Spatiotemporal Segregation of Neural Response to Auditory Stimulation: An fMRI Study Using Independent Component Analysis and Frequency-Domain Analysis

The hearing ear is always found close to the speaking tongue: Review of the role of the motor system in speech perception

Does "the motor system" play "a role" in speech perception? If so, where, how, and when? We conducted a systematic review that addresses these questions using both qualitative and quantitative methods. The qualitative review of behavioural, computational modelling, non-human animal, brain damage/disorder, electrical stimulation/recording, and neuroimaging research suggests that distributed brain regions involved in producing speech play specific, dynamic, and contextually determined roles in speech perception. The quantitative review employed region and network based neuroimaging meta-analyses and a novel text mining method to describe relative contributions of nodes in distributed brain networks. Supporting the qualitative review, results show a specific functional correspondence between regions involved in non-linguistic movement of the articulators, covertly and overtly producing speech, and the perception of both nonword and word sounds. This distributed set of cortical and subcortical speech production regions are ubiquitously active and form multiple networks whose topologies dynamically change with listening context. Results are inconsistent with motor and acoustic only models of speech perception and classical and contemporary dual-stream models of the organization of language and the brain. Instead, results are more consistent with complex network models in which multiple speech production related networks and subnetworks dynamically self-organize to constrain interpretation of indeterminant acoustic patterns as listening context requires.

Large-scale functional network overlap is a general property of brain functional organization: Reconciling inconsistent fMRI findings from general-linear-model-based analyses

Functional magnetic resonance imaging (fMRI) studies regularly use univariate general-linear-model-based analyses (GLM). Their findings are often inconsistent across different studies, perhaps because of several fundamental brain properties including functional heterogeneity, balanced excitation and inhibition (E/I), and sparseness of neuronal activities. These properties stipulate heterogeneous neuronal activities in the same voxels and likely limit the sensitivity and specificity of GLM. This paper selectively reviews findings of histological and electrophysiological studies and fMRI spatial independent component analysis (sICA) and reports new findings by applying sICA to two existing datasets. The extant and new findings consistently demonstrate several novel features of brain functional organization not revealed by GLM. They include overlap of large-scale functional networks (FNs) and their concurrent opposite modulations, and no significant modulations in activity of most FNs across the whole brain during any task conditions. These novel features of brain functional organization are highly consistent with the brain's properties of functional heterogeneity, balanced E/I, and sparseness of neuronal activity, and may help reconcile inconsistent GLM findings.

Implications of cortical balanced excitation and inhibition, functional heterogeneity, and sparseness of neuronal activity in fMRI

Blood-oxygenation-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) studies often report inconsistent findings, probably due to brain properties such as balanced excitation and inhibition and functional heterogeneity. These properties indicate that different neurons in the same voxels may show variable activities including concurrent activation and deactivation, that the relationships between BOLD signal and neural activity (i.e., neurovascular coupling) are complex, and that increased BOLD signal may reflect reduced deactivation, increased activation, or both. The traditional general-linear-model-based-analysis (GLM-BA) is a univariate approach, cannot separate different components of BOLD signal mixtures from the same voxels, and may contribute to inconsistent findings of fMRI. Spatial independent component analysis (sICA) is a multivariate approach, can separate the BOLD signal mixture from each voxel into different source signals and measure each separately, and thus may reconcile previous conflicting findings generated by GLM-BA. We propose that methods capable of separating mixed signals such as sICA should be regularly used for more accurately and completely extracting information embedded in fMRI datasets.

Effects of scanner acoustic noise on intrinsic brain activity during auditory stimulation

INTRODUCTION

Although the effects of scanner background noise (SBN) during functional magnetic resonance imaging (fMRI) have been extensively investigated for the brain regions involved in auditory processing, its impact on other types of intrinsic brain activity has largely been neglected. The present study evaluated the influence of SBN on a number of intrinsic connectivity networks (ICNs) during auditory stimulation by comparing the results obtained using sparse temporal acquisition (STA) with those using continuous acquisition (CA).

METHODS

Fourteen healthy subjects were presented with classical music pieces in a block paradigm during two sessions of STA and CA. A volume-matched CA dataset (CAm) was generated by subsampling the CA dataset to temporally match it with the STA data. Independent component analysis was performed on the concatenated STA-CAm datasets, and voxel data, time courses, power spectra, and functional connectivity were compared.

RESULTS

The ICA revealed 19 ICNs; the auditory, default mode, salience, and frontoparietal networks showed greater activity in the STA. The spectral peaks in 17 networks corresponded to the stimulation cycles in the STA, while only five networks displayed this correspondence in the CA. The dorsal default mode and salience networks exhibited stronger correlations with the stimulus waveform in the STA.

CONCLUSIONS

SBN appeared to influence not only the areas of auditory response but also the majority of other ICNs, including attention and sensory networks. Therefore, SBN should be regarded as a serious nuisance factor during fMRI studies investigating intrinsic brain activity under external stimulation or task loads.