Cognitive Mode Detectable with Task-Based fMRI: Default Mode A (DMA)
Bibliographic record
Abstract
In the context of task-based functional magnetic resonance imaging (fMRI), cognitive modes can be defined as task-general cognitive/sensory/motor processes which reliably elicit specific blood-oxygen-level-dependent (BOLD) signal pattern configurations. A number of cognitive modes are detectable with task-based fMRI, and here we focus on Default Mode A (DMA), a task-negative cognitive mode. The BOLD signal configurations associated with DMA are modulated by a range of tasks, and here we present four. For each task, we report: (1) specific pattern-based (as opposed to coordinate-based) anatomical details essential for distinguishing DMA from other BOLD-based cognitive modes, and (2) task-induced BOLD signal changes associated with DMA over a range of task conditions. In order to facilitate recognition, we nick-named the anatomical patterns specific to DMA as follows: (1) Snowman Mouth, (2) Muted Medial Temporal Dots, (3) Penguin, (4) Kitten, and (5) You’re in Trouble. Evidence for DMA was derived from the timing and magnitude of task-induced BOLD signal changes induced by phonological and semantic judgement tasks, lexical decision, and task switching. The DMA cognitive mode was inversely correlated with the language mode, and therefore showed greater deactivation when linguistic processing was required, but less deactivation when suppression of linguistic processes enhanced performance. We also provide an anatomical and functional comparison to Default Mode B (DMB).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".