In the past few years, several studies have been directed to understanding the complexity of functional interactions between different brain regions during various human behaviors. Among these, neuroimaging research installed the notion that speech and language require an orchestration of brain regions for comprehension, planning, and integration of a heard sound with a spoken word. However, these studies have been largely limited to mapping the neural correlates of separate speech elements and examining distinct cortical or subcortical circuits involved in different aspects of speech control. As a result, the complexity of the brain network machinery controlling speech and language remained largely unknown. Using graph theoretical analysis of functional MRI (fMRI) data in healthy subjects, we quantified the large-scale speech network topology by constructing functional brain networks of increasing hierarchy from the resting state to motor output of meaningless syllables to complex production of real-life speech as well as compared to non-speech-related sequential finger tapping and pure tone discrimination networks. We identified a segregated network of highly connected local neural communities (hubs) in the primary sensorimotor and parietal regions, which formed a commonly shared core hub network across the examined conditions, with the left area 4p playing an important role in speech network organization. These sensorimotor core hubs exhibited features of flexible hubs based on their participation in several functional domains across different networks and ability to adaptively switch long-range functional connectivity depending on task content, resulting in a distinct community structure of each examined network. Specifically, compared to other tasks, speech production was characterized by the formation of six distinct neural communities with specialized recruitment of the prefrontal cortex, insula, putamen, and thalamus, which collectively forged the formation of the functional speech connectome. In addition, the observed capacity of the primary sensorimotor cortex to exhibit operational heterogeneity challenged the established concept of unimodality of this region.
Extensive neuroimaging research over the past two decades installed the notion that speech and language require an orchestration between several brain regions for comprehension, planning, and integration of a heard sound with a spoken word [1–6]. However, studies investigating brain networks of speech and language control have been largely limited to the examination of distinct cortical and subcortical circuits involved in a range of speech controlling components, such as speech motor output [7–13], verbal fluency [14–16], phonological and semantic processing [17–21], verbal and tonal working memory [22–26], speech monitoring and discrimination [27–29], neural synchronization [30,31], and integration [32–38]. Moreover, the majority of these studies were directed toward mapping the neural correlates of separate speech elements, such as production of meaningless syllable sequences or single words [7,13,39–46], with only a handful of studies examining real-life speech production [6,9, 38,47,48]. As a result, our understanding of the complexity of brain network machinery controlling speech and language is very limited. One particularly significant and outstanding question concerns the large-scale architecture, interactions, and functional specialization of brain regions within the speech network for shaping the production of spoken language. Here, we applied graph theoretical analysis [49–52] to functional MRI (fMRI) data of healthy adult individuals during the resting state, production of meaningless syllables as a motor task relevant to speaking but with minimal linguistic meaning, and production of grammatically correct, meaningful real-life English sentences in order to examine functional networks of increasing hierarchy and to quantify the intermediate steps in the formation of the speech production network. To further delineate speech network characteristics and community-based architecture, we conducted a follow up study to investigate the formation of nodal communities across all examined conditions, as well as in comparison with the modular structure of functional networks during the performance of a nonlinguistic task (i. e. , auditory temporal discrimination of pure tones) and a simple nonspeech motor task (i. e. , sequential finger tapping). In the first experiment, we hypothesized that the speech production network (SPN), compared to the resting state (RSN) and syllable production (SylPN) networks, would exhibit enhanced functional segregation with densely interconnected local communities (hubs) centered on the sensorimotor cortex. Importantly, functional specialization of these sensorimotor hubs during speech production would ensure the distinct and unique recruitment of the multimodal integrative cortical regions, such as the prefrontal and inferior parietal cortices, into the SPN but not the RSN or SylPN. In the second experiment, we hypothesized that, while the functional network during each condition (i. e. , resting state, syllable production, speech production, finger tapping, and auditory discrimination) would be characterized by a distinct community-based structure, the emergence of specialized processing communities and refined modular architecture of SPN would uniquely reflect the complexity of network configuration for speech production.
