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Record W3111625461 · doi:10.1002/alz.041193

Whole brain generative model identifies neurotransmitter alterations underlying Alzheimer's disease progression

2020· article· en· W3111625461 on OpenAlexaff
Ahmed Faraz Khan, Nicola Palomero‐Gallagher, Karl Zilles, Yasser Iturria‐Medina

Bibliographic record

VenueAlzheimer s & Dementia · 2020
Typearticle
Languageen
FieldNeuroscience
TopicFunctional Brain Connectivity Studies
Canadian institutionsMcGill UniversityMontreal Neurological Institute and Hospital
Fundersnot available
KeywordsNeuroimagingNeurosciencePsychologyAlzheimer's diseaseFunctional neuroimagingNeurotransmitterBiologyDiseaseMedicineInternal medicineCentral nervous system

Abstract

fetched live from OpenAlex

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).

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.129
GPT teacher head0.331
Teacher spread0.203 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2020
Admission routes1
Has abstractyes

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