Music can be reconstructed from human auditory cortex activity using nonlinear decoding models.

IF 7.8 1区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY PLoS Biology Pub Date : 2023-08-01 DOI:10.1371/journal.pbio.3002176
Ludovic Bellier, Anaïs Llorens, Déborah Marciano, Aysegul Gunduz, Gerwin Schalk, Peter Brunner, Robert T Knight
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引用次数: 5

Abstract

Music is core to human experience, yet the precise neural dynamics underlying music perception remain unknown. We analyzed a unique intracranial electroencephalography (iEEG) dataset of 29 patients who listened to a Pink Floyd song and applied a stimulus reconstruction approach previously used in the speech domain. We successfully reconstructed a recognizable song from direct neural recordings and quantified the impact of different factors on decoding accuracy. Combining encoding and decoding analyses, we found a right-hemisphere dominance for music perception with a primary role of the superior temporal gyrus (STG), evidenced a new STG subregion tuned to musical rhythm, and defined an anterior-posterior STG organization exhibiting sustained and onset responses to musical elements. Our findings show the feasibility of applying predictive modeling on short datasets acquired in single patients, paving the way for adding musical elements to brain-computer interface (BCI) applications.

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利用非线性解码模型可以从人类听觉皮层的活动中重构音乐。
音乐是人类体验的核心,但音乐感知背后的精确神经动力学仍然未知。我们分析了29名患者的独特颅内脑电图(iEEG)数据集,这些患者听了Pink Floyd的歌曲,并应用了先前在语音领域使用的刺激重建方法。我们成功地从直接神经录音中重建了可识别的歌曲,并量化了不同因素对解码精度的影响。结合编码和解码分析,我们发现右半球主导音乐感知,颞上回(STG)起主要作用,证明了一个新的颞上回亚区适应音乐节奏,并定义了一个对音乐元素表现出持续和开始反应的前后侧STG组织。我们的研究结果表明,在单个患者获得的短数据集上应用预测建模是可行的,为在脑机接口(BCI)应用程序中添加音乐元素铺平了道路。
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来源期刊
PLoS Biology
PLoS Biology 生物-生化与分子生物学
CiteScore
14.40
自引率
2.00%
发文量
359
审稿时长
3 months
期刊介绍: PLOS Biology is an open-access, peer-reviewed general biology journal published by PLOS, a nonprofit organization of scientists and physicians dedicated to making the world's scientific and medical literature freely accessible. The journal publishes new articles online weekly, with issues compiled and published monthly. ISSN Numbers: eISSN: 1545-7885 ISSN: 1544-9173
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