Locus coeruleus tonic upregulation increases selectivity to inconspicuous auditory information in autistic compared to non-autistic individuals: a combined pupillometry and electroencephalography study
Bibliographic record
Abstract
BACKGROUND: Sensory processing requires selectivity to salient sensory input. Many autistic individuals report different sensory processing, which has been associated with altered sensory selectivity. The locus-coeruleus norepinephrine (LC-NE) system modulates the neuronal gain of sensory input, which represents a neurophysiological mechanism of sensory selectivity. In autistic individuals, we hypothesized that LC-NE tonic upregulation reduces sensory selectivity and underlies different sensory processing. METHODS: Autistic (n = 139) and non-autistic (n = 98) individuals were assessed during a passive auditory oddball task with pupillometry and electroencephalography. For every trial, a baseline pupil size (BPS) assessed LC-NE tonic activity that coincides with current arousal, while a stimulus-evoked pupillary response (SEPR) assessed LC-NE phasic activity that estimated sensory selectivity. Electroencephalography assessed amplitudes of mismatch negativity (MMN-amp) that estimated pre-attentive change detection as a brain-activity readout of sensory selectivity. Measures were modeled between groups within the task by combining Frequentist and Bayesian approaches. RESULTS: Across groups, higher BPS was associated with more negative MMN-amp to standards and oddballs. A more negative MMN-amp to standards was associated with a higher SEPR to standards. Controlling for these associations, autistic versus non-autistic individuals showed a higher SEPR in response to standards. In addition, a positive association of BPS and SEPR to standards was specific to autistic individuals. With task progression, autistic versus non-autistic individuals showed a higher initial increase and subsequently steeper decrease of BPS. This was supported by Bayesian posterior distribution estimates. LIMITATIONS: A short trial duration required concatenating trials to epochs and applying a linear-time invariant filter to capture the slow pupil changes. Without an LC-NE manipulation, we cannot rule out that pupil changes are evoked by other cortical pathways than the LC-NE. CONCLUSIONS: Across groups, LC-NE tonic upregulation is emphasized as a general mechanism that un-specifically increases pre-attentive change detection to all sensory stimuli, which then increases sensory selectivity to frequent stimuli. In autistic individuals, different sensory processing is characterized by increased sensory selectivity to frequent stimuli. This is likely caused by an LC-NE tonic upregulation. It associates autistic sensory processing with increased arousal upregulation that increases sensory selectivity to inconspicuous auditory information.
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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".