Painting by lesions: White matter hyperintensities disrupt functional networks and global cognition
Notice bibliographique
Résumé
White matter hyperintensities (WMH) are a prominent feature of cerebral small vessel disease and are associated with cognitive impairment. These deficits in cognition may be caused by the disruption of large-scale functional networks due to the presence of WMHs. However, knowledge regarding the relevance of these lesions on functional networks remains inconclusive. These inconsistencies may derive from issues with interpreting functional imaging data from clinical populations. Lesion network mapping is a technique that allows the overlaying of lesions from a patient population to the functional connectivity of a human connectome derived from healthy adults. This allows researchers to identify functional networks that would be disrupted in a healthy population should the WMHs seen in cerebral small vessel disease be present. We hypothesized that the extent to which these functional networks are disrupted by WMHs is associated with cognitive performance in older adults with cerebral small vessel disease. This cross-sectional study combined baseline data from four studies to create a total sample of 164 older adults (aged ≥55) from metropolitan Vancouver with cerebral small vessel disease. Using lesion network mapping, we assessed the percentage overlap between voxels functionally connected with both the WMHs (lesion network) and five common functional networks: (1) visual; (2) dorsal attention; (3) ventral attention; (4) sensorimotor; and (5) frontoparietal. Cognition was assessed using: (1) Montreal Cognitive Assessment (MoCA); (2) Stroop Colour Word Test (3-2); (3) Trail Making Tests (Part B-A); and (4) Digit Symbol Substitution Test. A One-Way ANOVA and Tukey post-hoc tests were performed to identify the functional networks with greatest percentage overlap with the lesion network. Partial correlations controlling for age were used to analyse whether the extent of the overlap between the lesion and functional networks was associated with poorer cognition. The visual, ventral attention, and frontoparietal networks had significantly greater overlap with the lesion network. After controlling for multiple comparisons, level of lesion network overlap with both the sensorimotor network (p<.001) and ventral attention network (p <. 001) was significantly correlated with MoCA score. Thus, the greater the disruption to the sensorimotor and ventral attention networks, the poorer the global cognition. Our results reveal that the visual, ventral attention, and frontoparietal networks are most vulnerable to disruptions stemming from WMHs. Additionally, we identified that disruption to the sensorimotor and ventral attention networks, as a result of WMHs, may underlie deficits in global cognition in older adults with cerebral small vessel disease.
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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 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».