Blood Brain Barrier permeability change in clinical stages of Alzheimer disease
Bibliographic record
Abstract
Abstract Background Inflammation and altered neurovascular dysfunction have been noted as features of Alzheimer's Disease (AD) pathophysiology 1‐3 . In this context understanding nature and degree of disruption of brain blood barrier (BBB) is important in AD studies 4‐6 , In this regard, brain region specific changes in well characterized AD subjects at distinct clinical stages of AD is yet to be well characterized. We therefore aimed to investigate the brain regional changes in BBB permeability in different clinical stages of AD versus healthy controls (HC). Method 41 subjects (70.2 ± 3.5 YO, F=26) were scanned at 3T under IRB consent. There were 6 AD dementia, 16 mild cognitive impairment from AD (MCI) and 19 HC based on clinical dementia rating scale, Montreal Cognitive assessment (MOCA) and positive AD biomarkers. Dynamic contrast enhanced (DCE) imaging was acquired with 3d GRE scans. Permeability (Ktrans) values were calculated with Patlak model 7 in subcortical gray matter (GM) and GM lobe parcellation 8 . Result We found the significant differences of averaged Ktrans values reflecting larger BBB permeability in thalamus, caudate, putamen, amygdala, hippocampus and occipital regions in AD than HC group. (see Table 1 and Figure 1). We find that the significantly negative correlation between Ktrans and MOCA scores ( p < 0.05), as shown in Figure 2. Conclusion We find that increased permeability in specific sub‐/cortical GM regions were more prominent in AD dementia than MCI and was significantly different from HC. These changes also linearly tracked severity of cognitive decline. References: 1. Jellinger KA. J Neural Transm (Vienna). 2020; 2. Knopman DS, et al. J Neuropathol Exp Neurol. 2003; 3. Price JL, et al. Neurobiol Aging. 2009; 4. Fiala M, et al. Eur J Clin Invest. 2002; 5. Pillai JA, et al. Ann Clin Transl Neurol. 2019; 6. Sweeney MD, et al. Nat Rev Neurol. 2018; 7. Patlak CS, et al. J Cereb Blood Flow Metab. 1983; 8. Fischl B. Neuroimage. 2012; 9. Montagne A, et al. Neuron. 2015; 10. Nation DA, et al. Nature Medicine. 2019; 11. Sweeney MD, et al. Alzheimers Dement. 2019; 12. van de Haar HJ, et al. Radiology. 2016;
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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 | 0.001 |
| 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 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".