Whole-Brain Network Connectivity Underlying the Human Speech Articulation as Emerged Integrating Direct Electric Stimulation, Resting State fMRI and Tractography

Abstract

Production of fluent speech in humans is based on a precise and coordinated articulation of sounds. A speech articulation network (SAN) has been observed in multiple brain studies typically using either neuroimaging or direct electrical stimulation (DES), thus giving limited knowledge about the whole brain structural and functional organization of this network. In this study, seven right-handed patients underwent awake surgery resection of low-grade gliomas (4) and cavernous angiomas. We combined pre-surgical resting state fMRI (rs-fMRI) and diffusion MRI together with speech arrest sites obtained intra-operatively with DES to address the following goals: (i) determine the cortical areas contributing to the intrinsic functional SAN using the speech arrest sites as functional seeds for rs-fMRI; (ii) evaluate the relative contribution of gray matter terminations from the two major language dorsal stream bundles, the superior longitudinal fasciculus (SLF III) and the arcuate fasciculus (AF); and (iii) evaluate the possible pre-surgical prediction of SAN with rs-fMRI. In all these right-handed patients the intrinsic functional SAN included frontal, inferior parietal, temporal, and insular regions symmetrically and bilaterally distributed across the two hemispheres regardless of the side (four right) of speech arrest evocation. The SLF III provided a much higher density of terminations in the cortical regions of SAN in respect to AF. Pre-surgical rs-fMRI data demonstrated moderate ability to predict the SAN. The set of functional and structural data provided in this multimodal study characterized, at a whole-brain level, a distributed and bi-hemispherical network subserving speech articulation.

A unified neurocomputational bilateral model of spoken language production in healthy participants and recovery in poststroke aphasia

“…Although the activation patterns are often leftward asymmetric, the degree of asymmetry largely depends on the nature of the tasks, with a subset showing stronger forms of asymmetric bias. For instance, propositional speech production is more left lateralized, whereas nonpropositional speech (e.g., counting) generates bilateral activations ( 27 – 29 ). Of course, identifying activations associated with language does not necessarily imply that the regions are necessary for language functions ( 30 ).…”

A normalized dataset of 1821 cortical and subcortical functional responses collected during direct electrical stimulation in patients undergoing awake brain surgery

“…In this video the probabilistic cortico-subcortical distribution of functional responses related to speech output [2] is reported in the axial, sagittal, and coronal planes of the MNI-152 template brain (T1-weighted sequence, 1mm resolution) as a color heat map representing cohort-level confidence intervals.Video 7 Speech articulation. In this video the probabilistic cortico-subcortical distribution of functional responses related to speech articulatory network [6] is reported in the axial, sagittal, and coronal planes of the MNI-152 template brain (T1-weighted sequence, 1mm resolution) as a color heat map representing cohort-level confidence intervals.Video 8 Anomia. In this video the probabilistic cortico-subcortical distribution of anomia is reported in the axial, sagittal, and coronal planes of the MNI-152 template brain (T1-weighted sequence, 1mm resolution) as a color heat map representing cohort-level confidence intervals.Video 9 Semantic.…”

“… Speech articulation. In this video the probabilistic cortico-subcortical distribution of functional responses related to speech articulatory network [6] is reported in the axial, sagittal, and coronal planes of the MNI-152 template brain (T1-weighted sequence, 1mm resolution) as a color heat map representing cohort-level confidence intervals.…”

ReStNeuMap: a tool for automatic extraction of resting-state functional MRI networks in neurosurgical practice

“…Specifically, we measure the amount of spatial overlap between each network determined by ICA with the network provided by an atlas representing the functional system that needs to be mapped. 33,39 We quantify this overlap in patient space by measuring the goodness of fit (GOF). 37 The optimal number of components in which the rs-fMRI signal is to be split to identify the networks can vary across patients.…”