Association between categorization of emotionally-charged and neutral visual scenes and parameters of event-related potentials in carriers of different COMT, HTR2A, BDNF gene genotypes
Bibliographic record
Abstract
<ns4:p> <ns4:bold>Background: </ns4:bold> This study aimed to discover the association between parameters of event-related potentials (ERPs) and categorization of images of visual scenes, both emotionally-charged and neutral, in carriers of different genotypes of the <ns4:italic>COMT, HTR2A, BDNF</ns4:italic> genes. </ns4:p> <ns4:p> <ns4:bold>Methods: </ns4:bold> Electroencephalogram (EEG) and ERPs were recorded at 128 leads, with two ear referents. Images of different visual scenes were presented to the study participants sequentially on a monitor screen. The participants’ task was to examine these images and indicate what emotions (negative, neutral or positive) they elicit. Comparison of event-related potentials was carried out using unpaired Student t-test in EEGLAB toolbox. </ns4:p> <ns4:p> <ns4:bold>Results: </ns4:bold> <ns4:italic> <ns4:underline>COMT.</ns4:underline> </ns4:italic> A stronger reaction, as reflected in the amplitude of the ERPs, in participants with the recessive homozygous Met/Met genotype was observed on latency around 200 ms to the stimuli, assessed as positive. Carriers of dominant homozygous Val/Val genotype had higher amplitude of 200 ms peak when assessed scene images as either neutral or negative in comparison to other genotypes. Participant with the Val/Met heterozygous genotype had higher amplitude of ERP that Met/Met group on same latency when assessed stimuli as negative. </ns4:p> <ns4:p> <ns4:italic> <ns4:underline>HTR2A</ns4:underline> </ns4:italic> <ns4:underline>.</ns4:underline> Significant increase in negativity in the parietal-occipital regions revealed in the range of 350-420 ms in participants with the recessive homozygous A/A genotype when choosing any type of assessment, compared to carriers of the heterozygous genotype A/G and the dominant homozygous G/G genotype. </ns4:p> <ns4:p> <ns4:italic> <ns4:underline>BDNF.</ns4:underline> </ns4:italic> Participants with Val/Val genotype categorized the visual images more thoroughly, as reflected in greater activation of the parietal-occipital zones and higher amplitude on ERP peak on 190 ms (negative assessment) and 160 ms (neutral assessment) then Val/Met carriers. </ns4:p> <ns4:p> <ns4:bold>Conclusions: </ns4:bold> The <ns4:italic>COMT, HTR2A, BDNF</ns4:italic> gene different genotypes are associated with the process of categorizing emotionally charged and neutral visual scenes, and this relationship is reflected in the ERP parameters. </ns4:p>
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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) | 0.001 | 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".