Cognitive impairments associated with silent brain lesions : profile and mechanisms
Notice bibliographique
Résumé
Silent brain lesions (SBLs) have been increasingly recognized as one of the underlying causes of insidious cognitive decline. However, it is still unclear to what extent SBLs affect brain function and what mediate the association of SBLs with cognitive impairments. In the present study, the spectrum of SBLs-related cognitive impairments was assessed and the role of reduced white matter integrity in the development of these impairments was examined. \n \n398 otherwise healthy hypertensive elderly Chinese subjects were included within this study. Demographical and related clinical information, performance on standard neuropsychological tests and multi-sequences MRI scans were obtained from all participants. Standard compound z scores were constructed across a wide range of cognitive domains. Global cognitive function was assessed using the Montreal Cognitive Assessment (MoCA) scale. Presence / load and location of silent brain infarcts (SBIs), brain microbleeds (BMBs), white matter hyperintensities (WMHs) were visually assessed using standard scales. The volume of WMHs was additionally measured automatically using open-source software. In order to determine the extent of white matter alterations, parameters derived from diffusion tensor imaging (DTI) were extracted from global and lobar normal appearing white matter (NAWM). Series of linear regression models and path analyses were used to examine the data. \n \nHigh prevalence of SBLs was identified in this study sample. Markers of SBLs (or their underlying pathology) showed varying impacts in certain cognitive function. BMBs load was significantly related to worse performance on tests of language-related function, and strictly lobar BMBs seemed to play a predominant role in this association. The presence of SBIs (predominantly the deep SBIs) was associated with cognitive deficits in executive function. Both the volume and Fazekas scores of WMHs showed significant association with worse performance on executive function, information processing speed and language-related function. The degree of periventricular white matter hyperintensities (PVHs), but not deep white matter hyperintensities (DWMHs) contributes to WMHs-related cognitive impairments. \n \nPVHs have the strongest impact on integrity of white matter microstructure. Other markers (DWMHs, deep SBIs, strictly lobar BMBs and deep BMBs) all showed additional effects on white matter integrity, though the effects were less extensive and weaker. The disruption of white matter integrity in turn predicted worse cognitive performance on specific domains. These results supported an intermediating role of white matter integrity in the correlation of SBLs with cognitive impairments. \n \nResults of path analyses further confirmed the independent role of white matter integrity as a mediator for cognitive impairments in individuals with SBLs. Furthermore, it suggested that SBLs-related cognitive declines across different cognitive domains were differentially mediated by white matter disruption / brain atrophy. However, white matter microstructure and brain volume changes cannot account for the whole cognitive declines, and other mechanisms like impaired network should be considered in further investigation. \n \nIn summary, this thesis provides a detailed profile of SBLs-related cognitive impairments, as well as evidence supporting the independent role of white matter disruption as a mediator. These findings paved the way for a more detailed analysis on the relation among SBI markers, DTI metrics and cognitive function.
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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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| 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 ».