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Decoding sound categories based on whole-brain functional connectivity patterns

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Abstract

2Sound decoding is important for patients with sensory loss, such as the blind. Previous studies on sound categorization were conducted by estimating brain activity using univariate analysis or voxel-wise multivariate decoding methods and suggested some regions were sensitive to auditory categories. It is proposed that feedback connections between brain areas may facilitate auditory object selection. Therefore, it is important to explore whether functional connectivity among regions can be used to decode sound category. In this study, we constructed whole-brain functional connectivity patterns when subjects perceived four different sound categories and combined them with multivariate pattern classification analysis for sound decoding. The categorical discriminative networks and regions were determined based on the weight maps. Results showed that a high accuracy in multi-category classification was obtained based on the whole-brain functional connectivity patterns and the results were verified by different preprocessing parameters. Insight into the category discriminative functional networks showed that contributive connections crossed the left and right brain, and ranged from primary regions to high-level cognitive regions, which provide new evidence for the distributed representation of auditory object. Further analysis of brain regions in the discriminative networks showed that superior temporal gyrus and Heschl’s gyrus significantly contributed to discriminating sound categories. Together, the findings reveal that functional connectivity based multivariate classification method provides rich information for auditory category decoding. The successful decoding results implicate the interactive properties of the distributed brain areas in auditory sound representation.

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Funding

This work was supported by the National Natural Science Foundation of China (No. U1736219, No. 61571327 and No. 61503278) and Peiyang Scholar Program of Tianjin University (No. 2018XRG-0037).

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Author notes

  1. Jinliang Zhang and Gaoyan Zhang contributed equally to this work.

Authors and Affiliations

  1. School of Computer Science and Technology, Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin University, Tianjin, 300350, People’s Republic of China

    Jinliang Zhang, Gaoyan Zhang & Baolin Liu

  2. Medical Imaging Research Institute, Binzhou Medical University, Yantai, Shandong, 264003, People’s Republic of China

    Xianglin Li & Bin Wang

  3. Department of Radiology, Yantai Affiliated Hospital of Binzhou Medical University, Yantai, Shandong, 264003, People’s Republic of China

    Peiyuan Wang

  4. State Key Laboratory of Intelligent Technology and Systems, National Laboratory for Information Science and Technology, Tsinghua University, Beijing, 100084, People’s Republic of China

    Baolin Liu

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  1. Jinliang Zhang
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  3. Xianglin Li
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  4. Peiyuan Wang
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  5. Bin Wang
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Correspondence to Baolin Liu.

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Conflict of interest

Jinliang Zhang, Gaoyan Zhang, Xianglin Li, Peiyuan Wang, Bin Wang, and Baolin Liu declare that they have no actual or potential conflict of interest including any financial, personal or other relationships with other people or organizations that can inappropriately influence our work. All of the authors declare that the work described in the manuscript was original research that has not been published previously, and was not under consideration for publication elsewhere.

Ethical approval

This study was approved by the Research Ethics Committee of Tianjin University. All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, and the applicable revisions at the time of the investigation.

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Informed consent was obtained from all subjects for being included in the study.

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Zhang, J., Zhang, G., Li, X. et al. Decoding sound categories based on whole-brain functional connectivity patterns. Brain Imaging and Behavior 14, 100–109 (2020). https://doi.org/10.1007/s11682-018-9976-z

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  • DOI: https://doi.org/10.1007/s11682-018-9976-z

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