外源性功能连接体的效率解释皮质中线和突出区计算损伤后胼胝体非运动特征的变化。

IF 2.4 3区 医学 Q3 NEUROSCIENCES
Brain connectivity Pub Date : 2023-09-01 Epub Date: 2023-07-24 DOI:10.1089/brain.2022.0074
Drew E Winters, Daniel R Leopold, Joseph T Sakai, R McKell Carter
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引用次数: 0

摘要

引言:Callous Unmotional(CU)特征是一种青年反社会表型,被认为是多个大脑系统整合差异的结果。然而,对这些大脑系统的机械洞察仍然是一个挑战。在先前的工作描述激活和连接的情况下,可以通过去除节点和量化网络特性的变化(以下称为计算损伤)来表征连接体的弹性和脆弱性,从而获得对大脑功能连接体的新的机制见解。方法:在这里,我们通过估计计算损伤个体水平连接体后效率的变化来研究CU性状中连接体整合的弹性。来自86名参与者(48%为女性,年龄14.52岁)的静息状态数据 ± 1.31)来自Nathan Kline研究所的Rockland研究,使用图形套索估计个体水平的连接体。计算损伤是按顺序进行的,并以全球和本地中心为目标。应用弹性网络回归来确定这些变化如何解释CU性状的方差。后续分析表征了建模的节点中枢,检查了适度性,确定了靶向的影响,并通过将区域与元分析图进行比较来解码大脑掩码。结果:弹性网络回归表明,23个节点的计算损伤、网络模块性和Tanner阶段解释了CU性状的方差。所选枢纽的枢纽分配在较高的CU性状上存在差异。没有发现模拟病变和CU特征之间存在调节的证据。以全球枢纽为目标提高了效率,而以本地枢纽为目标对更高的CU性状没有影响。已识别的大脑掩蔽元分析与更多的情绪和认知术语相关。尽管在参与者中发现了可靠的模式,但即使对于那些CU特征得分相似的人来说,青少年的大脑也是异质的。结论:青少年大脑对模拟损伤的反应揭示了一种连接体弹性和脆弱性模式,可以解释CU特征的变化,这有助于预测青少年具有更高CU特征的风险。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Efficiency of Heterogenous Functional Connectomes Explains Variance in Callous-Unemotional Traits After Computational Lesioning of Cortical Midline and Salience Regions.

Introduction: Callous-unemotional (CU) traits are a youth antisocial phenotype hypothesized to be a result of differences in the integration of multiple brain systems. However, mechanistic insights into these brain systems are a continued challenge. Where prior work describes activation and connectivity, new mechanistic insights into the brain's functional connectome can be derived by removing nodes and quantifying changes in network properties (hereafter referred to as computational lesioning) to characterize connectome resilience and vulnerability. Methods: Here, we study the resilience of connectome integration in CU traits by estimating changes in efficiency after computationally lesioning individual-level connectomes. From resting-state data of 86 participants (48% female, age 14.52 ± 1.31) drawn from the Nathan Kline institute's Rockland study, individual-level connectomes were estimated using graphical lasso. Computational lesioning was conducted both sequentially and by targeting global and local hubs. Elastic net regression was applied to determine how these changes explained variance in CU traits. Follow-up analyses characterized modeled node hubs, examined moderation, determined impact of targeting, and decoded the brain mask by comparing regions to meta-analytic maps. Results: Elastic net regression revealed that computational lesioning of 23 nodes, network modularity, and Tanner stage explained variance in CU traits. Hub assignment of selected hubs differed at higher CU traits. No evidence for moderation between simulated lesioning and CU traits was found. Targeting global hubs increased efficiency and targeting local hubs had no effect at higher CU traits. Identified brain mask meta-analytically associated with more emotion and cognitive terms. Although reliable patterns were found across participants, adolescent brains were heterogeneous even for those with a similar CU traits score. Conclusion: Adolescent brain response to simulated lesioning revealed a pattern of connectome resiliency and vulnerability that explains variance in CU traits, which can aid prediction of youth at greater risk for higher CU traits.

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来源期刊
Brain connectivity
Brain connectivity Neuroscience-General Neuroscience
CiteScore
4.80
自引率
0.00%
发文量
80
期刊介绍: Brain Connectivity provides groundbreaking findings in the rapidly advancing field of connectivity research at the systems and network levels. The Journal disseminates information on brain mapping, modeling, novel research techniques, new imaging modalities, preclinical animal studies, and the translation of research discoveries from the laboratory to the clinic. This essential journal fosters the application of basic biological discoveries and contributes to the development of novel diagnostic and therapeutic interventions to recognize and treat a broad range of neurodegenerative and psychiatric disorders such as: Alzheimer’s disease, attention-deficit hyperactivity disorder, posttraumatic stress disorder, epilepsy, traumatic brain injury, stroke, dementia, and depression.
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