Reciprocal Benefits of Mass-Univariate and Multivariate Modeling in Brain Mapping: Applications to Event-Related Functional MRI, H215O-, and FDG-PET
In brain mapping studies of sensory, cognitive, and motor operations, specific waveforms of dynamic neural activity are predicted based on theoretical models of human information processing. For example in event-related functional MRI (fMRI), the general linear model (GLM) is employed in mass-univar...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2006-01-01
|
| Series: | International Journal of Biomedical Imaging |
| Online Access: | http://dx.doi.org/10.1155/IJBI/2006/79862 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850157240442421248 |
|---|---|
| author | James R. Moeller Christian G. Habeck |
| author_facet | James R. Moeller Christian G. Habeck |
| author_sort | James R. Moeller |
| collection | DOAJ |
| description | In brain mapping studies of sensory, cognitive, and motor operations, specific waveforms of dynamic neural activity are predicted based on theoretical models of human information processing. For example in event-related functional MRI (fMRI), the general linear model (GLM) is employed in mass-univariate analyses to identify the regions whose dynamic activity closely matches the expected waveforms. By comparison multivariate analyses based on PCA or ICA provide greater flexibility in detecting spatiotemporal properties of experimental data that may strongly support alternative neuroscientific explanations. We investigated conjoint multivariate and mass-univariate analyses that combine the capabilities to (1) verify activation of neural machinery we already understand and (2) discover reliable signatures of new neural machinery. We examined combinations of GLM and PCA that recover latent neural signals (waveforms and footprints) with greater accuracy than either method alone. Comparative results are illustrated with analyses of real fMRI data, adding to Monte Carlo simulation support. |
| format | Article |
| id | doaj-art-2c680a9be7894090b1da477c394eefb5 |
| institution | OA Journals |
| issn | 1687-4188 1687-4196 |
| language | English |
| publishDate | 2006-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Biomedical Imaging |
| spelling | doaj-art-2c680a9be7894090b1da477c394eefb52025-08-20T02:24:14ZengWileyInternational Journal of Biomedical Imaging1687-41881687-41962006-01-01200610.1155/IJBI/2006/7986279862Reciprocal Benefits of Mass-Univariate and Multivariate Modeling in Brain Mapping: Applications to Event-Related Functional MRI, H215O-, and FDG-PETJames R. Moeller0Christian G. Habeck1New York State Psychiatric Institute, College of Physicians and Surgeons, Columbia University, New York, NY 10032, USACognitive Neuroscience Division, Taub Institute, College of Physicians and Surgeons, Columbia University, New York, NY 10032, USAIn brain mapping studies of sensory, cognitive, and motor operations, specific waveforms of dynamic neural activity are predicted based on theoretical models of human information processing. For example in event-related functional MRI (fMRI), the general linear model (GLM) is employed in mass-univariate analyses to identify the regions whose dynamic activity closely matches the expected waveforms. By comparison multivariate analyses based on PCA or ICA provide greater flexibility in detecting spatiotemporal properties of experimental data that may strongly support alternative neuroscientific explanations. We investigated conjoint multivariate and mass-univariate analyses that combine the capabilities to (1) verify activation of neural machinery we already understand and (2) discover reliable signatures of new neural machinery. We examined combinations of GLM and PCA that recover latent neural signals (waveforms and footprints) with greater accuracy than either method alone. Comparative results are illustrated with analyses of real fMRI data, adding to Monte Carlo simulation support.http://dx.doi.org/10.1155/IJBI/2006/79862 |
| spellingShingle | James R. Moeller Christian G. Habeck Reciprocal Benefits of Mass-Univariate and Multivariate Modeling in Brain Mapping: Applications to Event-Related Functional MRI, H215O-, and FDG-PET International Journal of Biomedical Imaging |
| title | Reciprocal Benefits of Mass-Univariate and Multivariate Modeling in Brain Mapping: Applications to Event-Related Functional MRI, H215O-, and FDG-PET |
| title_full | Reciprocal Benefits of Mass-Univariate and Multivariate Modeling in Brain Mapping: Applications to Event-Related Functional MRI, H215O-, and FDG-PET |
| title_fullStr | Reciprocal Benefits of Mass-Univariate and Multivariate Modeling in Brain Mapping: Applications to Event-Related Functional MRI, H215O-, and FDG-PET |
| title_full_unstemmed | Reciprocal Benefits of Mass-Univariate and Multivariate Modeling in Brain Mapping: Applications to Event-Related Functional MRI, H215O-, and FDG-PET |
| title_short | Reciprocal Benefits of Mass-Univariate and Multivariate Modeling in Brain Mapping: Applications to Event-Related Functional MRI, H215O-, and FDG-PET |
| title_sort | reciprocal benefits of mass univariate and multivariate modeling in brain mapping applications to event related functional mri h215o and fdg pet |
| url | http://dx.doi.org/10.1155/IJBI/2006/79862 |
| work_keys_str_mv | AT jamesrmoeller reciprocalbenefitsofmassunivariateandmultivariatemodelinginbrainmappingapplicationstoeventrelatedfunctionalmrih215oandfdgpet AT christianghabeck reciprocalbenefitsofmassunivariateandmultivariatemodelinginbrainmappingapplicationstoeventrelatedfunctionalmrih215oandfdgpet |