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Research ArticleNew Research, Sensory and Motor Systems

Hearing Scenes: A Neuromagnetic Signature of Auditory Source and Reverberant Space Separation

Santani Teng, Verena R. Sommer, Dimitrios Pantazis and Aude Oliva
eNeuro 13 February 2017, 4 (1) ENEURO.0007-17.2017; DOI: https://doi.org/10.1523/ENEURO.0007-17.2017
Santani Teng
1Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139
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Verena R. Sommer
1Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139
2Amsterdam Brain and Cognition Centre, University of Amsterdam, 1018 WS Amsterdam, The Netherlands
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Dimitrios Pantazis
3McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139
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Aude Oliva
1Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139
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  • Figure 1.
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    Figure 1.

    Stimulus conditions, MEG classification scheme, and single-sound decoding time course. A, Stimulus design. Three brief sounds were convolved with three different RIRs to produce nine sound sources spatialized in reverberant environments. B, MEG pattern vectors were used to train an SVM classifier to discriminate every pair of stimulus conditions (three sound sources in three different space sizes each). Decoding accuracies across every pair of conditions were arranged in 9 × 9 decoding matrices, one per time point t. C, Averaging across all condition pairs (shaded matrix partition) for each time point t resulted in a single-sound decoding time course. Lines below time course indicates significant time points (N = 14, cluster-definition threshold, p < 0.05, 1000 permutations). Decoding peaked at 156 ms; error bars represent 95% CI.

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    Figure 2.

    Separable space and source identity decoding. A, Individual conditions were pooled across source identity (left, top) or space size (left, bottom) in separate analyses. Classification analysis was then performed on the orthogonal stimulus dimension to establish the time course with which the brain discriminated between space (red) and source identity (blue). Sound-source classification peaked at 130 ms, while space classification peaked at 386 ms. Significance indicators and latency error bars on plots same as in Figure 1. B, Space was classified across sound sources and vice versa. Left panel, Cross-classification example in which a classifier was trained to discriminate between spaces on sound sources 1 and 2, then tested on space discrimination on source 3. Right panel, Sound-source cross-classification example in which a classifier was trained to discriminate between sound sources on space sizes 1 and 2, then tested on sound-source discrimination on space 3. B, Results from all nine such pairwise train-test combinations were averaged to produce a classification time course in which the train and test conditions contained different experimental factors. Sound-source cross-classification peaked at 132 ms, while space cross-classification peaked at 385 ms. Significance bars below time courses and latency error bars same as in Figure 1.

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    Figure 3.

    Sensorwise decoding of source identity and space size. MEG decoding time courses were computed separately for 102 sensor locations yielding decoding sensor maps. A, Sensor map of sound source decoding at the peak of the effect (130 ms). B, Sensor map of space size decoding at the peak of the effect (386 ms). Significant decoding is indicated with a black circle over the sensor position (p < 0.01; corrected for false discovery rate (FDR) across sensors and time).

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    Figure 4.

    Temporal generalization matrix of auditory source and space decoding time courses. Left column shows the generalized decoding profiles of space (A) and source (B) decoding. Right column shows the statistically significant results (t test against 50%, p < 0.05, FDR corrected).

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    Figure 5.

    Behavior correlates with MEG decoding data. Assessment of linear relationships between response times and MEG peak decoding latencies (A), as well as behavioral and decoding accuracies (B). Bootstrapping the participant sample (N = 14, p < 0.05) 10,000 times revealed significant correlations between RT and latency (r = 0.66, p = 0.0060) and behavioral and decoding accuracy (r = 0.59, p < 0.0001). Individual condition pairs are denoted by source (So; red) or space (Sp; blue) labels, with numerals indicating which conditions were compared. For space conditions: 1, small; 2, medium; 3, large. For source conditions: 1, hand pat; 2, pole tap; 3, ball bounce.

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    Figure 6.

    Stimulus dissimilarity analysis based on cochleogram data. A, Cochleograms were generated for each stimulus, discretized into 200 5-ms bins and 64 frequency subbands. Each cochleogram thus comprised 200 64 × 1 pattern vectors. For each pair of stimuli, pattern vectors across frequency subbands were correlated at corresponding time points and subtracted from 1. B, Overall cochleogram-based dissimilarity. The final dissimilarity value at time t is an average of all pairwise correlations at that time point. Peak overall cochleogram dissimilarity occurred at 500 ms; peak MEG dissimilarity (decoding accuracy) is shown for comparison. C, Pooled cochleogram-based dissimilarity across space size and source identity. Pairwise correlations were performed and averaged analogously to pooled decoding analysis. MEG pooled decoding peaks for source identity and space size are shown for reference; corresponding stimulus dissimilarity peaks were significantly offset (p < 0.05 for both source identity and space).

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    Figure 7.

