Analysis of brain structural connectivity networks and white matter integrity in patients with mild cognitive impairment
Bibliographic record
Abstract
Abstract Background Mild cognitive impairment (MCI) is considered a prodromal phase of Alzheimer’s disease (AD) and patients with MCI convert to AD at a rate of about 10–15% per year. Because of this high conversion rate to AD, studies of MCI may yield insight into early pathological changes in AD. Here, we used a novel approach to analyze structural connectivity and white matter (WM) integrity using fractional anisotropy (FA), intra‐cellular (IC), extra‐cellular (EC), and isotropic (ISO) maps between patients with MCI and healthy controls (HC). Method Thirty‐two HC (age mean ± S.D. = 69 ± 6 years; 22 females) and 20 MCI (71 ± 9 years; 7 females) were included in this study. All subjects completed the Mini‐Mental State Exam (MMSE) and the Montreal Cognitive Assessment (MoCA). Multi‐shell DTI were download from ADNI (http://adni.loni.usc.edu/). Pre‐processing was performed by Mrtrix3, FSL, and ANTs. Tractography was performed with 5 million seeds using the iFOD2 algorithm. Connectome was generated from the Desikan‐Killiany parcellation file and was subsequently filtered using the COMMIT2 algorithm. A three‐compartment Stick‐Zeppelin‐Ball model was used to create the IC, the EC, and the ISO compartments, respectively. Statistical analyses (ANCOVA) were performed by connectomestats (Mrtrix3), randomise (FLS), and by an in‐house Matlab script for Spearman’s correlations. Result The two groups did differ in MMSE and MoCA (Mann–Whitney U test: Z=2.031; p=0.021; Z=1.683; p=0.046, respectively). Differences between HC and MCI were found principally in the fornix for FA and in several WM areas for IC and ISO (Figure 1, Table 1). Connectometry analysis found differences between groups in two distinct tracts: Tract#1 between the left‐lateraloccipital and the left‐insula (t=4.25, p=0.023) and Tract#2 between the left‐middletemporal and the left‐parsopercularis (t=3.34, p=0.013) (Figure 2). Figure 3 and Table 2 show the results for the correlations between DTI metrics and the neurological scores. Conclusion In this study, by using a multi‐shell diffusion MRI acquisition and robust processing of DTI data, we detected alterations in WM integrity and structural connectivity in MCI subjects. Our results demonstrate the potential of these advanced diffusion MRI methods as biomarkers to distinguish MCI and HC.
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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 | 0.001 |
| 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) | 0.001 | 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".