Behavioural and electrophysiological measures of visual processing for early detection of Alzheimer’s disease
Bibliographic record
Abstract
Alzheimer’s disease (AD) begins years before clinical diagnosis, but there are no simple, cost-effective methods to identify individuals in preclinical stages of AD, when interventions are most likely to succeed. Individuals with AD show deficits in multiple visual functions thought to reflect changes in parieto-occipital and temporal brain regions. However, we know little about how vision changes during preclinical AD – a critical step in determining whether visual tasks can predict AD. Here, we present psychophysical and electrophysiological results for two simplified tasks collected from individuals with mild cognitive impairment (MCI; N=8; Age=61-88, MoCA=20-26) and normal cognition (NC; N=8; Age=62-82, MoCA=23-30). Methods: Face Identification: participants selected which of two briefly presented faces matched a target face identity, measuring accuracy and response time. Contour Integration: we measured density thresholds to identify the global orientation of a spiral contour embedded in a field of cluttering elements. In both tasks, event-related potentials (ERPs) were acquired using the consumer-focused Muse system. Results: Face Identification: the MCI group showed slightly, but not significantly, worse accuracy (Mdiff=0.06, 95%CI=[-0.06,0.19]) and slower response times (Mdiff=-0.35, 95%CI=[-0.96,0.26]) than the NC group. MCI N170s were reduced in amplitude (Mdiff=-1.42𝜇V, 95%CI=[-3.96,1.12]) and delayed (Mdiff=-15.1ms, 95%CI=[-33.8,3.56]) relative to NCs, although these differences also were not significant. Contour Integration: In contrast to the results from face identification, there was a large group difference in both density thresholds (Mdiff=0.45, 95%CI=[0.24,3.56]) and N1 latency (Mdiff=-40.5ms, 95%CI=[-67.7,-13.4]), but not in N1 amplitude (Mdiff=-0.29𝜇V, 95%CI=[-2.84, 2.27]). Delayed N1s were significantly correlated with worse density thresholds (r=-0.78) and lower MoCA scores (r=-0.62), and lower MoCA scores correlated with worse density thresholds (r=0.69). These are the first results showing that behavioural and ERP measures of contour perception may distinguish between normal cognition and MCI. Thus, simple visual tasks may provide viable candidates for early markers of preclinical AD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| 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) | 0.003 | 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".