Validita e affidabilita dei biomarcatori digitali per la demenza frontotemporale

Frontotemporal dementia is a neurological disease that has been a concern for humanity for a long time. Patients with this disease suffer from various consequences, such as cognitive dysfunction, motor problems, and emotional dysregulation. Unfortunately, there is no permanent treatment for it. Therefore, finding ways to collect more data would be highly beneficial in addressing this problem. In response, a mobile application (ALLFTD Mobile App) has been developed that its purpose is to simulate the data collected from diagnostic tests. This application includes mobile games designed to assess the cognitive function of patients, which can be an insight information about the level of dementia in them. By gathering more data from patients, this application facilitates research efforts aimed at finding a treatment and making better predictions of the severity of the disease in patients. In our analysis, we will demonstrate that this mobile application has the potential to replace certain diagnostic tests that are typically performed in hospitals, allowing patients to undergo fewer procedures. This will be achieved by using data from games’ performances over time from participants and evaluating their association with diagnostic tests such as volumes of brain regions from MRI scans. In this study, two techniques- Mixed-effects models and Structural Equation modeling(SEM)-will be used to analyze the relationship between game performance and brain volumes.