Functional MRI of brain physiology in aging and neurodegenerative diseases
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Brain aging and associated neurodegeneration constitute a major societal challenge as well as one for the neuroimaging community. A full understanding of the physiological mechanisms underlying neurodegeneration still eludes medical researchers, fuelling the development of in vivo neuroimaging markers. Hence it is increasingly recognized that our understanding of neurodegenerative processes likely will depend upon the available information provided by imaging techniques. At the same time, the imaging techniques are often developed in response to the desire to observe certain physiological processes. In this context, functional MRI (fMRI), which has for decades provided information on neuronal activity, has evolved into a large family of techniques well suited for in vivo observations of brain physiology. Given the rapid technical advances in fMRI in recent years, this review aims to summarize the physiological basis of fMRI observations in healthy aging as well as in age-related neurodegeneration. This review focuses on in-vivo human brain imaging studies in this review and on disease features that can be imaged using fMRI methods. In addition to providing detailed literature summaries, this review also discusses future directions in the study of brain physiology using fMRI in the clinical setting.
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.
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) | 0.001 | 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 it