Spatial Bayesian GLM on the cortical surface produces reliable task activations in individuals and groups
The general linear model (GLM) is a widely popular and convenient tool for estimating the functional brain response and identifying areas of significant activation during a task or stimulus. However, the classical GLM is based on a massive univariate approach that does not explicitly leverage the si...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-04-01
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| Series: | NeuroImage |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1053811922000386 |
| _version_ | 1818293547839782912 |
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| author | Daniel Spencer Yu Ryan Yue David Bolin Sarah Ryan Amanda F. Mejia |
| author_facet | Daniel Spencer Yu Ryan Yue David Bolin Sarah Ryan Amanda F. Mejia |
| author_sort | Daniel Spencer |
| collection | DOAJ |
| description | The general linear model (GLM) is a widely popular and convenient tool for estimating the functional brain response and identifying areas of significant activation during a task or stimulus. However, the classical GLM is based on a massive univariate approach that does not explicitly leverage the similarity of activation patterns among neighboring brain locations. As a result, it tends to produce noisy estimates and be underpowered to detect significant activations, particularly in individual subjects and small groups. A recently proposed alternative, a cortical surface-based spatial Bayesian GLM, leverages spatial dependencies among neighboring cortical vertices to produce more accurate estimates and areas of functional activation. The spatial Bayesian GLM can be applied to individual and group-level analysis. In this study, we assess the reliability and power of individual and group-average measures of task activation produced via the surface-based spatial Bayesian GLM. We analyze motor task data from 45 subjects in the Human Connectome Project (HCP) and HCP Retest datasets. We also extend the model to multi-run analysis and employ subject-specific cortical surfaces rather than surfaces inflated to a sphere for more accurate distance-based modeling. Results show that the surface-based spatial Bayesian GLM produces highly reliable activations in individual subjects and is powerful enough to detect trait-like functional topologies. Additionally, spatial Bayesian modeling enhances reliability of group-level analysis even in moderately sized samples (n=45). Notably, the power of the spatial Bayesian GLM to detect activations above a scientifically meaningful effect size is nearly invariant to sample size, exhibiting high power even in small samples (n=10). The spatial Bayesian GLM is computationally efficient in individuals and groups and is convenient to implement with the open-source BayesfMRI R package. |
| first_indexed | 2024-12-13T03:17:36Z |
| format | Article |
| id | doaj.art-f06ecade14814e3ea63971eebc172f24 |
| institution | Directory Open Access Journal |
| issn | 1095-9572 |
| language | English |
| last_indexed | 2024-12-13T03:17:36Z |
| publishDate | 2022-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage |
| spelling | doaj.art-f06ecade14814e3ea63971eebc172f242022-12-22T00:01:25ZengElsevierNeuroImage1095-95722022-04-01249118908Spatial Bayesian GLM on the cortical surface produces reliable task activations in individuals and groupsDaniel Spencer0Yu Ryan Yue1David Bolin2Sarah Ryan3Amanda F. Mejia4Department of Statistics, Indiana University, Myles Brand Hall E104 901 E. 10th Street Bloomington, IN, 47408, USA; Corresponding author.Paul H. Chook Department of Information Systems and Statistics, Baruch College, The City University of New York, New York, NY, 10010, USACEMSE Division, King Abdullah University of Science and Technology, Thuwal, Makkah Province, 23955-6900, Saudi ArabiaDepartment of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USADepartment of Statistics, Indiana University, Myles Brand Hall E104 901 E. 10th Street Bloomington, IN, 47408, USAThe general linear model (GLM) is a widely popular and convenient tool for estimating the functional brain response and identifying areas of significant activation during a task or stimulus. However, the classical GLM is based on a massive univariate approach that does not explicitly leverage the similarity of activation patterns among neighboring brain locations. As a result, it tends to produce noisy estimates and be underpowered to detect significant activations, particularly in individual subjects and small groups. A recently proposed alternative, a cortical surface-based spatial Bayesian GLM, leverages spatial dependencies among neighboring cortical vertices to produce more accurate estimates and areas of functional activation. The spatial Bayesian GLM can be applied to individual and group-level analysis. In this study, we assess the reliability and power of individual and group-average measures of task activation produced via the surface-based spatial Bayesian GLM. We analyze motor task data from 45 subjects in the Human Connectome Project (HCP) and HCP Retest datasets. We also extend the model to multi-run analysis and employ subject-specific cortical surfaces rather than surfaces inflated to a sphere for more accurate distance-based modeling. Results show that the surface-based spatial Bayesian GLM produces highly reliable activations in individual subjects and is powerful enough to detect trait-like functional topologies. Additionally, spatial Bayesian modeling enhances reliability of group-level analysis even in moderately sized samples (n=45). Notably, the power of the spatial Bayesian GLM to detect activations above a scientifically meaningful effect size is nearly invariant to sample size, exhibiting high power even in small samples (n=10). The spatial Bayesian GLM is computationally efficient in individuals and groups and is convenient to implement with the open-source BayesfMRI R package.http://www.sciencedirect.com/science/article/pii/S1053811922000386Task fMRIBayesianGeneral linear modelCortical surface |
| spellingShingle | Daniel Spencer Yu Ryan Yue David Bolin Sarah Ryan Amanda F. Mejia Spatial Bayesian GLM on the cortical surface produces reliable task activations in individuals and groups NeuroImage Task fMRI Bayesian General linear model Cortical surface |
| title | Spatial Bayesian GLM on the cortical surface produces reliable task activations in individuals and groups |
| title_full | Spatial Bayesian GLM on the cortical surface produces reliable task activations in individuals and groups |
| title_fullStr | Spatial Bayesian GLM on the cortical surface produces reliable task activations in individuals and groups |
| title_full_unstemmed | Spatial Bayesian GLM on the cortical surface produces reliable task activations in individuals and groups |
| title_short | Spatial Bayesian GLM on the cortical surface produces reliable task activations in individuals and groups |
| title_sort | spatial bayesian glm on the cortical surface produces reliable task activations in individuals and groups |
| topic | Task fMRI Bayesian General linear model Cortical surface |
| url | http://www.sciencedirect.com/science/article/pii/S1053811922000386 |
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