Changes in EEG microstate dynamics and cognition post-chemotherapy in people with breast cancer: A pilot study
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 electroencephalography (EEG) data of women with breast cancer before and following chemotherapy treatment. Methods: Data from 8 female participants between the ages of 30 and 52 (mean age = 44.8 yrs, SD = 7.3 yrs) were analyzed. Cognitive function was assessed using the PROMIS (Patient-Reported Outcomes Measurement Information System) and the Trail Making Test (TMT). Five minutes of resting state, 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. Overall, durations of microstates were significantly longer (p < 0.001, d = 2.837) and less evenly distributed following chemotherapy. The mean duration of microstate D significantly increased following chemotherapy (p = 0.007, d = 1.339). No significant correlations between microstate features and the PROMIS score were observed. Conclusions: We observed self-reported cognitive impairment and disturbed functional dynamics in the resting state brain following chemotherapy treatment. These results introduce a potential novel biomarker to evaluate the changes in large scale brain dynamics related to the cognitive effects of chemotherapy.
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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 | 0.002 |
| 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) | 0.001 | 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".