The Combination of Functional and Structural MRI Is a Potential Screening Tool in Alzheimer’s Disease
Bibliographic record
Abstract
Introduction: This study aimed to survey the discrimination power of parameters from cerebrospinal fluid (CSF) biomarkers, fluorodeoxyglucose uptake on PET (FDG-PET), structural magnetic resonance imaging (MRI) and functional MRI in high and low risk subjects or in converters and stable subjects of normal and mild cognitive impairment (MCI) statuses. Methods: We used baseline resting-state functional MRI (rfMRI) from the Alzheimer’s disease neuroimaging initiative (ADNI) dataset to analyze functional networks and recorded subjects’ characteristics and results of CSF study, FDG-PET, and structural MRI from ADNI website. All parameters were evaluated between-group difference among normal (NC), MCI and Alzheimer’s disease (AD) groups. The parameters other than CSF results were included to study difference between high and low AD risk subjects in NC or MCI groups, based on CSF results. On the basis of two-year follow-up conditions, all parameters were compared between stable subjects and converters in NC and MCI. Results: CSF biomarkers, FDG-PET, structural MRI and functional MRI are all able to differentiate AD from MCI or NC but not between MCI and NC. As compared with low AD risk subjects, high-risk subjects present decreased FDG-PET in both MCI and NC groups but structural MRI change only in MCI status and rfMRI alteration only in NC status. As compared with stable subjects, converters have decreased FDG-PET, functional network changes and structural changes in both MCI and NC groups. Conclusion: The combination of functional and structural MRI is a safer screening tool but with similar power as FDG-PET to reflect CSF change in AD pathological process and to identify high-risk subjects and converters in NC and MCI.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".