To identify the global configuration and characteristic features of the SPN, we performed a community-based analysis of functional networks during the RSN, SylPN, and SPN, as well as during the performance of an unrelated motor task of sequential finger tapping (finger-tapping network; FTN) and an unrelated auditory task of pure tone temporal discrimination (auditory discrimination network; ADN). To estimate network communication patterns, we classified high-strength hubs into connector (linking communities) and provincial hubs (connecting nodes within the same module) based on their participation in intermodular versus intramodular links [58]. All functional networks exhibited different patterns of modularity; while the RSN consisted of five distinct modules, the SylPN, FTN, and ADN scaled down to three modules, and the SPN expanded to six modules (Fig 6). During the resting state, functional network communities spanned (I) the frontoparietal cortex, (II) the temporooccipital cortex, (III) the insula/opercular cortex, basal ganglia, and thalamus, (IV) the sensorimotor and parietal cortex, and (V) the cerebellum. The RSN was characterized by a large number of connector hubs compared to relatively few provincial hubs (22 versus 6 hubs, respectively). Connector hubs linking different communities originated from modules I–IV and included sensorimotor and parietal cortical regions, whereas provincial hubs were centered on the primary sensorimotor cortex and exclusively recruited from module IV (Fig 7). Task performance dramatically changed the RSN modular configuration. The nodal community structure of the SylPN consisted of three modules and was characterized by two large communities (modules I and III), which were located within the left and right hemispheres (Fig 6), respectively, comprising 70% of all network nodes (105 out of a total 150 nodes, Fig 7). These relatively symmetrical modules predominantly included the sensorimotor, inferior frontal, inferior parietal, and superior temporal cortical regions, the basal ganglia, and the thalamus, whereas a smaller module II included mainly cerebellar and occipital regions. Hub classification revealed that all SylPN hubs participated as connector hubs and were situated around the sensorimotor and inferior parietal cortical regions with the left-sided module I containing the majority of connectors (10 out of 15 hubs). Speech production introduced the most complex network community architecture with the emergence of six distinct modules, which represented the largest number of modules compared across all experimental conditions. While left- and right-hemispheric modules I and IV of the SPN included predominantly sensorimotor, inferior frontal, inferior parietal, insular/opercular, and temporal cortical regions and largely corresponded to modules I and III of the SylPN, the new and extended nodal communities were formed and centered around the frontoparietooccipital cortex (module II), the basal ganglia and thalamus (module III), the hippocampus and thalamus (module V), and the cerebellum (module VI). The SPN was further characterized by a low ratio of connector to provincial hubs (7 versus 12 hubs, respectively), suggesting a high degree of network segregation with only a few network-wide coordinators. Similar to the SylPN, nonlinguistic ADN and nonspeech motor FTN were comprised of three network communities of a distinct spatial profile (Fig 7). The FTN showed a community structure comparable to SylPN with symmetrical modules I and III located within the left and right hemispheres (Fig 6), respectively, and connector-only hubs linking the three nodal communities (Fig 7). This finding points to a lack of locally controlled information flow within nodal communities in both the FTN and SylPN, which is reflective of the low complexity of these two motor production tasks. In contrast to the SylPN and FTN, which largely lacked the involvement of the frontal cortical regions, the ADN formed an extensive module II, which included the vast majority of frontal and parietal regions, bilaterally. Compared to the ADN, the SPN recruited a smaller subset of the frontoparietal regions, which were distributed across five different modules (I–IV and VI), suggesting intramodular importance of particular frontoparietal regions within the SPN. To quantify the observed differences in network community divisions across all experimental conditions, we assessed the partition distance pd between network community structures by calculating normalized mutual information coefficients between the respective partition vectors across all networks. Comparing the SPN to all other conditions, the highest degree of similarity with respect to their partitions was found with the SylPN (pd (SPN, SylPN) = 0. 27), followed by the RSN (pd (SPN, RSN) = 0. 25). The FTN community structure exhibited less correspondence to the SPN (pd (SPN, FTN) = 0. 23), whereas the ADN showed the most discordance in its modular architecture compared to the SPN (pd (SPN, ADN) = 0. 15), confirming the distinctly different topologies of the non-speech-related networks. Finally, based on our finding that primary sensorimotor and premotor cortices contributed to the formation of a shared hub network across all experimental conditions (Figs 4,6, and 7), we examined the existence of adaptive flexible hubs in these sensorimotor regions by comparing their participation coefficients, pci, estimating the uniformity of connections of these regions across all networks [59,60]. Compared to the frontoparietal regions, which were recently reported to host flexible hubs rapidly adapting their connectivity patterns depending on task demands [60], we found that the shared sensorimotor hubs in areas 6,4a, 4p, 3b, and 1 across the RSN, SylPN, SPN, FTN, and ADN also show high participation coefficients similar to shared frontoparietal hubs (sensorimotor versus frontoparietal pci: 0. 62 ± 0. 15 versus 0. 67 ± 0. 07, p = 0. 11).
