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Decoding Accuracy in Supplementary Motor Cortex Correlates with Perceptual Sensitivity to Tactile Roughness.

Kim J, Chung YG, Park JY, Chung SC, Wallraven C, Bülthoff HH, Kim SP - PLoS ONE (2015)

Bottom Line: The searchlight MVPA revealed four brain regions showing significant decoding results: the supplementary motor area (SMA), contralateral postcentral gyrus (S1), and superior portion of the bilateral temporal pole (STP).We found that only the SMA showed a significant correlation between neuronal decoding accuracy and JND across individuals; Participants with a smaller JND (i.e., better discrimination ability) exhibited higher decoding accuracy from their voxel response patterns in the SMA.Our findings suggest that multivariate voxel response patterns presented in the SMA represent individual perceptual sensitivity to tactile roughness and people with greater perceptual sensitivity to tactile roughness are likely to have more distinct neural representations of different roughness levels in their SMA.

View Article: PubMed Central - PubMed

Affiliation: Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea.

ABSTRACT
Perceptual sensitivity to tactile roughness varies across individuals for the same degree of roughness. A number of neurophysiological studies have investigated the neural substrates of tactile roughness perception, but the neural processing underlying the strong individual differences in perceptual roughness sensitivity remains unknown. In this study, we explored the human brain activation patterns associated with the behavioral discriminability of surface texture roughness using functional magnetic resonance imaging (fMRI). First, a whole-brain searchlight multi-voxel pattern analysis (MVPA) was used to find brain regions from which we could decode roughness information. The searchlight MVPA revealed four brain regions showing significant decoding results: the supplementary motor area (SMA), contralateral postcentral gyrus (S1), and superior portion of the bilateral temporal pole (STP). Next, we evaluated the behavioral roughness discrimination sensitivity of each individual using the just-noticeable difference (JND) and correlated this with the decoding accuracy in each of the four regions. We found that only the SMA showed a significant correlation between neuronal decoding accuracy and JND across individuals; Participants with a smaller JND (i.e., better discrimination ability) exhibited higher decoding accuracy from their voxel response patterns in the SMA. Our findings suggest that multivariate voxel response patterns presented in the SMA represent individual perceptual sensitivity to tactile roughness and people with greater perceptual sensitivity to tactile roughness are likely to have more distinct neural representations of different roughness levels in their SMA.

No MeSH data available.


Results of the whole brain searchlight MVPA.Searchlight analysis identified four brain regions showing significant decoding performance in the prediction of five different levels of roughness. For each region, the left panel shows a sagittal slice of the brain (Z-coordinate of slice indicated in bottom left corner). The right panel shows the decoding accuracies for each of the 13 participants and the rightmost value indicates the average accuracy across the participants. Error bars indicate standard errors and a chance level is marked by the dashed line (20%). Note that the data from participants 6, 7, and 10 were excluded from the analysis.
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pone.0129777.g002: Results of the whole brain searchlight MVPA.Searchlight analysis identified four brain regions showing significant decoding performance in the prediction of five different levels of roughness. For each region, the left panel shows a sagittal slice of the brain (Z-coordinate of slice indicated in bottom left corner). The right panel shows the decoding accuracies for each of the 13 participants and the rightmost value indicates the average accuracy across the participants. Error bars indicate standard errors and a chance level is marked by the dashed line (20%). Note that the data from participants 6, 7, and 10 were excluded from the analysis.

Mentions: A random-effects group analysis (N = 13) revealed that four neural clusters exhibited above-chance decoding accuracy results of discriminating five roughness levels (p < 0.001 uncorrected, cluster size > 30) (Fig 2 and Table 2). These four clusters were located in the supplementary motor area (SMA), the contralateral postcentral gyrus (S1), and the superior portions of the bilateral temporal pole (STP), respectively. The clusters that we found were unlikely to have occurred by chance: a bootstrap procedure [25] revealed that the probabilities of obtaining a cluster as large as ours were less than 5%. Therefore, our clusters remained significant after the correction for multiple comparisons [25, 26]. Decoding accuracies from each significant cluster were obtained as follows (presented as mean ± standard deviation, highest and lowest accuracy for each cluster): 40.1 ± 4.6%, 47%, and 32% for the SMA cluster; 37.4 ± 4.5%, 46%, and 27% for the contralateral S1 cluster; 34.6 ± 4.2%, 41%, and 26% for the contralateral STP cluster; and 33.6 ± 4.3%, 40%, and 28% for the ipsilateral STP. A one sample t-test verified that these group-wise decoding accuracy results significantly exceeded the chance level for every cluster (SMA: t12 = 14.91, p < 0.01; contralateral S1: t12 = 9.51, p < 0.01; contralateral STP: t12 = 11.66, p < 0.01; ipsilateral STP: t12 = 15.85, p < 0.01). Furthermore, we measured the decoding accuracies of excluded participants (i.e., participants 6, 7, and 10) in the identified brain regions. Although these participants could not discriminate the degrees of roughness in the behavioral experiments, the decoding performances were significantly higher than the chance level (20%) across the brain regions (p < 0.05). However, their decoding performances were largely lower compared to data from the 13 participants (S1 Table).


