Altered activity in functional brain networks involved in lexical decision making in bipolar disorder: An fMRI case-control study
Bibliographic record
Abstract
BACKGROUND: Brain networks involved in language, attentional and response processes are detectable by fMRI during lexical decision (LD). Here, we investigated possible abnormalities in the functional networks involved in LD in patients with bipolar disorder (BD). METHODS: fMRI and behavioural data were compared between BD (n = 25) and control (n = 21), with groups matched for age and sex. The functional brain networks involved in LD were extracted by manipulating the "word-likeness" of LD stimuli and using a multidimensional analysis method. RESULTS: Attentional, response and language processes were captured in separate function-specific brain networks (default mode network, response network, linguistic processing network, respectively) in the BD and control groups, replicating the results of our previous study in an independent group of healthy adults. Behaviourally, the BD group showed higher performance than the control group in the LD task. Activity in the default mode network (DMN) and the linguistic processing network (LPN) did not differ between the groups, but the BD group had higher activation than the control group in the response network (RESP). LIMITATIONS: Due to the small sample, the study is underpowered, capable of only detecting large effects. CONCLUSIONS: The results suggest that BD may be associated with sustained activity in the RESP network, which might contribute to psychomotor dysfunction in BD. Future studies should investigate the possible link between altered RESP activation and psychomotor disturbances in BD, as well as the basis for altered RESP activity in 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".