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Kujala, J.

Publications and source records attributed to Kujala, J..

2 recordsLinked to original sources

Feeling the Music: Preceding Vibroacoustic Stimulation Modulates Oscillatory Brain Dynamics During Music Listening

Background: Although typically considered an auditory experience, music listening engages multiple sensory systems, including somatosensory and motor pathways, making it an inherently multisensory phenomenon. However, research has predominantly examined the influence of music on other sensory systems, while the reciprocal question - how the existing state of a sensory system modulates the music listening experience- has received considerably less attention. To address this gap, we examined neural activity during music listening in two somatosensory states: one preceded by vibroacoustic stimulation (VAS) and one preceded by rest alone. Methods: Forty participants completed two MEG sessions in a within-subject crossover design. In one session, they received 20 minutes of 40 Hz VAS before listening to 10 minutes of self-selected relaxing music (VAS_ML); in the other, they lay on the same mattress without stimulation (NoVAS_ML). Oscillatory and aperiodic activity were estimated using DICS beamforming and FOOOF decomposition for the whole music period and for early and late listening segments. Results: Across the full listening period, the VAS condition was associated with reduced alpha power in the posterior temporal lobe and increased low-gamma power in the medial somatosensory and motor cortices compared to the NoVAS condition, suggesting enhanced cortical excitability and stronger auditory-motor engagement. Over time, both music listening conditions showed increases in alpha and beta power, consistent with habituation to the musical stimulus, though the spatial distribution differed qualitatively: changes were widespread across temporal and occipital regions in the NoVAS condition but remained localized to temporal areas after VAS. Additionally, VAS uniquely increased temporal-lobe theta power over time, whereas the NoVAS condition showed a decrease in the aperiodic exponent. Subjectively, participants reported stronger emotional intensity during music listening after VAS. Conclusion: These findings suggest that preceding VAS induces a more engaged neural state and qualitatively alters the temporal dynamics of music processing.

neuroscience

Analysis of functional connectivity and oscillatory power using DICS: from raw MEG data to group-level statistics in Python

Communication between brain regions is thought to be facilitated by the synchronization of oscillatory activity. Hence, large-scale functional networks within the brain may be estimated by measuring synchronicity between regions. Neurophysiological recordings, such as magnetoencephalography (MEG) and electroencephalography (EEG), provide a direct measure of oscillatory neural activity with millisecond temporal resolution. In this paper, we describe a full data analysis pipeline for functional connectivity analysis based on dynamic imaging of coherent sources (DICS) of MEG data. DICS is a beamforming technique in the frequency-domain that enables the study of the cortical sources of oscillatory activity and synchronization between brain regions. All the analysis steps, starting from the raw MEG data up to publication-ready group-level statistics and visualization, are discussed in depth, including methodological considerations, rules of thumb and tradeoffs. We start by computing cross-spectral density (CSD) matrices using a wavelet approach in several frequency bands (alpha, theta, beta, gamma). We then provide a way to create comparable source spaces across subjects and discuss the cortical mapping of spectral power. For connectivity analysis, we present a canonical computation of coherence that facilitates a stable estimation of all-to-all connectivity. Finally, we use group-level statistics to limit the network to cortical regions for which significant differences between experimental conditions are detected and produce vertex-and parcel-level visualizations of the different brain networks. Code examples using the MNE-Python package are provided at each step, guiding the reader through a complete analysis of the freely available openfMRI ds000117 \"familiar vs. unfamiliar vs. scrambled faces\" dataset. The goal is to educate both novice and experienced data analysts with the \"tricks of the trade\" necessary to successfully perform this type of analysis on their own data.

neuroscience