Background: 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. Methods: 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. Results: 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 #x3c; 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. Conclusion: 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.
Neuropsychobiology

中文翻译：

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持久性听觉幻觉患者的语言和自我参考网络功能分离。

背景：越来越多的神经影像证据表明，精神分裂症患者的内在神经活动异常可能是听觉言语幻觉（AVH）的基础。然而，关于不同的内在神经网络之间的功能相互作用及其与AVH的关联知之甚少。方法：我们研究了不同静止状态网络的功能网络连通性（FNC），以及FNC强度与AVH症状严重程度之间的关系。获得了14个健康对照和10例持续性AVH的精神分裂症患者在3 T时的静止状态功能MRI数据。使用独立于空间组的成分分析对数据进行分析，然后通过约束最大滞后相关性来确定组内和组之间的FNC。结果：选择了四个感兴趣的组件，包括语言，注意力，执行控制网络以及默认模式网络（DMN），用于后续的FNC分析。患有持续性AVH的患者在语言网络和DMN之间显示出较低的FNC（ p＃x3c； 0.05，错误发现率已校正）。但是，根据《精神病症状分级量表》，FNC强度与症状严重程度没有显着相关。结论：这些发现表明，语音相关系统与服务自参考处理的网络之间的FNC中断与AVH有关。数据与语音产生和感知的自我干扰破坏模型一致。
神经心理生物学