Alzheimer Disease, Amnestic-Mild Cognitive Impairment
Conditions
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
Interventions
DIAGNOSTIC_TESTEEG
EEG
GENETICApoE
ApoE
Sponsors
Catholic University of the Sacred Heart
Study design
Observational model
CASE_ONLY
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Exclusion criteria
for AD will be: * frontotemporal dementia; * behavioural variant of frontotemporal dementia; * vascular dementia; * extra-pyramidal syndromes; * reversible dementias (including pseudodementia of depression); * Lewy body dementia. The
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Biomarkers: EEG | 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 | 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
| Measure | Time frame | Description |
|---|---|---|
| Biomarker: Accuracy of digital classifier | 2 years | Secondary endpoint will be to investigate whether EEG connectivity markers (small world ) along with genetically determined risk-indicators for dementia, as represented by Apo-E testing can reach a greater sensitivity, specificity and accuracy for a digital classifier (i.e. an algorithm that solve the problem of identifying to which of a set of categories a new observation belongs) able to predict the MCI conversion to AD. The accuracy value is dimentionless number represented by a percentual value and it is the biomarker for the ability of the classifier for the early identification of AD (Vecchio F. et al., 2018 doi: 10.1002/ana.25289) |
Countries
Italy
Outcome results
None listed