NOVEL VIRTUAL REALITY‐ BASED METHOD FOR OBJECTIVE MEASUREMENT OF EMOTIONAL REACTIVITY IN PATIENTS WITH ALZHEIMER'S DISEASE
Bibliographic record
Abstract
Abstract Background Diminished emotional reactivity in cognitively normal (CN) older adults and in patients with Alzheimer's disease (AD) is associated with higher burden of neuropathology and predicts faster cognitive decline, stressing its potential to serve as a behavioral screening tool for individuals at high AD‐risk and for a more aggressive disease course. Its measurement is currently based on questionnaires which are prone to subjectivity biases. We present preliminary results of a study aimed to examine the feasibility of using autonomic nervous system (ANS) reactivity to emotional stimuli presented in a virtual reality (VR) environment, as an objective measurement tool of emotional reactivity. Specifically, we test the hypothesis that emotional reactivity, as expressed by galvanic skin response (GSR), will be reduced in patients with AD compared to CN individuals. Method Participants are patients with mild‐ moderate AD and cognitively normal age matched controls. Cognitive function is assessed via the Montreal Cognitive Assessment (MoCA) battery. Participants are exposed in the VR‐ environment to emotionally‐ laden stimuli (positive, neutral and aversive, 12 repetitions per stimulus type) while measuring GSR. Response weighted mean was calculated for each participant for each stimulus type. Non parametric group comparisons (AD vs. CN) were performed (U‐test) for each stimulus type separately (Bonferroni corrected). Result So far, 30 patients with AD and 21 CN individuals have been recruited. Participants' mean age is 75.6 years, 60.8 % females (Table 1). Patients with AD had lower scores on the MoCA (p<0.001), but did not differ from CN in demographic variables. VR‐based evaluation was well accepted by the participants with no adverse events. CN participants showed higher GSR reactivity as compared to AD patients for aversive stimuli (p<0.05) but not for neutral or positive stimuli (Figure 1). Conclusion GSR reactivity to emotional stimuli presented in the VR environment is a feasible method to study emotional reactivity in CN older adults and in participants with AD. GSR reactivity to aversive stimuli is reduced in AD participants compared to CN. These preliminary findings should be further established in larger populations while detecting the optimal type of stimulus required to differentiate between clinically distinguished cohorts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".