MRI-based intelligence quotient (IQ) estimation with sparse learning

Liye Wang, Chong Yaw Wee, Heung-Il Suk, Xiaoying Tang, Dinggang Shen

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

In this paper, we propose a novel framework for IQ estimation using Magnetic Resonance Imaging (MRI) data. In particular, we devise a new feature selection method based on an extended dirty model for jointly considering both element-wise sparsity and group-wise sparsity. Meanwhile, due to the absence of large dataset with consistent scanning protocols for the IQ estimation, we integrate multiple datasets scanned from different sites with different scanning parameters and protocols. In this way, there is large variability in these different datasets. To address this issue, we design a two-step procedure for 1) first identifying the possible scanning site for each testing subject and 2) then estimating the testing subject's IQ by using a specific estimator designed for that scanning site. We perform two experiments to test the performance of our method by using the MRI data collected from 164 typically developing children between 6 and 15 years old. In the first experiment, we use a multi-kernel Support Vector Regression (SVR) for estimating IQ values, and obtain an average correlation coefficient of 0.718 and also an average root mean square error of 8.695 between the true IQs and the estimated ones. In the second experiment, we use a singlekernel SVR for IQ estimation, and achieve an average correlation coefficient of 0.684 and an average root mean square error of 9.166. All these results show the effectiveness of using imaging data for IQ prediction, which is rarely done in the field according to our knowledge.

Original languageEnglish
Article numbere0117295
JournalPLoS One
Volume10
Issue number3
DOIs
Publication statusPublished - 2015 Mar 30

Fingerprint

Intelligence
magnetic resonance imaging
learning
Magnetic Resonance Imaging
Learning
Scanning
Mean square error
testing
selection methods
Experiments
Testing
image analysis
Feature extraction
prediction
seeds
Imaging techniques
methodology
Datasets

ASJC Scopus subject areas

  • Agricultural and Biological Sciences(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Medicine(all)

Cite this

MRI-based intelligence quotient (IQ) estimation with sparse learning. / Wang, Liye; Wee, Chong Yaw; Suk, Heung-Il; Tang, Xiaoying; Shen, Dinggang.

In: PLoS One, Vol. 10, No. 3, e0117295, 30.03.2015.

Research output: Contribution to journalArticle

Wang, Liye ; Wee, Chong Yaw ; Suk, Heung-Il ; Tang, Xiaoying ; Shen, Dinggang. / MRI-based intelligence quotient (IQ) estimation with sparse learning. In: PLoS One. 2015 ; Vol. 10, No. 3.
@article{a283b1395f2c46779bc7113a918b8d90,
title = "MRI-based intelligence quotient (IQ) estimation with sparse learning",
abstract = "In this paper, we propose a novel framework for IQ estimation using Magnetic Resonance Imaging (MRI) data. In particular, we devise a new feature selection method based on an extended dirty model for jointly considering both element-wise sparsity and group-wise sparsity. Meanwhile, due to the absence of large dataset with consistent scanning protocols for the IQ estimation, we integrate multiple datasets scanned from different sites with different scanning parameters and protocols. In this way, there is large variability in these different datasets. To address this issue, we design a two-step procedure for 1) first identifying the possible scanning site for each testing subject and 2) then estimating the testing subject's IQ by using a specific estimator designed for that scanning site. We perform two experiments to test the performance of our method by using the MRI data collected from 164 typically developing children between 6 and 15 years old. In the first experiment, we use a multi-kernel Support Vector Regression (SVR) for estimating IQ values, and obtain an average correlation coefficient of 0.718 and also an average root mean square error of 8.695 between the true IQs and the estimated ones. In the second experiment, we use a singlekernel SVR for IQ estimation, and achieve an average correlation coefficient of 0.684 and an average root mean square error of 9.166. All these results show the effectiveness of using imaging data for IQ prediction, which is rarely done in the field according to our knowledge.",
author = "Liye Wang and Wee, {Chong Yaw} and Heung-Il Suk and Xiaoying Tang and Dinggang Shen",
year = "2015",
month = "3",
day = "30",
doi = "10.1371/journal.pone.0117295",
language = "English",
volume = "10",
journal = "PLoS One",
issn = "1932-6203",
publisher = "Public Library of Science",
number = "3",

}

TY - JOUR

T1 - MRI-based intelligence quotient (IQ) estimation with sparse learning

AU - Wang, Liye

AU - Wee, Chong Yaw

AU - Suk, Heung-Il

AU - Tang, Xiaoying

AU - Shen, Dinggang

PY - 2015/3/30

Y1 - 2015/3/30

N2 - In this paper, we propose a novel framework for IQ estimation using Magnetic Resonance Imaging (MRI) data. In particular, we devise a new feature selection method based on an extended dirty model for jointly considering both element-wise sparsity and group-wise sparsity. Meanwhile, due to the absence of large dataset with consistent scanning protocols for the IQ estimation, we integrate multiple datasets scanned from different sites with different scanning parameters and protocols. In this way, there is large variability in these different datasets. To address this issue, we design a two-step procedure for 1) first identifying the possible scanning site for each testing subject and 2) then estimating the testing subject's IQ by using a specific estimator designed for that scanning site. We perform two experiments to test the performance of our method by using the MRI data collected from 164 typically developing children between 6 and 15 years old. In the first experiment, we use a multi-kernel Support Vector Regression (SVR) for estimating IQ values, and obtain an average correlation coefficient of 0.718 and also an average root mean square error of 8.695 between the true IQs and the estimated ones. In the second experiment, we use a singlekernel SVR for IQ estimation, and achieve an average correlation coefficient of 0.684 and an average root mean square error of 9.166. All these results show the effectiveness of using imaging data for IQ prediction, which is rarely done in the field according to our knowledge.

AB - In this paper, we propose a novel framework for IQ estimation using Magnetic Resonance Imaging (MRI) data. In particular, we devise a new feature selection method based on an extended dirty model for jointly considering both element-wise sparsity and group-wise sparsity. Meanwhile, due to the absence of large dataset with consistent scanning protocols for the IQ estimation, we integrate multiple datasets scanned from different sites with different scanning parameters and protocols. In this way, there is large variability in these different datasets. To address this issue, we design a two-step procedure for 1) first identifying the possible scanning site for each testing subject and 2) then estimating the testing subject's IQ by using a specific estimator designed for that scanning site. We perform two experiments to test the performance of our method by using the MRI data collected from 164 typically developing children between 6 and 15 years old. In the first experiment, we use a multi-kernel Support Vector Regression (SVR) for estimating IQ values, and obtain an average correlation coefficient of 0.718 and also an average root mean square error of 8.695 between the true IQs and the estimated ones. In the second experiment, we use a singlekernel SVR for IQ estimation, and achieve an average correlation coefficient of 0.684 and an average root mean square error of 9.166. All these results show the effectiveness of using imaging data for IQ prediction, which is rarely done in the field according to our knowledge.

UR - http://www.scopus.com/inward/record.url?scp=84926390489&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84926390489&partnerID=8YFLogxK

U2 - 10.1371/journal.pone.0117295

DO - 10.1371/journal.pone.0117295

M3 - Article

VL - 10

JO - PLoS One

JF - PLoS One

SN - 1932-6203

IS - 3

M1 - e0117295

ER -