The advent of neuroscience continues the longstanding debate on the origins of music and language—that fascinated Rousseau and Darwin (Kivy, 1959; Rousseau, 2009)—on new biological ground: evidence for the existence of selective and/or shared neural populations involved in their processing. The question on functional selectivity versus domain-general mechanisms is closely related to the question of the nature of the neural code: Are representations sparse (and localized) or distributed? While the former allows to explicitly represent any stimulus in a small number of neurons, it would require an intractable number of neurons to represent all possible stimuli. Experimental evidence instead suggests that stimulus identification is achieved through a population code, implemented by neural coupling in a distributed dynamical system (Bizley and Cohen, 2013; Rissman and Wagner, 2012). The question of the nature of the neural code has tremendous implications: it defines an epistemological posture on how to map cognitive and brain functions. This, in turn, affects both the definition of cognitive operations—what is actually computed—as well as the way we look at the data—looking for differences or similarities.

Neuroimaging studies report mixed evidence of selectivity and resource sharing. On one hand, one can find claims for a clear distinction between brain regions exclusively dedicated to language versus other cognitive processes (Chen et al., 2023; Fedorenko et al., 2011; Fedorenko and Blank, 2020; Friederici, 2020) and for the existence of specific and separate neural populations for speech, music, and song (Boebinger et al., 2021; Norman-Haignere et al., 2022). On the other hand, other neuroimaging studies suggest that the brain regions that support language and speech also support nonlinguistic functions (Albouy et al., 2020; Fadiga et al., 2009; Koelsch, 2011; Menon et al., 2002; Robert et al., 2023; Schön et al., 2010). This point is often put forward when interpreting the positive impact music training can have on different levels of speech and language processing (Flaugnacco et al., 2015; François et al., 2013; Kraus and Chandrasekaran, 2010; Schön et al., 2004).

Several elements may account for these different findings. The very first may rely on the definition of a brain region. This can be considered as a set of functionally homogeneous but spatially distributed voxels, or, alternatively, as an anatomical landmark as those used in brain atlases (e.g. inferior frontal gyrus). However, observing functional regional selectivity in a distributed pattern is not incompatible with the observation of an absence of anatomical regional selectivity: a selective set of voxels may exist within an anatomically non-selective region. A second element concerns the choice of the stimuli. Some of the studies claiming functional selectivity used rather short auditory stimuli (Boebinger et al., 2021; Norman-Haignere et al., 2015; Norman-Haignere et al., 2022). Besides the low ecological validity of such stimuli that may reduce the generalizability of the findings (Theunissen et al., 2000), their comparison further relies on the assumption that speech and music share similar cognitive time constants. However, speech unfolds faster than music (Ding et al., 2017), and while a linguistic phrase is typically shorter than a second (Inbar et al., 2020), a melodic phrase is an order of magnitude longer. Moreover, balancing the complexity/simplicity of linguistic and musical stimuli can be challenging, and musical stimuli are often reduced to very simple melodies played on a synthesizer. These simple melodies mainly induce pitch processing in associative auditory regions (Griffiths et al., 2010) but do not recruit the entire dual-stream auditory pathways (Zatorre et al., 2007). Overall, while short and simple stimuli may be sufficient to induce linguistic processing, they might not be cognitively relevant musical stimuli. Finally, another element concerns the data at stake. Most studies that compared language and music processing examined functional MRI data (Chen et al., 2023; Fedorenko et al., 2011; Nieto-Castañón and Fedorenko, 2012). Here, we would like to consider cognition as resulting from interactions among functionally specialized but widely distributed brain networks and adopt an approach in which large-scale and frequency-specific neural dynamics are characterized. This approach rests on the idea that the canonical computations that underlie cognition and behavior are anchored in population dynamics of interacting functional modules (Buzsáki and Vöröslakos, 2023; Safaie et al., 2023) and bound to spectral fingerprints consisting of network- and frequency-specific coherent oscillations (Siegel et al., 2012). This framework requires relying on time-resolved neurophysiological recordings (M/EEG) and—rather than focusing only on the amplitude of the high-frequency activity (HFa), a common approach in the literature involving human intracranial EEG recordings (Martin et al., 2019; Norman-Haignere et al., 2022; Oganian and Chang, 2019)—to investigate the entire frequency spectrum of neural activity. Indeed, while HFa amplitude is a good proxy of focal neural spiking (Le Van Quyen et al., 2010; Ray and Maunsell, 2011), large-scale neuronal interactions mainly rely on slower dynamics (Kayser et al., 2012; Kopell et al., 2000; Siegel et al., 2012).

