Drew E Winters, Daniel R Leopold, Joseph T Sakai, R McKell Carter
{"title":"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。","authors":"Drew E Winters, Daniel R Leopold, Joseph T Sakai, R McKell Carter","doi":"10.1089/brain.2022.0074","DOIUrl":null,"url":null,"abstract":"<p><p><b><i>Introduction:</i></b> 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. <b><i>Methods:</i></b> 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. <b><i>Results:</i></b> 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. <b><i>Conclusion:</i></b> 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.</p>","PeriodicalId":9155,"journal":{"name":"Brain connectivity","volume":"13 7","pages":"410-426"},"PeriodicalIF":2.4000,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10517336/pdf/","citationCount":"0","resultStr":"{\"title\":\"Efficiency of Heterogenous Functional Connectomes Explains Variance in Callous-Unemotional Traits After Computational Lesioning of Cortical Midline and Salience Regions.\",\"authors\":\"Drew E Winters, Daniel R Leopold, Joseph T Sakai, R McKell Carter\",\"doi\":\"10.1089/brain.2022.0074\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p><b><i>Introduction:</i></b> 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. <b><i>Methods:</i></b> 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. <b><i>Results:</i></b> 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. <b><i>Conclusion:</i></b> 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.</p>\",\"PeriodicalId\":9155,\"journal\":{\"name\":\"Brain connectivity\",\"volume\":\"13 7\",\"pages\":\"410-426\"},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2023-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10517336/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Brain connectivity\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1089/brain.2022.0074\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2023/7/24 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q3\",\"JCRName\":\"NEUROSCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Brain connectivity","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1089/brain.2022.0074","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/7/24 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"NEUROSCIENCES","Score":null,"Total":0}
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.
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.