Speech and music recruit frequency-specific distributed and overlapping cortical networks

Published: June 5, 2024

To what extent does speech and music processing rely on domain-specific domain-general neural networks? Using whole-brain intracranial EEG recordings in 18 epilepsy patients listening to natural, continuous or music, we investigated the presence of frequency-specific network-level brain activity. We combined it with a statistical approach which clear operational distinction is made between shared , preferred, domain- selective responses. show that majority focal activity processing. Our data also reveal an absence anatomical regional selectivity. Instead, domain-selective responses are restricted distributed coherent oscillations, typical spectral fingerprints. work highlights importance considering natural stimuli dynamics their full complexity map cognitive functions.

Language: Английский

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Analyzing the Potential Contribution of a Meta-Learning Approach to Robust and Effective Subject-Independent, Emotion-Related Time Series Analysis of Bio-signals

Studies in computational intelligence, Journal Year: 2024, Volume and Issue: unknown, P. 139 - 187

Published: Jan. 1, 2024

Language: Английский

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Interpretability of statistical approaches in speech and language neuroscience

Published: Nov. 29, 2019

Traditional theoretical models conceive the neural system of speech and language as a set hierarchical modules that transform continuous acoustic stream into discrete concepts. This modular view arises from traditional neuropsychology has largely been backed up by statistical allow for controlled variation few experimental factors at time, thus allowing clear interpretations to be made. Recently, exploration large datasets led emergence more complex can capture patterns distributed across space time. However, interpretation these is challenging due increased correlations spatio-temporal dependencies between variables, which obscure links activations linguistic functions. To guide experimenter data analyst through complexity approaches in neuroscience, we have designed taxonomy delineates trade-off model interpretability.

Language: Английский

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Speech and music recruit frequency-specific distributed and overlapping cortical networks

Research Square (Research Square), Journal Year: 2022, Volume and Issue: unknown

Published: Oct. 11, 2022

To what extent do speech and music processing rely on domain-specific domain-general neural networks? Adopting a dynamical system framework, we investigate the presence of frequency-specific network-level selectivity combine it with statistical approach in which clear distinction is made between shared, preferred, category-selective responses. Using intracranial EEG recordings 18 epilepsy patients listening to natural continuous music, show that majority focal activity shared processing. Our data also reveal an absence regional selectivity. Instead, restricted dis- tributed coherent oscillations, typical spectral fingerprints. work addresses longstanding debate redefines epistemological posture how map cognitive brain functions.

Language: Английский

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Beyond Linear Neural Envelope Tracking: A Mutual Information Approach

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2022, Volume and Issue: unknown

Published: Aug. 15, 2022

Abstract The human brain tracks the temporal envelope of speech, which contains essential cues for speech understanding. Linear models are most common tool to study neural tracking. However, information on how is processed can be lost since nonlinear relations precluded. As an alternative, mutual (MI) analysis detect both linear and relations. Yet, several different approaches calculating MI applied without consensus approach use. Furthermore, added value techniques remains a subject debate in field. To resolve this, we analyses electroencephalography (EEG) data participants listening continuous speech. Comparing approaches, conclude that results reliable robust using Gaussian copula approach, first transforms standard Gaussians. With this valid technique studying Like models, it allows spatial interpretations processing, peak latency analyses, applications multiple EEG channels combined. Finally, demonstrate components single-subject level, beyond limits models. We more informative Significance statement In present study, addressed key methodological considerations applications. Traditional methodologies require estimation probability distribution at first. show step introduce bias and, consequently, severely impact interpretations. propose parametric method, demonstrated against biases. Second, analysis, there variance explain proving its statistically powerful tracking than addition, retains characteristics processing when complex deep networks.

Language: Английский

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