Cognitive Dysfunction in the Addictions (CDiA): protocol for a neuron-to-neighbourhood collaborative research program
Bibliographic record
Abstract
Substance use disorders (SUDs), including Alcohol Use Disorder, are pressing global public health problems. Executive functions (EFs) are prominently featured in mechanistic models of addiction. However, significant gaps remain in our understanding of EFs in SUDs, including the dimensional relationships of EFs to underlying neural circuits, molecular biomarkers, disorder heterogeneity, and functional ability. Transforming health outcomes for people with SUDs requires an integration of clinical, biomedical, preclinical, and health services research. Through such interdisciplinary research, we can develop policies and interventions that align with biopsychosocial models of addiction, addressing the complex cognitive concerns of people with SUDs in a more holistic and effective way. Here, we introduce the design and procedures underlying Cognitive Dysfunction in the Addictions (CDiA), an integrative research program, which aims to fill these knowledge gaps and facilitate research discoveries to enhance treatments for people living with SUDs. The CDiA Program comprises seven interdisciplinary projects that aim to evaluate the central thesis that EF has a crucial role in functional outcomes in SUDs. The projects draw on a diverse sample of adults aged 18-60 (target N=400) seeking treatment for SUD, who are followed over one year to identify specific EF domains most associated with improved functioning. Projects 1-3 investigate SUD symptoms, brain circuits, and blood biomarkers and their associations with key EF domains (inhibition, working memory, and set-shifting) and functional outcomes (disability, quality of life). Projects 4 and 5 evaluate interventions for SUDs and their impacts on EF: a clinical trial of repetitive transcranial magnetic stimulation and a preclinical study of potential new pharmacological treatments in rodents. Project 6 links EF to healthcare utilization and is supplemented with a qualitative investigation of EF-related barriers to treatment engagement. Project 7 uses whole-person modeling to integrate the multi-modal data generated across projects, applying clustering and deep learning methods to identify patient subtypes and drive future cross-disciplinary initiatives. The CDiA Program will bring scientific domains together to uncover novel ways in which EFs are linked to SUD severity and functional recovery, and facilitate future discoveries to improve health outcomes in individuals living with SUDs.
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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 | 0.059 | 0.052 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.088 | 0.021 |
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".