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Record W4206039633 · doi:10.1093/brain/awab429

A multi-pronged investigation of option generation using depression, PET and modafinil

2021· article· en· W4206039633 on OpenAlexfundno aff
Cristina Cusin, Yoann Petibon, Daniel G. Dillon, Micah Breiger, Emily L. Belleau, Marc D. Normandin, Hans S. Schroder, Sean Boyden, Emma Hayden, Michael Levine, Aava Jahan, Min Su Kang, Devon Brunner, Steven E. Gelda, Jacob M. Hooker, Georges El Fakhri, Maurizio Fava, Diego A. Pizzagalli

Bibliographic record

VenueBrain · 2021
Typearticle
Languageen
FieldMedicine
TopicTreatment of Major Depression
Canadian institutionsnot available
FundersNational Institute on Drug AbuseAllerganNational Institutes of HealthTakeda OncologyMassachusetts General HospitalScience Foundation IrelandVistagen TherapeuticsCerecorEikonizo TherapeuticsDenali TherapeuticsMarinus PharmaceuticalsNeurocrine BiosciencesACADIA PharmaceuticalsMcGill UniversityConcert PharmaceuticalsStanley Medical Research InstituteEmory UniversityH. Lundbeck A/SNational Institute of Mental HealthAvanir PharmaceuticalsPatient-Centered Outcomes Research InstituteMemorial Sloan-Kettering Cancer CenterAlzheimer's Drug Discovery FoundationArbor PharmaceuticalsPfizerBioClinicaBiogenSanofi
KeywordsModafinilPsychologyMajor depressive disorderPutamenRacloprideDepression (economics)MedicineInternal medicineCognitionPsychiatryDopamineDopamine receptor D2

Abstract

fetched live from OpenAlex

Option generation is a critical process in decision making, but previous studies have largely focused on choices between options given by a researcher. Consequently, how we self-generate options for behaviour remain poorly understood. Here, we investigated option generation in major depressive disorder and how dopamine might modulate this process, as well as the effects of modafinil (a putative cognitive enhancer) on option generation in healthy individuals. We first compared differences in self-generated options between healthy non-depressed adults [n = 44, age = 26.3 years (SD 5.9)] and patients with major depressive disorder [n = 54, age = 24.8 years (SD 7.4)]. In the second study, a subset of depressed individuals [n = 22, age = 25.6 years (SD 7.8)] underwent PET scans with 11C-raclopride to examine the relationships between dopamine D2/D3 receptor availability and individual differences in option generation. Finally, a randomized, double-blind, placebo-controlled, three-way crossover study of modafinil (100 mg and 200 mg), was conducted in an independent sample of healthy people [n = 19, age = 23.2 years (SD 4.8)] to compare option generation under different doses of this drug. The first study revealed that patients with major depressive disorder produced significantly fewer options [t(96) = 2.68, P = 0.009, Cohen's d = 0.54], albeit with greater uniqueness [t(96) = -2.54, P = 0.01, Cohen's d = 0.52], on the option generation task compared to healthy controls. In the second study, we found that 11C-raclopride binding potential in the putamen was negatively correlated with fluency (r = -0.69, P = 0.001) but positively associated with uniqueness (r = 0.59, P = 0.007). Hence, depressed individuals with higher densities of unoccupied putamen D2/D3 receptors in the putamen generated fewer but more unique options, whereas patients with lower D2/D3 receptor availability were likely to produce a larger number of similar options. Finally, healthy participants were less unique [F(2,36) = 3.32, P = 0.048, partial η2 = 0.16] and diverse [F(2,36) = 4.31, P = 0.021, partial η2 = 0.19] after taking 200 mg versus 100 mg and 0 mg of modafinil, while fluency increased linearly with dosage at a trend level [F(1,18) = 4.11, P = 0.058, partial η2 = 0.19]. Our results show, for the first time, that option generation is affected in clinical depression and that dopaminergic activity in the putamen of patients with major depressive disorder may play a key role in the self-generation of options. Modafinil was also found to influence option generation in healthy people by reducing the creativity of options produced.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.439
Threshold uncertainty score0.275

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.081
GPT teacher head0.319
Teacher spread0.237 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2021
Admission routes1
Has abstractyes

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