    Comparison of MEG neural representations to a categorical versus an ordinal scene size model. Representational dissimilarity matrices (RDMs) of a categorical and an ordinal model (A) were correlated with the MEG data from 138–801 ms (the temporal window of significant space size decoding) to assess the nature of MEG scene size representations. B, Results indicate the MEG representations have significantly higher correlation with the ordinal than the categorical scene size model. Spearman correlation coefficients ρ were averaged across time points in the temporal window. Error bars represent ±SEM.

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    Figure 8.

    Space and sound source decoding with repetition-window stimuli. A, Representative waveforms of single and repeated stimuli. Repeated stimuli were produced by concatenation of anechoic stimuli, followed by RIR convolution and linear amplitude ramping. B, Source (blue) and space (red) decoding. Sound-source classification peaked at 167 (96-312) ms, while space classification peaked at 237 (71-790) ms. Color-coded lines below time courses indicate significant time points, as in experiment 1; latency error bars indicate bootstrapped confidence intervals as in experiment 1. Gray vertical lines indicate stimulus onset and approximate offset.

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    Table 1.

    Summary of key statistical tests

    LineData structureType of test95% confidence intervals
    aNone assumed: classification accuracy over timeBootstrap N = 14 participants 1000 times to obtain empirical distribution of significant decoding onsetOnset CI: 12–64 ms
    bNone assumed: classification accuracy over timeBootstrap N = 14 participants 1000 times to obtain empirical distribution of significant decoding peakPeak CI: 119–240 ms
    cNone assumed: classification accuracy over timeBootstrap N = 14 participants 1000 times to obtain empirical distribution of significant sound-source decoding onsetOnset CI: 37–60 ms
    dNone assumed: classification accuracy over timeBootstrap N = 14 participants 1000 times to obtain empirical distribution of significant sound-source decoding peakPeak CI: 116–140 ms
    eNone assumed: classification accuracy over timeBootstrap N = 14 participants 1000 times to obtain empirical distribution of significant space decoding onsetOnset CI: 71–150 ms
    fNone assumed: classification accuracy over timeBootstrap N = 14 participants 1000 times to obtain empirical distribution of significant space decoding peakPeak CI: 246–395 ms
    gNone assumed: onsets of source and space decodingCompare bootstrapped empirical distribution of space decoding onset with mean source decoding onsetSpace onset CI: 71–150 ms
    hNone assumed: peaks of source and space decodingCompare bootstrapped empirical distribution of space decoding peak with mean source decoding peakSpace peak CI: 246–395 ms
    iNone assumed: cross-classification accuracy over timeBootstrap N = 14 participants 1000 times to obtain empirical distribution of significant sound-source cross-decoding peaksOnset CI: 40–63 ms
    Peak CI: 111–139 ms
    jNone assumed: cross-classification accuracy over timeBootstrap N = 14 participants 1000 times to obtain empirical distribution of significant space cross-decoding peaksOnset CI: 125–356 ms
    Peak CI: 251–513 ms
    kNone assumed: MEG-behavior correlationsBootstrapping N = 14 pool, 10,000 iterations of Spearman correlation between behavioral reaction time and MEG peak latencyCI: .227–.895
    lNone assumed: MEG-behavior correlationsBootstrapping N = 14 pool, 10,000 iterations of Spearman correlation between behavioral accuracy and MEG peak accuracyCI: .325–.795
    mNone assumed: empirical distribution of source decoding peakCompare bootstrapped empirical distribution of source decoding peak with source dissimilarity peakPeak CI: 116–140 ms
    nNone assumed: empirical distribution of space decoding peakCompare bootstrapped empirical distribution of space decoding peak with mean space dissimilarity peakPeak CI: 246–395 ms
    oNormal distribution: MEG-model correlations over time pointsPaired t test between mean correlationsMean difference CI: 0.0470–0.0507
    pNone assumed: classification accuracy over timeBootstrap N = 16 participants 1000 times to obtain empirical distribution of significant source decoding onsetSource peak CI: 96–312 ms
    qNone assumed: classification accuracy over timeBootstrap N = 16 participants 1000 times to obtain empirical distribution of significant source decoding onsetSpace peak CI: 71–790 ms
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Hearing Scenes: A Neuromagnetic Signature of Auditory Source and Reverberant Space Separation
Santani Teng, Verena R. Sommer, Dimitrios Pantazis, Aude Oliva
eNeuro 13 February 2017, 4 (1) ENEURO.0007-17.2017; DOI: 10.1523/ENEURO.0007-17.2017

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Hearing Scenes: A Neuromagnetic Signature of Auditory Source and Reverberant Space Separation
Santani Teng, Verena R. Sommer, Dimitrios Pantazis, Aude Oliva
eNeuro 13 February 2017, 4 (1) ENEURO.0007-17.2017; DOI: 10.1523/ENEURO.0007-17.2017
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Keywords

  • audition
  • Auditory Scene Analysis
  • magnetoencephalography
  • multivariate pattern analysis
  • reverberation

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