The formation of hubs and their roles within and across network communities provided detailed insights into the functional specialization of the speech connectome (Figs 4 and 7). We found that the SPN shared common hubs with the RSN and SylPN, constituting a core hub network, which was centered on the left laryngeal and orofacial regions of the primary motor cortex and its main input regions in the surrounding premotor, somatosensory, and parietal cortices. Notably, the strength of SPN hubs was significantly greater than those of the RSN or SylPN, which may be explained by the task complexity. Our data further highlighted the role of the left area 4p as an important core hub during speaking. The area 4p (posterior part of the primary motor cortex) is known to be involved in initiation and execution of motor commands and the modulation of movement-related attention. A recent meta-analysis of speech-related fMRI literature showed that the peak of activity within the laryngeal motor cortex is located in the area 4p [63]. Conversely, the area 4a (anterior part of the primary motor cortex) functionally resembles the secondary motor cortex and requires higher order sensory feedback for motor execution [64–67]. Our findings demonstrate that the left area 4p but not either left or right area 4a has distinctive strength within the core hub network during speaking compared to syllable production and the resting state. The importance of area 4p within the SPN is further emphasized by its role as a provincial hub, which established dense intramodular connections and largely influenced its own nodal community comprised of the majority of sensorimotor core hub regions contributing to the final common cortical pathway of the SPN. In contrast, area 4a served as a connector hub, fulfilling its role in network-wide information integration not limited to a particular module. Based on the distinct involvement of area 4p and area 4a, these two primary motor cortical subdivisions may differentially influence the hub network connectivity for formation of long-range neural connections during speech production compared to other conditions. Major differences between the SPN and all other networks were found in their hub network connectivity patterns. Detailed investigation of the connectivity profiles of the SPN compared to the RSN and SylPN showed that, while these networks share the same sensorimotor hubs, the connections of the common hub network with other brain regions largely varied depending on the state of brain activity (i. e. , resting, syllable production, or speaking) (Figs 4 and 7). The same finding held true when comparing the architecture of the SPN with the FTN and ADN. Being centrally embedded within the network, the sensorimotor hubs recruited an exclusive set of high-degree and high-strength nodes in the SPN. Compared to the RSN, the SPN-only brain regions included the parietal operculum, insula, middle/posterior cingulate cortex, putamen, thalamus, and cerebellum, while the parietal and temporal cortex had different regions specifically designated to either the SPN or RSN (Fig 4A). Comparisons between the SylPN and SPN showed that the SPN recruited the prefrontal cortex, insula, putamen, and thalamus, whereas the SylPN involved the parietal, temporal, and occipital cortices, and the cerebellum (Fig 4B). These data demonstrate that the transition from the resting state to meaningless syllable production to meaningful speaking is contingent upon the involvement of the multimodal associative regions, such as the frontoparietal cortices as well as the basal ganglia and cerebellum (see Discussion in S1 Text for the discussion of subcortical structures). In particular, compared to the resting state, the SPN recruited a wide array of brain regions across multiple nodal communities (Fig 6). The SPN core sensorimotor hub network established connections with the brain regions responsible for sound perception and encoding (auditory cortex), phonological and semantic processing (parietal cortex), lexical decisions and narrative comprehension (middle/posterior cingulate cortex), motor planning and temporal processing of auditory stimuli (insula), control of learned voice production and suppression of unintended responses (basal ganglia), and modulation of vocal motor timing and sequence processing (cerebellum) (reviewed in [1,68]). In contrast to the SylPN, the SPN further refined its network properties and functional specializations by recruiting brain regions responsible for high-order planning and processing. Among these regions, the involvement of the prefrontal cortex was particularly prominent, as a strong relationship with the core hub network was found only in the SPN but not in the SylPN (Fig 2II–2IIIC). Similar to the SylPN, the speech-unrelated FTN also showed no prefrontal component within its large-scale network, which attests to the similarities between the simple vocal motor task of syllable production and the simple motor task of finger tapping, as well as further underscores the distinct prefrontal involvement in the SPN. However, the question arises as to whether the prefrontal