Changes in EEG Microstate Dynamics and Cognition Post‐Chemotherapy in People With Breast Cancer
Bibliographic record
Abstract
OBJECTIVE: Chemotherapy-related cognitive changes following breast cancer are commonly reported; however, changes in brain dynamics of large-scale neural networks remain unclear. Using data from the Aerobic exercise and CogniTIVe functioning in women with breAsT cancEr (ACTIVATE) trial, we conducted exploratory analyses to compare self-reported and objective measures of cognition and applied microstate analysis to resting state (RS) electroencephalography (EEG) data of women with breast cancer before and following chemotherapy treatment. METHODS: Data from eight female participants between the ages of 30 and 52 (mean age = 44.8 years, SD = 7.3 years) were analyzed. Cognitive function was assessed using the PROMIS (Patient-Reported Outcomes Measurement Information System) and the Trail Making Test (TMT). Five minutes of RS eyes-closed EEG data were also collected. Seven EEG microstates were extracted, and mean microstate duration and occurrence were computed. RESULTS: Following chemotherapy, there was a significant decrease in the PROMIS score (p = 0.003, d = 1.601), but no significant difference in the TMT score. Overall, microstate durations were significantly longer (p < 0.001, d = 2.837) and less evenly distributed following chemotherapy. The mean duration of microstate D (involved in attention/executive functions) significantly increased following chemotherapy (p = 0.007, d = 1.339). Comparing behavioral and microstate measures that exhibited a large effect size, no significant correlations were observed either before or after chemotherapy. CONCLUSIONS: We observed self-reported cognitive impairment and disturbed functional dynamics in the RS brain following chemotherapy. This exploratory study provides new evidence using a within-cohort design showing that changes occur in large scale brain dynamics related to the cognitive effects of chemotherapy. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT03277898.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".