A window into the brain mechanisms associated with noise sensitivity

Kliuchko, Marina; Heinonen-Guzejev, Marja; Vuust, Peter; Tervaniemi, Mari; Brattico, Elvira

doi:10.1038/srep39236

Cited by 71 publications

(58 citation statements)

References 65 publications

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“…The study protocol proceeded on acceptance by the ethics committee of the Coordinating Board of the Helsinki and Uusimaa Hospital District. The data collection was part of a broader project (Tunteet) involving additional tests and neuroimaging and neurophysiological measures [Alluri et al, ; Bogert et al, ; Burunat et al, ; Carlson et al, ; Haumann et al, ; Kliuchko et al, ].…”

Section: Methodsmentioning

confidence: 99%

Connectivity patterns during music listening: Evidence for action‐based processing in musicians

Alluri

Toiviainen

Burunat

et al. 2017

Human Brain Mapping

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Musical expertise is visible both in the morphology and functionality of the brain. Recent research indicates that functional integration between multi-sensory, somato-motor, default-mode (DMN), and salience (SN) networks of the brain differentiates musicians from non-musicians during resting state. Here, we aimed at determining whether brain networks differentially exchange information in musicians as opposed to non-musicians during naturalistic music listening. Whole-brain graph-theory analyses were performed on participants' fMRI responses. Group-level differences revealed that musicians' primary hubs comprised cerebral and cerebellar sensorimotor regions whereas non-musicians' dominant hubs encompassed DMN-related regions. Community structure analyses of the key hubs revealed greater integration of motor and somatosensory homunculi representing the upper limbs and torso in musicians. Furthermore, musicians who started training at an earlier age exhibited greater centrality in the auditory cortex, and areas related to top-down processes, attention, emotion, somatosensory processing, and non-verbal processing of speech. We here reveal how brain networks organize themselves in a naturalistic music listening situation wherein musicians automatically engage neural networks that are action-based while non-musicians use those that are perception-based to process an incoming auditory stream. Hum Brain Mapp 38:2955-2970, 2017. © 2017 Wiley Periodicals, Inc.

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Section: Methodsmentioning

confidence: 99%

Connectivity patterns during music listening: Evidence for action‐based processing in musicians

Alluri

Toiviainen

Burunat

et al. 2017

Human Brain Mapping

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“…The study did not involve any new data collection but instead utilized a subset of an existing dataset of combined electroencephalography (EEG) and magnetoencephalography (MEG) recordings called Tunteet, collected at the Biomag laboratory at Helsinki University Hospital (Vectorview TM 306-channel MEG scanner (Elekta Neuromag ® , Elekta Oy, Helsinki, Finland; sample rate: 600 Hz)) in the years 2012-2013 and already extensively published or under publication [19,[24][25][26][27][28][29][30][31][32][33][34][35][36] (ethical approval by the Coordinating Ethics Committee of the Hospital District of Helsinki and Uusimaa; approval number: 315/13/03/00/11, obtained on March the 11th, 2012). The subset here included EEG and MEG data from 48 healthy participants with self-reported normal hearing and no past cases of neurological or psychiatric disorder (28.3 years; st.dev.…”

Section: Eeg and Meg Datasetmentioning

confidence: 99%

“…During the measurement subjects were instructed to remain still, listen to the music through pneumatic headphones, and keep their eyes open. Further details about stimulus presentation and data acquisition are found e.g., in Reference [29]. Preprocessing of MEG data was done applying Elekta NeuromagTM MaxFilter 2.2 Temporal Signal Space Separation (tSSS), and the EEG and MEG data was further processed with ICA decomposition using FieldTrip version r9093 [37] and Matlab R2013b (MathWorks, Natick, MA, USA).…”

