Neurodegenerative Diseases Progression Markers (MARKERS-NDD)

Considering the current estimates and the global social and economic burden of neurodegenerative diseases, changes in the manner and timing of a diagnosis of these diseases are urgently needed as well as in the timeliness with which effective therapeutic interventions are carried out. The complexity of the molecular mechanisms underlying neuronal degeneration and the heterogeneity of the population of patients affected by neurodegenerative diseases present enormous challenges to the development of early diagnostic tools and systems capable of predicting the course of the disease. Despite intensive research in the field of pharmacology, surgery and rehabilitation, neurodegenerative diseases remain chronic progressive diseases without a therapy that can change the course. MARKERS-NDD is a prospective, observational, longitudinal study, which aims to collect data from patients affected by neurodegenerative diseases (NDD) followed longitudinally for routine examinations performed as part of normal clinical practice. Data collected from clinical evaluations, movement analysis, brain imaging, neuropsychological and electroencephalographic assessments, blood chemistry tests will be analysed to carry out statistical investigations and predictive analyses, also using artificial intelligence systems, which allow the identification of new early markers of diagnosis and prognosis of neurodegenerative diseases. Quantitative movement analysis, with the aid of standard motion capture systems (gait analysis) and with wearable inertial sensors, is a valid tool both for supporting clinical diagnostics and to assess the response to pharmacological treatment, and to monitor the progression of NDDs. From this perspective, the kinematic analysis of gait and graphic gesture can reveal early alterations of motor features to support the differential diagnosis and stratification of the patient and allow us to follow the progression of the disease over time. Although motor symptoms represent key aspects for differential diagnosis between parkinsonian syndrome and dementia, alterations of cognitive functions, and in particular, of executive functions can be also present in the early stages of the disease in patients with synucleinopathies such as PD and Lewy Body Dementia. In Parkinson's disease (PD), these cognitive disorders can evolve over time towards a mild cognitive decline (Mild cognitive impairment, PD-MCI) up to dementia (Parkinson Disease Dementia, PDD). The identification of neuropsychological markers that can predict the progress of these diseases and the risk of conversion in patients with PD is a crucial aspect for the treatment and management of patients in the different phases of the disease. In the era of artificial intelligence (AI), with the introduction of AI-driven computer vision, the human movement can be tracked and analysed in real time by the support of a simple camera of mobile devices, such as tablets and smartphones, potentially eliminating the need for additional sensors or specialized equipment. Therefore, an approach such as telemedicine using AI could offer new possibilities and challenges for remote diagnosis, telemonitoring and telerehabilitation in neurological disorders such as neurodegenerative diseases. Movement analysis based on AI-driven computer vision could reduce the number of patient movements, especially in the advanced stages of the disease, often characterized by severe disability, alleviate the burden of caregivers who are sometimes elderly or engaged in work activities, and allow consultations to be carried out in remote areas that are not easily reachable. The comparison and validation of these systems with the gold standard of movement analysis represented by gait analysis with motion-captures and the already validated analysis systems with inertial sensors, constitutes a key piece in the development of clinical tools that support remote diagnosis and telemonitoring of therapies pharmacological and rehabilitation treatments. Similarly, the application of artificial intelligence systems for the kinematic analysis of the graphic gesture has allowed the development of various pattern recognition systems for the automatic recognition of handwriting in different application fields. Dysgraphic features were related to abnormalities of motor and cognitive functions revealed by clinical and neuropsychological tests, as well as to neurophysiological correlates of handwriting-related cortical activity such as electroencephalogram (EEG). Handwriting analysis system which uses AI systems and involves the execution of validated neuropsychological tests, with the aid of commercial graphics tablets, carried out as part of normal clinical practice - such as a simple outpatient visit or during of a structural neuropsychological examination as a component of the now common multidisciplinary approach that characterizes the management of patients with NDD - could be particularly useful in PD-MCI to follow the progression of both motor and cognitive symptoms. As a cost-effective and non-invasive measure of motor and cognitive performance, graphical gesture analysis systems could be applied repeatedly over time without serial or meta-learning effects. A further key aspect in the early diagnosis of NDDs is represented by voice analysis: the production of the human voice occurs through complex and synergistic movements of systems and subsystems (vocal cords, larynx, glottis, oral cavity and more), which can be influenced by the health conditions of the speaker. In particular, among neurodegenerative diseases, PD and Parkinsonism involve dramatic, objective and measurable changes in vocal production, which may include (among others) increased noise levels (due to incomplete closure of the vocal folds) and loss of voice (dysarthrophonia, dysarthria and hypophonia). Although the evaluation of speech disorder can indeed be performed via laryngoscope and video-stroboscopic instruments, these are very expensive tests, requiring a lot of time and qualified personnel. Voice-based artificial intelligence systems that make use of commercial systems (mobile devices equipped with microphones with sound robustness) could allow the analysis of the voice with artificial intelligence algorithms, to diagnose the disease in its early stages. A long-term analysis of common laboratory blood chemistry parameters and analysis of different biological samples, such as fecal samples for microbiota analysis, performed as routine checks by patients with chronic diseases such as NDDs could allow the identification of associations and early markers useful in the diagnosis and monitoring of disease progression. At the same time, the introduction of increasingly powerful and accurate brain imaging systems, supported by machine learning systems and artificial intelligence techniques, to obtain an increasingly accurate etiological diagnosis. Relevant is that each neurodegenerative disease favours a specific brain network which in turn is associated with a specific loss of tissue in particular brain regions. Therefore, the possibility of identifying new neuroimaging markers through machine learning systems to guide the differential diagnosis and accurately evaluate the course of the disease. In conclusion, the approach adopted by the MARKERS-NDD study is fundamental to increase the potential for success of an ambitious strategy that aims to develop markers of progression of neurodegenerative diseases that accelerate the search for disease-modifying therapy.