Abnormal organization of white matter networks in patients with subjective cognitive decline and mild cognitive impairment
Bibliographic record
Abstract
// Xiao-Ni Wang 1,* , Yang Zeng 2,* , Guan-Qun Chen 1 , Yi-He Zhang 2 , Xuan-Yu Li 1 , Xu-Yang Hao 2 , Yang Yu 1 , Meng Zhang 2 , Can Sheng 1 , Yu-Xia Li 1 , Yu Sun 1 , Hong-Yan Li 1 , Yang Song 1 , Kun-Cheng Li 3 , Tian-Yi Yan 2 , Xiao-Ying Tang 2 and Ying Han 1,4 1 Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing, China 2 School of Life Science, Beijing Institute of Technology, Beijing, China 3 Department of Radiology, XuanWu Hospital of Capital Medical University, Beijing, China 4 Center of Alzheimer’s Disease, Beijing Institute for Brain Disorders, Beijing, China * These authors have contributed equally to this work Correspondence to: Ying Han, email: // Xiao-Ying Tang, email: // Tian-Yi Yan, email: // Keywords : subjective cognitive decline, amnestic mild cognitive impairment, network, white matter, diffusion tensor imaging, Pathology Section Received : October 31, 2015 Accepted : June 29, 2016 Published : July 13, 2016 Abstract Network analysis has been widely used in studying Alzheimer’s disease (AD). However, how the white matter network changes in cognitive impaired patients with subjective cognitive decline (SCD) (a symptom emerging during early stage of AD) and amnestic mild cognitive impairment (aMCI) (a pre-dementia stage of AD) is still unclear. Here, structural networks were constructed respectively based on FA and FN for 36 normal controls, 21 SCD patients, and 33 aMCI patients by diffusion tensor imaging and graph theory. Significantly lower efficiency was found in aMCI patients than normal controls (NC). Though not significant, the values in those with SCD were intermediate between aMCI and NC. In addition, our results showed significantly altered betweenness centrality located in right precuneus, calcarine, putamen, and left anterior cingulate in aMCI patients. Furthermore, association was found between network metrics and cognitive impairment. Our study suggests that the structural network properties might be preserved in SCD stage and disrupted in aMCI stage, which may provide novel insights into pathological mechanisms of AD.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 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".