Section: Eeg and Meg Datasetmentioning

confidence: 99%

Applying Acoustical and Musicological Analysis to Detect Brain Responses to Realistic Music: A Case Study

et al. 2018

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Music information retrieval (MIR) methods offer interesting possibilities for automatically identifying time points in music recordings that relate to specific brain responses. However, how the acoustical features and the novelty of the music structure affect the brain response is not yet clear. In the present study, we tested a new method for automatically identifying time points of brain responses based on MIR analysis. We utilized an existing database including brain recordings of 48 healthy listeners measured with electroencephalography (EEG) and magnetoencephalography (MEG). While we succeeded in capturing brain responses related to acoustical changes in the modern tango piece Adios Nonino, we obtained less reliable brain responses with a metal rock piece and a modern symphony orchestra musical composition. However, brain responses might also relate to the novelty of the music structure. Hence, we added a manual musicological analysis of novelty in the musical structure to the computational acoustic analysis, obtaining strong brain responses even to the rock and modern pieces. Although no standardized method yet exists, these preliminary results suggest that analysis of novelty in music is an important aid to MIR analysis for investigating brain responses to realistic music.

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“…The associations with sympathetic nervous system activity may re ect associations with state or trait anxiety rather than being speci c to noise sensitivity [11,56]. A study using electro-and magnetoencephalography measuring mismatch negativity found that noise sensitivity categorised with the Weinstein scale was associated with altered sensory processing in the auditory cortex implying a central cortical origin for noise sensitivity [57]. This very interesting study requires replication; it is a type of neurophysiological validation of a self-report noise sensitivity scale but it does not directly link these auditory processing characteristics to vulnerability to illhealth as might be expected if noise sensitivity is related to increased susceptibility to ill-health.…”

Section: Discussionmentioning

confidence: 99%

Road traffic noise, noise sensitivity, psychological and physical health and mortality

Stansfeld¹,

Clark²,

M³

et al. 2020

Preprint

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Background: Both physical and psychological health outcomes have been associated with exposure to environmental noise. It is not known whether all individuals are equally susceptible to these effects. Noise sensitivity has chiefly been examined in studies of annoyance where it has been shown to moderate the annoyance responses to transport-related noise. Noise sensitivity could have the same moderating effect on physical and psychological health outcomes related to environmental noise exposure but this has been little tested. Noise sensitivity which is also associated with sensitivity to chemicals, light and odours could be an indicator of a more pervasive susceptibility to ill-health related to environmental sources. Methods: A cohort of 2398 men between 45 and 59 years, the longitudinal Caerphilly Collaborative Heart Disease study, was established in 1984/88 and followed into the mid-1990 s. Road traffic noise maps were assessed at baseline. Baseline psychological ill-health measures were measured in phase 2 in 1984/88, at phase 3 follow up 1989/93 and phase 4 follow up in 1993/6.Ischaemic heart disease and risk factors were measured in clinic and by questionnaire at baseline and through hospital records and administrative records of deaths during follow up. This study aimed to test if noise sensitivity has a moderating effect on road traffic noise and psychological ill-health and secondly if noise sensitivity predicted physical and psychological ill- health and mortality, irrespective of exposure to road traffic noise. Results: Road traffic noise was associated with Phase 4 psychological ill-health but only among those exposed to 56-60dBA (OR = 1.98 95%CI 1.21, 3.24). High noise sensitivity was associated with lower mortality risk (HR = 0.71, 95%CI 0.54–0.94). High noise sensitivity was associated longitudinally with psychological ill-health at phase 3 (OR = 1.82 95%CI1.30, 2.56). There was also weak evidence that noise sensitivity moderated the association of road traffic noise exposure with psychological ill-health. Conclusions: Noise sensitivity is a specific predictor of psychological ill-health and may be an indicator of current psychological ill-health as part of a wider construct of environmental susceptibility. It may increase the risk of psychological ill-health when exposed to road traffic noise.

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A window into the brain mechanisms associated with noise sensitivity

Cited by 71 publications

References 65 publications

Connectivity patterns during music listening: Evidence for action‐based processing in musicians

Connectivity patterns during music listening: Evidence for action‐based processing in musicians

Applying Acoustical and Musicological Analysis to Detect Brain Responses to Realistic Music: A Case Study

Road traffic noise, noise sensitivity, psychological and physical health and mortality

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