IC‐P‐224: HETEROGENEOUS TAU‐PET SIGNAL IN THE HIPPOCAMPUS HELPS RESOLVE DISCREPANCIES BETWEEN IMAGING AND PATHOLOGY
Notice bibliographique
Résumé
In Alzheimer's disease (AD), neurofibrillary tau tangles (NFTs) are believed to first aggregate in the entorhinal cortex (ERC), and eventually spread to the hippocampus, possibly via trans-synaptic mechanisms. However, while NFTs can now be measured in living humans using positron emission tomography (PET), PET signal in the hippocampus has not corroborated findings from pathology studies. In the current study, we examine data-driven patterns of PET signal in the hippocampus, to establish whether discrepancies in this region are due to heterogeneous signal sources. AV1451 images were downloaded from the Alzheimer's Disease Neuroimaging Initiative website (143 older controls, 88 mild cognitive impairment, 27 AD dementia). In a previous study, we used an advanced clustering algorithm in the Swedish BioFINDER cohort to segregate different spatial distribution patterns of AV1451 across the AD spectrum. Average AV1451 signal was extracted across the whole hippocampus, as well as separately across hippocampal voxels belonging to each AV1451 cluster identified in the previous analysis. We then tested whether different AV1451 signal patterns were differentially related to clinical diagnosis, presence of Aβ pathology and episodic memory (EM) scores. Finally, we used diffusion tractography to measure the number of connections between the ERC and the different hippocampal signal clusters, based on a connectome extracted from 114 young controls using ndmg. AV1451 signal in the hippocampus covaried either with other regions involved in early NFT aggregation ("Early NFT"), or with regions susceptible to off-target AV1451 binding ("Off-Target"; Figure 1). Relationships between AV1451 and diagnosis, Aβ status and EM scores were substantially enhanced when only using voxels in the Early NFT cluster, compared with voxels in the Off-Target cluster, or the whole hippocampus (Figure 2). Tractography analysis revealed a greater number of anatomical connections between the ERC and the Early NFT cluster of the hippocampus compared to the Off-Target cluster (p<0.001; Figure 2). Clustering across AV1451 images revealed five spatial covariance networks. Two of these networks, were represented inside the hippocampus, indicating signal heterogeneity in this structure. (Left) The spatial extent of the two clusters. (Above) Membership of each hippocampal voxel in the two clusters projected onto a hippocampal surface. Individuals with higher hippocampal AV1451 signal (gray) were more likely to be Aβ positive (top left), had worse episodic memory scores (top right) and were more frequently diagnosed with AD dementia (bottom left). These associations were enhanced when only looking in hippocampal voxels within the early NFT cluster (turquoise), and were diminished when using voxels in the off-target cluster (purple). The Early NFT cluseter demonstrated greater anatomical connections with the entorhinal cortex as measured using diffusion tractography imaging (bottom right). Using data-driven methods, we were able to enhance the associations expected from pathology studies between hippocampal AV1451 signal and other pathological and cognitive markers. These findings partially resolve discrepancies between previous PET and pathology studies and provide a template for future AV1451 studies.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».