Parietal Alpha Asymmetry as a Correlate of Internet Use Severity in Healthy Adults
Bibliographic record
Abstract
Background: Problematic Internet Use (PIU) is associated with emotional and cognitive dysregulation, yet its neural correlates, particularly in non-clinical populations, remain poorly understood. This study investigated association of the resting-state alpha asymmetry and desynchronization with psychological correlates of internet use within healthy regular internet users. Methods: A total of 129 participants (49 males, aged 18–35) were assessed using the Nine-Item Problematic Internet Use Questionnaire (PIUQ 9), alongside measures of anxiety, depression, and obsessive–compulsive symptoms. Resting-state EEG was recorded across Eyes Open (EO) and Eyes Closed (EC) conditions, with frontal and parietal alpha asymmetry and desynchronization indices analyzed in relation to internet use severity (Spearman rank correlations with non-parametric bootstrapping, 5000 replicates; FDR-corrected). For further analysis, participants with the lowest (n = 36) and highest (n = 33) PIUQ-9 scores were classified as low and high PIU groups, respectively, and their neurophysiological profiles were compared (Mann–Whitney U tests). Results: Higher internet use severity was associated with greater right parietal alpha power during EO condition, indicating greater left hemisphere parietal activity among individuals with higher internet engagement. Individuals with higher internet use severity also exhibited reduced absolute frontal and parietal alpha power, while alpha desynchronization was not associated with PIU severity or psychological symptoms. Conclusions: These findings suggest that posterior asymmetry patterns may serve as a neurophysiological correlate of PIU in non-clinical populations, warranting further investigation in future research.
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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 | 0.000 |
| 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) | 0.002 | 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".