Novel Multimodal Imaging Framework for Synaptic Integrity Characterization in Alzheimer’s Disease
Bibliographic record
Abstract
Abstract Background Dynamic loss of synapses is well understood to be a crucial endpoint in many neurodegenerative diseases especially that of Alzheimer’s Disease (AD). Hence, successful in vivo characterization of synaptic integrity (SI) provides huge potential for opportunities in therapeutics targeting synaptic damage restoration. Though recent studies using synaptic glycoprotein 2A (SV2A) PET imaging remains as the most direct measure of SI, such methods are not readily available for clinical applications. Here, we examine a new framework for in vivo characterization of SI in the AD continuum by incorporating widely available imaging parameters such as atrophy, connectivity, and neuronal metabolism. Method 53 amyloid‐negative cognitively normal (CN‐), 92 amyloid‐positive mild cognitive impairment (MCI+), and 50 amyloid‐positive AD (AD+) subjects with T1‐weighted MRI, diffusion‐weighted MRI and 18F‐fluorodeoxyglucose (FDG) PET images were collected from ADNI. Selected imaging modalities were each theorized to portray distinct parts of SI such as pre‐synaptic disruption, synaptic connectivity, and synaptic dysfunction. Hence, using an exogenous common factor analysis model, with each modality measures as primary manifestation variables, an amalgamated regional SI score was calculated. Using regional values, factor analysis was implemented again to produce global SI scores for each subject. Regional scores were normalized and adjusted for age, sex, and education for groupwise comparison. Result Regional SI W‐scores showed progressive decline along the AD continuum in the entorhinal, lateral temporal, lateral parietal, and isthmus cingulate cortices, showing significant differences among most regions between CN‐ and AD+ groups. Bilateral entorhinal and isthmus cingulate cortices and left inferior temporal region showed strong significant differences between CN‐ and MCI+ groups, suggesting early synaptic loss in these areas, which agree well with previous results from SV2A PET studies. Global scores also declined significantly through disease progression and showed associations with decreased cognitive performance as measured by MMSE and CDR‐SOB. Conclusion Various neuroimaging biomarkers have been extensively verified to observe different traits of AD, yet successful characterization of SI without SV2A PET is first to our knowledge. Our model has shown that synaptic change can be indirectly visualized using widely available imaging modalities, providing immense opportunities for applications in both clinical and research contexts.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".