Skip to main content
Journal cover image

Magnetic resonance field strength effects on diffusion measures and brain connectivity networks.

Publication ,  Journal Article
Zhan, L; Mueller, BA; Jahanshad, N; Jin, Y; Lenglet, C; Yacoub, E; Sapiro, G; Ugurbil, K; Harel, N; Toga, AW; Lim, KO; Thompson, PM
Published in: Brain connectivity
January 2013

The quest to map brain connectivity is being pursued worldwide using diffusion imaging, among other techniques. Even so, we know little about how brain connectivity measures depend on the magnetic field strength of the scanner. To investigate this, we scanned 10 healthy subjects at 7 and 3 tesla-using 128-gradient high-angular resolution diffusion imaging. For each subject and scan, whole-brain tractography was used to estimate connectivity between 113 cortical and subcortical regions. We examined how scanner field strength affects (i) the signal-to-noise ratio (SNR) of the non-diffusion-sensitized reference images (b(0)); (ii) diffusion tensor imaging (DTI)-derived fractional anisotropy (FA), mean, radial, and axial diffusivity (MD/RD/AD), in atlas-defined regions; (iii) whole-brain tractography; (iv) the 113 × 113 brain connectivity maps; and (v) five commonly used network topology measures. We also assessed effects of the multi-channel reconstruction methods (sum-of-squares, SOS, at 7T; adaptive recombine, AC, at 3T). At 7T with SOS, the b0 images had 18.3% higher SNR than with 3T-AC. FA was similar for most regions of interest (ROIs) derived from an online DTI atlas (ICBM81), but higher at 7T in the cerebral peduncle and internal capsule. MD, AD, and RD were lower at 7T for most ROIs. The apparent fiber density between some subcortical regions was greater at 7T-SOS than 3T-AC, with a consistent connection pattern overall. Suggesting the need for caution, the recovered brain network was apparently more efficient at 7T, which cannot be biologically true as the same subjects were assessed. Care is needed when comparing network measures across studies, and when interpreting apparently discrepant findings.

Duke Scholars

Altmetric Attention Stats
Dimensions Citation Stats

Published In

Brain connectivity

DOI

EISSN

2158-0022

ISSN

2158-0014

Publication Date

January 2013

Volume

3

Issue

1

Start / End Page

72 / 86

Related Subject Headings

  • Neural Pathways
  • Nerve Net
  • Image Interpretation, Computer-Assisted
  • Humans
  • Diffusion Magnetic Resonance Imaging
  • Brain Mapping
  • Brain
  • 5202 Biological psychology
  • 3209 Neurosciences
  • 1109 Neurosciences
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Zhan, L., Mueller, B. A., Jahanshad, N., Jin, Y., Lenglet, C., Yacoub, E., … Thompson, P. M. (2013). Magnetic resonance field strength effects on diffusion measures and brain connectivity networks. Brain Connectivity, 3(1), 72–86. https://doi.org/10.1089/brain.2012.0114
Zhan, Liang, Bryon A. Mueller, Neda Jahanshad, Yan Jin, Christophe Lenglet, Essa Yacoub, Guillermo Sapiro, et al. “Magnetic resonance field strength effects on diffusion measures and brain connectivity networks.Brain Connectivity 3, no. 1 (January 2013): 72–86. https://doi.org/10.1089/brain.2012.0114.
Zhan L, Mueller BA, Jahanshad N, Jin Y, Lenglet C, Yacoub E, et al. Magnetic resonance field strength effects on diffusion measures and brain connectivity networks. Brain connectivity. 2013 Jan;3(1):72–86.
Zhan, Liang, et al. “Magnetic resonance field strength effects on diffusion measures and brain connectivity networks.Brain Connectivity, vol. 3, no. 1, Jan. 2013, pp. 72–86. Epmc, doi:10.1089/brain.2012.0114.
Zhan L, Mueller BA, Jahanshad N, Jin Y, Lenglet C, Yacoub E, Sapiro G, Ugurbil K, Harel N, Toga AW, Lim KO, Thompson PM. Magnetic resonance field strength effects on diffusion measures and brain connectivity networks. Brain connectivity. 2013 Jan;3(1):72–86.
Journal cover image

Published In

Brain connectivity

DOI

EISSN

2158-0022

ISSN

2158-0014

Publication Date

January 2013

Volume

3

Issue

1

Start / End Page

72 / 86

Related Subject Headings

  • Neural Pathways
  • Nerve Net
  • Image Interpretation, Computer-Assisted
  • Humans
  • Diffusion Magnetic Resonance Imaging
  • Brain Mapping
  • Brain
  • 5202 Biological psychology
  • 3209 Neurosciences
  • 1109 Neurosciences