Department of Radiology and Biomedical Imaging, University of California, San, Francisco, CA 94143, USA.
Medical Imaging Business Center, Ricoh Company, Ltd., Kanazawa, Japan.
Schizophr Bull. 2022 Nov 18;48(6):1384-1393. doi: 10.1093/schbul/sbac075.
Prior research has shown that patients with schizophrenia (SZ) show disruption in brain network connectivity that is thought to underlie their cognitive and psychotic symptoms. However, most studies examining functional network disruption in schizophrenia have focused on the temporally correlated coupling of the strength of network connections. Here, we move beyond correlative metrics to assay causal computations of connectivity changes in directed neural information flow, assayed from a neural source to a target in SZ.
This study describes a whole-brain magnetoencephalography-imaging approach to examine causal computations of connectivity changes in directed neural information flow between brain regions during resting states, quantified by phase-transfer entropy (PTE) metrics, assayed from a neural source to an endpoint, in 21 SZ compared with 21 healthy controls (HC), and associations with cognitive and clinical psychotic symptoms in SZ.
We found that SZ showed significant disruption in information flow in alpha (8-12 Hz) and beta (12-30 Hz) frequencies, compared to HC. Reduced information flow in alpha frequencies from the precuneus to the medio-ventral occipital cortex was associated with more severe clinical psychopathology (ie, positive psychotic symptoms), while reduced information flow between insula and middle temporal gyrus was associated with worsening cognitive symptoms.
The present findings highlight the importance of delineating dysfunction in neural information flow in specific oscillatory frequencies between distinct regions that underlie the cognitive and psychotic symptoms in SZ, and provide potential neural biomarkers that could lead to innovations in future neuromodulation treatment development.
先前的研究表明,精神分裂症(SZ)患者的大脑网络连接存在中断,这被认为是其认知和精神病症状的基础。然而,大多数研究都集中在精神分裂症中功能网络中断的时间相关的网络连接强度的相关性。在这里,我们超越了相关性指标,研究了在静息状态下,从神经源到 SZ 中的目标的定向神经信息流中连接变化的因果计算,通过相位传递熵(PTE)指标进行评估。
本研究描述了一种全脑脑磁图成像方法,用于检查在静息状态下,从神经源到目标的定向神经信息流中连接变化的因果计算,通过相位传递熵(PTE)指标进行评估,在 21 名 SZ 患者与 21 名健康对照组(HC)中进行评估,并与 SZ 的认知和临床精神病症状相关联。
我们发现,与 HC 相比,SZ 在 alpha(8-12 Hz)和 beta(12-30 Hz)频率下表现出明显的信息流中断。从楔前叶到中-腹侧枕叶的 alpha 频率的信息流减少与更严重的临床精神病学症状(即阳性精神病症状)相关,而岛叶和颞中回之间的信息流减少与认知症状的恶化相关。
本研究结果强调了在特定振荡频率下,明确区分不同区域之间的神经信息流功能障碍的重要性,这些区域是 SZ 认知和精神病症状的基础,并提供了潜在的神经生物标志物,这可能会导致未来神经调节治疗的创新。