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Record W4413982356 · doi:10.1139/jpn.090010

Medial prefrontal cortex activity during memory encoding of pictures and its relation to symptomatic improvement after citalopram treatment in patients with major depression

2010· article· en· W4413982356 on OpenAlexaffvenue
Martin Roy, Philippe‐Olivier Harvey, Marcelo T. Berlim, Firoza Mamdani, Marie-Martine Beaulieu, Gustavo Turecki, Martin Lepage

Bibliographic record

VenueJournal of Psychiatry and Neuroscience · 2010
Typearticle
Languageen
FieldNeuroscience
TopicMemory and Neural Mechanisms
Canadian institutionsUniversité LavalMcGill UniversityDouglas Mental Health University Institute
Fundersnot available
KeywordsCitalopramPrefrontal cortexDepression (economics)PsychologyNeuroscienceEncoding (memory)AntidepressantCognitionHippocampus

Abstract

fetched live from OpenAlex

Background: Brain imaging studies of major depressive disorder have shown alterations in the brain regions typically involved in episodic memory, including the prefrontal cortex and medial temporal areas. Some studies of major depressive disorder have linked episodic memory performance to treatment response. In this study, we sought to identify brain regions whose activity, measured during the encoding of pictures, predicted symptomatic improvement after 8 weeks of citalopram treatment. Methods: We included 20 unmedicated depressed patients. These patients performed an episodic recognition memory task during functional magnetic resonance imaging. During the encoding phase, 150 pictures depicting emotionally positive, negative or neutral content were presented, and the participants were required to classify each picture according to its emotional valence. The same 150 pictures were presented, along with 150 new ones, for a recognition task. We asked participants to distinguish the old pictures from the new ones. We assessed symptom severity by use of the 21-item Hamilton Rating Scale for Depression (HAM-D) at baseline and after 8 weeks of citalopram treatment. We performed subsequent memory effect analyses using SPM2 software. We explored the relation between brain activation during successful encoding of pictures and symptomatic improvement. Results: Patients showed a mean symptomatic improvement of 54.5% on the HAM-D after 8 weeks. Symptomatic improvement was significantly and positively correlated with picture recognition memory accuracy. We also found that the activity of the ventromedial prefrontal cortex and anterior cingulate cortex during successful encoding was significantly correlated with symptomatic improvement. Finally, we found greater activation in the ventromedial prefrontal cortex during the successful encoding of positive pictures in comparison with neutral pictures. Limitations: During the recognition memory task, 5 participants (among the best responders to treatment) were not included in the valence-specific analyses because they had very few errors. A more challenging task would have allowed the inclusion of most patients. Conclusion: Different types of functional imaging paradigms have been used to explore whether the activity of specific brain regions measured at baseline is predictive of a better response to treatment in major depressive disorder. Among these regions, the medial prefrontal cortex and anterior cingulate cortex usually show the strongest predictive value. According to our results, the medial prefrontal cortex and anterior cingulate cortex could have an effect on treatment response in major depressive disorder by contributing to the successful encoding of positively valenced information.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.013
GPT teacher head0.254
Teacher spread0.241 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations1
Published2010
Admission routes2
Has abstractyes

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