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Neuroanatomical morphometric characterization of sex differences in youth using statistical learning

Overview of attention for article published in NeuroImage, May 2018
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#19 of 7,490)
  • High Attention Score compared to outputs of the same age (99th percentile)
  • High Attention Score compared to outputs of the same age and source (98th percentile)

Mentioned by

twitter
299 tweeters
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1 Facebook page

Citations

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3 Dimensions

Readers on

mendeley
18 Mendeley
Title
Neuroanatomical morphometric characterization of sex differences in youth using statistical learning
Published in
NeuroImage, May 2018
DOI 10.1016/j.neuroimage.2018.01.065
Pubmed ID
Authors

Farshid Sepehrband, Kirsten M. Lynch, Ryan P. Cabeen, Clio Gonzalez-Zacarias, Lu Zhao, Mike D'Arcy, Carl Kesselman, Megan M. Herting, Ivo D. Dinov, Arthur W. Toga, Kristi A. Clark

Abstract

Exploring neuroanatomical sex differences using a multivariate statistical learning approach can yield insights that cannot be derived with univariate analysis. While gross differences in total brain volume are well-established, uncovering the more subtle, regional sex-related differences in neuroanatomy requires a multivariate approach that can accurately model spatial complexity as well as the interactions between neuroanatomical features. Here, we developed a multivariate statistical learning model using a support vector machine (SVM) classifier to predict sex from MRI-derived regional neuroanatomical features from a single-site study of 967 healthy youth from the Philadelphia Neurodevelopmental Cohort (PNC). Then, we validated the multivariate model on an independent dataset of 682 healthy youth from the multi-site Pediatric Imaging, Neurocognition and Genetics (PING) cohort study. The trained model exhibited an 83% cross-validated prediction accuracy, and correctly predicted the sex of 77% of the subjects from the independent multi-site dataset. Results showed that cortical thickness of the middle occipital lobes and the angular gyri are major predictors of sex. Results also demonstrated the inferential benefits of going beyond classical regression approaches to capture the interactions among brain features in order to better characterize sex differences in male and female youths. We also identified specific cortical morphological measures and parcellation techniques, such as cortical thickness as derived from the Destrieux atlas, that are better able to discriminate between males and females in comparison to other brain atlases (Desikan-Killiany, Brodmann and subcortical atlases).

Twitter Demographics

The data shown below were collected from the profiles of 299 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 18 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 18 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 8 44%
Student > Ph. D. Student 5 28%
Student > Master 3 17%
Student > Postgraduate 2 11%
Readers by discipline Count As %
Psychology 4 22%
Unspecified 3 17%
Neuroscience 3 17%
Computer Science 3 17%
Agricultural and Biological Sciences 2 11%
Other 3 17%

Attention Score in Context

This research output has an Altmetric Attention Score of 199. 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 08 December 2018.
All research outputs
#55,865
of 12,271,878 outputs
Outputs from NeuroImage
#19
of 7,490 outputs
Outputs of similar age
#3,292
of 331,316 outputs
Outputs of similar age from NeuroImage
#2
of 172 outputs
Altmetric has tracked 12,271,878 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,490 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 99% 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 331,316 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 99% of its contemporaries.
We're also able to compare this research output to 172 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 98% of its contemporaries.