulnerability through ion channel gene dysregulation in spinocerebellar ataxia type 1

Selective neuronal vulnerability in neurodegenerative disease is poorly understood. Using the ATXN1[82Q] model of spinocerebellar ataxia type 1 (SCA1), we explored the hypothesis that regional differences in Purkinje neuron degeneration could provide novel insights into selective vulnerability. ATXN1[82Q] Purkinje neurons from the anterior cerebellum were found to degenerate earlier than those from the nodular zone, and this early degeneration was associated with selective dysregulation of ion channel transcripts and altered Purkinje neuron spiking. Efforts to understand the basis for selective dysregulation of channel transcripts revealed modestly increased expression of the ATXN1 co-repressor Capicua (Cic) in anterior cerebellar Purkinje neurons. Importantly, disrupting the association between ATXN1 and Cic rescued the levels of these ion channel transcripts, and lentiviral overexpression of Cic in the nodular zone accelerated both aberrant Purkinje neuron spiking and neurodegeneration. These findings reinforce the central role for Cic in SCA1 cerebellar pathophysiology and suggest that only modest reductions in Cic are needed to have profound therapeutic impact in SCA1.

Neuroimaging Applications in Chronic Ataxias

Magnetic resonance imaging (MRI), single photon emission computed tomography (SPECT) and positron emission tomography (PET) are the main instruments for neuroimaging investigation of patients with chronic ataxia. MRI has a predominant diagnostic role in the single patient, based on the visual detection of three patterns of atrophy, namely, spinal atrophy, cortical cerebellar atrophy and olivopontocerebellar atrophy, which correlate with the aetiologies of inherited or sporadic ataxia. In fact spinal atrophy is observed in Friedreich ataxia, cortical cerebellar atrophy in Ataxia Telangectasia, gluten ataxia and Sporadic Adult Onset Ataxia and olivopontocerebellar atrophy in Multiple System Atrophy cerebellar type. The 39 types of dominantly inherited spinocerebellar ataxias show either cortical cerebellar atrophy or olivopontocerebellar atrophy. T2 or T2* weighted MR images can contribute to the diagnosis by revealing abnormally increased or decreased signal with a characteristic distribution. These include symmetric T2 hyperintensity of the posterior and lateral columns of the cervical spinal cord in Friedreich ataxia, diffuse and symmetric hyperintensity of the cerebellar cortex in Infantile Neuro-Axonal Dystrophy, symmetric hyperintensity of the peridentate white matter in Cerebrotendineous Xanthomatosis, and symmetric hyperintensity of the middle cerebellar peduncles and peridentate white matter, cerebral white matter and corpus callosum in Fragile X Tremor Ataxia Syndrome. Abnormally decreased T2 or T2* signal can be observed with a multifocal distribution in Ataxia Telangectasia and with a symmetric distribution in the basal ganglia in Multiple System Atrophy. T2 signal hypointensity lining diffusely the outer surfaces of the brainstem, cerebellum and cerebrum enables diagnosis of superficial siderosis of the central nervous system. The diagnostic role of nuclear medicine techniques is smaller. SPECT and PET show decreased uptake of radiotracers investigating the nigrostriatal system in Multiple System Atrophy and in patients with Fragile X Tremor Ataxia Syndrome. Semiquantitative or quantitative MRI, SPECT and PET data describing structural, microstructural and functional changes of the cerebellum, brainstem, and spinal cord have been widely applied to investigate physiopathological changes in patients with chronic ataxias. Moreover they can track diseases progression with a greater sensitivity than clinical scales. So far, a few small-size and single center studies employed neuroimaging techniques as surrogate markers of treatment effects in chronic ataxias.

Landmark Based Shape Analysis for Cerebellar Ataxia Classification and Cerebellar Atrophy Pattern Visualization

Cerebellar dysfunction can lead to a wide range of movement disorders. Studying the cerebellar atrophy pattern associated with different cerebellar disease types can potentially help in diagnosis, prognosis, and treatment planning. In this paper, we present a landmark based shape analysis pipeline to classify healthy control and different ataxia types and to visualize the characteristic cerebellar atrophy patterns associated with different types. A highly informative feature representation of the cerebellar structure is constructed by extracting dense homologous landmarks on the boundary surfaces of cerebellar sub-structures. A diagnosis group classifier based on this representation is built using partial least square dimension reduction and regularized linear discriminant analysis. The characteristic atrophy pattern for an ataxia type is visualized by sampling along the discriminant direction between healthy controls and the ataxia type. Experimental results show that the proposed method can successfully classify healthy controls and different ataxia types. The visualized cerebellar atrophy patterns were consistent with the regional volume decreases observed in previous studies, but the proposed method provides intuitive and detailed understanding about changes of overall size and shape of the cerebellum, as well as that of individual lobules.

