Neurobiological And Neuroethical Perspectives On The Contribution Of Functional Neuroimaging To The Study Of Aging In The Brain
Bibliographic record
Abstract
It is crucial to improve the understanding of healthy and pathological processes of cognitive aging. This article aims to provide an overview of the contribution of neuroimaging to the understanding of neurocognitive aging, and highlights the neuroethical considerations and legal implications of using neuroimaging to conduct research on aging in the brain. Neuroimaging studies have contributed the most to the understanding of such cognitive variability, by documenting both structural and functional changes related to aging. Neuroimaging has enabled researchers to determine which specific brain regions are more vulnerable to age-related structural changes, and when such changes begin. This article presents the most recent and popular models and theories on these age-related brain activation patterns. Further concerns are raised when these human subjects have clinical conditions such as brain damage or other neurodegenerative conditions that might compromise their cognitive capacities and hence their ability to understand fully the nature of the research and to provide their informed consent.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".