GLORIA

GEOMAR Library Ocean Research Information Access

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
  • 1
    In: Alzheimer's & Dementia, Wiley, Vol. 18, No. S6 ( 2022-12)
    Abstract: The value of Electroencephalography (EEG) to unveil pathophysiological signatures in neurodegenerative diseases that cause dementia has been recently highlighted. To grant EEG tools the necessary validity, reliability, and scalability to support the diagnosis of dementia globally, efforts will need to integrate knowledge developed by EEG labs across diverse countries and develop protocols that can be swiftly implemented by such labs to harmonize research and clinical practices. These are the aims of EuroLaD‐EEG, a consortium that brings together five Latin American (LAC) and five European countries to develop a harmonised EEG database (Aim 1) that can help improve dementia phenotyping and diagnosis (Aim 2). Methods EuroLaD‐EEG has developed a global EEG database that comprises 1234 EEG recordings from groups of healthy adults (n=1223), patients with mild cognitive impairment (n=199), and with familial and sporadic variants of neurodegenerative diseases (e.g., AD and FTD) (n=501). We have recently published the harmonization pipeline that will allow EuroLaD‐EEG meet its Aim 1 (Prado et al., 2022, Int J Psychophysiol, 172, 24–38). We have now developed a multi‐feature multimodal approach that combines demographic, neuropsychological, fMRI and EEG data as inputs for a gradient boosting machine‐learning classifier. Multicentric data including those from underrepresented samples will enter such a classification algorithm towards our Aim 2. Results Preliminary results with a subsample from LAC (n=282) revealed high classification (AUC 〉 0.90 for all the classes) and robustness towards heterogeneity, sociodemographic variability, and missing data. We are now planning to combine data from European and LAC to explore sources of phenotypic variability linked to socio‐demographic and cultural factors. Conclusion By broadening our understanding of dementia phenotypes, risk factors, and affordable diagnostic approaches, and adding new evidence on variability across developed and developing countries, the EEG will contribute unique evidence that will help enhance both dementia phenotyping and diagnostic strategies.
    Type of Medium: Online Resource
    ISSN: 1552-5260 , 1552-5279
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2022
    detail.hit.zdb_id: 2201940-6
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
  • 2
    In: Journal of Neural Engineering, IOP Publishing, Vol. 19, No. 4 ( 2022-08-01), p. 046048-
    Abstract: Objective. The differential diagnosis of behavioral variant frontotemporal dementia (bvFTD) and Alzheimer’s disease (AD) remains challenging in underrepresented, underdiagnosed groups, including Latinos, as advanced biomarkers are rarely available. Recent guidelines for the study of dementia highlight the critical role of biomarkers. Thus, novel cost-effective complementary approaches are required in clinical settings. Approach . We developed a novel framework based on a gradient boosting machine learning classifier, tuned by Bayesian optimization, on a multi-feature multimodal approach (combining demographic, neuropsychological, magnetic resonance imaging (MRI), and electroencephalography/functional MRI connectivity data) to characterize neurodegeneration using site harmonization and sequential feature selection. We assessed 54 bvFTD and 76 AD patients and 152 healthy controls (HCs) from a Latin American consortium (ReDLat). Main results . The multimodal model yielded high area under the curve classification values (bvFTD patients vs HCs: 0.93 (±0.01); AD patients vs HCs: 0.95 (±0.01); bvFTD vs AD patients: 0.92 (±0.01)). The feature selection approach successfully filtered non-informative multimodal markers (from thousands to dozens). Results . Proved robust against multimodal heterogeneity, sociodemographic variability, and missing data. Significance . The model accurately identified dementia subtypes using measures readily available in underrepresented settings, with a similar performance than advanced biomarkers. This approach, if confirmed and replicated, may potentially complement clinical assessments in developing countries.
    Type of Medium: Online Resource
    ISSN: 1741-2560 , 1741-2552
    Language: Unknown
    Publisher: IOP Publishing
    Publication Date: 2022
    detail.hit.zdb_id: 2135187-9
    SSG: 12
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...