Validation of High-Resolution Tractography Against In Vivo Tracing in the Macaque Visual Cortex
Open accessAbstract
Diffusion magnetic resonance imaging (MRI) allows for the noninvasive in vivo examination of anatomical connections in the human brain, which has an important role in understanding brain function. Validation of this technique is vital, but has proved difficult due to the lack of an adequate gold standard. In this work, the macaque visual system was used as a model as an extensive body of literature of in vivo and postmortem tracer studies has established a detailed understanding of the underlying connections. We performed probabilistic tractography on high angular resolution diffusion imaging data of 2 ex vivo, in vitro macaque brains. Comparisons were made between identified connections at different thresholds of probabilistic connection "strength," and with various tracking optimization strategies previously proposed in the literature, and known connections from the detailed visual system wiring map described by Felleman and Van Essen (1991; FVE91). On average, 74% of connections that were identified by FVE91 were reproduced by performing the most successfully optimized probabilistic diffusion MRI tractography. Further comparison with the results of a more recent tracer study ( Markov et al. 2012) suggests that the fidelity of tractography in estimating the presence or absence of interareal connections may be greater than this.
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The FA threshold had significantly more effect on connectome reconstruction sensitivity and specificity than the step size and angle threshold.
“The FA threshold had significantly more effect on connectome reconstruction sensitivity and specificity than the step size and angle threshold, in line with previous research (Azadbakht et al. 2015; Chen et al. 2015).”
From Diffusion MRI-based cortical connectome reconstruction: dependency on tractography procedures and neuroanatomical characteristics (2018) · cites this paper as Azadbakht et al. 2015
The Results sections on FA termination and streamline curvature report that higher FA thresholds reduced connection accuracy, while curvature constraints had no clear effect. However, the paper does not report a statistical comparison showing a significantly greater effect on sensitivity and specificity, and the Discussion refers to a default step size rather than a tested step-size comparison. Page numbers are unavailable in our copy. (LLM, conf=0.85)
Experimental — accuracy not yet measuredAn increasing FA threshold negatively influenced sensitivity and the Jaccard index.
“In fact, an increasing FA threshold negatively influenced sensitivity and the Jaccard index, suggesting that an FA threshold should be omitted for this type of datasets and analyses (Azadbakht et al. 2015; Chen et al. 2015).”
From Diffusion MRI-based cortical connectome reconstruction: dependency on tractography procedures and neuroanatomical characteristics (2018) · cites this paper as Azadbakht et al. 2015
The Results section on FA termination constraints reports that higher FA thresholds reduced tracking accuracy and suggests omitting them under the study’s experimental conditions. It does not report that sensitivity or the Jaccard index decreased; those specific measures are not supported by this source. (LLM, conf=0.88)
Experimental — accuracy not yet measuredThe considerable amount of false positive and false negative connections generated by diffusion-based tractography in rat brain is in line with related studies in macaque brain, mouse brain, and human connectome.
“The considerable amount of false positive and false negative connections generated by diffusion-based tractography in rat brain is in line with related studies in macaque brain (Thomas et al. 2014; Azadbakht et al. 2015; Donahue et al. 2016), and mouse brain (Calabrese et al. 2015; Chen et al. 2015), and similar findings have been reported for the human connectome (Maier-Hein et al. 2016).”
From Diffusion MRI-based cortical connectome reconstruction: dependency on tractography procedures and neuroanatomical characteristics (2018) · cites this paper as Azadbakht et al. 2015
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- Authors
- Azadbakht, H., Parkes, L. M., Haroon, H. A., Augath, M., Logothetis, N. K., de Crespigny, A., D'Arceuil, H. E., & Parker, G. J. M.
- Affiliations
- Manchester Academic Health Science Centre (GB)
- Max Planck Institute for Biological Cybernetics (DE)
- Massachusetts General Hospital (US)
- Venue
- Cerebral Cortex
- Publisher
- Oxford University Press (OUP)
- Published
- 2015-03-18
- Volume
- 25
- Issue
- 11
- Pages
- 4299-4309
- ISSN
- 1047-3211 (ISSN-L)
- 1460-2199
- PMC
- PMC4816782
- OpenAlex
- W2145544236
- Cited by
- 103
- References
- 67
- Citations by year
- 2026: 3 · 2025: 7 · 2024: 2 · 2023: 4 · 2022: 4 · 2021: 8 · 2020: 15 · 2019: 12 · 2018: 17 · 2017: 9 · 2016: 16 · 2015: 6
- FWCI
- 12.95
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Cerebral Cortex (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Research Explorer (The University of Manchester) (publishedVersion)
- Max Planck Digital Library (submittedVersion, other-oa)
- UCL Discovery (University College London) (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Funders
- Research Councils UK
- University of Manchester
- Biotechnology and Biological Sciences Research Council (BB/E002226/1)
- 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
- probabilistic tractography, high angular resolution diffusion imaging, macaque visual cortex
- MeSH terms
- Algorithms, Animals, Brain Mapping, Macaca mulatta, Neural Pathways, ROC Curve, Visual Cortex, Reproducibility of Results, Imaging, Three-Dimensional, Diffusion Magnetic Resonance Imaging
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 ›
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