cortical participation is related more to general cognitive processing than speech production. Comparisons between the SPN and the nonlinguistic decision-making ADN showed that while both networks shared common nodes in the prefrontal cortex, the SPN required only a specialized subset of the prefrontal region, which was additionally assigned to different neural communities as opposed to forming one large frontoparietal community in the ADN. Such network topology suggests that the prefrontal cortex may play a specialized role in the formation of cognitive aspects of speech control, such as verbal fluency, semantic context associations and violations, word retrieval and sentence generation, stimulus monitoring, and attention-demanding speech comprehension [69–76], which are not characteristic of the production of meaningless syllables, other simple motor or speech-unrelated cognitive tasks. Taken together, our study demonstrates that the production of a syllable, as a speech building block, leads to an integrated network configuration dominated by connector hub regions with almost no participation of the prefrontal cortex in the large-scale network, while the ability to produce and monitor meaningful speech requires locally segregated information processing by specialized communities and information transfer through the prefrontal cortical regions within the SPN. To emphasize that the changes in network topology observed from resting to syllable production to speaking truly represent characteristics of speech-relevant motor networks, we contrasted the network architecture of the RSN, SPN, and SylPN with the topology of the FTN representing a nonspeech related motor control task and of the ADN as an example of a nonlinguistic decision-making task. Our findings demonstrated once again that among all examined conditions, the SPN showed a unique functional architecture (Figs 6 and 7) that reflected the high level of complexity of meaningful speech as opposed to repetitive syllable production, finger tapping, and auditory temporal discrimination. While the SylPN exhibited a connectivity profile similar to the SPN on a nodal level (Figs 2 and 3), the recruitment of nodes into network communities showed a distinctly more complex and segregated network organization in the SPN than in the SylPN (Fig 6). In contrast, the topology of the SylPN, FTN, and ADN was characterized by the emergence of three nodal communities, which were, however, distinctly configured depending on task demands. The SylPN had two large, relatively symmetrical left and right communities with the left component containing the majority of the connector hubs; the FTN had its connector hubs evenly distributed across both hemispheres, whereas the community partition of the ADN illustrated the distributed functional couplings of frontoparietal areas across the network (orange module in Fig 6). As all examined networks had commonly shared hubs in the primary sensorimotor and premotor cortical regions, it is plausible to suggest that behaviorally driven organization and functional specialization of the core sensorimotor network (hubs) defined the unique topological arrangement of large-scale networks, especially in the SPN and SylPN. Based on the participation of the same sensorimotor hubs in several functional domains across different networks and their ability to adaptively establish connectivity with a large range of brain regions (Fig 7), the detected sensorimotor hubs may be considered as flexible network hubs [60,77] with a potential capacity for operational heterogeneity. This novel finding challenges the previous concept of the sensorimotor cortex to exert only low order unimodal influences on other regions [77–80]. In summary, several new findings emerged from our utilization of multivariate graph theoretical analysis of the SPN. Specifically, combining the analysis of functional interactions at the level of network communities with the assessment of individual nodes provided detailed quantitative evidence suggesting that speech production requires specialized network organization around the core local communities centered on the sensorimotor cortex. Because of their high strength, degree, and heterogeneity of functional connections and participation across various behaviors, these sensorimotor regions may be considered to be multimodal flexible network hubs. Among these, area 4p of the primary motor cortex emerged as a particularly important cortical hub in speech controlling network. Furthermore, the production of real-life speech depended on the proper orchestration of a large-scale network, comprised of specialized cortical and subcortical nodes in the prefrontal cortex, insula, putamen, and thalamus, which were less important for other networks, including the SylPN. Collectively, these individual nodes and their roles within functionally specialized nodal communities determined the reconfiguration of global network architecture from the resting state to syllable production to speaking and clarified the distinct functional specializations of core sensorimotor hub regions within large-scale neural networks.
