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Measuring brain connectivity: Diffusion tensor imaging validates resting state temporal correlations
Open accessAbstract
Diffusion tensor imaging (DTI) and resting state temporal correlations (RSTC) are two leading techniques for investigating the connectivity of the human brain. They have been widely used to investigate the strength of anatomical and functional connections between distant brain regions in healthy subjects, and in clinical populations. Though they are both based on magnetic resonance imaging (MRI) they have not yet been compared directly. In this work both techniques were employed to create global connectivity matrices covering the whole brain gray matter. This allowed for direct comparisons between functional connectivity measured by RSTC with anatomical connectivity quantified using DTI tractography. We found that connectivity matrices obtained using both techniques showed significant agreement. Connectivity maps created for a priori defined anatomical regions showed significant correlation, and furthermore agreement was especially high in regions showing strong overall connectivity, such as those belonging to the default mode network. Direct comparison between functional RSTC and anatomical DTI connectivity, presented here for the first time, links two powerful approaches for investigating brain connectivity and shows their strong agreement. It provides a crucial multi-modal validation for resting state correlations as representing neuronal connectivity. The combination of both techniques presented here allows for further combining them to provide richer representation of brain connectivity both in the healthy brain and in clinical conditions.
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Metadata
- Authors
- Skudlarski, P., Jagannathan, K., Calhoun, V. D., Hampson, M., Skudlarska, B. A., & Pearlson, G.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2008-11
- Volume
- 43
- Issue
- 3
- Pages
- 554-561
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W1982609124
- Cited by
- 457
- References
- 10
- FWCI
- 15.42
- Type
- journal article
- Language
- en
- Open access
- green
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