Consensus paper: radiological biomarkers of cerebellar diseases

Hereditary and sporadic cerebellar ataxias represent a vast and still growing group of diseases whose diagnosis and differentiation cannot only rely on clinical evaluation. Brain imaging including magnetic resonance (MR) and nuclear medicine techniques allows for characterization of structural and functional abnormalities underlying symptomatic ataxias. These methods thus constitute a potential source of radiological biomarkers, which could be used to identify these diseases and differentiate subgroups of them, and to assess their severity and their evolution. Such biomarkers mainly comprise qualitative and quantitative data obtained from MR including proton spectroscopy, diffusion imaging, tractography, voxel-based morphometry, functional imaging during task execution or in a resting state, and from SPETC and PET with several radiotracers. In the current article, we aim to illustrate briefly some applications of these neuroimaging tools to evaluation of cerebellar disorders such as inherited cerebellar ataxia, fetal developmental malformations, and immune-mediated cerebellar diseases and of neurodegenerative or early-developing diseases, such as dementia and autism in which cerebellar involvement is an emerging feature. Although these radiological biomarkers appear promising and helpful to better understand ataxia-related anatomical and physiological impairments, to date, very few of them have turned out to be specific for a given ataxia with atrophy of the cerebellar system being the main and the most usual alteration being observed. Consequently, much remains to be done to establish sensitivity, specificity, and reproducibility of available MR and nuclear medicine features as diagnostic, progression and surrogate biomarkers in clinical routine.

Automated cerebellar lobule segmentation with application to cerebellar structural analysis in cerebellar disease

The cerebellum plays an important role in both motor control and cognitive function. Cerebellar function is topographically organized and diseases that affect specific parts of the cerebellum are associated with specific patterns of symptoms. Accordingly, delineation and quantification of cerebellar sub-regions from magnetic resonance images are important in the study of cerebellar atrophy and associated functional losses. This paper describes an automated cerebellar lobule segmentation method based on a graph cut segmentation framework. Results from multi-atlas labeling and tissue classification contribute to the region terms in the graph cut energy function and boundary classification contributes to the boundary term in the energy function. A cerebellar parcellation is achieved by minimizing the energy function using the α-expansion technique. The proposed method was evaluated using a leave-one-out cross-validation on 15 subjects including both healthy controls and patients with cerebellar diseases. Based on reported Dice coefficients, the proposed method outperforms two state-of-the-art methods. The proposed method was then applied to 77 subjects to study the region-specific cerebellar structural differences in three spinocerebellar ataxia (SCA) genetic subtypes. Quantitative analysis of the lobule volumes shows distinct patterns of volume changes associated with different SCA subtypes consistent with known patterns of atrophy in these genetic subtypes.

Macro- and microstructural changes in patients with spinocerebellar ataxia type 6: assessment of phylogenetic subdivisions of the cerebellum and the brain stem

BACKGROUND AND PURPOSE

Site-specific degeneration patterns of the infratentorial brain in relation to phylogenetic origins may relate to symptoms in patients with spinocerebellar degeneration, but the patterns are still unclear. We investigated macro- and microstructural changes of the infratentorial brain based on phylogenetic origins and their correlation with symptoms in patients with spinocerebellar ataxia type 6.

MATERIALS AND METHODS

MR images of 9 patients with spinocerebellar ataxia type 6 and 9 age- and sex-matched controls were obtained. We divided the infratentorial brain on the basis of phylogenetic origins and performed an atlas-based analysis. Comparisons of the 2 groups and a correlation analysis assessed with the International Cooperative Ataxia Rating Scale excluding age effects were performed.

RESULTS

A significant decrease of fractional volume and an increase of mean diffusivity were seen in all subdivisions of the cerebellum and in all the cerebellar peduncles except mean diffusivity in the inferior cerebellar peduncle in patients compared with controls (P < .0001 to <.05). The bilateral anterior lobes showed the strongest atrophy. Fractional volume decreased mainly in old regions, whereas mean diffusivity increased mainly in new regions of the cerebellum. Reflecting this tendency, the International Cooperative Ataxia Rating Scale total score showed strong correlations in fractional volume in the right flocculonodular lobe and the bilateral deep structures and in mean diffusivity in the bilateral posterior lobes (r = 0.73 to ±0.87).

