↓ Skip to main content

Connectome sensitivity or specificity: which is more important?

Overview of attention for article published in NeuroImage, November 2016
Altmetric Badge

About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (92nd percentile)
  • High Attention Score compared to outputs of the same age and source (91st percentile)

Mentioned by

twitter
44 tweeters
facebook
1 Facebook page

Citations

dimensions_citation
62 Dimensions

Readers on

mendeley
161 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Connectome sensitivity or specificity: which is more important?
Published in
NeuroImage, November 2016
DOI 10.1016/j.neuroimage.2016.06.035
Pubmed ID
Authors

Andrew Zalesky, Alex Fornito, Luca Cocchi, Leonardo L. Gollo, Martijn P. van den Heuvel, Michael Breakspear

Abstract

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. For clinically typical diffusion MRI data (e.g. ~30 directions, b=1000s/mm(2)), we advise caution in performing graph analysis on dense connectomes reconstructed with unconstrained crossing fiber models. Despite their poor sensitivity and limitations, the use of simple unimodal models may be warranted in these cases.

Twitter Demographics

The data shown below were collected from the profiles of 44 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 161 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Canada 3 2%
Germany 2 1%
United Kingdom 2 1%
South Africa 1 <1%
Switzerland 1 <1%
Netherlands 1 <1%
Cuba 1 <1%
Spain 1 <1%
Unknown 149 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 53 33%
Researcher 32 20%
Student > Master 22 14%
Unspecified 13 8%
Student > Doctoral Student 13 8%
Other 28 17%
Readers by discipline Count As %
Neuroscience 36 22%
Unspecified 34 21%
Medicine and Dentistry 19 12%
Psychology 16 10%
Engineering 15 9%
Other 41 25%

Attention Score in Context

This research output has an Altmetric Attention Score of 24. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 19 December 2016.
All research outputs
#557,087
of 12,212,281 outputs
Outputs from NeuroImage
#513
of 7,453 outputs
Outputs of similar age
#20,276
of 270,182 outputs
Outputs of similar age from NeuroImage
#23
of 276 outputs
Altmetric has tracked 12,212,281 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,453 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.0. This one has done particularly well, scoring higher than 93% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 270,182 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 92% of its contemporaries.
We're also able to compare this research output to 276 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 91% of its contemporaries.