Decoding Accuracy in Supplementary Motor Cortex Correlates with Perceptual Sensitivity to Tactile Roughness.

Kim J, Chung YG, Park JY, Chung SC, Wallraven C, Bülthoff HH, Kim SP - PLoS ONE (2015)

Results of the whole brain searchlight MVPA.Searchlight analysis identified four brain regions showing significant decoding performance in the prediction of five different levels of roughness. For each region, the left panel shows a sagittal slice of the brain (Z-coordinate of slice indicated in bottom left corner). The right panel shows the decoding accuracies for each of the 13 participants and the rightmost value indicates the average accuracy across the participants. Error bars indicate standard errors and a chance level is marked by the dashed line (20%). Note that the data from participants 6, 7, and 10 were excluded from the analysis.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4465937&req=5

pone.0129777.g002: Results of the whole brain searchlight MVPA.Searchlight analysis identified four brain regions showing significant decoding performance in the prediction of five different levels of roughness. For each region, the left panel shows a sagittal slice of the brain (Z-coordinate of slice indicated in bottom left corner). The right panel shows the decoding accuracies for each of the 13 participants and the rightmost value indicates the average accuracy across the participants. Error bars indicate standard errors and a chance level is marked by the dashed line (20%). Note that the data from participants 6, 7, and 10 were excluded from the analysis.
Mentions: A random-effects group analysis (N = 13) revealed that four neural clusters exhibited above-chance decoding accuracy results of discriminating five roughness levels (p < 0.001 uncorrected, cluster size > 30) (Fig 2 and Table 2). These four clusters were located in the supplementary motor area (SMA), the contralateral postcentral gyrus (S1), and the superior portions of the bilateral temporal pole (STP), respectively. The clusters that we found were unlikely to have occurred by chance: a bootstrap procedure [25] revealed that the probabilities of obtaining a cluster as large as ours were less than 5%. Therefore, our clusters remained significant after the correction for multiple comparisons [25, 26]. Decoding accuracies from each significant cluster were obtained as follows (presented as mean ± standard deviation, highest and lowest accuracy for each cluster): 40.1 ± 4.6%, 47%, and 32% for the SMA cluster; 37.4 ± 4.5%, 46%, and 27% for the contralateral S1 cluster; 34.6 ± 4.2%, 41%, and 26% for the contralateral STP cluster; and 33.6 ± 4.3%, 40%, and 28% for the ipsilateral STP. A one sample t-test verified that these group-wise decoding accuracy results significantly exceeded the chance level for every cluster (SMA: t12 = 14.91, p < 0.01; contralateral S1: t12 = 9.51, p < 0.01; contralateral STP: t12 = 11.66, p < 0.01; ipsilateral STP: t12 = 15.85, p < 0.01). Furthermore, we measured the decoding accuracies of excluded participants (i.e., participants 6, 7, and 10) in the identified brain regions. Although these participants could not discriminate the degrees of roughness in the behavioral experiments, the decoding performances were significantly higher than the chance level (20%) across the brain regions (p < 0.05). However, their decoding performances were largely lower compared to data from the 13 participants (S1 Table).

Bottom Line: The searchlight MVPA revealed four brain regions showing significant decoding results: the supplementary motor area (SMA), contralateral postcentral gyrus (S1), and superior portion of the bilateral temporal pole (STP).We found that only the SMA showed a significant correlation between neuronal decoding accuracy and JND across individuals; Participants with a smaller JND (i.e., better discrimination ability) exhibited higher decoding accuracy from their voxel response patterns in the SMA.Our findings suggest that multivariate voxel response patterns presented in the SMA represent individual perceptual sensitivity to tactile roughness and people with greater perceptual sensitivity to tactile roughness are likely to have more distinct neural representations of different roughness levels in their SMA.

View Article: PubMed Central - PubMed

Affiliation: Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea.

ABSTRACT
Perceptual sensitivity to tactile roughness varies across individuals for the same degree of roughness. A number of neurophysiological studies have investigated the neural substrates of tactile roughness perception, but the neural processing underlying the strong individual differences in perceptual roughness sensitivity remains unknown. In this study, we explored the human brain activation patterns associated with the behavioral discriminability of surface texture roughness using functional magnetic resonance imaging (fMRI). First, a whole-brain searchlight multi-voxel pattern analysis (MVPA) was used to find brain regions from which we could decode roughness information. The searchlight MVPA revealed four brain regions showing significant decoding results: the supplementary motor area (SMA), contralateral postcentral gyrus (S1), and superior portion of the bilateral temporal pole (STP). Next, we evaluated the behavioral roughness discrimination sensitivity of each individual using the just-noticeable difference (JND) and correlated this with the decoding accuracy in each of the four regions. We found that only the SMA showed a significant correlation between neuronal decoding accuracy and JND across individuals; Participants with a smaller JND (i.e., better discrimination ability) exhibited higher decoding accuracy from their voxel response patterns in the SMA. Our findings suggest that multivariate voxel response patterns presented in the SMA represent individual perceptual sensitivity to tactile roughness and people with greater perceptual sensitivity to tactile roughness are likely to have more distinct neural representations of different roughness levels in their SMA.

No MeSH data available.