The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Functional connectivity in the motor cortex of resting human brain using echo‐planar mri
Biswal, B., Zerrin Yetkin, F., Haughton, V. M., & Hyde, J. S. (1995) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910340409 · Cited by 9,812
Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging
Fox, M. D., & Raichle, M. E. (2007) · Nature Reviews Neuroscience · doi:10.1038/nrn2201 · Cited by 6,700
Functional connectivity in the resting brain: a network analysis of the default mode hypothesis
Greicius, M. D., Krasnow, B., Reiss, A. L., & Menon, V. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0135058100 · Cited by 6,358
Greicius, M. D., Srivastava, G., Reiss, A. L., & Menon, V. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0308627101 · Cited by 3,684
Disruption of Large-Scale Brain Systems in Advanced Aging
Andrews-Hanna, J. R., Snyder, A. Z., Vincent, J. L., Lustig, C., Head, D., Raichle, M. E., & Buckner, R. L. (2007) · Neuron · doi:10.1016/j.neuron.2007.10.038 · Cited by 1,592
Brain connectivity related to working memory performance
Hampson, M., Driesen, N. R., Skudlarski, P., Gore, J. C., & Constable, R. T. (2006) · The Journal of Neuroscience · doi:10.1523/jneurosci.3408-06.2006 · Cited by 853
Self-citationRombouts, S. A. R. B., Barkhof, F., Goekoop, R., Stam, C. J., & Scheltens, P. (2005) · Human Brain Mapping · doi:10.1002/hbm.20160 · Cited by 734
Detection of functional connectivity using temporal correlations in MR images
Hampson, M., Peterson, B. S., Skudlarski, P., Gatenby, J. C., & Gore, J. C. (2002) · Human Brain Mapping · doi:10.1002/hbm.10022 · Cited by 699
Self-citationAn investigation of functional and anatomical connectivity using magnetic resonance imaging
Koch, M. A., Norris, D. G., & Hund-Georgiadis, M. (2002) · NeuroImage · doi:10.1006/nimg.2001.1052 · Cited by 509
DTI mapping of human brain connectivity: Statistical fibre tracking and virtual dissection
Hagmann, P., Thiran, J.-P., Jonasson, L., Vandergheynst, P., Clarke, S., Maeder, P., & Meuli, R. (2003) · NeuroImage · doi:10.1016/s1053-8119(03)00142-3 · Cited by 326
Cited by
Predicting human resting-state functional connectivity from structural connectivity
Honey, C. J., Sporns, O., Cammoun, L., Gigandet, X., Thiran, J. P., Meuli, R., & Hagmann, P. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0811168106 · Cited by 3,307
Network-based statistic: identifying differences in brain networks
Zalesky, A., Fornito, A., & Bullmore, E. T. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.06.041 · Cited by 2,750
The global signal and observed anticorrelated resting state brain networks
Fox, M. D., Zhang, D., Snyder, A. Z., & Raichle, M. E. (2009) · Journal of Neurophysiology · doi:10.1152/jn.90777.2008 · Cited by 1,877
Whole-brain anatomical networks: does the choice of nodes matter?
Zalesky, A., Fornito, A., Harding, I. H., Cocchi, L., Yücel, M., Pantelis, C., & Bullmore, E. T. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2009.12.027 · Cited by 1,169
Precuneus shares intrinsic functional architecture in humans and monkeys
Margulies, D. S., Vincent, J. L., Kelly, C., Lohmann, G., Uddin, L. Q., Biswal, B. B., Villringer, A., Castellanos, F. X., Milham, M. P., & Petrides, M. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0905314106 · Cited by 965
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Measuring brain connectivity: Diffusion tensor imaging validates resting state temporal correlations. https://app.cochat.ai/papers/10.1016/j.neuroimage.2008.07.063
- The paper
- Skudlarski, P., Jagannathan, K., Calhoun, V. D., Hampson, M., Skudlarska, B. A., & Pearlson, G. (2008). Measuring brain connectivity: Diffusion tensor imaging validates resting state temporal correlations. NeuroImage. https://doi.org/10.1016/j.neuroimage.2008.07.063
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
