Background: Spinocerebellar ataxia type 7 (SCA7) is a genetic disorder characterized by degeneration of the motor and visual systems. Besides neural deterioration, these patients also show functional connectivity changes linked to the degenerated brain areas. However, it is not known if there are functional connectivity changes in regions not necessarily linked to the areas undergoing structural deterioration. Therefore, in this study we have explored the whole-brain functional connectivity of SCA7 patients in order to find the overall abnormal functional pattern of this disease. Twenty-six patients and age-and-gender-matched healthy controls were recruited. Whole-brain functional connectivity analysis was performed in both groups. A classification algorithm was used to find the discriminative power of the abnormal connections by classifying patients and healthy subjects.
Results: Nineteen abnormal functional connections involving cerebellar and cerebral regions were selected for the classification stage. Support vector machine classification reached 92.3% accuracy with 95% sensitivity and 89.6% specificity using a 10-fold cross-validation. Most of the selected regions were well known degenerated brain regions including cerebellar and visual cortices, but at the same time, our whole-brain connectivity analysis revealed new regions not previously reported involving temporal and prefrontal cortices.
Conclusion: Our whole-brain connectivity approach provided information that seed-based analysis missed due to its region-specific searching method. The high classification accuracy suggests that using resting state functional connectivity may be a useful biomarker in SCA 7.
Keywords: Functional connectivity; MVPA; Resting state; SCA; Spinocerebellar ataxia.