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Record W2218904422 · doi:10.3389/fncel.2015.00399

Does non-invasive brain stimulation applied over the dorsolateral prefrontal cortex non-specifically influence mood and emotional processing in healthy individuals?

2015· review· en· W2218904422 on OpenAlexafffund
Marine Mondino, François Thiffault, Shirley Fecteau

Bibliographic record

VenueFrontiers in Cellular Neuroscience · 2015
Typereview
Languageen
FieldNeuroscience
TopicTranscranial Magnetic Stimulation Studies
Canadian institutionsUniversité LavalInstitut Universitaire en Santé Mentale de Québec
FundersCanadian Institutes of Health Research
KeywordsDorsolateral prefrontal cortexPsychologyAngerMoodNeurosciencePrefrontal cortexBrain stimulationAffect (linguistics)Cognitive psychologyStimulationCognitionSocial psychologyCommunication

Abstract

fetched live from OpenAlex

The dorsolateral prefrontal cortex (DLPFC) is often targeted with non-invasive brain stimulation (NIBS) to modulate in vivo human behaviors. This brain region plays a key role in mood, emotional processing, and attentional processing of emotional information. In this article, we ask the question: when we target the DLPFC with NIBS, do we modulate these processes altogether, non-specifically, or can we modulate them selectively? We thus review articles investigating the effects of NIBS applied over the DLPFC on mood, emotional processing, and attentional processing of emotional stimuli in healthy subjects. We discuss that NIBS over the DLPFC can modulate emotional processing and attentional processing of emotional stimuli, without specifically influencing mood. Indeed, there seems to be a lack of evidence that NIBS over the DLPFC influences mood in healthy individuals. Finally, there appears to be a hemispheric lateralization: when applied over the left DLPFC, NIBS improved processing of positive stimuli and reduced selective attention for stimuli expressing anger, whereas when applied over the right DLPFC, it increased selective attention for stimuli expressing anger.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.842
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.035
GPT teacher head0.298
Teacher spread0.262 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations63
Published2015
Admission routes2
Has abstractyes

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