State of the art
Cognitive decline related to dementia and other neurodegenerative disorders represents a growing public health concern. Each year, 9.9 million new cases are identified worldwide. It is reported that someone develops dementia every three seconds on average. Improvements in lifestyle in adults - including increasing access to education and greater attention to cardiovascular issues - have reduced the incidence of the disease in recent years, but total numbers of dementia are still going up because of the rising age of the population, combined with declining birth rates. As a matter of fact, in 2020, the Alzheimer's Disease (AD) International Association estimated that over 55 million people were affected by this pathology globally. The number will almost double every 20 years, reaching 78 million in 2030 and 139 million in 2050.
The global health community has recognized the need for action and has placed dementia on the public agenda (cf. The United Nations 2030 Agenda for Sustainable Development - Goal 3).
However, since current drug treatments can not modify the neuropathological substrate of AD or reverse its signs, research is focusing on pre-symptomatic stages of the disease.
The neurodegenerative process leading to dementia begins much earlier than the clinical symptoms. This "prodromal'' phase, a grey area between normal ageing and abnormal cognitive functioning, can provide a key opportunity for pharmacological treatment development and therapeutic intervention.
To date, timely diagnosis of the preclinical stages of dementia remains a big challenge for healthcare systems: many assessment tools have been proposed over recent years, but the commonest paper-and-pencil screening instruments are largely unreliable for detecting early subtle changes in cognition. They are time-consuming, not ecologically valid, and often prone to human bias.
Nonetheless, over the last few years, there has been a growing interest in the employment of verbal productions as digital biomarkers, namely «objective, quantifiable behavioural data that can be collected and measured by means of digital devices, allowing for a low-cost pathology detection, classification, and monitoring». Promising research has been conducted on the automatic detection of changes in cognition due to dementia and other medical conditions common among elderly patients (e.g., cognitive dysfunctions associated with metabolic disorders, dysarthria) through the identification of linguistic alteration in speech and written productions. Compared to classical neuropsychological tests, this analysis showed many advantages, notably non-intrusive and time-effective application, adherence to real-world settings, and low cost. Moreover, they canprovide both offline and online measures that serve as a proxy for cognitive processing.
Project Objectives
The Project aims at tackling a comprehensive range of modelling and clinical issues concerning MCI through the following activity lines:
The three lines of activity are interdependent:
ReMind will push progress in the early diagnosis of MCI, a pivotal challenge to the promotion of the optimal management of cognitive frailty, both at the individual and societal levels, paving the way to timely identification of risk, and personalised intervention.
Progress will likely reduce the psychological burden of patients and caregivers, enabling the implementation of preventive measures and appropriate treatment, and containing the economic impact of dementia on social welfare and healthcare systems.