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Brain Struct Funct. 2017 Apr;222(3):1131-1151. doi: 10.1007/s00429-016-1264-3. Epub 2016 Jul 2.

A seed-based cross-modal comparison of brain connectivity measures.

Author information

1
Institute for Neuroscience and Medicine (INM-1), Jülich Research Center, Wilhelm-Johnen-Straße, 52428, Jülich, Germany. a.reid@donders.ru.nl.
2
Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen, Nijmegen, The Netherlands. a.reid@donders.ru.nl.
3
Institute for Neuroscience and Medicine (INM-1), Jülich Research Center, Wilhelm-Johnen-Straße, 52428, Jülich, Germany.
4
Department of Clinical Neuroscience and Medicine, Heinrich Heine University, Düsseldorf, Germany.
5
School of Brain and Cognitive Sciences, National Key Laboratory of Cognitive Neuroscience and Learning, Beijing, China.
6
Department of Physics, Florida International University, Miami, FL, USA.
7
University of Texas Health Sciences Center at San Antonio, San Antonio, TX, USA.
8
South Texas Veterans Health Care System, San Antonio, TX, USA.
9
McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal, Canada.
10
C. & O. Vogt Institute for Brain Research, Heinrich Heine University, Düsseldorf, Germany.

Abstract

Human neuroimaging methods have provided a number of means by which the connectivity structure of the human brain can be inferred. For instance, correlations in blood-oxygen-level-dependent (BOLD) signal time series are commonly used to make inferences about "functional connectivity." Correlations across samples in structural morphometric measures, such as voxel-based morphometry (VBM) or cortical thickness (CT), have also been used to estimate connectivity, putatively through mutually trophic effects on connected brain areas. In this study, we have compared seed-based connectivity estimates obtained from four common correlational approaches: resting-state functional connectivity (RS-fMRI), meta-analytic connectivity modeling (MACM), VBM correlations, and CT correlations. We found that the two functional approaches (RS-fMRI and MACM) had the best agreement. While the two structural approaches (CT and VBM) had better-than-random convergence, they were no more similar to each other than to the functional approaches. The degree of correspondence between modalities varied considerably across seed regions, and also depended on the threshold applied to the connectivity distribution. These results demonstrate some degrees of similarity between connectivity inferred from structural and functional covariances, particularly for the most robust functionally connected regions (e.g., the default mode network). However, they also caution that these measures likely capture very different aspects of brain structure and function.

KEYWORDS:

Cortical thickness; MACM; Multimodal comparison; Resting-state fMRI; VBM

PMID:
27372336
PMCID:
PMC5205581
DOI:
10.1007/s00429-016-1264-3
[Indexed for MEDLINE]
Free PMC Article

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