Written informed consent was obtained from all subjects prior to study participation, which was approved by the Institutional Review Boards of the Icahn School of Medicine at Mount Sinai and National Institute of Neurological Disorders and Stroke, National Institutes of Health. Twenty-seven right-handed monolingual English-speaking healthy subjects (18 females, 9 males, age 52. 2 ± 11. 3 years [mean ± SD]) without any history of neurological, psychiatric, or laryngeal disorders participated in the study. Among these, 20 subjects (13 females, 7 males, age 55. 2 ± 9. 8 years) participated in the initial resting state and speech production fMRI study. As a follow-up study, 14 subjects (5 same and 9 new; total: 7 females, 7 males, age 52. 0 ± 13. 1 years) participated in the other task-production fMRI studies, including syllable production, sequential finger tapping, and pure tone auditory temporal discrimination. To ensure the compatibility of data from different subject groups, we conducted a comparison of time series in all hub regions in the two groups of subjects from the original and follow-up studies and found no statistically significant differences between these groups (all p > 0. 07 adjusted for family wise error [FWE] based on the maximal statistic Tmax [81]), indicating that these data were coherent and not biased by intersubject differences. Written informed consent was obtained from all subjects prior to study participation, which was approved by the Institutional Review Boards of the Icahn School of Medicine at Mount Sinai and National Institute of Neurological Disorders and Stroke, National Institutes of Health. Brain images were acquired on a 3. 0 Tesla GE scanner equipped with a quadrature birdcage radio frequency head coil (Milwaukee, WI). Data preprocessing was performed using AFNI software [85] following standard steps. Based on the cytoarchitectonic maximum probability maps and macrolabel atlas [91], the whole brain was parcellated into 212 regions of interest (ROIs), including 142 cortical, 36 subcortical, and 34 cerebellar regions (Fig 8A). For each ROI, a voxelwise-averaged time series of rs-fMRI and task-production fMRI was computed. Because the main focus of this study was to investigate statistical dependence of neural processing sites distributed throughout the entire brain, zero-lag Pearson’s correlation coefficients were calculated for each pair of regions and each condition, giving rise to 212 x 212 correlation matrices (Fig 8B). Visual inspection of per-subject correlation histograms revealed only a negligible number of negative entries, which were removed from the matrices [92,93]. All connectivity matrices are publicly available at http: //figshare. com/articles/The_Functional_Connectome_of_Speech_Control/1431873; the codes used to transform the fMRI data to networks can be found at http: //research. mssm. edu/simonyanlab/analytical-tools/. Visual inspection of rs-fMRI data in six subjects showed pronounced atypical variations in average correlation strength, which were suggestive of susceptibility artifacts in the resting-state data. These subjects were excluded from rs-fMRI analysis, and their speech-related fMRI datasets were removed for consistency of data analysis, thus reducing the number of subjects to 14 per group in each condition. None of these six subjects were among the recruited for the follow-up syllable production study. As a next step, the individual datasets during each condition (i. e. , rest, speech, syllable production) were used to construct weighted undirected graphs by using the 212 brain regions as nodes vi of a network with the associated correlation coefficients representing the weights of the graph' s edges. The density (or cost) of each network was computed as the number of actual connections divided by the number of maximal possible connections in the graph [93], which yielded an average density of 88% ± 4% (mean ± SD) for RSN, 92% ± 6% for SPN, and 92% ± 5% for SylPN. Because networks with a density > 50% tend to exhibit random network characteristics [93,94], we reduced the cost of the network by removing edges with a weight less than a given percentage of the maximum weight in the network until the network had a density of 50%. Because our principal goal was to examine the organization of the SPN in comparison to the RSN and SylPN, we first applied this thresholding to the SPN and then adjusted the RSN and SylPN accordingly so that all graphs had