The interpretability of gait analysis studies in people with rare diseases, such as those with primary hereditary cerebellar ataxia (pwCA), is frequently limited by the small sample sizes and unbalanced datasets. The purpose of this study was to assess the effectiveness of data balancing and generative artificial intelligence (AI) algorithms in generating synthetic data reflecting the actual gait abnormalities of pwCA. Gait data of 30 pwCA (age: 51.6 ± 12.2 years; 13 females, 17 males) and 100 healthy subjects (age: 57.1 ± 10.4; 60 females, 40 males) were collected at the lumbar level with an inertial measurement unit. Subsampling, oversampling, synthetic minority oversampling, generative adversarial networks, and conditional tabular generative adversarial networks (ctGAN) were applied to generate datasets to be input to a random forest classifier. Consistency and explainability metrics were also calculated to assess the coherence of the generated dataset with known gait abnormalities of pwCA. ctGAN significantly improved the classification performance compared with the original dataset and traditional data augmentation methods. ctGAN are effective methods for balancing tabular datasets from populations with rare diseases, owing to their ability to improve diagnostic models with consistent explainability.

Background: A complicated form of recessive hereditary spastic paraplegias (HSPs) with thin corpus callosum (TCC) was first described in Japan, and most of the Japanese families showed linkage to chromosome 15q13–15. A recessive HSP locus (SPG11) has also been mapped to chromosome 15q13–15 in Italian and North American families with and without TCC, and it overlaps the region identified in the Japanese families. Objective: To study clinically and genetically 12 Italian families with HSP and TCC. Methods: The authors investigated 18 affected and 30 healthy individuals from 12 unrelated Italian families with recessive HSP-TCC. Clinical, neurophysiologic, and neuroradiologic studies were undertaken. All patients were negative for SPG7 mutations. Genetic linkage analyses were carried out with polymorphic DNA markers on 15q13–15. Results: Five families were consistent with linkage, thus defining a 19.8-cM region between markers D15S1007 and D15S978, encompassing the SPG11 interval. In one consanguineous family, linkage could be firmly excluded, confirming genetic heterogeneity. Two families appeared not linked to the region, but this could not be firmly proved because of the small family size. The remaining four families were uninformative for linkage purposes. Conclusion: HSP-TCC is common in Italy. The phenotype is fairly homogeneous and is associated with impaired cognition. There are at least two loci for HSP-TCC, one of which is on chromosome 15q13–15.

The term hereditary ataxia (HA) refers to a heterogeneous group of neurological disorders with multiple genetic etiologies and a wide spectrum of ataxia-dominated phenotypes. Massive gene analysis in next-generation sequencing has entered the HA scenario, broadening our genetic and clinical knowledge of these conditions. In this study, we employed a targeted resequencing panel (TRP) in a large and highly heterogeneous cohort of 377 patients with a clinical diagnosis of HA, but no molecular diagnosis on routine genetic tests. We obtained a positive result (genetic diagnosis) in 33.2% of the patients, a rate significantly higher than those reported in similar studies employing TRP (average 19.4%), and in line with those performed using exome sequencing (ES, average 34.6%). Moreover, 15.6% of the patients had an uncertain molecular diagnosis. STUB1, PRKCG, and SPG7 were the most common causative genes. A comparison with published literature data showed that our panel would have identified 97% of the positive cases reported in previous TRP-based studies and 92% of those diagnosed by ES. Proper use of multigene panels, when combined with detailed phenotypic data, seems to be even more efficient than ES in clinical practice.