Working memory-related brain activations and deactivations linked with adolescent substance use via alexithymia
Bibliographic record
Abstract
INTRODUCTION: Adolescent substance use (SU) rates remain high, speaking to continued need for enhanced insight into etiological factors. While working memory-related task performance and brain activity have been highlighted as potential predictors, mechanistic links to SU remain unclear. One possible link explored here is alexithymia, which is characterized by difficulty describing, identifying, and recognizing emotions and associated with altered prefrontal cortex (PFC) and superior temporal gyrus (STG) activity. METHODS: Adolescents (n = 137) from a longitudinal study completed a n-back working memory (WM) task during fMRI scanning at baseline. Utilizing serial mediation models, we considered the interrelations between WM-related brain activity (i.e., lateral PFC [lPFC], STG), task performance (i.e., d-prime), Toronto Alexithymia Scale scores (i.e., difficulty describing feelings), and self-reported SU variables at follow-up. Six models estimated the influence of lPFC/STG activity on e-cigarette, cannabis, and alcohol use via task performance and alexithymia. RESULTS: In the absence of serial mediation, we observed a simple mediation effect linking brain activity and SU via alexithymia. Specifically, less lPFC WM-related activation was linked with higher alexithymia which, in turn, predicted more e-cigarette use assessed over a year later. Conversely, less STG deactivation was linked with higher alexithymia, which predicted more e-cigarette use. Significant indirect effects were not detected in the cannabis or alcohol models. CONCLUSIONS: These outcomes highlight alexithymia as a mechanistic link between WM abilities and e-cigarette use. Functional alterations in WM-related brain regions may render some adolescents prone to difficulty communicating feelings and potential nicotine use to modulate emotions or connect socially.
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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 | 0.001 |
| 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) | 0.000 | 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".