Literature DB >> 24269803

Capturing the musical brain with Lasso: Dynamic decoding of musical features from fMRI data.

Petri Toiviainen1, Vinoo Alluri2, Elvira Brattico3, Mikkel Wallentin4, Peter Vuust5.   

Abstract

We investigated neural correlates of musical feature processing with a decoding approach. To this end, we used a method that combines computational extraction of musical features with regularized multiple regression (LASSO). Optimal model parameters were determined by maximizing the decoding accuracy using a leave-one-out cross-validation scheme. The method was applied to functional magnetic resonance imaging (fMRI) data that were collected using a naturalistic paradigm, in which participants' brain responses were recorded while they were continuously listening to pieces of real music. The dependent variables comprised musical feature time series that were computationally extracted from the stimulus. We expected timbral features to obtain a higher prediction accuracy than rhythmic and tonal ones. Moreover, we expected the areas significantly contributing to the decoding models to be consistent with areas of significant activation observed in previous research using a naturalistic paradigm with fMRI. Of the six musical features considered, five could be significantly predicted for the majority of participants. The areas significantly contributing to the optimal decoding models agreed to a great extent with results obtained in previous studies. In particular, areas in the superior temporal gyrus, Heschl's gyrus, Rolandic operculum, and cerebellum contributed to the decoding of timbral features. For the decoding of the rhythmic feature, we found the bilateral superior temporal gyrus, right Heschl's gyrus, and hippocampus to contribute most. The tonal feature, however, could not be significantly predicted, suggesting a higher inter-participant variability in its neural processing. A subsequent classification experiment revealed that segments of the stimulus could be classified from the fMRI data with significant accuracy. The present findings provide compelling evidence for the involvement of the auditory cortex, the cerebellum and the hippocampus in the processing of musical features during continuous listening to music.
Copyright © 2013 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Decoding; Music; Music Information Retrieval; Naturalistic paradigm; Time series; fMRI

Mesh:

Year:  2013        PMID: 24269803     DOI: 10.1016/j.neuroimage.2013.11.017

Source DB:  PubMed          Journal:  Neuroimage        ISSN: 1053-8119            Impact factor:   6.556


  27 in total

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Authors:  Asim H Dar; Adina S Wagner; Michael Hanke
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2.  Connectivity patterns during music listening: Evidence for action-based processing in musicians.

Authors:  Vinoo Alluri; Petri Toiviainen; Iballa Burunat; Marina Kliuchko; Peter Vuust; Elvira Brattico
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3.  Decoding Auditory Saliency from Brain Activity Patterns during Free Listening to Naturalistic Audio Excerpts.

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Journal:  Neuroinformatics       Date:  2018-10

Review 4.  Identifying a brain network for musical rhythm: A functional neuroimaging meta-analysis and systematic review.

Authors:  Anna V Kasdan; Andrea N Burgess; Fabrizio Pizzagalli; Alyssa Scartozzi; Alexander Chern; Sonja A Kotz; Stephen M Wilson; Reyna L Gordon
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5.  High-Order Areas and Auditory Cortex Both Represent the High-Level Event Structure of Music.

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Journal:  J Cogn Neurosci       Date:  2022-03-05       Impact factor: 3.420

6.  Neocortical substrates of feelings evoked with music in the ACC, insula, and somatosensory cortex.

Authors:  Stefan Koelsch; Vincent K M Cheung; Sebastian Jentschke; John-Dylan Haynes
Journal:  Sci Rep       Date:  2021-05-12       Impact factor: 4.379

7.  Post-stroke enriched auditory environment induces structural connectome plasticity: secondary analysis from a randomized controlled trial.

Authors:  Aleksi J Sihvonen; Seppo Soinila; Teppo Särkämö
Journal:  Brain Imaging Behav       Date:  2022-03-29       Impact factor: 3.224

8.  ECoG high gamma activity reveals distinct cortical representations of lyrics passages, harmonic and timbre-related changes in a rock song.

Authors:  Irene Sturm; Benjamin Blankertz; Cristhian Potes; Gerwin Schalk; Gabriel Curio
Journal:  Front Hum Neurosci       Date:  2014-10-13       Impact factor: 3.169

9.  Shaping pseudoneglect with transcranial cerebellar direct current stimulation and music listening.

Authors:  Silvia Picazio; Chiara Granata; Carlo Caltagirone; Laura Petrosini; Massimiliano Oliveri
Journal:  Front Hum Neurosci       Date:  2015-03-26       Impact factor: 3.169

10.  Improving the prediction of going concern of Taiwanese listed companies using a hybrid of LASSO with data mining techniques.

Authors:  Yeung-Ja James Goo; Der-Jang Chi; Zong-De Shen
Journal:  Springerplus       Date:  2016-04-27
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