Hallucination‐ and speech‐specific hypercoupling in frontotemporal auditory and language networks in schizophrenia using combined task‐based fMRI data: An fBIRN study

Abstract

Hypercoupling of activity in speech-perception-specific brain networks has been proposed to play a role in the generation of auditory-verbal hallucinations (AVHs) in schizophrenia; however, it is unclear whether this hypercoupling extends to nonverbal auditory perception. We investigated this by comparing schizophrenia patients with and without AVHs, and healthy controls, on task-based functional magnetic resonance imaging (fMRI) data combining verbal speech perception (SP), inner verbal thought generation (VTG), and nonverbal auditory oddball detection (AO). Data from two previously published fMRI studies were simultaneously analyzed using group constrained principal component analysis for fMRI (group fMRI-CPCA), which allowed for comparison of task-related functional brain networks across groups and tasks while holding the brain networks under study constant, leading to determination of the degree to which networks are common to verbal and nonverbal perception conditions, and which show coordinated hyperactivity in hallucinations. Three functional brain networks emerged: (a) auditory-motor, (b) language processing, and (c) default-mode (DMN) networks. Combining the AO and sentence tasks allowed the auditory-motor and language networks to separately emerge, whereas they were aggregated when individual tasks were analyzed. AVH patients showed greater coordinated activity (deactivity for DMN regions) than non-AVH patients during SP in all networks, but this did not extend to VTG or AO. This suggests that the hypercoupling in AVH patients in speech-perception-related brain networks is specific to perceived speech, and does not extend to perceived nonspeech or inner verbal thought generation.

Abnormalities of intrinsic regional brain activity in first-episode and chronic schizophrenia: a meta-analysis of resting-state functional MRI

“…84 The DMN has recently attracted attention as a novel means of understanding the neural mechanisms of schizophrenia. Aberrant intrinsic connectivity and activity patterns of the DMN in schizophrenia have been documented in many reports and also associated with social cognition and social functioning impairments, 13,85,86 executive deficits, 87 self-referential processing abnormalities, 88 dysfunction of autobiographical memory, 89 auditory-verbal hallucinations 90,91 and abnormal attentional control processing. 92 As the core of the DMN, the mPFC is important for the affective value of re inforcers, decision-making, the generation and regulation of negative emotion, social cognition and expectation.…”

Exploring concomitant neuroimaging and genetic alterations in patients with and patients without auditory verbal hallucinations: A pilot study and mini review

“…The development of qualitative diagnostic and therapeutic efficacy prediction models in patients with AVHs who have various diagnoses may improve the accuracy and repeatability of prediction models, and may, ultimately, facilitate the delivery of early, potentially curative treatments. 7 , 52 – 60 …”

Hemodynamic responses are abnormal in isolated cervical dystonia

“…Briefly, fMRI-CPCA combines multivariate multiple regression analysis and principal component analysis into a unified framework to reveal multiple orthogonal sources of stimulus-associated fluctuations in brain activity (Lavigne & Woodward, 2018;Metzak et al, 2011;Woodward et al, 2016). Briefly, fMRI-CPCA combines multivariate multiple regression analysis and principal component analysis into a unified framework to reveal multiple orthogonal sources of stimulus-associated fluctuations in brain activity (Lavigne & Woodward, 2018;Metzak et al, 2011;Woodward et al, 2016).…”

“…CPCA analysis was carried out using the fMRI-CPCA toolbox (Version 16, www.nitrc.org/proje cts/fmricpca) available for SPM12 using a FIR model. Briefly, fMRI-CPCA combines multivariate multiple regression analysis and principal component analysis into a unified framework to reveal multiple orthogonal sources of stimulus-associated fluctuations in brain activity (Lavigne & Woodward, 2018;Metzak et al, 2011;Woodward et al, 2016). Whole-brain BOLD signal activity variance is partitioned into task-related and task-unrelated fluctuations using multivariate multiple regression.…”

“…First, voxels showing high task-related covariance in BOLD signal (regression) can be grouped into functional networks involved in that particular task (PCA of task-related signals). Second, hemodynamic response function (HRF) patterns associated with each of these functional networks are estimated by a finite impulse response (FIR) model for the regression portion of the analysis (Lavigne & Woodward, 2018;Metzak et al, 2011;Woodward, Leong, Sanford, Tipper, & Lavigne, 2016). In other words, CPCA enables both the quantification of taskdependent functional networks and the comparison of neural activity time courses in these networks between groups and conditions.…”