An examination of white matter integrity and functional network organization in Subjective Cognitive Decline using Diffusional Kurtosis Imaging‐based tractography resting state fMRI
Bibliographic record
Abstract
Abstract Background Subjective Cognitive Decline (SCD) may increase the risk of Alzheimer’s Disease and related dementias (ADRD). Brain network changes may occur at both the functional and structural levels in SCD. To better understand these network changes, diffusional kurtosis imaging (DKI) and functional connectome (FC) measures were examined in SCD and healthy controls (HC). We hypothesized that DKI and FC measures in the medial temporal lobe would be sensitive to differences between SCD and healthy controls (HC). Method SCD classification was determined by clinician evaluation or Everyday Cognition average item score > 1.6, with objectively healthy cognitive performance (Montreal Cognitive Assessment score > 22). DKI and fMRI images were acquired on a Siemens PRISMA scanner in 33 SCD subjects and 33 age/sex matched HCs (mean age=69.1 yr). DSI Studio’s Automatic fiber tracking extracted the DKI measure mean kurtosis (MK) in 3 bilateral white matter bundles associated with ADRD: inferior longitudinal fasciculus (ILF), cingulum‐parahippocampus (CP), and uncinate fasciculus (UF). FC measure eigenvector centrality (Evc) was calculated in 19 medial temporal nodes using the Brain Connectivity Toolbox. Generalized Linear Models were conducted for each bundle to determine whether EVC in the left ventrolateral amygdala (LAMG) was predicted by MK, Diagnosis or the MK*Diagnosis interaction. Result In the left ILF, the effect of MK approached significance (p = .054) as did the MK*Diagnosis interaction (p = .059). Lower MK was associated with higher LAMG EVC in HC (r = ‐.40) but there was no association between MK and EVC for SCD. In the left UF, effect of MK was significant (p = .003), but no effect of Diagnosis and no interaction. Lower MK was associated with higher LAMG EVC (r = ‐.359) in both SCD and HC. Conclusion This preliminary study showed that in HC, white matter integrity of the ILF was associated with LAMG functional connectivity, but this association was absent in SCD. While present results should be interpreted with caution due to small sample sizes, these findings indicate that healthy aging structural‐functional associations are disrupted in SCD, but future studies should determine whether this is a reliable marker of brain organization changes.
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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) | 0.001 | 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".