Identifying Inter‐Individual Differences in Cognitive Decline Using the Brain Connectome in Osteoporosis
Bibliographic record
Abstract
ABSTRACT Background Osseous structures have been recognized as an endocrine organ that bidirectionally interacts with the brain. Osteoporosis (OP) is a systemic endocrine disorder linked to neurodegenerative disorders. This bone–brain axis interdependence highlights the necessity of cognitive monitoring in OP management to detect early neurodegeneration markers, particularly given individual variability in brain reserve that may predispose patients to accelerated cognitive decline. Purpose To investigate the individual differences in functional connectome and its association with cognitive ability in OP. Study Design Longitudinal human study. Subjects A total of 31 OP patients (Age: 56.7 ± 13.2, 17 Male) and 31 healthy controls (HC, age: 55.1 ± 11.3, 15 male). Field Strength/Sequence 3 T, gradient‐echo EPI sequence, MP2RAGE sequence. Assessment Individual identification analyses were performed to investigate the individual‐specific pattern of brain functional connectome in both OP and HC by leveraging longitudinal test–retest fMRI data to map individual variabilities in brain functional connectomes. Cognitive abilities were assessed using the Montreal Cognitive Assessment (MoCA) and Mini‐Mental State Examination (MMSE). Statistical Tests Two‐sample t tests, support vector regression, permutation tests, and Bonferroni correction. A p < 0.05 was considered statistically significant. Results Significant inter‐individual variability (SDOP = 3.27, SDHC = 2.35) and robust intra‐individual stability (P OP = 0.17, P HC = 0.59) in cognitive performance for both OP and HC groups were observed. In addition, functional connectivity profiles could reliably identify individuals across sessions (SuccessRate, SROP = 85%, SRHC = 92%). The support vector regression model revealed that connectivity profiles could predict cognitive ability both within (r MoCA‐OP = 0.63, r MMSE‐OP = 0.54, r MoCA‐HC = 0.58, r MMSE‐HC = 0.61) and between sessions (r MoCA‐OP = 0.47, r MMSE‐OP = 0.41, r MoCA‐HC = 0.53; r MMSE‐HC = 0.54), with the medial‐frontal and default‐mode networks emerging as the most predictive contributors. Conclusion These findings underscore the potential of resting‐state functional connectomes characterizing individual variability for cognitive ability in OP patients. Evidence Level 4. Technical Efficacy Stage 2.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".