Network mechanisms of ongoing brain activity’s influence on conscious visual perception
Bibliographic record
Abstract
Spontaneous brain activity is energetically expensive and spatiotemporally organized in an intricate manner with important clinical implications. Yet, little is known regarding how spontaneous brain activity participates in online, task-oriented brain functions. While previous work has demonstrated that prestimulus ongoing activity can predict task performance from trial to trial, the underlying mechanisms remain elusive. Here, we systematically investigated prestimulus ongoing activity’s influences on visual perceptual decision-making and conscious object recognition. We employed whole-brain 7 Tesla fMRI data acquired during a threshold-level visual object recognition task in 25 healthy human subjects. Our objective was to dissect the influences of prestimulus brain activity from distributed cortical and subcortical brain regions on multiple facets of perceptual behavior, including the sensitivity and criterion of conscious object recognition, and discrimination accuracy in a categorization task. To shed light on the mechanisms linking prestimulus ongoing activity and perceptual behavior, we further investigated how prestimulus activity modulates stimulus-related processing. Our findings reveal a diverse set of effects on perceptual behavior exerted by prestimulus ongoing activity originating from distributed brain regions. High prestimulus activity in the ventromedial prefrontal cortex enhances sensitivity and promotes a more conservative criterion in object recognition by reducing the trial-to-trial variability of distributed stimulus-triggered responses. Prestimulus activity in the cingulo-opercular and visual networks had opposite influences on recognition-related criterion and discrimination accuracy, with prestimulus visual network activity modulating the variability and stimulus encoding in sensory-evoked responses, and prestimulus cingulo-opercular network activity exerting a pattern of influences consistent with the modulation of tonic alertness. In sum, our study sheds light on the intricate contributions of spontaneous brain activity from distributed brain networks to perceptual decision-making and conscious visual perception. Our findings further illuminate how prestimulus activity from these distributed brain regions shapes multiple aspects of stimulus-related processing, providing concrete mechanistic insights into these behavioral effects.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".