Aberrant auditory prediction patterns robustly characterize tinnitus
- Centre for Cognitive Neuroscience and Department of Psychology, Paris-Lodron-University of Salzburg, Austria
- Department of Psychology, University of Lübeck, Germany
- Center of Brain, Behavior and Metabolism, University of Lübeck, Germany
- Translational and Clinical Research Institute, Newcastle University, United Kingdom
- Wellcome Centre for Human Neuroimaging, University College London, United Kingdom
- Department of Otolaryngology, University Hospital Regensburg, Germany
- Department of Neurology, Christian Doppler University Hospital, Paracelsus Medical University, Austria
- Neuroscience Institute, Christian Doppler University Hospital, Paracelsus Medical University, Austria
- Department of Psychiatry and Psychotherapy, University of Regensburg, Germany
Figures
Figure 1
Experimental design and analysis rationale.
Upper panel: Transition matrices used to generate sound sequences according to the different conditions (random, midminus, midplus, and ordered) with a schematic example of a brief sound sequence. The ‘Testing Time’ window corresponds to one trial with the to-be-decoded carrier frequency in the center (at 0 ms; marked by solid line), preceded and followed by two other tones (marked by dashed lines). Middle panel: Individual-level analysis. For multivariate pattern analysis (MVPA), time-shifted classifiers were trained on events in the random condition (left panel) and applied in a condition- and time-generalized manner to all conditions (middle panel). Lower panel: Group comparisons. At a group level, the resulting slopes (β-coefficients) of the regression analysis were compared between the tinnitus group and the control group. Notably, Study 2 solely included random and ordered sound conditions, as well as a narrower frequency range between ~400 and 1000 Hz. Analyses approaches did not vary except statistics were based on differences in decoding accuracy between conditions (referred to as ‘neural predictions scores’) rather than β-coefficients.
Figure 2 with 1 supplement
Random tone decoding.
Left panel: Temporal decoding of carrier frequencies in the random sound sequence for the tinnitus and control groups, respectively. In both groups, peak accuracy is reached after ~100 ms following sound onset. Above chance decoding accuracy is observed in a sustained manner up to ~600 ms (p < 0.05, Bonferroni corrected). No differences were observed between the groups. Right panel: Source-level depiction of informative activity for different periods: 50–125 ms (T1) and 450–550 ms (T2) after decoded sound presentation. The latter corresponds to the training time interval, yielding pronounced group differences in the condition generalized analysis.
Figure 2—figure supplement 1
Time-resolved bias score.
Quantification of the difference in the average decoding scores of the first and second upper diagonals of the confusion matrix for the random tone sequence in Study 1. Results showed no difference between tinnitus and controls.
Figure 3
Group comparisons before and after sound onset.
(A) Prediction scores (i.e. group comparison of β-coefficient values) between tinnitus and control groups in the time-generalized matrix. Colors indicate t-values and solid black borders delimiting periods of significant difference (p < 0.05, cluster corrected). (B) Time courses of β-coefficients averaged over the training time window (450–550 ms). (C) Individual β-coefficient values within the pre- and post-stimulus clusters are shown. Asterisks indicate significance levels with p<0.001.
Figure 4
Random tone decoding accuracies.
(A) Comparison of random tone decoding accuracies between the two studies. Gray areas indicate relevant peaks in decoding accuracies. (B) Source plots demonstrating activity in the auditory cortex for both time windows in Study 2.
Figure 5
Time generalization matrices over training and testing time for the tinnitus and control groups.
Black masks indicate significant difference clusters between the ordered and random sound condition.
Figure 6
Differences in decoding accuracies between groups and correlations with tinnitus distress.
(A) Prediction scores with the second time window (470–570 ms, i.e. the relevant time window in Study 1) as training time. The gray area depicts the time window used for statistical analyses and vertical lines indicate sound onsets. (B) For illustration purposes, depiction of individual prediction scores at the time point of the most pronounced group difference (–380 ms). Squares indicate the mean of each group. Asterisks indicate significance levels with p<0.001. (C) Random tone decoding accuracy over time for both groups. Vertical lines indicate sound onsets, and the dashed horizontal line depicts decoding at chance level. (D) Positive, non-significant correlation between prediction scores and tinnitus distress reported in the tinnitus group.
Figure 7
Exploratory analyses.
(A) Bayes factor analyses of the differences in decoding accuracies between the tinnitus and control group for both datasets indicated higher effects in Study 1. (B) Different numbers of trials used for decoding were drawn from Study 1. Comparing these, Bayes factor analyses demonstrated higher and relevant effects solely in datasets with a high amount of trials. Horizontal, dashed lines in both figures indicate Bayes factor supporting a difference between samples (see Schönbrodt and Wagenmakers, 2018).
Figure 8
Logistic regression indicates that higher prediction scores significantly predict the status as tinnitus participant (P(Tinnitus) = 1).
Additional files
Download links
A two-part list of links to download the article, or parts of the article, in various formats.
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Aberrant auditory prediction patterns robustly characterize tinnitus
eLife 13:RP99757.
https://doi.org/10.7554/eLife.99757.4
