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Dynamic reconfiguration of functional brain networks during working memory training.工作记忆训练期间功能性大脑网络的动态重组。
Nat Commun. 2020 May 15;11(1):2435. doi: 10.1038/s41467-020-15631-z.
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Pairwise common variant meta-analyses of schizophrenia with other psychiatric disorders reveals shared and distinct gene and gene-set associations.对精神分裂症与其他精神障碍的常见变异进行两两元分析,揭示了共享和独特的基因和基因集关联。
Transl Psychiatry. 2020 May 12;10(1):134. doi: 10.1038/s41398-020-0817-7.
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Translating insights from neuropsychiatric genetics and genomics for precision psychiatry.将神经精神遗传学和基因组学的见解转化为精准精神病学。
Genome Med. 2020 Apr 29;12(1):43. doi: 10.1186/s13073-020-00734-5.
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Polygenic Risk Scores Shed Light on the Relationship between Schizophrenia and Cognitive Functioning: Review and Meta-Analysis.多基因风险评分揭示精神分裂症与认知功能之间的关系:综述与荟萃分析
J Clin Med. 2020 Jan 25;9(2):341. doi: 10.3390/jcm9020341.
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Novel Approaches for Identifying the Molecular Background of Schizophrenia.鉴定精神分裂症分子背景的新方法。
Cells. 2020 Jan 18;9(1):246. doi: 10.3390/cells9010246.
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PRSice-2: Polygenic Risk Score software for biobank-scale data.PRSice-2:用于生物库规模数据的多基因风险评分软件。
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The Relationship Between Cognitive Dysfunction and Symptom Dimensions Across Schizophrenia, Bipolar Disorder, and Major Depressive Disorder.精神分裂症、双相情感障碍和重度抑郁症中认知功能障碍与症状维度之间的关系。
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A systematic review of associations between functional MRI activity and polygenic risk for schizophrenia and bipolar disorder.一项关于功能磁共振成像活动与精神分裂症和双相情感障碍多基因风险之间关联的系统评价。
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Structural and Functional Neuroimaging of Polygenic Risk for Schizophrenia: A Recall-by-Genotype-Based Approach.多基因风险与精神分裂症的结构和功能神经影像学:基于基因型召回的方法。
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10
Identification of Two Distinct Working Memory-Related Brain Networks in Healthy Young Adults.识别健康年轻成年人中两个不同的工作记忆相关脑网络。
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全球大脑在工作记忆期间的灵活性在精神分裂症高遗传风险组中降低。

Global Brain Flexibility During Working Memory Is Reduced in a High-Genetic-Risk Group for Schizophrenia.

机构信息

Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, United Kingdom; Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom; MRC Centre for Neuropsychiatric Genetics and Genomics, Institute of Psychological Medicine and Clinical Neurosciences, Cardiff School of Medicine, Cardiff University, Cardiff, United Kingdom; Neuroinformatics Group, School of Psychology, Cardiff University, Cardiff, United Kingdom.

Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, United Kingdom; Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom; School of Psychology, Bath University, Bath, United Kingdom.

出版信息

Biol Psychiatry Cogn Neurosci Neuroimaging. 2021 Dec;6(12):1176-1184. doi: 10.1016/j.bpsc.2021.01.007. Epub 2021 Jan 29.

DOI:
10.1016/j.bpsc.2021.01.007
PMID:33524599
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7613444/
Abstract

BACKGROUND

Altered functional brain connectivity has been proposed as an intermediate phenotype between genetic risk loci and clinical expression of schizophrenia. Genetic high-risk groups of healthy subjects are particularly suited for the investigation of this proposition because they can be tested in the absence of medication or other secondary effects of schizophrenia.

METHODS

Here, we applied dynamic functional connectivity analysis to functional magnetic resonance imaging data to reveal the reconfiguration of brain networks during a cognitive task. We recruited healthy carriers of common risk variants using the recall-by-genotype design. We assessed 197 individuals: 99 individuals (52 female, 47 male) with low polygenic risk scores (schizophrenia risk profile scores [SCZ-PRSs]) and 98 individuals (52 female, 46 male) with high SCZ-PRSs from both tails of the SCZ-PRS distribution from a genotyped population cohort, the Avon Longitudinal Study of Parents and Children (N = 8169). We compared groups both on conventional brain activation profiles, using the general linear model of the experiment, and on the neural flexibility index, which quantifies how frequent a brain region's community affiliation changes over experimental time.

RESULTS

Behavioral performance and standard brain activation profiles did not differ significantly between groups. High SCZ-PRS was associated with reduced flexibility index and network modularity across n-back levels. The whole-brain flexibility index and that of the frontoparietal working memory network was associated with n-back performance. We identified a dynamic network phenotype related to high SCZ-PRS.

CONCLUSIONS

Such neurophysiological markers can become important for the elucidation of biological mechanisms of schizophrenia and, particularly, the associated cognitive deficit.

摘要

背景

功能连接的改变被认为是精神分裂症遗传风险位点与临床表型之间的中间表型。健康的遗传高风险人群特别适合于研究这一假说,因为他们可以在没有药物或其他精神分裂症继发效应的情况下进行测试。

方法

我们采用动态功能连接分析方法对功能磁共振成像数据进行分析,以揭示认知任务期间大脑网络的重新配置。我们采用基于基因型的回忆设计招募了常见风险变异的健康携带者。我们评估了 197 名个体:99 名个体(52 名女性,47 名男性)低多基因风险评分(精神分裂症风险评分[SCZ-PRS])和 98 名个体(52 名女性,46 名男性)来自基因型人群队列,即阿冯纵向研究父母和儿童(N=8169)的 SCZ-PRS 分布的两个尾端的高 SCZ-PRS。我们使用实验的一般线性模型比较了两个组的常规大脑激活图谱,以及量化大脑区域在实验过程中改变其社区隶属关系的频率的神经灵活性指数。

结果

行为表现和标准大脑激活图谱在组间没有显著差异。高 SCZ-PRS 与 n-back 水平的灵活性指数和网络模块性降低相关。全脑灵活性指数和额顶叶工作记忆网络的灵活性指数与 n-back 表现相关。我们确定了与高 SCZ-PRS 相关的动态网络表型。

结论

这些神经生理标志物可以成为精神分裂症生物学机制,特别是相关认知缺陷的阐明的重要工具。