Affective cognition in bipolar disorder: A systematic review by the ISBD targeting cognition task force
Bibliographic record
Abstract
BACKGROUND: Impairments in affective cognition are part of the neurocognitive profile and possible treatment targets in bipolar disorder (BD), but the findings are heterogeneous. The International Society of Bipolar Disorder (ISBD) Targeting Cognition Task Force conducted a systematic review to (i) identify the most consistent findings in affective cognition in BD, and (ii) provide suggestions for affective cognitive domains for future study and meta-analyses. METHODS: The review included original studies reporting behavioral measures of affective cognition in BD patients vs controls following the procedures of the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) statement. Searches were conducted on PubMed/MEDLINE, EMBASE, and PsychInfo from inception until November 2018. RESULTS: A total of 106 articles were included (of which nine included data for several affective domains); 41 studies assessed emotional face processing; 23 studies investigated reactivity to emotional words and images; 3 investigated explicit emotion regulation; 17 assessed implicit emotion regulation; 31 assessed reward processing and affective decision making. In general, findings were inconsistent. The most consistent findings were trait-related difficulties in facial emotion recognition and implicit emotion regulation, and impairments in reward processing and affective decision making during mood episodes. Studies using eye-tracking and facial emotion analysis revealed subtle trait-related abnormalities in emotional reactivity. CONCLUSION: The ISBD Task Force recommends facial expression recognition, implicit emotion regulation, and reward processing as domains for future research and meta-analyses. An important step to aid comparability between studies in the field would be to reach consensus on an affective cognition test battery for BD.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.011 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".