Optimized Simultaneous Assessment of Subcortical and Cortical Auditory Responses Through a Frequency‐Tagged Roving Paradigm
Bibliographic record
Abstract
Assessment of auditory-evoked responses across multiple stages of the ascending auditory pathway provides complementary insights into neural integrity for research and clinical contexts. However, traditional approaches, constrained by conflicting optimal parameters, require separate sessions for different response types, limiting efficiency and preventing simultaneous multi-level assessment, while evidence of individual-level sensitivity and reliability remains limited. We aimed to develop and validate a paradigm enabling concurrent, single-subject assessment of frequency-following responses (FFRs), auditory steady-state responses (ASSRs), and event-related potentials (ERPs) spanning subcortical to cortical levels. Two amplitude-modulated tones (carriers at 220/440 Hz, modulated at 40/80 Hz) were presented in a roving sequence so that each tone served as both standard and deviant, and EEG was recorded using a two-electrode montage (Fz, Cz) EEG setup. In 32 healthy participants, the paradigm achieved 100% sensitivity for high-frequency FFRs and gamma-band ASSRs, confirmed by permutation-based spectral analysis. Machine-learning classification distinguished stimulus conditions from resting state based on N1 and sustained negativity in all participants (32/32), confirming robust single-subject detection of obligatory cortical responses. Directional asymmetry was observed in transition responses: ascending frequency transitions predominantly elicited enhanced N1-P2-like responses (32/32), whereas descending transitions evoked mismatch negativity-like (MMN-like) responses in 30/32 participants. Recording-duration analysis showed that overall detection sensitivity across response components reached 0.91 after 27 min of recording. Collectively, these findings indicate that the frequency-tagged roving paradigm provides a framework for characterizing auditory processing across hierarchical levels within a single session, supporting potential use in future experimental and translational studies.
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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".