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NeuroImage. Clinical · Jan 2018
Microstructure of the superior temporal gyrus and hallucination proneness - a multi-compartment diffusion imaging study.
- Amy Spray, Anton L Beer, Richard P Bentall, Vanessa Sluming, and Georg Meyer.
- University of Liverpool, Liverpool, UK. Electronic address: ajspray@liv.ac.uk.
- Neuroimage Clin. 2018 Jan 1; 20: 1-6.
AbstractPrevious studies reported that the volume of the left superior temporal gyrus (STG) is reduced in patients with schizophrenia and negatively correlated with hallucination severity. Moreover, diffusion-tensor imaging studies suggested a relationship between the brain microstructure in the STG of patients and auditory hallucinations. Hallucinations are also experienced in non-patient groups. This study investigated the relationship between hallucination proneness and the brain structure of the STG. Hallucination proneness was assessed by the Launey Slade Hallucination Scale (LSHS) in 25 healthy individuals who varied in their propensity to hear voices. Brain volume and microstructure of the STG was assessed by magnetic resonance imaging (MRI). Microstructure was examined by conventional diffusion-tensor imaging as well as by neurite orientation dispersion and density imaging (NODDI). The latter decomposes diffusion-based MRI into multiple compartments that characterize the brain microstructure by its neurite complexity known as orientation dispersion (ODI) and by its neurite density (NDI). Hallucination proneness was negatively correlated with the volume and microstructure (fractional anisotropy, neurite complexity) of the left but not the right STG. The strongest relationship (r = -0.563) was observed for neurite complexity (ODI). No correlation was observed for neurite density (NDI). These findings suggest that there is a relationship between the volume and the microstructure of the left STG and hallucination proneness. Dendritic complexity (but not neurite density) is inversely related to hallucination proneness. Metrics based on multi-compartment diffusion models seem to be more sensitive for hallucination-related neural processes than conventional MRI-based metrics.
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