Functional network interactions in patients with schizophrenia with persistent auditory verbal hallucinations: A multimodal MRI fusion approach using three-way pICA

Abstract

Over the last decade, there have been an increasing number of functional magnetic resonance imaging (fMRI) studies examining brain activity in schizophrenia (SZ) patients with persistent auditory verbal hallucinations (AVH) using either task-based or resting-state fMRI (rs-fMRI) paradigms. Such data have been conventionally collected and analyzed as distinct modalities, disregarding putative crossmodal interactions. Recently, it has become possible to incorporate two or more modalities in one comprehensive analysis to uncover hidden patterns of neural dysfunction not sufficiently captured by separate analysis. A novel multivariate fusion approach to multimodal data analysis, i.e., parallel independent component analysis (pICA), has been previously shown to be a powerful tool in this regard. We utilized three-way pICA to study covarying components among fractional amplitude of low-frequency fluctuations (fALFF) for rs-MRI and task-based activation computed from an alertness and a working memory (WM) paradigm of 15 SZ patients with AVH, 16 non-hallucinating SZ patients (nAVH), and 19 healthy controls (HC). The strongest connected triplet (false discovery rate (FDR)-corrected pairwise correlations) comprised a frontostriatal/temporal network (fALFF), a temporal/sensorimotor network (alertness task), and a frontoparietal network (WM task). Frontoparietal and frontostriatal/temporal network strength significantly differed between AVH patients and HC. Phenomenological features such as omnipotence and malevolence of AVH were associated with temporal/sensorimotor and frontoparietal network strength. The transmodal data confirm a complex interplay of neural systems subserving attentional processes and cognitive control interacting with speech and language processing networks. In addition, the data emphasize the importance of sensorimotor regions modulating specific symptom dimensions of AVH.