Marvin Petersen, Mirthe Coenen, Charles DeCarli, Alberto De Luca, Ewoud van der Lelij, Frederik Barkhof, Thomas Benke, Christopher P L H Chen, Peter Dal-Bianco, Anna Dewenter, Marco Duering, Christian Enzinger, Michael Ewers, Lieza G Exalto, Evan M Fletcher, Nicolai Franzmeier, Saima Hilal, Edith Hofer, Huiberdina L Koek, Andrea B Maier, Pauline M Maillard, Cheryl R McCreary, Janne M Papma, Yolande A L Pijnenburg, Reinhold Schmidt, Eric E Smith, Rebecca M E Steketee, Esther van den Berg, Wiesje M van der Flier, Vikram Venkatraghavan, Narayanaswamy Venketasubramanian, Meike W Vernooij, Frank J Wolters, Xin Xu, Andreas Horn, Kaustubh R Patil, Simon B Eickhoff, Götz Thomalla, J Matthijs Biesbroek, Geert Jan Biessels, Bastian Cheng
{"title":"通过白质超强度连通性评估加强认知能力预测","authors":"Marvin Petersen, Mirthe Coenen, Charles DeCarli, Alberto De Luca, Ewoud van der Lelij, Frederik Barkhof, Thomas Benke, Christopher P L H Chen, Peter Dal-Bianco, Anna Dewenter, Marco Duering, Christian Enzinger, Michael Ewers, Lieza G Exalto, Evan M Fletcher, Nicolai Franzmeier, Saima Hilal, Edith Hofer, Huiberdina L Koek, Andrea B Maier, Pauline M Maillard, Cheryl R McCreary, Janne M Papma, Yolande A L Pijnenburg, Reinhold Schmidt, Eric E Smith, Rebecca M E Steketee, Esther van den Berg, Wiesje M van der Flier, Vikram Venkatraghavan, Narayanaswamy Venketasubramanian, Meike W Vernooij, Frank J Wolters, Xin Xu, Andreas Horn, Kaustubh R Patil, Simon B Eickhoff, Götz Thomalla, J Matthijs Biesbroek, Geert Jan Biessels, Bastian Cheng","doi":"10.1093/brain/awae315","DOIUrl":null,"url":null,"abstract":"White matter hyperintensities of presumed vascular origin (WMH) are associated with cognitive impairment and are a key imaging marker in evaluating brain health. However, WMH volume alone does not fully account for the extent of cognitive deficits and the mechanisms linking WMH to these deficits remain unclear. Lesion network mapping (LNM) enables to infer if brain networks are connected to lesions and could be a promising technique for enhancing our understanding of the role of WMH in cognitive disorders. Our study employed LNM to test the following hypotheses: (1) LNM-informed markers surpass WMH volumes in predicting cognitive performance, and (2) WMH contributing to cognitive impairment map to specific brain networks. We analyzed cross-sectional data of 3,485 patients from 10 memory clinic cohorts within the Meta VCI Map Consortium, using harmonized test results in 4 cognitive domains and WMH segmentations. WMH segmentations were registered to a standard space and mapped onto existing normative structural and functional brain connectome data. We employed LNM to quantify WMH connectivity to 480 atlas-based gray and white matter regions of interest (ROI), resulting in ROI-level structural and functional LNM scores. We compared the capacity of total and regional WMH volumes and LNM scores in predicting cognitive function using ridge regression models in a nested cross-validation. LNM scores predicted performance in three cognitive domains (attention/executive function, information processing speed, and verbal memory) significantly better than WMH volumes. LNM scores did not improve prediction for language functions. ROI-level analysis revealed that higher LNM scores, representing greater connectivity to WMH, in gray and white matter regions of the dorsal and ventral attention networks were associated with lower cognitive performance. Measures of WMH-related brain network connectivity significantly improve the prediction of current cognitive performance in memory clinic patients compared to WMH volume as a traditional imaging marker of cerebrovascular disease. This highlights the crucial role of network integrity, particularly in attention-related brain regions, improving our understanding of vascular contributions to cognitive impairment. Moving forward, refining WMH information with connectivity data could contribute to patient-tailored therapeutic interventions and facilitate the identification of subgroups at risk of cognitive disorders.","PeriodicalId":9063,"journal":{"name":"Brain","volume":null,"pages":null},"PeriodicalIF":10.6000,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Enhancing cognitive performance prediction by white matter hyperintensity connectivity assessment\",\"authors\":\"Marvin Petersen, Mirthe Coenen, Charles DeCarli, Alberto De Luca, Ewoud van der Lelij, Frederik Barkhof, Thomas Benke, Christopher P L H Chen, Peter Dal-Bianco, Anna Dewenter, Marco Duering, Christian Enzinger, Michael Ewers, Lieza G Exalto, Evan M Fletcher, Nicolai Franzmeier, Saima Hilal, Edith Hofer, Huiberdina L Koek, Andrea B Maier, Pauline M Maillard, Cheryl R McCreary, Janne M Papma, Yolande A L Pijnenburg, Reinhold Schmidt, Eric E Smith, Rebecca M E Steketee, Esther van den Berg, Wiesje M van der Flier, Vikram Venkatraghavan, Narayanaswamy Venketasubramanian, Meike W Vernooij, Frank J Wolters, Xin Xu, Andreas Horn, Kaustubh R Patil, Simon B Eickhoff, Götz Thomalla, J Matthijs Biesbroek, Geert Jan Biessels, Bastian Cheng\",\"doi\":\"10.1093/brain/awae315\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"White matter hyperintensities of presumed vascular origin (WMH) are associated with cognitive impairment and are a key imaging marker in evaluating brain health. However, WMH volume alone does not fully account for the extent of cognitive deficits and the mechanisms linking WMH to these deficits remain unclear. Lesion network mapping (LNM) enables to infer if brain networks are connected to lesions and could be a promising technique for enhancing our understanding of the role of WMH in cognitive disorders. Our study employed