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Adolescent Brain Cognitive Development Neurocognitive Prediction

Overview of attention for book
Cover of 'Adolescent Brain Cognitive Development Neurocognitive Prediction'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 A Combined Deep Learning-Gradient Boosting Machine Framework for Fluid Intelligence Prediction
  3. Altmetric Badge
    Chapter 2 Predicting Fluid Intelligence of Children using T1-weighted MR Images and a StackNet
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    Chapter 3 Deep Learning vs. Classical Machine Learning: A Comparison of Methods for Fluid Intelligence Prediction
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    Chapter 4 Surface-Based Brain Morphometry for the Prediction of Fluid Intelligence in the Neurocognitive Prediction Challenge 2019
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    Chapter 5 Prediction of Fluid Intelligence from T1-Weighted Magnetic Resonance Images
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    Chapter 6 Ensemble of SVM, Random-Forest and the BSWiMS Method to Predict and Describe Structural Associations with Fluid Intelligence Scores from T1-Weighed MRI
  8. Altmetric Badge
    Chapter 7 Predicting Intelligence Based on Cortical WM/GM Contrast, Cortical Thickness and Volumetry
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    Chapter 8 Predict Fluid Intelligence of Adolescent Using Ensemble Learning
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    Chapter 9 Predicting Fluid Intelligence in Adolescent Brain MRI Data: An Ensemble Approach
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    Chapter 10 Predicting Fluid Intelligence from Structural MRI Using Random Forest regression
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    Chapter 11 Nu Support Vector Machine in Prediction of Fluid Intelligence Using MRI Data
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    Chapter 12 An AutoML Approach for the Prediction of Fluid Intelligence from MRI-Derived Features
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    Chapter 13 Predicting Fluid Intelligence from MRI Images with Encoder-Decoder Regularization
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    Chapter 14 ABCD Neurocognitive Prediction Challenge 2019: Predicting Individual Residual Fluid Intelligence Scores from Cortical Grey Matter Morphology
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    Chapter 15 Ensemble Modeling of Neurocognitive Performance Using MRI-Derived Brain Structure Volumes
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    Chapter 16 ABCD Neurocognitive Prediction Challenge 2019: Predicting Individual Fluid Intelligence Scores from Structural MRI Using Probabilistic Segmentation and Kernel Ridge Regression
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    Chapter 17 Predicting Fluid Intelligence Using Anatomical Measures Within Functionally Defined Brain Networks
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    Chapter 18 Sex Differences in Predicting Fluid Intelligence of Adolescent Brain from T1-Weighted MRIs
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    Chapter 19 Ensemble of 3D CNN Regressors with Data Fusion for Fluid Intelligence Prediction
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    Chapter 20 Adolescent Fluid Intelligence Prediction from Regional Brain Volumes and Cortical Curvatures Using BlockPC-XGBoost
  22. Altmetric Badge
    Chapter 21 Cortical and Subcortical Contributions to Predicting Intelligence Using 3D ConvNets
Attention for Chapter 2: Predicting Fluid Intelligence of Children using T1-weighted MR Images and a StackNet
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  • Good Attention Score compared to outputs of the same age and source (71st percentile)

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Chapter title
Predicting Fluid Intelligence of Children using T1-weighted MR Images and a StackNet
Chapter number 2
Book title
Adolescent Brain Cognitive Development Neurocognitive Prediction
Published in
arXiv, April 2019
DOI 10.1007/978-3-030-31901-4_2
Book ISBNs
978-3-03-031900-7, 978-3-03-031901-4
Authors

Po-Yu Kao, Angela Zhang, Michael Goebel, Jefferson W. Chen, B. S. Manjunath, Kao, Po-Yu, Zhang, Angela, Goebel, Michael, Chen, Jefferson W., Manjunath, B. S.

X Demographics

X Demographics

The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 7 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 7 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 3 43%
Other 1 14%
Student > Doctoral Student 1 14%
Unknown 2 29%
Readers by discipline Count As %
Computer Science 3 43%
Unknown 4 57%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 13 May 2020.
All research outputs
#14,345,282
of 24,099,692 outputs
Outputs from arXiv
#237,404
of 1,020,419 outputs
Outputs of similar age
#165,709
of 322,925 outputs
Outputs of similar age from arXiv
#7,251
of 27,697 outputs
Altmetric has tracked 24,099,692 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,020,419 research outputs from this source. They receive a mean Attention Score of 4.0. This one has gotten more attention than average, scoring higher than 74% 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 322,925 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 46th percentile – i.e., 46% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 27,697 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.