Spatially-enhanced clusterwise inference for testing and localizing intermodal correspondence
With the increasing availability of neuroimaging data from multiple modalities—each providing a different lens through which to study brain structure or function—new techniques for comparing, integrating, and interpreting information within and across modalities have emerged. Recent developments inc...
| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-12-01
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| Series: | NeuroImage |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1053811922008333 |
| _version_ | 1828268374976626688 |
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| author | Sarah M. Weinstein Simon N. Vandekar Erica B. Baller Danni Tu Azeez Adebimpe Tinashe M. Tapera Ruben C. Gur Raquel E. Gur John A. Detre Armin Raznahan Aaron F. Alexander-Bloch Theodore D. Satterthwaite Russell T. Shinohara Jun Young Park |
| author_facet | Sarah M. Weinstein Simon N. Vandekar Erica B. Baller Danni Tu Azeez Adebimpe Tinashe M. Tapera Ruben C. Gur Raquel E. Gur John A. Detre Armin Raznahan Aaron F. Alexander-Bloch Theodore D. Satterthwaite Russell T. Shinohara Jun Young Park |
| author_sort | Sarah M. Weinstein |
| collection | DOAJ |
| description | With the increasing availability of neuroimaging data from multiple modalities—each providing a different lens through which to study brain structure or function—new techniques for comparing, integrating, and interpreting information within and across modalities have emerged. Recent developments include hypothesis tests of associations between neuroimaging modalities, which can be used to determine the statistical significance of intermodal associations either throughout the entire brain or within anatomical subregions or functional networks. While these methods provide a crucial foundation for inference on intermodal relationships, they cannot be used to answer questions about where in the brain these associations are most pronounced. In this paper, we introduce a new method, called CLEAN-R, that can be used both to test intermodal correspondence throughout the brain and also to localize this correspondence. Our method involves first adjusting for the underlying spatial autocorrelation structure within each modality before aggregating information within small clusters to construct a map of enhanced test statistics. Using structural and functional magnetic resonance imaging data from a subsample of children and adolescents from the Philadelphia Neurodevelopmental Cohort, we conduct simulations and data analyses where we illustrate the high statistical power and nominal type I error levels of our method. By constructing an interpretable map of group-level correspondence using spatially-enhanced test statistics, our method offers insights beyond those provided by earlier methods. |
| first_indexed | 2024-04-13T05:17:01Z |
| format | Article |
| id | doaj.art-e650d1c441124d158c7c0d906852c2c5 |
| institution | Directory Open Access Journal |
| issn | 1095-9572 |
| language | English |
| last_indexed | 2024-04-13T05:17:01Z |
| publishDate | 2022-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage |
| spelling | doaj.art-e650d1c441124d158c7c0d906852c2c52022-12-22T03:00:52ZengElsevierNeuroImage1095-95722022-12-01264119712Spatially-enhanced clusterwise inference for testing and localizing intermodal correspondenceSarah M. Weinstein0Simon N. Vandekar1Erica B. Baller2Danni Tu3Azeez Adebimpe4Tinashe M. Tapera5Ruben C. Gur6Raquel E. Gur7John A. Detre8Armin Raznahan9Aaron F. Alexander-Bloch10Theodore D. Satterthwaite11Russell T. Shinohara12Jun Young Park13Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104, USADepartment of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, 37232, USADepartment of Psychiatry, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104, USAPenn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104, USADepartment of Psychiatry, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104, USA; Strategy Innovation & Deployment Section, Johnson and Johnson, Raritan, NJ, 08869, USADepartment of Psychiatry, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104, USADepartment of Psychiatry, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104, USADepartment of Psychiatry, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104, USADepartment of Neurology, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104, USASection on Developmental Neurogenomics, National Institute of Mental Health Intramural Research Program, Bethesda, MD 20892, USADepartment of Psychiatry, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104, USA; Department of Child and Adolescent Psychiatry and Behavioral Science, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USADepartment of Psychiatry, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104, USAPenn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104, USACorresponding author.; Department of Statistical Sciences and Department of Psychology, University of Toronto, Toronto, ON, M5G 1Z5, CanadaWith the increasing availability of neuroimaging data from multiple modalities—each providing a different lens through which to study brain structure or function—new techniques for comparing, integrating, and interpreting information within and across modalities have emerged. Recent developments include hypothesis tests of associations between neuroimaging modalities, which can be used to determine the statistical significance of intermodal associations either throughout the entire brain or within anatomical subregions or functional networks. While these methods provide a crucial foundation for inference on intermodal relationships, they cannot be used to answer questions about where in the brain these associations are most pronounced. In this paper, we introduce a new method, called CLEAN-R, that can be used both to test intermodal correspondence throughout the brain and also to localize this correspondence. Our method involves first adjusting for the underlying spatial autocorrelation structure within each modality before aggregating information within small clusters to construct a map of enhanced test statistics. Using structural and functional magnetic resonance imaging data from a subsample of children and adolescents from the Philadelphia Neurodevelopmental Cohort, we conduct simulations and data analyses where we illustrate the high statistical power and nominal type I error levels of our method. By constructing an interpretable map of group-level correspondence using spatially-enhanced test statistics, our method offers insights beyond those provided by earlier methods.http://www.sciencedirect.com/science/article/pii/S1053811922008333Clusterwise inferenceData integrationIntermodal correspondencePermutationSpatial autocorrelation |
| spellingShingle | Sarah M. Weinstein Simon N. Vandekar Erica B. Baller Danni Tu Azeez Adebimpe Tinashe M. Tapera Ruben C. Gur Raquel E. Gur John A. Detre Armin Raznahan Aaron F. Alexander-Bloch Theodore D. Satterthwaite Russell T. Shinohara Jun Young Park Spatially-enhanced clusterwise inference for testing and localizing intermodal correspondence NeuroImage Clusterwise inference Data integration Intermodal correspondence Permutation Spatial autocorrelation |
| title | Spatially-enhanced clusterwise inference for testing and localizing intermodal correspondence |
| title_full | Spatially-enhanced clusterwise inference for testing and localizing intermodal correspondence |
| title_fullStr | Spatially-enhanced clusterwise inference for testing and localizing intermodal correspondence |
| title_full_unstemmed | Spatially-enhanced clusterwise inference for testing and localizing intermodal correspondence |
| title_short | Spatially-enhanced clusterwise inference for testing and localizing intermodal correspondence |
| title_sort | spatially enhanced clusterwise inference for testing and localizing intermodal correspondence |
| topic | Clusterwise inference Data integration Intermodal correspondence Permutation Spatial autocorrelation |
| url | http://www.sciencedirect.com/science/article/pii/S1053811922008333 |
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