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Research ArticleResearch Article: New Research, Cognition and Behavior

Gustatory Cortex Is Involved in Evidence Accumulation during Food Choice

Ali Ataei, Arash Amini and Ali Ghazizadeh
eNeuro 4 May 2022, 9 (3) ENEURO.0006-22.2022; DOI: https://doi.org/10.1523/ENEURO.0006-22.2022
Ali Ataei
Electrical Engineering Department, Sharif University of Technology, Tehran 1458889694, Iran
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Arash Amini
Electrical Engineering Department, Sharif University of Technology, Tehran 1458889694, Iran
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Ali Ghazizadeh
Electrical Engineering Department, Sharif University of Technology, Tehran 1458889694, Iran
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Figures

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  • Figure 1.
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    Figure 1.

    Task design, behavioral, and modeling results and EEG. a, Schematic representation of the experimental paradigm. After a variable delay (2–4 s), two stimuli (snack items) were presented on the screen for 1.25 s, and participants had to indicate their preferred item by pressing a button. The central fixation dimmed briefly when a response was registered. Snack stimuli shown here are for illustration purposes only. Participants viewed real branded items during the experiments. b, Behavioral performance (red circles) and modeling results (black crosses). Participants’ average (N = 21) reaction time (RT) and accuracy (top and bottom, respectively) improved as the value difference (VD) between the alternatives increased. An SSM that assumes a noisy moment-by-moment accumulation of the Vd signal fit the behavioral data well. c, Average (N = 21) model predicted evidence accumulation (EA; black) and EEG activity (red) in the time window leading up to the response (on average, 600–100 ms before the response), arising from a centroparietal electrode cluster (darker circles in the inset) that exhibited significant correlation between the two signals. Shaded error bars represent standard error across participants (reproduced with permission from Pisauro et al., 2017).

  • Figure 2.
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    Figure 2.

    EEG energy of electrodes is a good correlate of energy consumption across voxels for modest levels of dipole coherences. a, Effect of dipole coherences on the maximum of the error percentage (‘z’) in approximating EEG energy as a sum of voxel BOLD values among 63 EEG channels averaged across simulations. b, Same as a but for the average of ‘z’ rather than its maximum across EEG electrodes. c, The histogram of the maximum of ‘z’ among EEG channels for the highest coherence level simulated (coherence = 0.2).

  • Figure 3.
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    Figure 3.

    GC shows a significant positive correlation with EEG energy during food choice (GLMs 1–2). Group-average activation map (t stats) for the “EEG energy” regressor in GLM2 (step-wise regression on the residuals of original GLM done by Pisauro et al., 2017) showing activity in the bilateral insular, opercular and inferior somatosensory cortices; p < 0.05, cluster-corrected (right cluster = 293, left cluster = 218 > threshold = 136); a, axial and multiple coronal views, b, lateral and medial views on the inflated cortex. See Extended Data Figure 3-1 for the illustration of the regressors used in the single-subject GLMs. See Extended Data Figure 3-2 for the activation maps regarding to the nuisance regressors.

  • Figure 4.
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    Figure 4.

    Right GC activation is robust to the addition of visual stimulus offset as a nuisance regressor (GLMs 3–9). Group-average activation map (t stats) for the “EEG energy” regressor in GLM4 showing activity in the right insular, opercular and inferior somatosensory cortices (p < 0.05, cluster-corrected (cluster = 214 > threshold = 121); a, axial and multiple coronal views, b, lateral and medial views on the inflated cortex. See Extended Data Figure 4-1 to compare the effect of addition of “vstim-off” regressor on the activation map for the raw EEG regressor. See Extended Data Figure 4-2 for a similar result for EEG energy when the raw EEG and EEG energy regressors are simultaneously used in the second-step GLM. See Extended Data Figure 4-3, for activations revealed by the raw EEG and EEG energy if instead of vstim-off, one uses a boxcar function for the duration of visual stimulus.

  • Figure 5.
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    Figure 5.

    Right GC activation is robust to normalization of EEG-driven regressors (GLMs 10–11). Group-average activation map (t stats) for the normalized “EEG energy” regressor in GLM11 showing activity in the insular, opercular and inferior somatosensory cortices (p < 0.05, cluster-corrected (cluster = 567 > threshold = 295); a, axial and multiple coronal views, b, lateral and medial views on the inflated cortex. See Extended Data Figure 5-1 for variability of average energy of EEG over a total run between subjects.

  • Figure 6.
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    Figure 6.

    Higher order EEG powers 3 and 4 do not reveal significant positive clusters of brain activation during food choice (GLMs 12–13). Group-average activation map (t stats) for higher powers of EEG in GLM12 and GLM13. a, Negative correlations with EEG pow3 (p < 0.05, cluster-corrected; clusters > threshold = 121). b, No significant correlations with EEG pow4 (p < 0.05, cluster-corrected; cluster threshold = 71).

