Resting-State microstates in Mild Cognitive Impairment: A high-density EEG study
Bibliographic record
Abstract
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.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".