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      Cortical encoding of melodic expectations in human temporal cortex

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          Abstract

          Humans engagement in music rests on underlying elements such as the listeners’ cultural background and interest in music. These factors modulate how listeners anticipate musical events, a process inducing instantaneous neural responses as the music confronts these expectations. Measuring such neural correlates would represent a direct window into high-level brain processing. Here we recorded cortical signals as participants listened to Bach melodies. We assessed the relative contributions of acoustic versus melodic components of the music to the neural signal. Melodic features included information on pitch progressions and their tempo, which were extracted from a predictive model of musical structure based on Markov chains. We related the music to brain activity with temporal response functions demonstrating, for the first time, distinct cortical encoding of pitch and note-onset expectations during naturalistic music listening. This encoding was most pronounced at response latencies up to 350 ms, and in both planum temporale and Heschl’s gyrus.

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          Brain correlates of music-evoked emotions.

          Music is a universal feature of human societies, partly owing to its power to evoke strong emotions and influence moods. During the past decade, the investigation of the neural correlates of music-evoked emotions has been invaluable for the understanding of human emotion. Functional neuroimaging studies on music and emotion show that music can modulate activity in brain structures that are known to be crucially involved in emotion, such as the amygdala, nucleus accumbens, hypothalamus, hippocampus, insula, cingulate cortex and orbitofrontal cortex. The potential of music to modulate activity in these structures has important implications for the use of music in the treatment of psychiatric and neurological disorders.
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            Spectral changes in cortical surface potentials during motor movement.

            In the first large study of its kind, we quantified changes in electrocorticographic signals associated with motor movement across 22 subjects with subdural electrode arrays placed for identification of seizure foci. Patients underwent a 5-7 d monitoring period with array placement, before seizure focus resection, and during this time they participated in the study. An interval-based motor-repetition task produced consistent and quantifiable spectral shifts that were mapped on a Talairach-standardized template cortex. Maps were created independently for a high-frequency band (HFB) (76-100 Hz) and a low-frequency band (LFB) (8-32 Hz) for several different movement modalities in each subject. The power in relevant electrodes consistently decreased in the LFB with movement, whereas the power in the HFB consistently increased. In addition, the HFB changes were more focal than the LFB changes. Sites of power changes corresponded to stereotactic locations in sensorimotor cortex and to the results of individual clinical electrical cortical mapping. Sensorimotor representation was found to be somatotopic, localized in stereotactic space to rolandic cortex, and typically followed the classic homunculus with limited extrarolandic representation.
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              The Multivariate Temporal Response Function (mTRF) Toolbox: A MATLAB Toolbox for Relating Neural Signals to Continuous Stimuli

              Understanding how brains process sensory signals in natural environments is one of the key goals of twenty-first century neuroscience. While brain imaging and invasive electrophysiology will play key roles in this endeavor, there is also an important role to be played by noninvasive, macroscopic techniques with high temporal resolution such as electro- and magnetoencephalography. But challenges exist in determining how best to analyze such complex, time-varying neural responses to complex, time-varying and multivariate natural sensory stimuli. There has been a long history of applying system identification techniques to relate the firing activity of neurons to complex sensory stimuli and such techniques are now seeing increased application to EEG and MEG data. One particular example involves fitting a filter—often referred to as a temporal response function—that describes a mapping between some feature(s) of a sensory stimulus and the neural response. Here, we first briefly review the history of these system identification approaches and describe a specific technique for deriving temporal response functions known as regularized linear regression. We then introduce a new open-source toolbox for performing this analysis. We describe how it can be used to derive (multivariate) temporal response functions describing a mapping between stimulus and response in both directions. We also explain the importance of regularizing the analysis and how this regularization can be optimized for a particular dataset. We then outline specifically how the toolbox implements these analyses and provide several examples of the types of results that the toolbox can produce. Finally, we consider some of the limitations of the toolbox and opportunities for future development and application.
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                Author and article information

                Contributors
                Role: Reviewing Editor
                Role: Senior Editor
                Journal
                eLife
                Elife
                eLife
                eLife
                eLife Sciences Publications, Ltd
                2050-084X
                03 March 2020
                2020
                : 9
                : e51784
                Affiliations
                [1 ]Laboratoire des systèmes perceptifs, Département d’études cognitives, École normale supérieure, PSL University, CNRS 75005 ParisFrance
                [2 ]Department of Psychology, New York University New YorkUnited States
                [3 ]Institut de Neurosciences des Système, UMR S 1106, INSERM, Aix Marseille Université MarseilleFrance
                [4 ]UCL Ear Institute LondonUnited Kingdom
                [5 ]Department of Electrical Engineering, Columbia University New YorkUnited States
                [6 ]Mortimer B Zuckerman Mind Brain Behavior Institute, Columbia University New YorkUnited States
                [7 ]Department of Neurosurgery, Zucker School of Medicine at Hofstra/Northwell ManhassetUnited States
                [8 ]Feinstein Institute of Medical Research, Northwell Health ManhassetUnited States
                [9 ]Institute for Systems Research, Electrical and Computer Engineering, University of Maryland College ParkUnited States
                Washington University in St. Louis United States
                Carnegie Mellon University United States
                Washington University in St. Louis United States
                Author notes
                [†]

                These authors contributed equally to this work.

                Author information
                https://orcid.org/0000-0002-7361-0980
                http://orcid.org/0000-0001-7293-1101
                https://orcid.org/0000-0002-2987-759X
                Article
                51784
                10.7554/eLife.51784
                7053998
                32122465
                25402b82-232a-4988-9644-703fc9a4d7ab
                © 2020, Di Liberto et al

                This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

                History
                : 11 September 2019
                : 20 January 2020
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100010663, H2020 European Research Council;
                Award ID: 787836
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100010669, H2020 LEIT Information and Communication Technologies;
                Award ID: 644732
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: NIMH MH114166-01
                Award Recipient :
                The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
                Categories
                Research Article
                Neuroscience
                Custom metadata
                Computational models of musical structure reveal cortical encoding of pitch and rhythm expectations during naturalistic music listening.

                Life sciences
                cortical signals,sensory,music,expectations,pitch,markov model,human
                Life sciences
                cortical signals, sensory, music, expectations, pitch, markov model, human

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