Sustainable Method for Alzheimer's Prediction in Mild Cognitive Impairment: EEG Connectivity and Graph Theory Combined With ApoE Testing.
Overview
- Phase
- Not Applicable
- Intervention
- Not specified
- Conditions
- Alzheimer Disease
- Sponsor
- Catholic University of the Sacred Heart
- Enrollment
- 150
- Locations
- 1
- Primary Endpoint
- Biomarkers: EEG
- Status
- Completed
- Last Updated
- 5 years ago
Overview
Brief Summary
This is an observational study with the aim of validating, in a consistent population sample, with appropriate follow-up, whether EEG connectivity analysis combined with the neuropsychological evaluation and ApoE genotype testing in aMCI could be of help in early identification of converted aMCI as a first-line screening method in order to intercept early those subjects with a high risk for rapid progression to AD.
Detailed Description
Primary aim of the present project is to investigate the dynamic connectivity among brain centers by using a mathematical (Small World) approach to the analysis of EEG-related neural networks. The aim is to provide reliable discrimination of amnesic-Mild Cognitive Impairment (a MCI) subjects who, on individual basis, will rapidly convert to Alzheimer Disease (AD) after a relatively brief follow-up. Moreover, keeping in mind that the epsilon-4 allele of the ApoE gene is a genetically determined risk factor for pathogenesis of late-onset AD, a secondary endpoint is introduced to investigate whether the EEG connectivity markers together with a genetically determined risk of dementia as represented by ApoE testing can reach higher sensitivity/specificity for early discrimination of MCI converting to AD
Investigators
Paolo Maria Rossini
Full Professor
Catholic University of the Sacred Heart
Eligibility Criteria
Inclusion Criteria
- Not provided
Exclusion Criteria
- Not provided
Outcomes
Primary Outcomes
Biomarkers: EEG
Time Frame: 2 years
EEG recording will be performed at rest, with closed eyes from routine electrode scalp positions according to the International 10-20 system. Functional connectivity analysis will be performed using eLORETA evaluating intracortical Lagged Linear Coherence. Weighted and undirected networks will be built from the above measure. Small World parameter is a dimentionless number that will be assessed as Biomarker of brain connectivity networks, since it measures the balance between local connectedness and the global integration of a network, representing brain network organization. Small world index will be computed in the seven EEG frequency bands delta (2-4 Hz), theta (4-8 Hz), alpha 1 (8-10.5 Hz), alpha 2 (10.5-13 Hz), beta 1 (13-20 Hz), beta 2 (20-30 Hz) and gamma (30-45 Hz) (Vecchio et al., 2018 doi: 10.1002/ana.25289)
Biomarker: ApoE4
Time Frame: 2 years
It will be evaluated the allele of the Apo-E gene as biomarker for the pathogenesis of late-onset and sporadic AD. The Apo-E test provides a dimentionless value represented by the type of the allele (ε2, ε3,ε4).
Secondary Outcomes
- Biomarker: Accuracy of digital classifier(2 years)