A SHARED NEURAL MECHANISM UNDERLYING DEFICITS IN SELECTIVE VISUAL ATTENTION ACROSS MILD COGNITIVE IMPAIRMENT SUBTYPES
Poster
Authors/Editors
Strategic Research Themes
- Assistive Technology for the Aged & Disabled & Rehabilitation (Smart Healthcare)
- Brain-based Learning (Future Learning)
- digital learning (Future Learning)
- Digital Transformation (Strategic Research Themes)
- Information Technology (Digital Transformation)
- Medical Diagnostics (Smart Healthcare)
- Medical Treatment & Prevention (Smart Healthcare)
- Smart Healthcare (Strategic Research Themes)
Publication Details
Author list: K. Boonyadhammakul , P. Jetsadapattarakul , M. Hema , J. Oboun , T. Phangwiwat , P. Sookprao , K. Benjasupawan , C. Chunharas , I. Chatnuntawech , S. Itthipuripat
Publication year: 2024
Abstract
Prior research indicates that while the fidelity of neural representations in the visuocortical system supporting selective attention remains intact in late adulthood, it substantially diminishes in individuals with mild cognitive impairment (MCI), a transitional stage preceding dementia. However, it remains unclear to what extent neural substrates underlying attentional deficits vary across various MCI subtypes, commonly classified based on Montreal Cognitive Assessment (MoCA) performance. Here, employing an inverted encoding model (IEM) approach, we reconstructed spatially selective attention representations from visuocortical alpha band activity (9-12Hz EEG oscillations) during a modified Eriksen Flanker task. We compared the fidelity of these
representations across healthy aging and distinct MCI subtypes: single-domain amnesic, single-domain non-amnesic, multiple-domain amnesic, and multiple-domain non-amnesic. Our findings reveal that all MCI subtypes exhibit reduced fidelity of alpha-based selective attention representations compared to healthy controls, even in the absence of significant attentional deficits as per MoCA assessment, as observed in the single-domain amnesic subtype. These results suggest a shared neural mechanism underlying attention deficits across diverse MCI subtypes. Furthermore, the introduced model-based EEG approach holds promise as a brain-based diagnostic tool for quantifying attention deficits in different MCI subtypes.
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