insight - Neuroscience - # Neural Dynamics of Speech and Music Perception

Shared and Frequency-Specific Neural Networks Underlie Speech and Music Processing in the Human Brain

Q: How do the frequency-specific and distributed neural networks underlying speech and music processing adapt to different task demands or contexts (e.g., active listening, production, or learning)?

The frequency-specific and distributed neural networks involved in speech and music processing demonstrate adaptability based on task demands and contexts. For instance, during active listening, these networks show increased engagement and synchronization to track acoustic features and extract meaningful information from the auditory input. In the context of production, such as speaking or singing, these networks exhibit a shift towards motor planning and execution, integrating sensory feedback to ensure accurate production. Learning scenarios involve plasticity within these networks, allowing for the acquisition and refinement of language and musical skills over time. The adaptability of these networks is evident in the differential recruitment of frequency bands depending on the task demands. For example, low-frequency oscillations (delta and theta bands) play a crucial role in speech tracking, segmentation, and decoding, aiding in the comprehension of linguistic content. In contrast, high-frequency activity (beta, low-gamma, and HFa bands) may be more involved in processing fine acoustic details, such as pitch perception in music or phonemic distinctions in speech. Overall, the flexibility of these networks to adjust their activity patterns based on task demands highlights the dynamic nature of speech and music processing in the human brain. This adaptability allows individuals to effectively engage with and respond to various auditory stimuli in different contexts, showcasing the intricate interplay between neural oscillations and cognitive functions.

Q: To what extent do the shared neural resources between speech and music processing reflect common computational principles, and how might this inform our understanding of the evolutionary origins of these cognitive abilities?

The shared neural resources observed in speech and music processing suggest the existence of common computational principles underlying these cognitive abilities. These shared resources indicate overlapping neural mechanisms that are recruited for both speech and music tasks, emphasizing the interconnected nature of auditory processing in the brain. The presence of shared neural responses across different frequency bands and distributed networks implies a fundamental similarity in the cognitive processes involved in processing linguistic and musical stimuli. From an evolutionary perspective, the shared neural resources between speech and music processing could stem from the evolutionary origins of these cognitive abilities. It is plausible that the neural circuits responsible for processing complex auditory information, such as speech sounds and musical melodies, have evolved from a common ancestral system. This shared foundation may have provided an adaptive advantage to early humans in communication, social interaction, and cognitive development. By understanding the common computational principles that underlie speech and music processing, we can gain insights into the evolutionary trajectory of these cognitive abilities. Exploring the neural basis of shared resources can shed light on the evolutionary pressures that shaped the development of language and music in humans, offering valuable clues about the origins and adaptive functions of these complex cognitive skills.

Conceitos essenciais

The majority of neural responses are shared between natural speech and music processing, with selectivity restricted to distributed and frequency-specific coherent oscillations.

Resumo

The study investigated the neural processing of natural, continuous speech and music in 18 epilepsy patients using intracranial EEG recordings. The results reveal that:

The majority of neural responses are shared between speech and music processing, with only a small percentage being selective to one domain or the other. Selectivity is mostly observed in the lower frequency bands (up to alpha) and is rare in the high-frequency activity.
There is an absence of anatomical regional selectivity, i.e., no single brain region is exclusively dedicated to speech or music processing. Instead, selective responses coexist in space across different frequency bands.
The low-frequency neural activity best encodes the acoustic dynamics of both speech and music, with the strongest encoding observed in the auditory cortex and extending to other regions involved in language and music processing.
The auditory cortex is mostly connected to the rest of the brain through slow neural dynamics, and these connections are also mostly non-domain selective to speech or music.

Overall, the findings highlight the importance of considering the full complexity of natural stimuli and brain dynamics, including the spectral fingerprints of neural activity, to map cognitive and brain functions.

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Estatísticas

The majority of channels (∼70%) showed shared neural responses to speech and music.
Selective responses were more present in the lower frequency bands (∼30% up to the alpha band) and quite marginal in the high-frequency activity band (6-12%).
The low-frequency neural activity significantly encoded the acoustic dynamics of both speech and music in a distributed network extending beyond the auditory cortex.
∼33% of the channels showed coherence values higher than the surrogate distribution at delta rate, and only ∼12% at high-frequency activity.

