The aging brain: A set of functional MRI data acquired at rest and during exposure to tactile or muscle proprioceptive stimulation in healthy young and older volunteers
Bibliographic record
Abstract
There is a growing interest in understanding functional brain decline with aging. The dataset provides raw anatomical and functional images recorded in a group of 20 young volunteers and in another group of 19 older volunteers during a 10-minute period of resting state followed by four consecutive task-related runs. During each task-related run, the participants were exposed to two types of sensory stimulation: a tactile stimulation consisting in a textured-disk rotation under the palm of their right hand or a muscle proprioceptive stimulation consisting in a mechanical vibration applied to the muscle tendon of their wrist abductor. These two stimulations are known to evoke illusory sensations of hand movement, while the hand remains actually still. Therefore, the dataset is meant to be used to assess age-related functional brain changes during the perception of hand movements based on muscle proprioception or touch individually. It also allows to explore any structural changes or functional resting connectivity alteration with aging. The dataset is a supplement to the research findings in the paper 'Functional brain changes in the elderly for the perception of hand movements: a greater impairment occurs in proprioception than touch published in NeuroImage.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".