Using diffusion tractography to predict cortical connection strength and distance: a quantitative comparison with tracers in the monkey
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UNLABELLED: Tractography based on diffusion MRI offers the promise of characterizing many aspects of long-distance connectivity in the brain, but requires quantitative validation to assess its strengths and limitations. Here, we evaluate tractography's ability to estimate the presence and strength of connections between areas of macaque neocortex by comparing its results with published data from retrograde tracer injections. Probabilistic tractography was performed on high-quality postmortem diffusion imaging scans from two Old World monkey brains. Tractography connection weights were estimated using a fractional scaling method based on normalized streamline density. We found a correlation between log-transformed tractography and tracer connection weights of r = 0.59, twice that reported in a recent study on the macaque. Using a novel method to estimate interareal connection lengths from tractography streamlines, we regressed out the distance dependence of connection strength and found that the correlation between tractography and tracers remains positive, albeit substantially reduced. Altogether, these observations provide a valuable, data-driven perspective on both the strengths and limitations of tractography for analyzing interareal corticocortical connectivity in nonhuman primates and a framework for assessing future tractography methodological refinements objectively. SIGNIFICANCE STATEMENT: Tractography based on diffusion MRI has great potential for a variety of applications, including estimation of comprehensive maps of neural connections in the brain ("connectomes"). Here, we describe methods to assess quantitatively tractography's performance in detecting interareal cortical connections and estimating connection strength by comparing it against published results using neuroanatomical tracers. We found the correlation of tractography's estimated connection strengths versus tracer to be twice that of a previous study. Using a novel method for calculating interareal cortical distances, we show that tractography-based estimates of connection strength have useful predictive power beyond just interareal separation. By freely sharing these methods and datasets, we provide a valuable resource for future studies in cortical connectomics.
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Ex vivo imaging and tracing or physically and numerically simulated phantoms allow validation to some extent, and in specific circumstances such as basic fiber configurations.
“Ex vivo imaging and tracing17–23 or physically24–30 and numerically simulated phantoms31–34 allow validation to some extent, and in specific circumstances such as basic fiber configurations.”
From Tractography-based connectomes are dominated by false-positive connections (2016) · cites this paper as [23]
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, 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 Donahue et al. 2016
Most neuroanatomical connections are at least to a certain degree bidirectional and contain nested sub-regions with their own connections and corresponding weights.
“While this was recently done to compare measures of connectivity strength between diffusion tractography and tract tracing data from macaque brain (van den Heuvel et al. 2015; Donahue et al. 2016), most neuroanatomical connections are at least to a certain degree bidirectional and contain nested sub-regions with their own connections and corresponding weights.”
From Diffusion MRI-based cortical connectome reconstruction: dependency on tractography procedures and neuroanatomical characteristics (2018) · cites this paper as Donahue et al. 2016
In the monkey, whole-brain tract-tracer connection networks and tractography networks show a significant but only moderately high correspondence.
“In the monkey, whole-brain tract-tracer connection networks and tractography networks demonstrate a significant, but only moderately high correspondence.”
From Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature (2017) · cites this paper as Donahue et al., 2016
The highest reported correlation between monkey tract-tracer and tractography networks is r = 0.59, though it is often much lower.
“Currently, the highest reported correlation stands at r = 0.59 (Donahue et al., 2016), though it is often much lower (van den Heuvel et al., 2015a).”
From Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature (2017) · cites this paper as Donahue et al., 2016
Structure-function modeling using MRI data suffers from inherent limitations in DWI-tractography that have not been overcome by state-of-the-art acquisition and processing approaches.
“structure-function modeling using MRI data suffers from a number of inherent limitations in DWI-tractography that have not been overcome by state-of-the-art acquisition and processing approaches (Calabrese et al., 2015; Donahue et al., 2016; Reveley et al., 2015; Thomas et al., 2014).”
From Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature (2017) · cites this paper as Donahue et al., 2016
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- Authors
- Donahue, C. J., Sotiropoulos, S. N., Jbabdi, S., Hernandez-Fernandez, M., Behrens, T. E., Dyrby, T. B., Coalson, T., Kennedy, H., Knoblauch, K., Van Essen, D. C., & Glasser, M. F.
- Affiliations
- Washington University in St. Louis (US)
- University of Oxford (GB)
- Copenhagen University Hospital (DK)
- Institut Cellule Souche et Cerveau (FR)
- ORCID
- Venue
- The Journal of Neuroscience
- Publisher
- Society for Neuroscience
- Published
- 2016-06-22
- Volume
- 36
- Issue
- 25
- Pages
- 6758-6770
- ISSN
- 0270-6474 (ISSN-L)
- 1529-2401
- PMC
- PMC4916250
- OpenAlex
- W2463784123
- Cited by
- 380
- References
- 56
- Citations by year
- 2026: 29 · 2025: 40 · 2024: 41 · 2023: 27 · 2022: 32 · 2021: 50 · 2020: 51 · 2019: 32 · 2018: 48 · 2017: 21 · 2016: 9
- FWCI
- 31.81
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Neuroscience (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Repository@Nottingham (University of Nottingham) (publishedVersion, other-oa)
- Digital Commons@Becker (Washington University School of Medicine) (submittedVersion)
- Repository@Nottingham (University of Nottingham) (submittedVersion, cc-by)
- Nottingham ePrints (University of Nottingham) (submittedVersion)
- Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU) (submittedVersion, other-oa)
- Funders
- Wellcome Trust (104765/Z/14/Z)
- Agence Nationale de la Recherche (ANR-11-BSV4-501, ANR-11-BSV4-0005, ANR-14-CE13-0033, ANR-11-IDEX-0007, ANR-11-IDEX, ANR-15-CE32-0016, ANR-14-CE13-0003, ANR-11-LABX-0042, 11 IDEX - 0007)
- National Institutes of Health (EP/L023067/1, ANR-11-LABX-0042, P51OD011132)
- Yerkes National Primate Research Center, Emory University (P51OD11132, /OD P51OD11132)
- Medical Research Council (MR/L009013/1)
- Engineering and Physical Sciences Research Council (L023067, EP/L023067, EP/L023067/1)
- National Institute on Aging (P01AG026423)
- National Institute of Mental Health (R01MH060974, F30MH097312)
- Office of Research Infrastructure Programs, National Institutes of Health (ODP51OD11132, P51OD11132)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Keywords
- diffusion tractography, corticocortical connectivity, connection strength, probabilistic tractography, connectomics, non-human primates
- MeSH terms
- Animals, Brain Mapping, Cercopithecidae, Cerebral Cortex, Image Processing, Computer-Assisted, Functional Laterality, Models, Neurological, Nerve Fibers, Nerve Net, Diffusion Tensor Imaging, Connectome
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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.
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