Center for Psychosocial Medicine, Department of General Psychiatry, Heidelberg University, Heidelberg, Germany.
Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
Neuropsychobiology. 2020;79(4-5):345-351. doi: 10.1159/000507630. Epub 2020 Jun 2.
Accumulating neuroimaging evidence suggests that abnormal intrinsic neural activity could underlie auditory verbal hallucinations (AVH) in patients with schizophrenia. However, little is known about the functional interplay between distinct intrinsic neural networks and their association with AVH.
We investigated functional network connectivity (FNC) of distinct resting-state networks as well as the relationship between FNC strength and AVH symptom severity. Resting-state functional MRI data at 3 T were obtained for 14 healthy controls and 10 patients with schizophrenia presenting with persistent AVH. The data were analyzed using a spatial group independent component analysis, followed by constrained maximal lag correlations to determine FNC within and between groups.
Four components of interest, comprising language, attention, executive control networks, as well as the default-mode network (DMN), were selected for subsequent FNC analyses. Patients with persistent AVH showed lower FNC between the language network and the DMN (p < 0.05, corrected for false discovery rate). FNC strength, however, was not significantly related to symptom severity, as measured by the Psychotic Symptom Rating Scale.
These findings suggest that disrupted FNC between a speech-related system and a network subserving self-referential processing is associated with AVH. The data are consistent with a model of disrupted self-attribution of speech generation and perception.
越来越多的神经影像学证据表明，异常的内在神经活动可能是精神分裂症患者出现听觉言语幻觉（AVH）的基础。然而，对于不同内在神经网络之间的功能相互作用及其与 AVH 的关系知之甚少。
我们研究了不同静息态网络的功能网络连接（FNC），以及 FNC 强度与 AVH 症状严重程度之间的关系。我们使用 3T 的静息态功能磁共振成像数据对 14 名健康对照者和 10 名持续性 AVH 的精神分裂症患者进行了研究。数据分析采用空间组独立成分分析，然后采用约束最大滞后相关来确定组内和组间的 FNC。
选择了四个感兴趣的成分，包括语言、注意力、执行控制网络以及默认模式网络（DMN），用于随后的 FNC 分析。持续性 AVH 患者的语言网络与 DMN 之间的 FNC 较低（p < 0.05，经假发现率校正）。然而，FNC 强度与以精神病症状评定量表（PSYRATS）测量的症状严重程度没有显著相关性。
这些发现表明，与言语相关系统和自我参照处理网络之间的 FNC 中断与 AVH 有关。这些数据与言语产生和感知的自我归因中断模型一致。
