Abstract

The myotonic dystrophies (DM1 and DM2) are dominantly inherited disorders that cause pathological changes throughout the body. Many individuals with DM experience cognitive, behavioral and other functional central nervous system effects that impact their quality of life. The extent of psychological impairment that will develop in each patient is variable and unpredictable. Hence, it is difficult to get strong supervision information like fully ground truth labels for all cognitive involvement patterns. This study is to assess cognitive involvement among healthy controls and patients with DM. The DM cognitive impairment pattern observation is modeled in a weakly supervised setting and supervision information is used to transform the input feature space to a more discriminative representation suitable for pattern observation. This study incorporated results from 59 adults with DM and 92 control subjects. The developed system categorized the neuropsychological testing data into five cognitive clusters. The quality of the obtained clustering solution was assessed using an internal validity metric. The experimental results show that the proposed algorithm can discover interesting patterns and useful information from neuropsychological data, which will be be crucial in planning clinical trials and monitoring clinical performance. The proposed system resulted in an average classification accuracy of 88%, which is very promising considering the unique challenges present in this population.

View details for DOI 10.1109/EMBC48229.2022.9871626

View details for PubMedID 36086274