Tables

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    Table 1

    Summary of all GLMs used in the study

    GLM
    index
    Signal to
    regress
    Regressors
    GLM1BOLDvstim – VD – rt – EEG
    GLM2Residual of GLM1EEG energy
    GLM3BOLDvstim – VD – rt – vstim
    off – EEG
    GLM4Residual of GLM3EEG energy
    GLM5BOLDvstim – VD – rt – vstim off
    GLM6Residual of GLM5EEG, EEG energy
    GLM7BOLDvstim (onset) – VD – rt
    – vstim boxcar
    GLM8Residual of GLM7EEG
    GLM9Residual of GLM7EEG energy
    GLM10Residual of GLM5EEG (normalized)
    GLM11Residual of GLM5EEG energy (normalized)
    GLM12Residual of GLM3EEG pow3
    GLM13Residual of GLM4EEG pow 4
    • View popup
    Table 2

    Number of voxels and location of peak activity for both positive and negative clusters

    Region/cluster#(voxels)HemispherePeak XPeak YPeak ZBA
    Energy, GLM2 (+):
     Inferior somatosensory (gustatory cortex)293Right−5681443/6
     Insula (gustatory cortex)213Left4122013
    Energy, GLM4 (+):
     Inferior somatosensory (gustatory cortex)214Right−5481443/6
    Energy, GLM6 (+):
     Inferior somatosensory (gustatory cortex)213Right−5481443/6
    Energy, GLM9 (+):
     Inferior somatosensory (gustatory cortex)219Right−5481443/6
    Energy, GLM11 (+):
     Insula (gustatory cortex)567Right−44102013
    Energy, GLM4 (–):
     Superior frontal gyrus10945Right−18−56309
     Inferior parietal lobe3351Right−48545640
     Supramarginal gyrus2157Left58543640
     Superior temporal gyrus2048Left30−14−3438
     Middle temporal gyrus1668Right−5222−1221
     Cingulate gyrus928Left12443031
     Cuenus872Left28902219
     Cingulate gyrus791Right−4443431
     Middle occipital gyrus705Left3864−237
     Declive (cerebellum)683Right−3468−26—
     putamen603Right−22−120—
     Superior temporal gyrus528Right−44−10−2038
     Inferior frontal gyrus488Left28−10−1847
     Caudate head257Left8−46—
     Thalamus150Right−141212—
     Culmen (cerebellum)148Right−3046−32—
    EEG pow3, GLM12 (–):
     Middle frontal gyrus275Left40−50−1011
     Inferior parietal lobe140Right−46684840
     Inferior parietal lobe135Left40664840
    • BA, Broadman Area.

Extended Data

  • Figures
  • Tables
  • Extended Data Figure 3-1

    The regressors used in fMRI analyses. a, The four nuisance regressors; the visual onset, the value difference, the reaction time, and the visual offset regressors. b, The regressors of interest; the raw EEG and the EEG energy regressors. Download Figure 3-1, TIF file.

  • Extended Data Figure 3-2

    Activation maps for the nuisance regressors (GLM 3). Group-average activation map (t stats) for the nuisance regressors all with p < 0.01 and cluster-corrected: (a) visual onset (vstim) regressor, (b) visual offset (vstim-off) regressor, (c) value difference (Vd) regressor, and (d) reaction time (rt) regressor. Download Figure 3-2, TIF file.

  • Extended Data Figure 4-1

    Comparing the activity map for the raw EEG regressor with and without considering stimulus offset as a nuisance regressor (GLMs 1, 3) Group-average activation map for the raw EEG regressor (a) without considering “vstim-off” nuisance regressor as in GLM1 showing the activity in pMFC and premotor cortex (p < 0.05, cluster = 1281> threshold = 911) and in (b) with considering “vstim-off” nuisance regressor, GLM3 (no significant activity with p < 0.05). Download Figure 4-1, TIF file.

  • Extended Data Figure 4-2

    Robustness of the activity in the right GC for simultaneous regression with raw EEG and EEG energy regressors in the second-step GLM (GLMs 5, 6). Group-average activation map (t stats) for the “EEG energy” regressor in GLM6 showing activity in the insular, opercular, and inferior somatosensory cortices (p < 0.05, cluster-corrected (cluster = 213 > threshold = 146). a, Axial and multiple coronal views. b, Lateral and medial views on the inflated cortex. Download Figure 4-2, TIF file.

  • Extended Data Figure 4-3

    Activations revealed by the raw EEG and EEG energy when using a boxcar function for the duration of visual stimulus (GLMs 7–9). Group-average activation map (t stats) for (a) the “EEG energy” regressor in GLM9 (p < 0.05, cluster-corrected, cluster = 219 > threshold = 138) and (b) the raw EEG regressor in GLM8 with no significant (p < 0.05) activity. Download Figure 4-3, TIF file.

  • Extended Data Figure 5-1

    Between subject variability of average energy of the “raw EEG” over a whole run. a, Average energy of the electric potential over all decision periods in an experimental run corresponding to the best electrode of each subject. b, Histogram of average energy of subjects’ best electrode EEG Download Figure 5-1, TIF file.

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Gustatory Cortex Is Involved in Evidence Accumulation during Food Choice
Ali Ataei, Arash Amini, Ali Ghazizadeh
eNeuro 4 May 2022, 9 (3) ENEURO.0006-22.2022; DOI: 10.1523/ENEURO.0006-22.2022

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Gustatory Cortex Is Involved in Evidence Accumulation during Food Choice
Ali Ataei, Arash Amini, Ali Ghazizadeh
eNeuro 4 May 2022, 9 (3) ENEURO.0006-22.2022; DOI: 10.1523/ENEURO.0006-22.2022
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Keywords

  • EEG energy
  • EEG-informed fMRI analysis
  • food choice
  • gustatory cortex
  • value-based decision-making

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