Loss of callosal fibre integrity in healthy elderly with age-related white matter changes Martin GriebeAlex ForsterMichele WessaChristina RossmanithHansjorg Bazner • Tamara SauerKathrin ZohselChristian BlahakAndrea V. KingJulia Linke • Michael G. HennericiAchim GassKristina Szabo
Bibliographic record
Abstract
Age-related white matter changes (ARWMC) appear to correspond to a continuum from normal func- tioning to clinically overt neurological syndromes. Dis- turbance of the structural integrity of cerebral fibre tracts— the so-called cerebral network—by ARWMC might be one explanation for this development. From 3 T magnetic resonance imaging (MRI) data of 34 healthy elderly sub- jects (60-82 years) we calculated the lesion volume of ARWMC and the area of the corpus callosum (CC). Gait, balance and cognition were assessed. We compared these findings in those with mild (n = 22) and advanced (n = 12) ARWMC and performed tract-based spatial sta- tistics (TBSS) to analyze white matter structural integrity. In subjects with advanced ARWMC, TBSS showed a sig- nificant decrease of fractional anisotropy (FA) in several large tracts of the white matter including the CC; total CC, CC2 and CC5 areas were significantly smaller. Despite these morphological changes, tests of gait, balance and cognition as measured by the Mini-Mental State Exami- nation (MMSE) were in the normal range for both groups; only the Montreal Cognitive Assessment (MoCA) detected executive and language dysfunction in those with advanced ARWMC. Loss of tissue integrity and atrophy of the CC secondary to spatially remote lesions in the peri- and paraventricular white matter in ARWMC appear to be already detectable in healthy elderly individuals.
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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) | 0.000 | 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) | 0.002 | 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".