Citações

"The majority of neural responses are shared between natural speech and music processing, with selectivity restricted to distributed and frequency-specific coherent oscillations."
"There is an absence of anatomical regional selectivity, i.e., no single brain region is exclusively dedicated to speech or music processing."
"The low-frequency neural activity best encodes the acoustic dynamics of both speech and music, with the strongest encoding observed in the auditory cortex and extending to other regions involved in language and music processing."

Principais Insights Extraídos De

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

by te Rietmolen... às www.biorxiv.org 10-09-2022

https://www.biorxiv.org/content/10.1101/2022.10.08.511398v5

Perguntas Mais Profundas

How do the frequency-specific and distributed neural networks underlying speech and music processing adapt to different task demands or contexts (e.g., active listening, production, or learning)?

The frequency-specific and distributed neural networks involved in speech and music processing demonstrate adaptability based on task demands and contexts. For instance, during active listening, these networks show increased engagement and synchronization to track acoustic features and extract meaningful information from the auditory input. In the context of production, such as speaking or singing, these networks exhibit a shift towards motor planning and execution, integrating sensory feedback to ensure accurate production. Learning scenarios involve plasticity within these networks, allowing for the acquisition and refinement of language and musical skills over time.
The adaptability of these networks is evident in the differential recruitment of frequency bands depending on the task demands. For example, low-frequency oscillations (delta and theta bands) play a crucial role in speech tracking, segmentation, and decoding, aiding in the comprehension of linguistic content. In contrast, high-frequency activity (beta, low-gamma, and HFa bands) may be more involved in processing fine acoustic details, such as pitch perception in music or phonemic distinctions in speech.
Overall, the flexibility of these networks to adjust their activity patterns based on task demands highlights the dynamic nature of speech and music processing in the human brain. This adaptability allows individuals to effectively engage with and respond to various auditory stimuli in different contexts, showcasing the intricate interplay between neural oscillations and cognitive functions.

To what extent do the shared neural resources between speech and music processing reflect common computational principles, and how might this inform our understanding of the evolutionary origins of these cognitive abilities?

The shared neural resources observed in speech and music processing suggest the existence of common computational principles underlying these cognitive abilities. These shared resources indicate overlapping neural mechanisms that are recruited for both speech and music tasks, emphasizing the interconnected nature of auditory processing in the brain. The presence of shared neural responses across different frequency bands and distributed networks implies a fundamental similarity in the cognitive processes involved in processing linguistic and musical stimuli.
From an evolutionary perspective, the shared neural resources between speech and music processing could stem from the evolutionary origins of these cognitive abilities. It is plausible that the neural circuits responsible for processing complex auditory information, such as speech sounds and musical melodies, have evolved from a common ancestral system. This shared foundation may have provided an adaptive advantage to early humans in communication, social interaction, and cognitive development.
By understanding the common computational principles that underlie speech and music processing, we can gain insights into the evolutionary trajectory of these cognitive abilities. Exploring the neural basis of shared resources can shed light on the evolutionary pressures that shaped the development of language and music in humans, offering valuable clues about the origins and adaptive functions of these complex cognitive skills.

What insights could be gained by applying computational models that account for the structural and temporal properties of speech and music to further elucidate the neural mechanisms underlying their processing?

Applying computational models that integrate the structural and temporal properties of speech and music can provide valuable insights into the neural mechanisms underlying their processing. By incorporating detailed representations of acoustic features, linguistic structures, and musical elements, these models can simulate the complex interactions between sensory input and neural activity in the brain.
One key insight that can be gained from such computational models is the elucidation of the hierarchical organization of neural processing in speech and music. By modeling how different levels of acoustic information are processed and integrated within the brain, these models can reveal the sequential and parallel pathways involved in speech comprehension and music perception. This hierarchical organization can help uncover the neural dynamics of feature extraction, pattern recognition, and semantic interpretation in auditory processing.
Furthermore, computational models that account for the temporal properties of speech and music can elucidate the role of neural oscillations in encoding and representing auditory information. By simulating the dynamic interplay between different frequency bands and their synchronization patterns, these models can uncover how neural networks coordinate their activity to support speech and music processing. This temporal dimension can provide insights into the timing of neural responses, the coordination of sensory-motor integration, and the predictive coding mechanisms involved in auditory perception.
Overall, by leveraging computational models that capture the structural and temporal properties of speech and music, researchers can gain a deeper understanding of the neural mechanisms that underlie these cognitive processes. These models offer a powerful tool for exploring the complex interactions between sensory input, cognitive processing, and neural activity, ultimately advancing our knowledge of how the brain processes and interprets auditory stimuli in the context of speech and music.