the same number of nodes and edges for between-network comparisons [95]. Specifically, the least densely interconnected nodes were first removed from the 212-node SPN, and then the same nodes were excluded from the RSN and SylPN. Such an elimination strategy allowed us to create a refinement of the initial whole-brain parcellation based on the speech production task. We tested the validity and efficacy of the employed nodal elimination strategy by means of random networks. As a first step, synthetic reference networks with the same number of nonzero elements as the original 212-node per-subject networks were constructed. This gave rise to three groups corresponding to the RSN, SPN, and SylPN with 14 random networks per group. Then the randomized SPN group was thresholded down to 50% density, and the same nodes were then removed from the randomized RSN and SylPN graphs. The resulting downsized networks exhibited a connectivity structure comparable to the initial 212-node random graphs, demonstrating that the employed elimination strategy did not diminish the underlying topological structure of the networks (see details in Methods of S1 Text and S2 Fig). As a last step in network construction, we removed sparsely connected, low-degree nodes with fewer connections than 5% of the maximal number of nodal links in the speech graphs. Based on this multistep thresholding, a total of 62 nodes were detached from the SPN, which reduced the number of regions from 212 to 150 (73 in the left hemisphere and 77 in the right hemisphere) at a target density of 50% (Fig 8B). The same 62 nodes were then removed from the RSN and SylPN in order to allow for comparisons with SPN properties. The majority of removed regions were located in the ventral parts of the brain, comprising areas that are especially prone to fMRI susceptibility artifacts [96]. All resultant 42 (14 RSN, 14 SPN, and 14 SylPN) weighted undirected graphs with n = 150 nodes were thresholded again to obtain a common density range of 77%–86% (10 values, 1% increments). Over this range, group-averaged networks were computed as reported earlier [53] and had a density of 60%–78% (10 values, 2% increments) (Fig 8B). In the follow-up second experiment, we assessed the global reconfiguration of brain networks during speech production in contrast to the resting state, syllable production, sequential finger tapping, and an auditory discrimination task. The same 212 ROI whole-brain parcellation was used to construct weighted, undirected networks for all experimental conditions, where sparsely-occurring negative correlation values were assigned the edge weight zero [92,93]. In order to avoid a restriction of nodal community formation to an SPN-based subset of nodes, the above-employed nodal elimination strategy was replaced by individual thresholding of the RSN, SylPN, FTN, and ADN independently from the SPN. To ensure comparability of results between different networks and to the findings from the first experiment, all networks were downsized to 150 nodes.
Speech production is a complex process that requires the orchestration of multiple brain regions. However, our current understanding of the large-scale neural architecture during speaking remains scant, as research has mostly focused on examining distinct brain circuits involved in distinct aspects of speech control. Here, we performed graph theoretical analyses of functional MRI data acquired from healthy subjects in order to reveal how brain regions relate to one another while speaking. We constructed functional brain networks of increasing hierarchy from rest to simple vocal motor output to the production of real-life speech, and compared these to nonspeech control tasks such as finger tapping and pure tone discrimination. We discovered a specialized network of densely connected sensorimotor regions, which formed a common processing core across all conditions. Specifically, the primary sensorimotor cortex participated in multiple functional domains across different networks and modulated long-range connections depending on task content, which challenges the established concept of low-order unimodal function of this region. Compared to other tasks, speech production was characterized by the formation of six distinct neural communities with specialized recruitment of the prefrontal cortex, insula, putamen, and thalamus, which collectively formed the functional speech connectome.
The Functional Connectome of Speech Control