The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Connectivity-based fixel enhancement: Whole-brain statistical analysis of diffusion MRI measures in the presence of crossing fibres
Open accessAbstract
In brain regions containing crossing fibre bundles, voxel-average diffusion MRI measures such as fractional anisotropy (FA) are difficult to interpret, and lack within-voxel single fibre population specificity. Recent work has focused on the development of more interpretable quantitative measures that can be associated with a specific fibre population within a voxel containing crossing fibres (herein we use fixel to refer to a specific fibre population within a single voxel). Unfortunately, traditional 3D methods for smoothing and cluster-based statistical inference cannot be used for voxel-based analysis of these measures, since the local neighbourhood for smoothing and cluster formation can be ambiguous when adjacent voxels may have different numbers of fixels, or ill-defined when they belong to different tracts. Here we introduce a novel statistical method to perform whole-brain fixel-based analysis called connectivity-based fixel enhancement (CFE). CFE uses probabilistic tractography to identify structurally connected fixels that are likely to share underlying anatomy and pathology. Probabilistic connectivity information is then used for tract-specific smoothing (prior to the statistical analysis) and enhancement of the statistical map (using a threshold-free cluster enhancement-like approach). To investigate the characteristics of the CFE method, we assessed sensitivity and specificity using a large number of combinations of CFE enhancement parameters and smoothing extents, using simulated pathology generated with a range of test-statistic signal-to-noise ratios in five different white matter regions (chosen to cover a broad range of fibre bundle features). The results suggest that CFE input parameters are relatively insensitive to the characteristics of the simulated pathology. We therefore recommend a single set of CFE parameters that should give near optimal results in future studies where the group effect is unknown. We then demonstrate the proposed method by comparing apparent fibre density between motor neurone disease (MND) patients with control subjects. The MND results illustrate the benefit of fixel-specific statistical inference in white matter regions that contain crossing fibres.
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
Voxel-Based Morphometry—The Methods
Ashburner, J., & Friston, K. J. (2000) · NeuroImage · doi:10.1006/nimg.2000.0582 · Cited by 8,676
Nonparametric permutation tests for functional neuroimaging: A primer with examples
Nichols, T. E., & Holmes, A. P. (2002) · Human Brain Mapping · doi:10.1002/hbm.1058 · Cited by 6,256
SMITH, S., & NICHOLS, T. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2008.03.061 · Cited by 5,250
Permutation inference for the general linear model
Winkler, A. M., Ridgway, G. R., Webster, M. A., Smith, S. M., & Nichols, T. E. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.01.060 · Cited by 3,563
Self-citationTuch, D. S. (2004) · Magnetic Resonance in Medicine · doi:10.1002/mrm.20279 · Cited by 1,880
Detecting activations in PET and fMRI: levels of inference and power
Friston, K. J., Holmes, A., Poline, J.-B., Price, C. J., & Frith, C. D. (1996) · NeuroImage · doi:10.1006/nimg.1996.0074 · Cited by 1,148 · cited 1×
“Cluster-levelinference can be more sensitivethan voxel-level inference by exploiting spatial correlations in voxel intensities due to shared underlying anatomy and pathology (Friston et al., 1996).”
BackgroundJeurissen, B., Leemans, A., Tournier, J., Jones, D. K., & Sijbers, J. (2013) · Human Brain Mapping · doi:10.1002/hbm.22099 · Cited by 1,088
Self-citationComposite hindered and restricted model of diffusion (CHARMED) MR imaging of the human brain
Assaf, Y., & Basser, P. J. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2005.03.042 · Cited by 856
SIFT: Spherical-deconvolution informed filtering of tractograms
Smith, R. E., Tournier, J.-D., Calamante, F., & Connelly, A. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2012.11.049 · Cited by 752
Self-citationCOMMIT: Convex Optimization Modeling for Microstructure Informed Tractography
Daducci, A., Dal Palu, A., Lemkaddem, A., & Thiran, J.-P. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2014.2352414 · Cited by 255
Cited by
MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation
Tournier, J.-D., Smith, R., Raffelt, D., Tabbara, R., Dhollander, T., Pietsch, M., Christiaens, D., Jeurissen, B., Yeh, C.-H., & Connelly, A. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116137 · Cited by 2,974
Self-citationInvestigating white matter fibre density and morphology using fixel-based analysis.
Raffelt, D. A., Tournier, J.-D., Smith, R. E., Vaughan, D. N., Jackson, G., Ridgway, G. R., & Connelly, A. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2016.09.029 · Cited by 620
Self-citationThe development of brain white matter microstructure
Lebel, C., & Deoni, S. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2017.12.097 · Cited by 602
Fibre-specific white matter reductions in Alzheimer’s disease and mild cognitive impairment
Mito, R., Raffelt, D., Dhollander, T., Vaughan, D. N., Tournier, J.-D., Salvado, O., Brodtmann, A., Rowe, C. C., Villemagne, V. L., & Connelly, A. (2018) · Brain · doi:10.1093/brain/awx355 · Cited by 309
Self-citationGorgolewski, K. J., Alfaro-Almagro, F., Auer, T., Bellec, P., Capotă, M., Chakravarty, M. M., Churchill, N. W., Cohen, A. L., Craddock, R. C., Devenyi, G. A., et al. (2017) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1005209 · Cited by 311
Self-citationChen, Y., Wang, Y., Song, Z., Fan, Y., Gao, T., & Tang, X. (2023) · Ageing Research Reviews · doi:10.1016/j.arr.2023.101911 · Cited by 152
Tractography in the presence of multiple sclerosis lesions
Lipp, I., Parker, G. D., Tallantyre, E. C., Goodall, A., Grama, S., Patitucci, E., Heveron, P., Tomassini, V., & Jones, D. K. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2019.116471 · Cited by 51
Zarkali, A., McColgan, P., Leyland, L. A., Lees, A. J., & Weil, R. S. (2022) · Journal of Neurology, Neurosurgery & Psychiatry · doi:10.1136/jnnp-2021-326630 · Cited by 32
Sleurs, C., Lemiere, J., Christiaens, D., Billiet, T., Peeters, R., Sunaert, S., Uyttebroeck, A., & Deprez, S. (2018) · Human Brain Mapping · doi:10.1002/hbm.24082 · Cited by 26
Fixel-based analysis reveals tau-related white matter changes in early stages of Alzheimer's disease
Ahmadi, K., Pereira, J. B., Westen, D. V., Pasternak, O., Zhang, F., Nilsson, M., Stomrud, E., Spotorno, N., & Hansson, O. (2024) · The Journal of Neuroscience · doi:10.1523/jneurosci.0538-23.2024 · Cited by 25
Finkelstein, A., Faiyaz, A., Weber, M. T., Qiu, X., Uddin, M. N., Zhong, J., & Schifitto, G. (2021) · Frontiers in Neurology · doi:10.3389/fneur.2021.725059 · Cited by 15
Metadata
- Authors
- Raffelt, D. A., Smith, R. E., Ridgway, G. R., Tournier, J.-D., Vaughan, D. N., Rose, S., Henderson, R., & Connelly, A.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2015-08
- Volume
- 117
- Pages
- 40-55
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W239929985
- Cited by
- 386
- References
- 11
- FWCI
- 8.62
- Type
- journal article
- Language
- en
- Open access
- hybrid
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
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.
