P4‐203: Developing Brain Vital Signs: Initial Assessments Across the Adult Lifespan
Bibliographic record
Abstract
The ability to accurately assess brain functional status is critical for functional monitoring in aging and Alzheimer’s disease (AD). Event related potentials (ERPs) provide a reliable, physiology-based measure of brain function, independent of potential behavioral deficits. Changes in ERP characteristics have been shown to precede manifestation of clinical symptoms in AD, and enable relatively accurate prognostication of decline in MCI patients. However, to translate laboratory-based ERP advances into improved care in AD and dementia, there remains a need for a framework for clinically accessible, objective, and physiology-based measures of key (modal) brain functions common across individuals– i.e., the equivalent of brain vital signs (BVS). Three 5-minute sessions of ERP data encompassing the entire information-processing spectrum, from auditory sensation (N100) to basic attention (P300) to cognitive processing (N400) were collected on 16 healthy individuals (range: 22-82years). Concomitant MMSE and MoCA assessed participants’ cognitive status. ERP processing used established methods, including spectral filtering (1-20Hz bandpass and 60Hz notch), segmentation (-100ms to 900ms relative to stimulus), and conditional averaging. Machine learning approaches identified ERP components at the individual level. The complex ERP data were then transformed into the BVS framework. The 3 targeted ERP components were successfully elicited in all participants. Machine learning methods identified the ERP responses at the individual level across young and older adults with high accuracy (>86.81%), sensitivity (>0.84) and specificity (>0.90). While global cognitive performance scores were in the healthy range for young (MMSE/MoCA =30±0) and older (MMSE=30, MoCA=29.3±0.5) adults, there was a significant increase in the P300 latency for the older group (p<0.05) and a similar trend was observed for the N400 latency (p=0.07). Importantly, transformation to the BVS framework retained these age-related patterns, while demonstrating a decrease in the within session variance (p<0.05). Initial validation across the adult lifespan confirmed the sensitivity of ERPs to detect age-related functional brain changes. Transformation to the BVS framework stabilized the neuronal responses while maintaining the age-related differences. The findings represent the first step in identifying and characterizing ERPs as vital signs, critical for subsequent evaluation of dysfunction.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".