Neuroimaging meta regression for coordinate based meta analysis data with a spatial model
Coordinate-based meta-analysis combines evidence from a collection of neuroimaging studies to estimate brain activation. In such analyses, a key practical challenge is to find a computationally efficient approach with good statistical interpretability to model the locations of activation foci. In th...
| Main Authors: | , , , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
Oxford University Press
2024
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| _version_ | 1811140443028586496 |
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| author | Yu, Y Pintos Lobo, R Riedel, MC Bottenhorn, K Laird, AR Nichols, T |
| author_facet | Yu, Y Pintos Lobo, R Riedel, MC Bottenhorn, K Laird, AR Nichols, T |
| author_sort | Yu, Y |
| collection | OXFORD |
| description | Coordinate-based meta-analysis combines evidence from a collection of neuroimaging studies to estimate brain activation. In such analyses, a key practical challenge is to find a computationally efficient approach with good statistical interpretability to model the locations of activation foci. In this article, we propose a generative coordinate-based meta-regression (CBMR) framework to approximate a smooth activation intensity function and investigate the effect of study-level covariates (e.g. year of publication, sample size). We employ a spline parameterization to model the spatial structure of brain activation and consider four stochastic models for modeling the random variation in foci. To examine the validity of CBMR, we estimate brain activation on 20 meta-analytic datasets, conduct spatial homogeneity tests at the voxel level, and compare the results to those generated by existing kernel-based and model-based approaches. |
| first_indexed | 2024-09-25T04:22:03Z |
| format | Journal article |
| id | oxford-uuid:658fced4-316c-44ba-b0a2-b8248e1a50b2 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-09-25T04:22:03Z |
| publishDate | 2024 |
| publisher | Oxford University Press |
| record_format | dspace |
| spelling | oxford-uuid:658fced4-316c-44ba-b0a2-b8248e1a50b22024-08-12T14:54:00ZNeuroimaging meta regression for coordinate based meta analysis data with a spatial modelJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:658fced4-316c-44ba-b0a2-b8248e1a50b2EnglishSymplectic ElementsOxford University Press2024Yu, YPintos Lobo, RRiedel, MCBottenhorn, KLaird, ARNichols, TCoordinate-based meta-analysis combines evidence from a collection of neuroimaging studies to estimate brain activation. In such analyses, a key practical challenge is to find a computationally efficient approach with good statistical interpretability to model the locations of activation foci. In this article, we propose a generative coordinate-based meta-regression (CBMR) framework to approximate a smooth activation intensity function and investigate the effect of study-level covariates (e.g. year of publication, sample size). We employ a spline parameterization to model the spatial structure of brain activation and consider four stochastic models for modeling the random variation in foci. To examine the validity of CBMR, we estimate brain activation on 20 meta-analytic datasets, conduct spatial homogeneity tests at the voxel level, and compare the results to those generated by existing kernel-based and model-based approaches. |
| spellingShingle | Yu, Y Pintos Lobo, R Riedel, MC Bottenhorn, K Laird, AR Nichols, T Neuroimaging meta regression for coordinate based meta analysis data with a spatial model |
| title | Neuroimaging meta regression for coordinate based meta analysis data with a spatial model |
| title_full | Neuroimaging meta regression for coordinate based meta analysis data with a spatial model |
| title_fullStr | Neuroimaging meta regression for coordinate based meta analysis data with a spatial model |
| title_full_unstemmed | Neuroimaging meta regression for coordinate based meta analysis data with a spatial model |
| title_short | Neuroimaging meta regression for coordinate based meta analysis data with a spatial model |
| title_sort | neuroimaging meta regression for coordinate based meta analysis data with a spatial model |
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