Research ArticleNEUROSCIENCE

Neurite architecture of the planum temporale predicts neurophysiological processing of auditory speech

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Science Advances  11 Jul 2018:
Vol. 4, no. 7, eaar6830
DOI: 10.1126/sciadv.aar6830
  • Fig. 1 Neurophysiological results for ERPs and sLORETA.

    (A) Averaged ERP waveforms for left (C5) and right (C6) electrodes. Blue tones represent the dichotic condition. Green tones represent the noise condition. (B) Source localization of the dichotic condition. sLORETA was used to show the cortical distribution of activation during the peak onset of the N1 component of the dichotic condition. Warm colors reflect positive current source density.

  • Fig. 2 Methodological sequence for the estimation of brain properties.

    T1-weighted images were automatically segmented into gray and white matter using surface-based methods in FreeSurfer (top left). From the reconstructed cortical surface, the PT in each hemisphere was defined according to the Destrieux atlas (top right) and volume estimates (VOL) were obtained. The PT was linearly transformed into the native space of the diffusion-weighted NODDI images, and different microstructural measures (INVF, ODI, and ISO) were computed (bottom). ROIs, regions of interest.

  • Fig. 3 Association between auditory speech processing and neurite architecture.

    Scatter plot illustrating the relationship between INVF of the left PT and N1 latency of the left electrode (C5).

  • Fig. 4 Hypothetical model of the microstructural asymmetries of human area PT as revealed by current in vivo study.

    Highly schematized depiction of two microcolumns in left and right hemispheric PT. The density of dendrites and axons is higher on the left. In addition, left PT neurons have a higher degree of arborization. On the basis of previously published postmortem data, microcolumns are wider and further apart in the left hemisphere such that dendritic arbors do not overlap. Furthermore, afferents axons innervate smaller numbers of neighboring microcolumns, possibly enabling sharper tonotopic mapping of columnar frequencies. The higher density of dendrites and afferents on the left side could enable near-synchronous activation of frequency-specific microcolumnar neurons, thereby decreasing the latency of left PT cells and increasing their temporal precision.

  • Table 1 Correlations between INVF of the PT and N1 latency during dichotic condition.

    P values are two-tailed and Bonferroni-corrected for multiple comparisons.

    INVF left PTINVF right PT
    Left N1 latency
    dichotic (C5)
    r = −0.25, P < 0.05r = −0.07, P = 0.99
    Right N1 latency
    dichotic (C6)
    r = 0.00, P = 0.99r = 0.05, P = 0.99
  • Table 2 Multiple regression analysis predicting left N1 latency during dichotic condition by structural predictors.
    PredictorsβtP
    Left PT INVF−0.23−2.340.02
    Left PT ODI0.100.990.32
    Left PT ISO0.131.340.18
    Left PT VOL0.151.500.14
    Dependent variable: left N1 latency
    dichotic (C5)
    R2 = 0.11, P = 0.024

Supplementary Materials

  • Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/4/7/eaar6830/DC1

    Table S1. Correlations between VOL of PT and N1 latency during dichotic condition.

    Table S2. Correlations between ODI of PT and N1 latency during dichotic condition.

    Table S3. Correlations between ISO of PT and N1 latency during dichotic condition.

    Table S4. Correlations between VOL of PT and N1 latency during noise condition.

    Table S5. Correlations between INVF of PT and N1 latency during noise condition.

    Table S6. Correlations between ODI of PT and N1 latency during noise condition.

    Table S7. Correlations between ISO of PT and N1 latency during noise condition.

    Table S8. Multiple regression analysis (alternative model 1) predicting left N1 latency during dichotic condition by structural predictors.

    Table S9. Multiple regression analysis predicting left N1 latency during dichotic condition by structural predictors.

    Table S10. Correlations between VOL of AF and N1 latency during dichotic condition.

    Table S11. Correlations between INVF of AF and N1 latency during dichotic condition.

    Table S12. Correlations between ODI of AF and N1 latency during dichotic condition.

    Table S13. Correlations between ISO of AF and N1 latency during dichotic condition.

    Fig. S1. Sagittal view of the left AF.

  • Supplementary Materials

    This PDF file includes:

    • Table S1. Correlations between VOL of PT and N1 latency during dichotic condition.
    • Table S2. Correlations between ODI of PT and N1 latency during dichotic condition.
    • Table S3. Correlations between ISO of PT and N1 latency during dichotic condition.
    • Table S4. Correlations between VOL of PT and N1 latency during noise condition.
    • Table S5. Correlations between INVF of PT and N1 latency during noise condition.
    • Table S6. Correlations between ODI of PT and N1 latency during noise condition.
    • Table S7. Correlations between ISO of PT and N1 latency during noise condition.
    • Table S8. Multiple regression analysis (alternative model 1) predicting left N1 latency during dichotic condition by structural predictors.
    • Table S9. Multiple regression analysis predicting left N1 latency during dichotic condition by structural predictors.
    • Table S10. Correlations between VOL of AF and N1 latency during dichotic condition.
    • Table S11. Correlations between INVF of AF and N1 latency during dichotic condition.
    • Table S12. Correlations between ODI of AF and N1 latency during dichotic condition.
    • Table S13. Correlations between ISO of AF and N1 latency during dichotic condition.
    • Fig. S1. Sagittal view of the left AF.

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