Connectome sensitivity or specificity: which is more important?
Open accessAbstract
Connectomes with high sensitivity and high specificity are unattainable with current axonal fiber reconstruction methods, particularly at the macro-scale afforded by magnetic resonance imaging. Tensor-guided deterministic tractography yields sparse connectomes that are incomplete and contain false negatives (FNs), whereas probabilistic methods steered by crossing-fiber models yield dense connectomes, often with low specificity due to false positives (FPs). Densely reconstructed probabilistic connectomes are typically thresholded to improve specificity at the cost of a reduction in sensitivity. What is the optimal tradeoff between connectome sensitivity and specificity? We show empirically and theoretically that specificity is paramount. Our evaluations of the impact of FPs and FNs on empirical connectomes indicate that specificity is at least twice as important as sensitivity when estimating key properties of brain networks, including topological measures of network clustering, network efficiency and network modularity. Our asymptotic analysis of small-world networks with idealized modular structure reveals that as the number of nodes grows, specificity becomes exactly twice as important as sensitivity to the estimation of the clustering coefficient. For the estimation of network efficiency, the relative importance of specificity grows linearly with the number of nodes. The greater importance of specificity is due to FPs occurring more prevalently between network modules rather than within them. These spurious inter-modular connections have a dramatic impact on network topology. We argue that efforts to maximize the sensitivity of connectome reconstruction should be realigned with the need to map brain networks with high specificity.
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False positive tract reconstructions may lead to biased characterization of connectome topology.
“This may lead to biased characterization of connectome topology, particularly in the presence of false positive tract reconstructions (Drakesmith et al. 2015; Zalesky et al. 2016).”
From Diffusion MRI-based cortical connectome reconstruction: dependency on tractography procedures and neuroanatomical characteristics (2018) · cites this paper as Zalesky et al. 2016
Reconstruction sensitivity and specificity may be influenced by streamline thresholding or group-based analysis (incidence thresholds).
“Lastly, reconstruction sensitivity and specificity may be influenced by streamline thresholding or group-based analysis (i.e., incidence thresholds, which might increase robustness against inter-subject variation), which might be employed to decrease the number of false positive reconstructions to satisfy the suggested 2:1 rule (Zalesky et al. 2016).”
From Diffusion MRI-based cortical connectome reconstruction: dependency on tractography procedures and neuroanatomical characteristics (2018) · cites this paper as Zalesky et al. 2016
Specific DT and global tractography parameter settings can satisfy the 2:1 rule (at least twice as many false negatives compared to false positives).
“Our data show that specific DT and global tractography parameter settings can satisfy this rule.”
From Diffusion MRI-based cortical connectome reconstruction: dependency on tractography procedures and neuroanatomical characteristics (2018) · cites this paper as Zalesky et al. 2016
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- Authors
- Zalesky, A., Fornito, A., Cocchi, L., Gollo, L. L., van den Heuvel, M. P., & Breakspear, M.
- Affiliations
- The University of Melbourne (AU)
- Monash University (AU)
- QIMR Berghofer Medical Research Institute (AU)
- Corresponding author
- Zalesky, Andrew
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2016-11
- Volume
- 142
- Pages
- 407-420
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2467410077
- Cited by
- 275
- References
- 72
- Citations by year
- 2026: 13 · 2025: 22 · 2024: 15 · 2023: 29 · 2022: 35 · 2021: 34 · 2020: 27 · 2019: 42 · 2018: 27 · 2017: 28 · 2016: 3
- FWCI
- 28.69
- Type
- journal article
- Language
- en
- Open access
- green
- dspace.library.uu.nl
- Licence
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- Pure Amsterdam UMC (publishedVersion)
- Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
- Amsterdam UMC (VU Amsterdam) - Institutional Repository (submittedVersion)
- Utrecht University Repository (Utrecht University) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Funders
- Australian National Health and Medical Research Council (1050504, 1066779, 1047648, 1037196, 1103252, 1099082)
- Australian Research Council (FT130100589, CE140100007)
- Netherlands Organization for Scientific Research (451-12-001)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Keywords
- probabilistic tractography, deterministic tractography, false positives, false-negative results, brain network topology, clustering coefficient, network efficiency, network modularity, small-world networks, connectome reconstruction
- MeSH terms
- Animals, Brain, Humans, Models, Theoretical, Sensitivity and Specificity, Connectome
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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