Resting-State microstates in Mild Cognitive Impairment: A high-density EEG study
Notice bibliographique
Résumé
Background: Mild cognitive impairment (MCI) is a relatively recent concept that describes a neurological condition depicting the transitional stage between healthy aging and severe cognitive deficits due to dementia pathologies. Because of its intermediate character, MCI is increasingly at the center of scientific interest in an effort to prevent the transition to dementia. Resting-state EEG microstates represent brief periods of global neuronal synchronization of large-scale networks that dynamically change over time. \n Aim: This study aims to detect alterations in MCI patients compared to healthy controls during resting-state high-density EEG recordings using the EEG microstate approach and examine associations between intrinsic dynamics of EEG microstates and self-reported thoughts using the Amsterdam Resting-State Questionnaire (ARSQ) (Diaz et al., 2013). To my knowledge, the ARSQ has not been tested on MCI patients before. Cognitive abilities were measured using the Montreal Cognitive Assessment (MoCA) (Nasreddine et al., 2005). The MoCA is a validated and highly sensitive tool to detect cognitive decline due to MCI. \n Methods: 143 participants were recruited: 23 MCI patients (9 female; Mage= 70.9), 60 healthy older (HO; 34 female; Mage=71.5) and 60 healthy younger (HY; 33 female; Mage=25.9) participants. The resting-state activity was acquired for 5 minutes in eyes closed condition, using the 257-channel EGI system to characterize microstate alterations in global explained variance, duration, occurrence, and time coverage. After the recording, a subgroup of participants (22 MCIs, 50 HO, and 31 HY) completed the Amsterdam Resting-State Questionnaire (ARSQ), specifying their thoughts during the rest. Furthermore, every participant completed the MoCA to assess their cognitive performance in visuospatial abilities, executive functions, attention, concentration and working memory, language, memory, and orientation. \n Results: The four canonical microstates A, B, C, and D were found across the three groups. MCI patients showed significant differences from healthy participants, specifically in microstates A and B. Significant differences between healthy younger and older participants were found in all four microstates. The MoCA results allowed distinguishing the MCI group from the healthy control groups using the scores for executive functions, memory, orientation, and the overall score. The ARSQ presented significantly different values for healthy younger compared to older participants and MCI patients in the dimensions of self, sleepiness, discontinuity of mind, theory of mind, planning, visual thoughts, and verbal thoughts. Correlation analysis between EEG microstates, ARSQ and MoCA revealed associations between microstate A and sleepiness (ARSQ), microstate B and discontinuity of mind (ARSQ), theory of mind (ARSQ), visual thoughts (ARSQ) and verbal thoughts (ARSQ), microstate C and discontinuity of mind (ARSQ), planning (ARSQ), executive function (MoCA), memory (MoCA) and the overall MoCA score; microstate D and comfort (ARSQ), executive function (MoCA), language (MoCA), memory (ARSQ) and the overall MoCA score. \n Conclusion: These findings demonstrate the relevance of characterizing microstate dynamics in MCI patients and assessing spontaneous thought for understanding intrinsic brain activity.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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) | 0,000 | 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 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 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 ».