title: Magnetic resonance field strength effects on diffusion measures and brain connectivity networks.
abstract: 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.
authors.affiliation: Imaging Genetics Center, Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Los Angeles, California 90095-7334, USA.
authors.forename: Liang
authors.initials: L
authors.lastname: Zhan
authors.forename: Bryon A
authors.initials: BA
authors.lastname: Mueller
authors.forename: Neda
authors.initials: N
authors.lastname: Jahanshad
authors.forename: Yan
authors.initials: Y
authors.lastname: Jin
authors.forename: Christophe
authors.initials: C
authors.lastname: Lenglet
authors.forename: Essa
authors.initials: E
authors.lastname: Yacoub
authors.forename: Guillermo
authors.initials: G
authors.lastname: Sapiro
authors.forename: Kamil
authors.initials: K
authors.lastname: Ugurbil
authors.forename: Noam
authors.initials: N
authors.lastname: Harel
authors.forename: Arthur W
authors.initials: AW
authors.lastname: Toga
authors.forename: Kelvin O
authors.initials: KO
authors.lastname: Lim
authors.forename: Paul M
authors.initials: PM
authors.lastname: Thompson
mesh_terms: D001921:Brain / Q000033:anatomy & histology* / Q000502:physiology; D001931:Brain Mapping / Q000379:methods*; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D009415:Nerve Net / Q000033:anatomy & histology / Q000502:physiology; D009434:Neural Pathways / Q000033:anatomy & histology* / Q000502:physiology