Abstract
Functional connectivity between two voxels or regions of voxels can be measured by the correlation between voxel measurements from either PET CBF or BOLD fMRI images in 3D. We propose to look at the entire 6D matrix of correlations between all voxels and search for 6D local maxima. The main result is a new theoretical formula based on random field theory for the p‐value of these local maxima, which distinguishes true correlations from background noise. This can be applied to crosscorrelations between two different sets of images—such as activations under two different tasks, as well as autocorrelations within the same set of images. Hum. Brain Mapping 6:364–367, 1998. © 1998 Wiley‐Liss, Inc.
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References
- Adler RJ (1981): The geometry of random fields. Wiley, New York: [Google Scholar]
- Bullmore ET, Rabe‐Hesketh S, Morris RG, Williams SCR, Gregory L, Gray JA, Brammer MJ (1996): Functional magnetic resonance image analysis of a large‐scale neurocognitive network. Neuro Image 4: 16–33. [DOI] [PubMed] [Google Scholar]
- Cao J, Worsley KJ (1998): The geometry of correlation fields, with an application to functional connectivity of the brain. Annals of Applied Probability submitted.
- Friston KJ, Frith CD, Liddle PF, Frackowiak RSJ (1993): Functional connectivity: The principal‐component analysis of large (PET) data sets. J Cereb Blood Flow Metab 13: 5–14. [DOI] [PubMed] [Google Scholar]
- Friston KJ, Frith CD, Frackowiak RSJ, Turner R (1995): Characterizing dynamic brain responses with fMRI: A multivariate approach. NeuroImage 2: 166–172. [DOI] [PubMed] [Google Scholar]
- Friston KJ, Büchel C, Fink GR, Morris J, Rolls E, Dolan RJ (1997): Psychophysiological and modulatory interactions in neuroimaging. NeuroImage 6: 218–229. [DOI] [PubMed] [Google Scholar]
- Horowitz B, Grady CL, Mentis MJ, Pietrini P, Ungerleider LG, Rapoport SI, Haxby JV (1996): Brain functional connectivity changes as task difficulty is altered. NeuroImage 3: S248. [DOI] [PubMed] [Google Scholar]
- McIntosh AR, Gonzalez‐Lima F (1994): Structural equation modeling and its application to network analysis of functional brain imaging. Hum Brain Mapping 2: 2–22. [Google Scholar]
- McIntosh AR, Bookstein FL, Haxby JV, Grady CL (1996): Spatial pattern analysis of functional brain images using Partial Least Squares. NeuroImage 3: 143–157. [DOI] [PubMed] [Google Scholar]
- Paus T, Zatorre RJ, Hofle N, Zografos C, Gotman J, Petrides M, Evans AC (1997): Time‐related changes in neural systems underlying attention and arousal during the performance of an auditory vigilance task. J Cogn Neurosci 9: 392–408. [DOI] [PubMed] [Google Scholar]
- Strother SC, Anderson JR, Schaper KA, Sidtis JJ, Liow JS, Woods RP, Rottenberg DA (1995): Principal component analysis and the scaled subprofile model compared to intersubject averaging and statistical parametric mapping: I. “Functional connectivity” of the human motor system studied with O15 water PET. J Cereb Blood Flow Metab 15: 738–753. [DOI] [PubMed] [Google Scholar]
- Worsley KJ, Marrett S, Neelin P, Vandal AC, Friston KJ, Evans AC (1996): A unified statistical approach for determining significant signals in images of cerebral activation. Hum Brain Mapping 4: 58–73. [DOI] [PubMed] [Google Scholar]
- Worsley KJ, Poline J‐B, Friston KJ, Evans AC (1997): Characterizing the response of fMRI data using multivariate linear models. Neuroimage 6: 305–319. [DOI] [PubMed] [Google Scholar]