{"title":"利用功能和结构连接性建立基于连接组的认知控制预测模型","authors":"","doi":"10.1016/j.bandc.2024.106221","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><p>Cognitive control involves flexibly configuring mental resources and adjusting behavior to achieve goal-directed actions. It is associated with the coordinated activity of brain networks, although it remains unclear how both structural and functional brain networks can predict cognitive control. Connectome-based predictive modeling (CPM) is a powerful tool for predicting cognitive control based on brain networks.</p></div><div><h3>Methods</h3><p>The study used CPM to predict cognitive control in 102 healthy adults from the UCLA Consortium for Neuropsychiatric Phenomics dataset and further compared structural and functional connectome characteristics that support cognitive control.</p></div><div><h3>Results</h3><p>Our results showed that both structural (<em>r</em> values 0.263–0.375) and functional (<em>r</em> values 0.336–0.503) connectomes can significantly predict individuals’ cognitive control subcomponents. There is overlap between the functional and structural networks of all three cognitive control subcomponents, particularly in the frontoparietal (FP) and motor (Mot) networks, while each subcomponent also has its own unique weight prediction network. Overall, the functional and structural connectivity that supports different cognitive control subcomponents manifests overlapping and distinct spatial patterns.</p></div><div><h3>Conclusions</h3><p>The structural and functional connectomes provide complementary information for predicting cognitive control ability. Integrating information from both connectomes offers a more comprehensive understanding of the neural underpinnings of cognitive control.</p></div>","PeriodicalId":55331,"journal":{"name":"Brain and Cognition","volume":null,"pages":null},"PeriodicalIF":2.2000,"publicationDate":"2024-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Connectome-based prediction modeling of cognitive control using functional and structural connectivity\",\"authors\":\"\",\"doi\":\"10.1016/j.bandc.2024.106221\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background</h3><p>Cognitive control involves flexibly configuring mental resources and adjusting behavior to achieve goal-directed actions. It is associated with the coordinated activity of brain networks, although it remains unclear how both structural and functional brain networks can predict cognitive control. Connectome-based predictive modeling (CPM) is a powerful tool for predicting cognitive control based on brain networks.</p></div><div><h3>Methods</h3><p>The study used CPM to predict cognitive control in 102 healthy adults from the UCLA Consortium for Neuropsychiatric Phenomics dataset and further compared structural and functional connectome characteristics that support cognitive control.</p></div><div><h3>Results</h3><p>Our results showed that both structural (<em>r</em> values 0.263–0.375) and functional (<em>r</em> values 0.336–0.503) connectomes can significantly predict individuals’ cognitive control subcomponents. There is overlap between the functional and structural networks of all three cognitive control subcomponents, particularly in the frontoparietal (FP) and motor (Mot) networks, while each subcomponent also has its own unique weight prediction network. Overall, the functional and structural connectivity that supports different cognitive control subcomponents manifests overlapping and distinct spatial patterns.</p></div><div><h3>Conclusions</h3><p>The structural and functional connectomes provide complementary information for predicting cognitive control ability. Integrating information from both connectomes offers a more comprehensive understanding of the neural underpinnings of cognitive control.</p></div>\",\"PeriodicalId\":55331,\"journal\":{\"name\":\"Brain and Cognition\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.2000,\"publicationDate\":\"2024-09-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Brain and Cognition\",\"FirstCategoryId\":\"102\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0278262624000988\",\"RegionNum\":3,\"RegionCategory\":\"心理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"NEUROSCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Brain and Cognition","FirstCategoryId":"102","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0278262624000988","RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"NEUROSCIENCES","Score":null,"Total":0}
Connectome-based prediction modeling of cognitive control using functional and structural connectivity
Background
Cognitive control involves flexibly configuring mental resources and adjusting behavior to achieve goal-directed actions. It is associated with the coordinated activity of brain networks, although it remains unclear how both structural and functional brain networks can predict cognitive control. Connectome-based predictive modeling (CPM) is a powerful tool for predicting cognitive control based on brain networks.
Methods
The study used CPM to predict cognitive control in 102 healthy adults from the UCLA Consortium for Neuropsychiatric Phenomics dataset and further compared structural and functional connectome characteristics that support cognitive control.
Results
Our results showed that both structural (r values 0.263–0.375) and functional (r values 0.336–0.503) connectomes can significantly predict individuals’ cognitive control subcomponents. There is overlap between the functional and structural networks of all three cognitive control subcomponents, particularly in the frontoparietal (FP) and motor (Mot) networks, while each subcomponent also has its own unique weight prediction network. Overall, the functional and structural connectivity that supports different cognitive control subcomponents manifests overlapping and distinct spatial patterns.
Conclusions
The structural and functional connectomes provide complementary information for predicting cognitive control ability. Integrating information from both connectomes offers a more comprehensive understanding of the neural underpinnings of cognitive control.
期刊介绍：
Brain and Cognition is a forum for the integration of the neurosciences and cognitive sciences. B&C publishes peer-reviewed research articles, theoretical papers, case histories that address important theoretical issues, and historical articles into the interaction between cognitive function and brain processes. The focus is on rigorous studies of an empirical or theoretical nature and which make an original contribution to our knowledge about the involvement of the nervous system in cognition. Coverage includes, but is not limited to memory, learning, emotion, perception, movement, music or praxis in relationship to brain structure or function. Published articles will typically address issues relating some aspect of cognitive function to its neurological substrates with clear theoretical import, formulating new hypotheses or refuting previously established hypotheses. Clinical papers are welcome if they raise issues of theoretical importance or concern and shed light on the interaction between brain function and cognitive function. We welcome review articles that clearly contribute a new perspective or integration, beyond summarizing the literature in the field; authors of review articles should make explicit where the contribution lies. We also welcome proposals for special issues on aspects of the relation between cognition and the structure and function of the nervous system. Such proposals can be made directly to the Editor-in-Chief from individuals interested in being guest editors for such collections.
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