Following the reasoning developed above, we suggest that the study of selectivity of music and language processing should carefully consider the following points: First, the use of ecologically valid stimuli, both in terms of content and duration. Second, a within-subject approach comparing both conditions. Third, aiming for high spatial sensitivity. Fourth, considering not only one type of neural activity (broadband, HFa amplitude) but the entire frequency spectrum of the neurophysiological signal. Fifth, use a broad range of complementary analyses, including connectivity, and take into account individual variability. Finally, we suggest that terms should be operationally defined based on statistical tests, which results in a clear distinction between shared, selective, and preferred activity. That is, let A and B be two investigated cognitive functions, ‘shared’ would be a neural population that (compared to a baseline) significantly and equally contributes to the processing of both A and B; ‘selective’ would be a neural population that exclusively contributes to the processing of A or B (e.g. significant for A but not B); and ‘preferred’ would be a neural population that significantly contributes to the processing of both A and B, but more prominently for A or B (Figure 1A).

Figure 1

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In an effort to take into account all the above challenges and to precisely quantify the degree of shared, preferred, and selective responses both at the levels of the channels and anatomical regions (Figure 1C and D), we conducted an experiment on 18 pharmacoresistant epileptic patients explored with stereotactic EEG (sEEG) electrodes. Patients listened to long and ecological audio-recordings of speech and music (10 min each). We investigated stimulus encoding, spectral content of the neural activity, and brain connectivity over the entire frequency spectrum (from 1 to 120 Hz; i.e. delta band to HFa). Finally, we carefully distinguished between the three different categories of neural responses described above: shared, selective, and preferred across the two investigated cognitive domains. Our results reveal that the majority of neural responses are shared between natural speech and music, and they highlight an absence of anatomical regional selectivity. Instead, we found neural selectivity to be restricted to distributed and frequency-specific coherent oscillations, typical of spectral fingerprints.

In this study, we investigated the existence of domain selectivity for speech and music under ecological conditions. We capitalized on the high spatiotemporal sensitivity of human stereotactic recordings (sEEG) to thoroughly evaluate the presence of selective neural responses—estimated both at the level of individual sEEG channels and anatomical cortical regions—when patients listened to a story or to instrumental music. More precisely, we statistically quantified the extent to which natural speech and music processing is performed by shared, preferred, or domain-selective neural populations. By combining sEEG investigations of HFa with the analyses of other frequency bands (from delta to low-gamma), the neural encoding of acoustic dynamics and spectrally resolved connectivity analyses, we obtained a thorough characterization of the neural dynamics at play during natural and continuous speech and music perception. Our results show that speech and music mostly rely on shared neural resources. Further, while selective responses seem absent at the level of atlas-based cortical regions, selectivity can be observed at the level of frequency-specific distributed networks in both power and connectivity analyses.

Previous work has reported that written or spoken language selectively activates a left-lateralized functional cortical network (Chen et al., 2023; Fedorenko et al., 2011; Fedorenko and Blank, 2020; Malik-Moraleda et al., 2022). In particular, in previous functional MRI studies, these strong and selective cortical responses were not visible during the presentation of short musical excerpts, and are hypothesized to index linguistic processes (Chen et al., 2023; Fedorenko et al., 2011). Moreover, in the superior temporal gyrus, specific and separate neural populations for speech, music, and song are visible (Boebinger et al., 2021; Norman-Haignere et al., 2022). These selective responses, not visible in primary cortical regions, seem independent of both low-level acoustic features and higher-order linguistic meaning (Norman-Haignere et al., 2015), and could subtend intermediate representations (Giordano et al., 2023) such as domain-dependent predictions (McCarty et al., 2023; Sankaran et al., 2023). Within this framework, the localizationism view applies to highly specialized processes (i.e. functional niches), while general cognitive domains are mostly spatially distributed. Recent studies have shown that some communicative signals (e.g. alarm, emotional, linguistic) can exploit distinct acoustic niches to target specific neural networks and trigger reactions adapted to the intent of the emitter (Albouy et al., 2020; Arnal et al., 2019). Using neurally relevant spectro-temporal representations (MPS), these studies show that different subspaces encode distinct information types: slow temporal modulations for meaning (speech), fast temporal modulations for alarms (screams), and spectral modulations for melodies (Albouy et al., 2020; Arnal et al., 2015; Arnal et al., 2019; Flinker et al., 2019). Which acoustic features—and which neural mechanisms—are necessary and sufficient to route communicative sounds toward selective neural networks remains a promising field of investigation to explore.

