Voxel-Based Morphometry Analysis of Gray Matter on Quran Memorizers
Bibliographic record
Abstract
Introduction: The brain is the most complex organ in the human body. Robust and vigorous daily activities may cause changes to the brain structure. Huffaz, individuals who memorise the Quran undergo intensive memorization training which may lead to structural changes in specific regions of the brain. Materials and method: This study looked at possible change that occurred on gray matter by characterising the textual memorization of brain structure using voxel-based morphometry (VBM). It involves voxel-by-voxel comparison of gray matter intensity of the MRI images. Forty-seven subjects (23 huffaz, 24 non-huffaz) aged between 21-25 years were voluntarily recruited. Subjects were scanned by 3 Tesla MRI system. Images were then re-aligned according to standardised Montreal Neurological Institute (MNI) coordinates. The MRIs were then segmented into gray matter, white matter and cerebrospinal fluid. Independent sample t-test was performed between the two groups. Results: No significant difference was found between the brain region of the huffaz and non-huffaz with appropriate corrections for family-wise error (FWE) at a threshold of p = 0.05. However, with a more lenient criteria (p = 0.001, uncorrected, cluster size = 50 mm3 ), we found that gray matter volume in Brodmann Area 6 and Brodmann Area 7 of the huffaz were significantly higher than the non-huffaz group. Conclusion: VBM is not sensitive enough to detect complex anatomical differences between huffaz and non-huffaz with the current sample size. Future study to explore possible image processing tools that can measure subtle structural change in human brain is warranted.
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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) | 0.003 | 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".