CONCLUSIONS

We found characteristic macro- and microstructural changes, depending on phylogenetic regions of the infratentorial brain, that strongly correlated with clinical symptoms in patients with spinocerebellar ataxia type 6.

Progression of brain atrophy in spinocerebellar ataxia type 2: a longitudinal tensor-based morphometry study

Spinocerebellar ataxia type 2 (SCA2) is the second most frequent autosomal dominant inherited ataxia worldwide. We investigated the capability of magnetic resonance imaging (MRI) to track in vivo progression of brain atrophy in SCA2 by examining twice 10 SCA2 patients (mean interval 3.6 years) and 16 age- and gender-matched healthy controls (mean interval 3.3 years) on the same 1.5 T MRI scanner. We used T1-weighted images and tensor-based morphometry (TBM) to investigate volume changes and the Inherited Ataxia Clinical Rating Scale to assess the clinical deficit. With respect to controls, SCA2 patients showed significant higher atrophy rates in the midbrain, including substantia nigra, basis pontis, middle cerebellar peduncles and posterior medulla corresponding to the gracilis and cuneatus tracts and nuclei, cerebellar white matter (WM) and cortical gray matter (GM) in the inferior portions of the cerebellar hemisphers. No differences in WM or GM volume loss were observed in the supratentorial compartment. TBM findings did not correlate with modifications of the neurological deficit. In conclusion, MRI volumetry using TBM is capable of demonstrating the progression of pontocerebellar atrophy in SCA2, supporting a possible role of MRI as biomarker in future trials.

Deep Learning for Cerebellar Ataxia Classification and Functional Score Regression

Cerebellar ataxia is a progressive neuro-degenerative disease that has multiple genetic versions, each with a characteristic pattern of anatomical degeneration that yields distinctive motor and cognitive problems. Studying this pattern of degeneration can help with the diagnosis of disease subtypes, evaluation of disease stage, and treatment planning. In this work, we propose a learning framework using MR image data for discriminating a set of cerebellar ataxia types and predicting a disease related functional score. We address the difficulty in analyzing high-dimensional image data with limited training subjects by: 1) training weak classifiers/regressors on a set of image subdomains separately, and combining the weak classifier/regressor outputs to make the decision; 2) perturbing the image subdomain to increase the training samples; 3) using a deep learning technique called the stacked auto-encoder to develop highly representative feature vectors of the input data. Experiments show that our approach can reliably classify between one of four categories (healthy control and three types of ataxia), and predict the functional staging score for ataxia.

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

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.

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A novel method to convert anatomical brain MRIs to feature vectors is introduced.

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Degree of local atlas–image disagreement is used to capture the anatomical features.

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The method was applied for pattern recognition of various neurodegenerative diseases.

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The feature vectors agreed well with the known pathological hallmarks of diseases.

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The method accurately categorized test images to the correct disease categories.

Clinical features, neurogenetics and neuropathology of the polyglutamine spinocerebellar ataxias type 1, 2, 3, 6 and 7

The spinocerebellar ataxias type 1 (SCA1), 2 (SCA2), 3 (SCA3), 6 (SCA6) and 7 (SCA7) are genetically defined autosomal dominantly inherited progressive cerebellar ataxias (ADCAs). They belong to the group of CAG-repeat or polyglutamine diseases and share pathologically expanded and meiotically unstable glutamine-encoding CAG-repeats at distinct gene loci encoding elongated polyglutamine stretches in the disease proteins. In recent years, progress has been made in the understanding of the pathogenesis of these currently incurable diseases: Identification of underlying genetic mechanisms made it possible to classify the different ADCAs and to define their clinical and pathological features. Furthermore, advances in molecular biology yielded new insights into the physiological and pathophysiological role of the gene products of SCA1, SCA2, SCA3, SCA6 and SCA7 (i.e. ataxin-1, ataxin-2, ataxin-3, α-1A subunit of the P/Q type voltage-dependent calcium channel, ataxin-7). In the present review we summarize our current knowledge about the polyglutamine ataxias SCA1, SCA2, SCA3, SCA6 and SCA7 and compare their clinical and electrophysiological features, genetic and molecular biological background, as well as their brain pathologies. Furthermore, we provide an overview of the structure, interactions and functions of the different disease proteins. On the basis of these comprehensive data, similarities, differences and possible disease mechanisms are discussed.