In this context, in the current study we did not observe a single anatomical region for which speech selectivity was present, in any of our analyses. In other words, 10 min of instrumental music was enough to activate cortical regions classically labeled as speech (or language)-selective. On the contrary, we report spatially distributed and frequency-specific patterns of shared, preferred, or selective neural responses and connectivity fingerprints. This indicates that domain-selective brain regions should be considered as a set of functionally homogeneous but spatially distributed voxels, instead of anatomical landmarks. Several non-exclusive explanations may account for this finding. First, our results part with the simple selective versus shared dichotomy and adopt a more biologically valid and continuous framework (Buzsáki, 2019; Zatorre and Gandour, 2008) by adding a new category that is often neglected in the literature: preferred responses (Figure 1A). Indeed, responses in this category are usually reported as shared or selective and most often the statistical approach does not allow a more nuanced view (Chen et al., 2023). However, preferred responses, namely responses that are stronger to a given class of stimuli but that are also present with other stimuli, are relevant and should not be collapsed into either the selective or shared categories. Introducing this intermediate category refines the epistemological and statistical approach on how to map cognitive and brain functions. It points toward the presence of gradients of neural activity across cognitive domains, instead of all-or-none responses. This framework is more compatible with the notion of distributed representations wherein specific regions are more or less recruited depending on their relative implication in a distributed manifold (Elman, 1991; Rissman and Wagner, 2012).

Second, most of the studies that reported regional selectivity are grounded on functional MRI data that lack a precise temporal resolution. Furthermore, the few studies assessing selectivity with intracranial EEG recordings analyzed only the HFa amplitude (Bellier et al., 2022; Norman-Haignere et al., 2020; Oganian and Chang, 2019). However, while this latter reflects local (Kopell et al., 2000) and possibly feedforward activity (Bastos et al., 2015; Fontolan et al., 2014; Fries, 2015), other frequency bands are also constitutive of the cortical dynamics and involved in cognition. For instance, alpha/beta rhythms play a role in predicting upcoming stimuli and modulating sensory processing and associated spiking (Arnal and Giraud, 2012; Bastos et al., 2020; Morillon and Baillet, 2017; Saleh et al., 2010; van Kerkoerle et al., 2014). Also slower dynamics in the delta/theta range have been described to play a major role in cognitive processes and in particular for speech perception, contributing to speech tracking, segmentation, and decoding (Ding et al., 2017; Doelling et al., 2014; Giraud and Poeppel, 2012; Gross et al., 2013; Keitel et al., 2018). Importantly, we here addressed both activations and deactivations that can co-occur in the same spatial location across different frequency bands (Pfurtscheller and Lopes da Silva, 1999; Proix et al., 2022) and indeed observed that the domain selectivity observed within our restricted stimulus set is frequency-specific, meaning that domain selectivity is marginal when considering the entire spectrum of activity of a given sEEG channel. Finally, most studies only investigated local neural activity and did not consider the brain as a distributed system, analyzed through the lens of functional connectivity analyses. While topological approaches are more complex, they also provide more nuanced and robust characterization of brain functions. Critically, our approach reveals the limitation of adopting a reductionist approach—either by considering the brain as a set of independent regions instead of distributed networks, or by overlooking the spectral complexity of the neural signal.

Third, the ecological auditory stimuli we used are longer and more complex than stimuli used in previous studies and hence more prone to elicit distributed and dynamical neural responses (Hasson et al., 2010; Sonkusare et al., 2019; Theunissen et al., 2000) and they require, in the case of music, for instance, more complex representations of melody and rhythm motifs contributing to stronger representations of meter, tonality, and groove (Boebinger et al., 2021). While listening to natural speech and music rests on cognitively relevant neural processes, our analytical approach, extending over a rather long period of time, does not allow to directly isolate specific brain operations. Computational models—which can be as diverse as acoustic (Chi et al., 2005), cognitive (Giordano et al., 2021), information-theoretic (Di Liberto et al., 2020; Donhauser and Baillet, 2020), or self-supervised neural networks (Donhauser and Baillet, 2020; Millet et al., 2022; Sankaran et al., 2023) models—are hence necessary to further our understanding of the type of computations performed by our reported frequency-specific distributed networks. Moreover, incorporating models accounting for musical and linguistic structure can help us avoid misattributing differences between speech and music driven by unmatched sensitivity factors (e.g. arousal, emotion, or attention) as inherent speech or music selectivity (Mas-Herrero et al., 2013; Nantais and Schellenberg, 1999).

