Default mode network perturbations in Alzheimer's disease: an fMRI study in Klang Valley, Malaysia
Bibliographic record
Abstract
The default mode network (DMN) is a large neural network that has a significant correlation with Alzheimer's disease (AD). Grey matter volume (GMV) and functional connectivity (FC) involving the regions of the DMN have been noted to differ significantly between AD and healthy older adults. Nevertheless, there is a paucity of data on the structural and functional changes in the DMN of AD patients in Malaysia. We conducted a cross-sectional study in Klang Valley, Malaysia, to evaluate AD subjects compared to healthy controls (HC) using a resting-state functional MRI (rs-fMRI) experiment. We recruited 22 subjects (AD=11, HC=11) and conducted neuropsychological tests such as the Montreal Cognitive Assessment (MoCA), Mini Mental State Examination (MMSE), and Clinical Dementia Rating (CDR). The subjects then underwent rs-fMRI scans, and subsequently, we quantitatively analysed the GMV by Voxel based Morphometry (VBM) using the structural data. We also utilised the CONN toolbox on Statistical Parametric Mapping (SPM) software to evaluate the FC and activation of the nodes of the DMN. In comparison with the HC group, the AD group demonstrated a reduction in GMV in the right and left inferior temporal gyrus, left superior frontal gyrus, right superior frontal gyrus medial segment, right gyrus rectus, right temporal lobe, left putamen, and right precuneus. Moreover, there was a significant decrease in the FC of the nodes of the DMN noted on rs-fMRI (cluster-size corrected p<0.05). In particular, the precuneus and anterior cingulate cortex had decreased FC in AD compared to HC. Hence, structural and resting-state fMRI can detect distinct imaging biomarkers of AD based on GMV and DMN functional connectivity profiles. This tool can be used as a non-invasive tool for improving the feature detection and diagnosis of AD in the Malaysian population.
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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 | 0.000 |
| 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) | 0.001 | 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".