Abnormalities in large-scale brain network dynamics in late-life depression with suicidal ideation: an EEG microstate analysis
Bibliographic record
Abstract
Background Patients with late-life depression (LLD) with suicidal ideation (SI) often have more explicit suicide plans, and suicide attempts among older adults are more highly lethal than in other age groups. Increasing evidence suggests that people with SI in depression exhibit abnormal brain network connectivity; however, the relationship between suicidal ideation in LLD and brain network dynamics is still unclear. Methods We recruited patients with LLD and SI (LLD-SI), patients with LLD without SI (LLD-NSI), and age-matched healthy older adults. We collected 64-channel resting state electroencephalography (EEG) recordings of all participants and used microstate analysis to explore large-scale brain network dynamics. Results We included 33 patients with LLD-SI, 29 patients with LLD-NSI, and 31 controls. We observed abnormal microstate parameters in the LLD-SI group, characterized by higher duration ( p = 0.04), occurrence ( p = 0.009), and contribution ( p = 0.001) of microstate C (reflecting activity of the salience network), compared with the LLD-NSI group, as well as higher occurrence ( p = 0.03) and contribution ( p = 0.009) of microstate C compared with the control group. Furthermore, transition probabilities from microstate class A to D ( r = −0.466, p = 0.04) and class D to A ( r = −0.506, p = 0.02) (involving coupling and sequential activation of auditory and executive control network) were negatively correlated with completion time of Stroop Colour and Word Test Part C (a neuropsychological test of executive function) in the LLD-SI group. Limitations The sample size was relatively small, the cross-sectional nature of this study prohibited exploring the causal relationship between abnormal microstate dynamics and suicidal ideation, and we did not include medication-naive patients with first-episode LLD. Conclusion The study reveals altered microstate dynamics among patients with LLD-SI, compared with patients with LLD-NSI and controls. Our findings suggest that microstate dynamics could serve as potential neurobiomarkers for identifying SI in LLD.
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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 | 0.001 |
| 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 | 0.000 | 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".