LNM to test the following hypotheses: (1) LNM-informed markers surpass WMH volumes in predicting cognitive performance, and (2) WMH contributing to cognitive impairment map to specific brain networks. We analyzed cross-sectional data of 3,485 patients from 10 memory clinic cohorts within the Meta VCI Map Consortium, using harmonized test results in 4 cognitive domains and WMH segmentations. WMH segmentations were registered to a standard space and mapped onto existing normative structural and functional brain connectome data. We employed LNM to quantify WMH connectivity to 480 atlas-based gray and white matter regions of interest (ROI), resulting in ROI-level structural and functional LNM scores. We compared the capacity of total and regional WMH volumes and LNM scores in predicting cognitive function using ridge regression models in a nested cross-validation. LNM scores predicted performance in three cognitive domains (attention/executive function, information processing speed, and verbal memory) significantly better than WMH volumes. LNM scores did not improve prediction for language functions. ROI-level analysis revealed that higher LNM scores, representing greater connectivity to WMH, in gray and white matter regions of the dorsal and ventral attention networks were associated with lower cognitive performance. Measures of WMH-related brain network connectivity significantly improve the prediction of current cognitive performance in memory clinic patients compared to WMH volume as a traditional imaging marker of cerebrovascular disease. This highlights the crucial role of network integrity, particularly in attention-related brain regions, improving our understanding of vascular contributions to cognitive impairment. Moving forward, refining WMH information with connectivity data could contribute to patient-tailored therapeutic interventions and facilitate the identification of subgroups at risk of cognitive disorders.\",\"PeriodicalId\":9063,\"journal\":{\"name\":\"Brain\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":10.6000,\"publicationDate\":\"2024-10-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Brain\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1093/brain/awae315\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CLINICAL NEUROLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Brain","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1093/brain/awae315","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}
Enhancing cognitive performance prediction by white matter hyperintensity connectivity assessment
White matter hyperintensities of presumed vascular origin (WMH) are associated with cognitive impairment and are a key imaging marker in evaluating brain health. However, WMH volume alone does not fully account for the extent of cognitive deficits and the mechanisms linking WMH to these deficits remain unclear. Lesion network mapping (LNM) enables to infer if brain networks are connected to lesions and could be a promising technique for enhancing our understanding of the role of WMH in cognitive disorders. Our study employed LNM to test the following hypotheses: (1) LNM-informed markers surpass WMH volumes in predicting cognitive performance, and (2) WMH contributing to cognitive impairment map to specific brain networks. We analyzed cross-sectional data of 3,485 patients from 10 memory clinic cohorts within the Meta VCI Map Consortium, using harmonized test results in 4 cognitive domains and WMH segmentations. WMH segmentations were registered to a standard space and mapped onto existing normative structural and functional brain connectome data. We employed LNM to quantify WMH connectivity to 480 atlas-based gray and white matter regions of interest (ROI), resulting in ROI-level structural and functional LNM scores. We compared the capacity of total and regional WMH volumes and LNM scores in predicting cognitive function using ridge regression models in a nested cross-validation. LNM scores predicted performance in three cognitive domains (attention/executive function, information processing speed, and verbal memory) significantly better than WMH volumes. LNM scores did not improve prediction for language functions. ROI-level analysis revealed that higher LNM scores, representing greater connectivity to WMH, in gray and white matter regions of the dorsal and ventral attention networks were associated with lower cognitive performance. Measures of WMH-related brain network connectivity significantly improve the prediction of current cognitive performance in memory clinic patients compared to WMH volume as a traditional imaging marker of cerebrovascular disease. This highlights the crucial role of network integrity, particularly in attention-related brain regions, improving our understanding of vascular contributions to cognitive impairment. Moving forward, refining WMH information with connectivity data could contribute to patient-tailored therapeutic interventions and facilitate the identification of subgroups at risk of cognitive disorders.
期刊介绍:
Brain, a journal focused on clinical neurology and translational neuroscience, has been publishing landmark papers since 1878. The journal aims to expand its scope by including studies that shed light on disease mechanisms and conducting innovative clinical trials for brain disorders. With a wide range of topics covered, the Editorial Board represents the international readership and diverse coverage of the journal. Accepted articles are promptly posted online, typically within a few weeks of acceptance. As of 2022, Brain holds an impressive impact factor of 14.5, according to the Journal Citation Reports.