The Functional Connectome of Speech Control

Abstract

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.

Mapping Critical Language Sites in Children Performing Verb Generation: Whole-Brain Connectivity and Graph Theoretical Analysis in MEG

“…The importance of a particular node may be determined by the amount or quality of its connectedness (where highly connection nodes are termed “hubs”; He and Evans, 2010; McIntosh and Mišić, 2013; Power et al, 2013; Fornito et al, 2015; Fuertinger et al, 2015). Investigating hubs can help to answer questions of how complex networks might develop, or how networks are altered in neuropsychiatric disease, such as Alzheimer’s or schizophrenia (Achard and Bullmore, 2007; Bassett et al, 2009; Stam et al, 2009; Supekar et al, 2009; Mandelli et al, 2016).…”

Stability of Network Communities as a Function of Task Complexity

“…Particularly, regions in the prefrontal cortex (orange, Fig. 3B), which have been shown to play a key role in the cognitive aspects of speech control (Benson et al, 2001; Burton, Locasto, Krebs-Noble, & Gullapalli, 2005; Giraud et al, 2004; Muller et al, 1997) and which are of less importance for the processing of meaningless syllables (Fuertinger et al, 2015), exhibited differing community assignments in G 0 syl as compared to F 0 syl . Thus, like in the resting state, modular deviations in the simulated graph G 0 sp from the community layout in F 0 sp were not evenly distributed across the entire network but predominantly affected specific, weakly integrated functional components of the graphs.…”

“…Wang, Sporns, & Burkhalter, 2012), task production induces a behaviorally driven reconfiguration of the functional architecture of the brain on a global scale (Fuertinger et al, 2015; Fuertinger et al, 2016; Stanley, Dagenbach, Lyday, Burdette, & Laurienti, 2014) and may therefore be more sensitive to inter-subject variability. Thus, the observed high within-group stability of resting-state functional partitions as compared to task-related modular layouts might be a consequence of the underlying neuroanatomical architecture.…”

“…As a simple vocal motor task, all subjects produced the same syllables /iʔi-iʔi/, which comprised the vowel /i/ (as in “beet”) and the glottal stop /ʔ/ followed again by /i/ which maximized vocal fold adduction, while minimizing orofacial articulation. As a more cognitively and linguistically complex speech motor task, all subjects produced the same ten meaningful, grammatically correct English sentences (e.g., “We are always away”, “Tom is in the army”), one per volume acquisition (Fuertinger, Horwitz, & Simonyan, 2015). The speech production task was designed to encompass higher-order cognitive and linguistic processing, whereas syllable production represented a simple vocal motor task that was relevant to speaking but had minimal cognitive and linguistic involvement (Fuertinger et al, 2015; Simonyan & Fuertinger, 2015; Simonyan, Herscovitch, & Horwitz, 2013).…”

“…Functional images were pre-processed using AFNI software (Cox, 1996) following a standard analysis pipeline as described earlier (Fuertinger et al, 2015). A whole-brain parcellation based on the cytoarchitectonic maximum probability maps and macrolabel atlas (Eickhoff et al, 2005) was used to extract regionally averaged task-production and resting-state BOLD fMRI residual time-series for all subjects (Fig.…”