Cited by 110Advanced Neuroimaging Techniques and ApplicationsFunctional Brain Connectivity StudiesAdvanced MRI Techniques and Applications

Fusion in diffusion MRI for improved fibre orientation estimation: an application to the 3T and 7T data of the human connectome project

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Sotiropoulos, Stamatios N., Hernández-Fernández, Moisés, Vu, An T., Andersson, Jesper L., Moeller, Steen, Yacoub, Essa, Lenglet, Christophe, Ugurbil, Kamil +2 more·2016·NeuroImage

Abstract

Determining the acquisition parameters in diffusion magnetic resonance imaging (dMRI) is governed by a series of trade-offs. Images of lower resolution have less spatial specificity but higher signal to noise ratio (SNR). At the same time higher angular contrast, important for resolving complex fibre patterns, also yields lower SNR. Considering these trade-offs, the Human Connectome Project (HCP) acquires high quality dMRI data for the same subjects at different field strengths (3T and 7T), which are publically released. Due to differences in the signal behavior and in the underlying scanner hardware, the HCP 3T and 7T data have complementary features in k- and q-space. The 3T dMRI has higher angular contrast and resolution, while the 7T dMRI has higher spatial resolution. Given the availability of these datasets, we explore the idea of fusing them together with the aim of combining their benefits. We extend a previously proposed data-fusion framework and apply it to integrate both datasets from the same subject into a single joint analysis. We use a generative model for performing parametric spherical deconvolution and estimate fibre orientations by simultaneously using data acquired under different protocols. We illustrate unique features from each dataset and how they are retained after fusion. We further show that this allows us to complement benefits and improve brain connectivity analysis compared to analyzing each of the datasets individually.

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Crossref · 10.1016/j.neuroimage.2016.04.014

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  • Construction of the connectivity matrix includes two major steps: parcellation of the whole-brain tractogram and definition of a connectivity metric for edge weights.

    “Construction of the connectivity matrix has been described in several reviews (Sotiropoulos et al., 2016; Yeh et al., 2020) and includes two major steps. First, parcellation of the whole-brain tractogram is needed to compute all possible fiber pathways in the brain, i.e., white matter parcels connecting all pairs of gray matter regions (as described in Section 4.2). Then, a metric of “connectivity”

    From Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review (2022) · cites this paper as Sotiropoulos et al., 2016

    In Results, under “Preserving the fibre spreading pattern to the cortex supported by the 7T data” (Fig. 9; page unavailable), the paper describes constructing a connectome matrix by recording tractography-derived path probabilities between pairs of surface vertices. That supports using a connectivity metric for matrix entries, but the paper does not describe the claimed two-step procedure of parcellating a whole-brain tractogram into pathways between gray-matter regions and then assigning edge weights. It is an application of fibre-orientation estimation, not a review of connectivity-matrix construction. (LLM, conf=0.86)

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Research works printed but not linked: 4

  1. Fan Q. Nummenmaa A. Witzel T. Huang S.Y. Polimeni J.R. Wedeen V.J. Rosen B.R. Wald L.L. Improved diffusion tractography at the cortical boundary using HARDI acquisitions with high-b/low-k in white matter and low-b/high-k within and near the cortex Proceedings of the ISMRM Meeting 2015 564 (Toronto)
  2. Fischl B. FreeSurfer NeuroImage 62 2012 774 781 22248573 PMC3685476
  3. Sotiropoulos S.N. Chen C. Dikranian K. Jbabdi S. Behrens T.E. Van Essen D.C. Glasser M.F. Comparison of diffusion MRI predictions and histology in the macaque brain Proceedings of the ISMRM Meeting 2013 835 (Salt Lake City)
  4. Van Essen DC, Jbabdi S, Sotiropoulos SN, Chen C, Dikranian K, Coalson T, Harwell J, Behrens TEJ, Glasser MF (2013a) Mapping connections in humans and nonhuman primates: Aspirations and challenges for diffusion imaging. In: Diffusion MRI: From quantitative measurement to in-vivo neuroanatomy (2nd edition) (Johansen-Berg, H. and Behrens, T. E., eds), pp 337–358: Elsevier.

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Authors
Sotiropoulos, S. N., Hernández-Fernández, M., Vu, A. T., Andersson, J. L., Moeller, S., Yacoub, E., Lenglet, C., Ugurbil, K., Behrens, T. E. J., & Jbabdi, S.
Affiliations
  • University of Oxford (GB)
  • University of Minnesota (US)
  • Wellcome Centre for Human Neuroimaging (GB)
Corresponding author
Sotiropoulos, Stamatios N.
Venue
NeuroImage
Publisher
Elsevier BV
Published
2016-07
Volume
134
Pages
396-409
ISSN
1053-8119 (ISSN-L)
OpenAlex
W2328941635
Cited by
110
References
57
Citations by year
2026: 2 · 2025: 14 · 2024: 15 · 2023: 6 · 2022: 13 · 2021: 13 · 2020: 13 · 2019: 8 · 2018: 11 · 2017: 10 · 2016: 4 · 2015: 1
FWCI
9.55
Type
journal article
Language
en
Open access
Locations
  • NeuroImage (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Repository@Nottingham (University of Nottingham) (submittedVersion, cc-by)
Funders
  • Human Connectome Project (1U54MH091657-01)
  • NIH (P41 EB015894)
  • UK EPSRC (EP/L023067/1)
  • UK MRC (MR/L009013/1)
Topics
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
Keywords
diffusion MRI, data fusion, Human Connectome Project, brain connectivity, generative models, high-field MRI
MeSH terms
Adult, Algorithms, Brain, Female, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Male, Sensitivity and Specificity, Subtraction Technique, Reproducibility of Results, Diffusion Tensor Imaging, Connectome, White Matter

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