Phd in CLINICAL EXPERIMENTAL NEUROSCIENCE AND PSYCHIATRY

Thesis title: Advanced Technologies for Axial Impairment in Parkinson's Disease: from Early Detection to Outcome Prediction

Axial motor symptoms—such as balance, gait, posture, and speech disorders—mark a critical milestone in the progression of Parkinson's disease (PD), significantly affecting patients’ prognosis and quality of life. However, the long-term evolution of these symptoms, especially in patients undergoing advanced therapies like subthalamic nucleus deep brain stimulation (STN-DBS), remains poorly understood. Also, current clinical evaluations lack sensitivity in detecting axial disorders at their early stages, and reliable predictors for patients at increased risk of developing these symptoms are still largely unknown. Recent technological advances in invasive neuromodulation (e.g., DBS), wearable sensors, and artificial intelligence-based computational methods provide new opportunities to address these challenges. This Thesis presents the main scientific findings from the PhD program, centred on the clinical and instrumental evaluation of axial impairment in PD using advanced technologies. The first section of the Thesis examines the short- and long-term clinical progression of axial disorders in a cohort of advanced PD patients treated with STN-DBS. Through a retrospective longitudinal analysis, a gradual worsening of axial symptoms, along with complications such as falls, was demonstrated over a period of up to 15 years post-surgery. The second section explores the use of wearable inertial and electromyographic sensors to assess gait and balance disorders in laboratory and home settings. These technologies accurately captured patient movement and quantified axial disturbances, highlighting their potential for telemedicine applications. Additionally, artificial intelligence algorithms were applied to automatically analyse speech in STN-DBS patients, revealing the impact of this therapy on vocal functions. In the third section, longitudinal regression analyses of a large PD cohort identified specific clinical predictors of axial symptoms, offering insight into patients at higher risk of developing related complications. A single wearable sensor was also used to accurately predict gait and balance impairments, demonstrating its utility for enhancing remote clinical assessments. In conclusion, the present Thesis underscores the potential of innovative monitoring and management strategies that leverage advanced technologies to improve clinical practice in PD.