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Most of our knowledge about the neuroanatomy of speech errors comes from lesion-symptom mapping studies in people with aphasia and laboratory paradigms designed to elicit primarily phonological errors in healthy adults, with comparatively little evidence from naturally occurring speech errors. In this study, we analyzed perfusion fMRI data from 24 healthy participants during a picture naming task, classifying their responses into correct and different speech error types (e.g., semantic, phonological, omission errors). Total speech errors engaged a wide set of left-lateralized frontal, parietal, and temporal regions that were almost identical to those involved during the production of correct responses. We observed significant perfusion signal decreases in the left posterior middle temporal gyrus and inferior parietal lobule (angular gyrus) for semantic errors compared to correct trials matched on various psycholinguistic variables. In addition, the left dorsal caudate nucleus showed a significant perfusion signal decrease for omission (i.e., anomic) errors compared with matched correct trials. Surprisingly, we did not observe any significant perfusion signal changes in brain regions proposed to be associated with monitoring mechanisms during speech production (e.g., ACC, superior temporal gyrus). Overall, our findings provide evidence for distinct neural correlates of semantic and omission error types, with anomic speech errors likely resulting from failures to initiate articulatory–motor processes rather than semantic knowledge impairments as often reported for people with aphasia. [ABSTRACT FROM AUTHOR]

It is well-established from fMRI experiments employing gradient echo echo-planar imaging (EPI) sequences that overt speech production introduces signal artefacts compromising accurate detection of task-related responses. Both design and post-processing (denoising) techniques have been proposed and implemented over the years to mitigate the various noise sources. Recently, fMRI studies of speech production have begun to adopt multiband EPI sequences that offer better signal-to-noise ratio (SNR) and temporal resolution allowing adequate sampling of physiological noise sources (e.g., respiration, cardiovascular effects) and reduced scanner acoustic noise. However, these new sequences may also introduce additional noise sources. In this study, we demonstrate the impact of applying several noise-estimation and removal approaches to continuous multiband fMRI data acquired during a naming-to-definition task, including rigid body motion regression and outlier censoring, principal component analysis for removal of cerebrospinal fluid (CSF)/edge-related noise components, and global fMRI signal regression (using two different approaches) compared to a baseline of realignment and unwarping alone. Our results show the strongest and most spatially extensive sources of physiological noise are the global signal fluctuations arising from respiration and muscle action and CSF/edge-related noise components, with residual rigid body motion contributing relatively little variance. Interestingly, denoising approaches tended to reduce and enhance task-related BOLD signal increases and decreases, respectively. Global signal regression using a voxel-wise linear model of the global signal estimated from unmasked data resulted in dramatic improvements in temporal SNR. Overall, these findings show the benefits of combining continuous multiband EPI sequences and denoising approaches to investigate the neurobiology of speech production., Competing Interests: Competing Interests: The authors have declared that no competing interests exist., (© 2024 Massachusetts Institute of Technology.)