Whole brain generative model identifies neurotransmitter alterations underlying Alzheimer's disease progression
Bibliographic record
Abstract
Abstract Background Alzheimer’s disease (AD) involves heterogeneous aberrations in multiple biological processes, although their causal molecular mechanisms are not fully understood. As the primary neuronal signalling molecules, neurotransmitters regulate a variety of biological processes pathological in AD, such as the coupling between neural activity and vascular response. However, due to the infeasibility of mapping most neurotransmitters in‐vivo, alterations during disease progression are not well characterized. We propose multi‐modal, subject‐specific models of neuroimaging alterations as functions of local neurotransmitter concentrations, which we combine with clinical data to identify specific neurotransmitters prominently altered during AD progression. Method (1) We used the methods described in [Iturria‐Medina et al., Neuroimage, 152:60–77, 2017] to pre‐process longitudinal neuroimaging data (amyloid β and tau protein distributions, cerebral blood flow, r‐fMRI, glucose metabolism, and grey matter density) for 141 healthy and 302 diseased subjects from ADNI. (2) Using the densities of 15 neurotransmitter receptors from multiple cortical areas [Palomero‐Gallagher & Zilles, Neuroimage, 197:716–741, 2019] and 4 serotonin‐type PET templates [Lanzenberger et al., Biological psychiatry, 61(9):1081–1089, 2007], we built subject‐specific, generative models of changes in neuroimaging in terms of (i) other neuroimaging modalities, (ii) network propagation, and (iii) first‐order receptor‐moderated interactions. (3) By evaluating the monotonicity of receptor‐associated coefficients with aging‐related cognitive measures (memory performance, executive function, MMSE, CDR, and ADAS), we identified receptors with a significant causal role in AD progression. Result Notably, the model explains 50%‐77% of observed variance in all imaging bio‐markers across clinical groups (HC, EMCI, LMCI, AD). Significant receptor‐imaging interactions with AD progression were identified for (i) NMDA and cholinergic receptors for amyloid distribution, (ii) nicotinic acetylcholine and serotonergic receptors for tau distribution, (iii) glutamatergic NMDA and D1 dopaminergic receptors for blood flow, (iv) GABAergic, cholinergic, dopaminergic and serotonergic receptors for neural activity, (v) GABA receptors for glucose metabolism, and (vi) GABAergic and serotonergic receptors for gray matter density. Conclusion A generative, multi‐modal neuroimaging model including 19 receptors allowed, for the first time, the in‐vivo identification of receptor alterations underlying AD progression and its associated cognitive/clinical deterioration. Critically, identified receptors are significant molecular controllers of typical multi‐modal alterations in AD (including tau and amyloid accumulation, vascular dysregulation, and extended brain atrophy).
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".