Clinical Research Directory

Validation of a Digital Intervention to Rehabilitate Cognitive Resources

The goal of this clinical trial is to learn if the use of a digital cognitive rehabilitation system named RICORDO, that is flexible and capable of adapting the rehabilitation pathway according to the needs and capacity of the patients will prove effective for subjects with Subjective Memory Complaint or with Mild Cognitive Impairment or with Mild Dementia. The main questions it aims to answer are: Will the RICORDO rehabilitation treatment, lead to an improvement in the global cognitive level? Will the RICORDO rehabilitation treatment lead to improved activation of participants in managing their own health and healthcare? Researchers will compare the multidomain cognitive rehabilitation strategy of RICORDO digital solution, with a standard paper pencil rehabilitation care (usual care). Participants will undergo a comprehensive neuropsychological evaluation immediately before, immediately after and six months after the completion of the rehabilitation program. Both interventions, the experimental and the usual care, will last 5 weeks, with 3 weekly sessions of 45 minutes each and can be done autonomously by the patient at home.

The Signature of Alzheimer's Disease in Subjective Cognitive Decline

This study focuses on improving early detection of Alzheimer's disease (AD) in patients with subjective cognitive decline (SCD), a preclinical stage of cognitive impairment, in the context of emerging disease-modifying therapies (DMTs). Current biomarkers, such as brain MRI, PET scans, and cerebrospinal fluid (CSF) markers, are highly accurate but costly, invasive, and not widely accessible. The study aims to provide cost-effective, scalable tools for early identification of individuals at risk, enabling personalized assessment and timely DMT administration. Objectives: * Evaluate the accuracy of innovative, easily accessible biomarkers in predicting biologically confirmed AD. * Assess the predictive utility of previously studied methods for SCD patients. * Explore new approaches, including automated speech analysis, to identify cognitive decline. * Evaluate genetic contributions to AD risk. * Integrate data from these various modalities using machine learning to create a predictive model for AD in SCD patients. Study Design: This is a multicenter, longitudinal, low-intervention study conducted at IRCCS Policlinico San Donato, San Donato Milanese, Milan, Italy (UO1) and the Center for Research and Innovation in Dementia, Careggi Hospital, Florence, Italy (UO2). Eligible participants are adults with SCD, intact daily functioning, and Mini-Mental State Examination (MMSE) scores \>24. Exclusion criteria include neurological or systemic diseases, major psychiatric disorders, substance use, or prior head injury. Participants undergo: * Detailed medical and family history collection. * Comprehensive neuropsychological, personality, and independence in daily activities assessment * EEG recording in resting state. * Blood sampling for plasma biomarkers (Aβ42, Aβ40, p-tau181, p-tau217, t-tau, NfL, GFAP). * CSF biomarker analysis (Aβ42, Aβ40, p-tau, t-tau). * Genetic analysis of AD-related genes (PSEN1, PSEN2, APOE, TREM2, ABCA7, BDNF, HTT). * Speech recording and analysis using standardized tasks to extract features for automated evaluation. The study expects to create a machine learning-based predictive model combining biomarker, neuropsychological, EEG, speech, and genetic data to improve early detection and guide personalized patient care. Procedures: * Neuropsychological evaluations occur at baseline and two-year follow-up. * Language recordings are conducted in controlled settings using standardized picture description tasks. * EEG is recorded using 21-channel systems. * Blood and CSF samples are collected, processed, and stored at -80°C for subsequent analysis at respective institutional laboratories. * Plasma biomarkers are analyzed with Simoa technology; CSF biomarkers are analyzed using chemiluminescent enzyme immunoassay (CLEIA). * Genetic analyses employ PCR, high-resolution melting analysis (HRMA), sequencing, and capillary electrophoresis as appropriate for specific genes or polymorphisms. The study expects to create a machine learning-based predictive model combining biomarker, neuropsychological, EEG, speech, and genetic data to improve early detection and guide personalized patient care.