Dario Alimonti


I graduated in Medicine in 1998, and then later took a specialisation in Neurology at the University of Pavia. In 2004 I got a Master of Science Degree (MSc) in Clinical Neuroscience, at the Institute of Neurology, University College of London, UK. The thesis of diploma was about the “Role of retinoic acid signaling in the neuronal regeneration in axotomised rat sensory ganglia: morphological correlation of RNA microarray data”.

My first job position was at the Hospital IRCCS “Fondazione Maugeri” of Pavia, where I was involved in the assistance of Amyotrophic Lateral Sclerosis patients and done basic research on this topic.

Keeping my interest on the neurodegenerative diseases, in 2008 I moved to Bergamo in another hospital (“ASST Papa Giovanni XXIII”), where I am still working. My principal interest is on movement disorders; particularly I deal in diagnosis and treatment of Parkinson’s Disease (PD) from the early to the advanced stages, for which I have experience on Deep Brain Stimulation (DBS). I further deepened my education on this topic obtaining a Master on the “Diagnosis and Treatment of Movement Disorders and other Neurodegenerative Diseases”, at the Institute of Neurology “Carlo Besta”, Università Cattolica del Sacro Cuore, Milan. In the last few years I contributed to the development of wearable sensors useful for detecting motor parameters in PD patients, in collaboration with the Department of Microelectronics, Faculty of Engineering, University of Bergamo. With the aim of optimize the clinical use of these sensors, in 2018 I enrolled a PhD program in Clinical Neuroscience at the University of Milan-Bicocca.


Motor and cognitive scores progression in Parkinson’s Disease.

  • Track: Clinical Neuroscience
  • Tutor: Prof. Carlo Ferrarese

Parkinson’s disease (PD) is the second most common neurodegenerative disease in humans. It is estimated that there are 6,3 millions people with Parkinson’s disease spreaded around the world. In accordance with the available statistics, in Europe there are 1.2 millions of PD patients. Age of onset is usually above 60 year, and incidence of the disease increases with age involving about 3 % of people with more than 80 years. Nonetheless, about 1 person in ten receives a PD diagnosis before his 50’s.

For demographic and social reasons, it has been calculated that in the Western Countries  the elderly population will significantly increase in the next decades. Being the neurodegenerative diseases directly age related, it follows that over the same period we will be facing a significant increase of such of diseases. In the absence of curative therapies, we must therefore study new methods for efficacely and inexpencely manage them.

Parkinon’s Disease is clinically defined by its motor signs (muscular plastic hypertonia; slowness and depletion fo movements; tremors) in different combinations among them, usually asimmetrically, and with variable trait patient by patient. Moreover, PD represents a paradimg for observing the gait, which is a general motor function studied in clinical practice to assess disease progression and its response to treatments, both farmacological and physiotherapeutic. In fact, in PD patients we may observe gait alterations: step asymmetry, augmented variability and slowing of pace; particularly these two variables associated to an higher risk of falls.

Motor symptoms are usually quantified with a specific clinical scale: Movement Disorder Society – Unified Parkinson’s Disease Rating Scale motor score (MDS-UPDRS part III). There are few studies which analyzed the UPDRS-III variations along time in PD patients. Whereas, for the clinical assessment of gait clinicians generally use qualitative descriptions or measure the speed of gait (i.e. TUG or eTUG) or the distance covered (i.e. 6MWT) in standardized tests. The use of sensors and gait analysis laboratories are limited to experimental contexts.

This project aims to measure the motor changes in Parkinson’s disease along one year of observation, comparing clinical scores to automated data from wearable electronic devices. As secondary endpoints we will evaluate a possible correlation between gait motor features and cognitive functions changes in the same patients; notably, to detect those specific motor issues which at an early stage may disclose an initial cognitive impairment; otherwise to detect which of them may be related to the risk of falls.


To get further details and the full pub list, visit: