This cross-sectional study investigated the correlation between the CAG repeat length and the degeneration of cerebellum in spinocerebellar ataxia type 3 (SCA3) patients based on neuroimaging approaches. Forty SCA3 patients were recruited and classified into two subgroups according to their CAG repeat lengths (≥ 74 and < 74). We measured each patient's Scale for the Assessment and Rating of Ataxia (SARA) score, N-acetylaspartate (NAA)/creatine (Cr) ratios based on magnetic resonance spectroscopy (MRS), and 3-dimensional fractal dimension (3D-FD) values derived from magnetic resonance imaging (MRI) results. Furthermore, the 3D-FD values were used to construct structural covariance networks based on graph theoretical analysis. The results revealed that SCA3 patients with a longer CAG repeat length demonstrated earlier disease onset. However, the CAG repeat length did not significantly correlate with their SARA scores, cerebellar NAA/Cr ratios or cerebellar 3D-FD values. Network dissociation between cerebellar regions and parietal-occipital regions was found in SCA3 patients with CAG ≥ 74, but not in those with CAG < 74. In conclusion, the CAG repeat length is uncorrelated with the change of SARA score, cerebellar function and cerebellar structure in SCA3. Nevertheless, a longer CAG repeat length may indicate early structural covariance network dissociation.

Background: Recent studies have shown that the patients with spinocerebellar ataxia type 3 (SCA3) may not only have disease involvement in the cerebellum and brainstem but also in the cerebral regions. However, the relations between the widespread degenerated brain regions remains incompletely explored.Methods: In the present study, we investigate the topological properties of the brain networks of SCA3 patients (n = 40) constructed based on the correlation of three-dimensional fractal dimension values. Random and targeted attacks were applied to measure the network resilience of normal and SCA3 groups.Results: The SCA3 networks had significantly smaller clustering coefficients (P < 0.05) and global efficiency (P < 0.05) but larger characteristic path length (P < 0.05) than the normal controls networks, implying loss of small-world features. Furthermore, the SCA3 patients were associated with reduced nodal betweenness (P < 0.001) in the left supplementary motor area, bilateral paracentral lobules, and right thalamus, indicating that the motor control circuit might be compromised.Conclusions: The SCA3 networks were more vulnerable to targeted attacks than the normal controls networks because of the effects of pathological topological organization. The SCA3 revealed a more sparsity and disrupted structural network with decreased values in the largest component size, mean degree, mean density, clustering coefficient, and global efficiency and increased value in characteristic path length. The cortico-cerebral circuits in SCA3 were disrupted and segregated into occipital-parietal (visual-spatial cognition) and frontal-pre-frontal (motor control) clusters. The cerebellum of SCA3 were segregated from cerebellum-temporal-frontal circuits and clustered into a frontal-temporal cluster (cognitive control). Therefore, the disrupted structural network presented in this study might reflect the clinical characteristics of SCA3.

Automatic identification of various perfusion compartments from dynamic susceptibility contrast magnetic resonance brain images can assist in clinical diagnosis and treatment of cerebrovascular diseases. The principle of segmentation methods was based on the clustering of bolus transit-time profiles to discern areas of different tissues. However, the cerebrovascular diseases may result in a delayed and dispersed local perfusion and therefore alter the hemodynamic signal profiles. Assessing the accuracy of the segmentation technique under delayed/dispersed circumstance is critical to accurately evaluate the severity of the vascular disease. In this study, we improved the segmentation method of expectation-maximization algorithm by using the results of hierarchical clustering on whitened perfusion data as initial parameters for a mixture of multivariate Gaussians model. In addition, Monte Carlo simulations were conducted to evaluate the performance of proposed method under different levels of delay, dispersion, and noise of signal profiles in tissue segmentation. The proposed method was used to classify brain tissue types using perfusion data from five normal participants, a patient with unilateral stenosis of the internal carotid artery, and a patient with moyamoya disease. Our results showed that the normal, delayed or dispersed hemodynamics can be well differentiated for patients, and therefore the local arterial input function for impaired tissues can be recognized to minimize the error when estimating the cerebral blood flow. Furthermore, the tissue in the risk of infarct and the tissue with or without the complementary blood supply from the communicating arteries can be identified.

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