White matter texture abnormalities are associated with delusional severity in a cognitively mixed sample of older adults
Bibliographic record
Abstract
Abstract Background Delusions in older adults correlate with white matter lesion burden [1], though the relationship with white matter texture (WMT) is unclear. White matter texture is the tissue organization in normal‐appearing brain matter (NABM) related to overall diffusion capabilities. Method Using NPI‐Q scores from the ADNI data set, a cognitively mixed sample of patients with delusions was extracted. Delusional severity was ranked from 1‐3 (1 being the lowest severity) based on the NPI‐Q scores along with downloads of FLAIR MRI volumes for each subject. Subjects and volumes were analyzed in each severity group. Normal‐appearing brain matter of FLAIR images were extracted with validated in‐house brain extraction and intensity standardization algorithms [2]. Volume texture maps representing the correlation in the NABM for each FLAIR image were generated as per Khademi et. al [3]. Result Boxplots for the NABM texture measures are shown for groups 1, 2 and 3 in Figure 1. Subjects with high delusional severity (group 3) had the most noticeable change in WMT relative to lowest delusional severity (group 1). Group 3 had a higher spatial correlation value of 6.0947 e+03 +/‐ 592.6178 than group 1 with values of 5.7630 e+03 +/‐ 832.5040. A higher value indicates more spatial correlation among pixels. This suggests that there are larger regions that are smooth in Group 3 compared to Group 1, indicating less WM “structure” in the more severe delusion group. Conclusion The severity of delusions in a cognitively diverse sample of older adults correlates with abnormalities in white matter texture, though further research is required to further validate our findings. References: [1] K. Misquitta, M. Dadar, D. L. Collins and M. C. Tartaglia and Alzheimer’s Disease Neuroimaging Initiative, White matter hyperintensities and neuropsychiatric symptoms in Alzheimer’s disease and mild cognitive impairment, bioRxiv, 2019. [2] A. Khademi, B. Reiche, G. Arezza, Method and System for Standardized Processing of MR Images, PCT Patent Application No. PCT/CA2018/051606, Filed.US. December 2018. [3] A. Khademi, D. Hosseinzadeh, A. Venetsanopoulos and A. Moody, "Nonparametric statistical tests for exploration of correlation and nonstationarity in images," 2009 16th International Conference on Digital Signal Processing, Santorini‐Hellas, 2009, pp 1‐6.
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 | 0.000 | 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 | 0.001 | 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".