Assessment of visual processing in Alzheimer’s Disease and Lewy Body Dementia using Fast Periodic Visual Stimulation
Bibliographic record
Abstract
BACKGROUND: It is vital that functional biomarkers of dementia are developed that can identify early cognitive change and stratify cognitive deficits in different aetiologies. Visuoperceptual impairment occurs to varying degrees in Lewy body disease (LBD) and Alzheimer's Disease (AD) and its quantification provides an opportunity to delineate the two diseases. Fast periodic visual stimulation (FPVS) is an electroencephalographic (EEG) marker of discrimination between two classes of frequency-tagged stimuli, that can be adapted to capture different cognitive functions. FPVS tasks provide a rapid (<3mins), objective measure of brain function, implicitly and passively, i.e. the participant is not required to respond or even understand the task. Here, we assess whether FPVS visuoperceptual tasks can differentiate between LBD, AD, and cognitively heathy older adults (HOA). METHOD: Six patients with LBD, nine with AD, and 40 age-matched HOAs, completed FPVS tasks that implicitly measured different forms of visuoperception: orientation discrimination, pseudo-object perception, real object recognition, and positional discrimination. Visuoperception was also assessed behaviourally using the Freiburg Visual Acuity and Contrast Test, and the Visual Object and Space Perception Battery (VOSP). Global cognitive functioning was assessed using the Montreal Cognitive Assessment (MoCA). Data collection is ongoing with target samples sizes of 20 per patient group. RESULT: Across FPVS tasks, implicit visuoperception was reduced in patients compared to HOAs, with greater impairment observed in LBD than in AD. Per task, the between group performance was: orientation discrimination, HOA > AD > LBD (η2 = .145, p =0.045); pseudo-object perception, HOA > AD > LBD (η2 = .232, p =0.020); real object recognition, HOA > AD = LBD (η2 = .096, p =0.080); positional discrimination, HOA > AD > LBD (η2 = .085, p =0.220). CONCLUSION: FPVS implicitly captures different forms of visuoperception in cognitively healthy older adults and is sensitive to impairment in AD and LDB. Furthermore, the technique can delineate DLB from AD using measures of orientation discrimination, pseudo-object perception and positional discrimination, potentially guiding differential diagnosis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".