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

Abstract

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.

Fig. 1

SCAs show disease-specific pattern of regional cerebellar atrophy. Sagittal (a–c), coronal (d–f), and axial (g–i) views of the cerebellum. As compared to control (d) and SCA6 (f), SCA2 (e) shows significant atrophy of the corpus medullare (central white matters of the cerebellum and the deep cerebellar nuclei). Furthermore, as compared to SCA6 (f), SCA2 shows relative sparing of the posterior-inferior regions of the cerebellum

Fig. 2

Principal component coefficients of the first three principal components. a An illustration of cerebellum with labeled subregions. Colormap indicates principal component coefficients or loadings. (The coefficient of each cerebellar subregion was classified into five equal-size intervals from minimum (coefficients) to maximum (coefficients) for each principal component [dark blue: positive coefficients (highest coefficient value); red: negative coefficients (lowest coefficient value)].) Principal component coefficients represent the relative “weight” of each original variable (relative cerebellar regional volume) in each principal component. The principal component coefficients are shown for the first three principal components (b–d)

Fig. 3

PCA separates disease groups based on the pattern of cerebellar neurodegeneration. Plot of the first three principal components. (μ indicates center of mass for each disease group.) Hotelling’s T-squared distribution test showed that within the first three principal components, SCA2 (p=0.0001) and SCA6 (p=0.003) differed from the controls. SCA2 also differed from SCA6 (p=0.002)

Fig. 4

SCA3 clusters with SCA2, while EA2 and ILOCAs associate with SCA6 in the PCA space. Archetype #1 (light green ellipse) consists of SCA3 (green circle) and SCA2 (white triangles) in the PCA space. Archetype #2 (light blue ellipse) consists of EA2 (pink circle), three idiopathic late-onset cerebellar ataxia (ILOCA) subjects (dark blue circles), and SCA6 (gray squares). The principal component scores of patients with SCA3 (n=1), EA2 (n=1), and ILOCA (n=3) were computed by regressing the relative regional volumes of each subject against the anatomic scores of controls, SCA2, and SCA6. Based on the first three principal component scores, SCA3 showed the highest partial probability of resembling SCA2 (A_SCA2=0.89) while EA2 resembled SCA6 (A_SCA6=0.85). The three ILOCA subjects showed the highest probability of resembling SCA6 (A_SCA6 =0.99, 0.70, and 0.84)