Our modeling approach, although lacking the modeling of melodic and linguistic features, was targeting the temporal dynamics of the speech and music stimuli. Beyond confirming that acoustic dynamics are strongly tracked by auditory neural dynamics, it revealed, investigating the entire cortex, that such neural tracking also occurs well outside of auditory regions—up to motor and inferior frontal areas (Figure 7B; see also Chalas et al., 2022; Zion Golumbic et al., 2013). Of note, this spatial map of speech dynamics encoding is very similar to former reports of the brain regions belonging to the language system (Diachek et al., 2020). But, here again, adopting an approach that investigates both low and high frequencies of the neural signal—an approach that is not enough embraced in intracranial EEG studies (Proix et al., 2022)—reveals that the LF activity clearly better encodes acoustic features than the HFa amplitude (Figure 7A).

In conclusion, our results point to a massive amount of shared neural response to speech and music, well beyond the auditory cortex. They also show the interest of considering shared, preferred, and selective responses when investigating domain selectivity. Importantly these three classes of responses should be considered in respect to (1) activation or deactivation patterns compared to a baseline, (2) different frequency bands, and (3) power spectrum (activity) and connectivity approaches. Combining all these points of view gives a richer although possibly more complex view of brain functions. While our data point to an absence of anatomical regional selectivity for speech and music, such a selectivity still exists at the level of a spatially distributed and frequency-specific network. Thus, the inconsistency with previous findings may be limited to the idea that some anatomical regions are selective to speech or music processing. However, the two points of view can be reconciled when considering a fine-grained network approach allowing selectivity to coexist for speech and music within the same anatomical region. Finally, in adopting here a comparative approach of speech and music—the two main auditory domains of human cognition—we only investigated one type of speech and of music during a passive listening task. Future work is needed to investigate for instance whether different sentences or melodies activate the same selective frequency-specific distributed networks and to what extent these results are related to the passive listening context compared to a more active and natural context (e.g. conversation).

The raw data investigated in the current manuscript is privileged patient data. Because of this, the conditions of our ethics approval do not permit public archiving of anonymised study data. Readers seeking access to the data should contact Dr. Daniele Schön (daniele.schon@univ-amu.fr). Access will be granted to named individuals in accordance with ethical procedures governing the reuse of clinical data, including completion of a formal data sharing agreement. Data analyses were performed using custom scripts in Python, which are available on GitHub along with preprocessed data necessary to reproduce the figures and results: https://github.com/noemietr/iSpeech (copy archived te Rietmolen, 2024).

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Author details

1. Noémie te Rietmolen

   1. Institute for Language, Communication, and the Brain, Aix-Marseille University, Marseille, France
   2. Aix Marseille Université, INSERM, INS, Institut de Neurosciences des Systèmes, Marseille, France

   Contribution

   Formal analysis, Visualization, Methodology, Writing – original draft, Writing – review and editing

   For correspondence

   noemieter@gmail.com

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5532-6118

2. Manuel R Mercier

   Aix Marseille Université, INSERM, INS, Institut de Neurosciences des Systèmes, Marseille, France

   Contribution

   Data curation, Formal analysis, Methodology, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-6358-4734

3. Agnès Trébuchon

   1. Institute for Language, Communication, and the Brain, Aix-Marseille University, Marseille, France
   2. Aix Marseille Université, INSERM, INS, Institut de Neurosciences des Systèmes, Marseille, France
   3. APHM, Hôpital de la Timone, Service de Neurophysiologie Clinique, Marseille, France

   Contribution

   Data curation, Writing – review and editing

   Competing interests

   No competing interests declared

4. Benjamin Morillon

   1. Institute for Language, Communication, and the Brain, Aix-Marseille University, Marseille, France
   2. Aix Marseille Université, INSERM, INS, Institut de Neurosciences des Systèmes, Marseille, France

   Contribution

   Conceptualization, Formal analysis, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing

   Contributed equally with

   Daniele Schön

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-0049-064X

5. Daniele Schön

   1. Institute for Language, Communication, and the Brain, Aix-Marseille University, Marseille, France
   2. Aix Marseille Université, INSERM, INS, Institut de Neurosciences des Systèmes, Marseille, France

   Contribution

   Conceptualization, Formal analysis, Funding acquisition, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing

   Contributed equally with

   Benjamin Morillon

   For correspondence

   daniele.schon@univ-amu.fr

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-4472-4150

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