Connectome-based predictive modeling of attention: Comparing different functional connectivity features and prediction methods across datasets

Connectome-based predictive modeling (CPM; Finn et al., 2015; Shen et al., 2017) was recently developed to predict individual differences in traits and behaviors, including fluid intelligence (Finn et al., 2015) and sustained attention (Rosenberg et al., 2016a), from functional brain connectivity (F...

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Published inNeuroImage (Orlando, Fla.) Vol. 167; pp. 11 - 22
Main Authors Yoo, Kwangsun, Rosenberg, Monica D., Hsu, Wei-Ting, Zhang, Sheng, Li, Chiang-Shan R., Scheinost, Dustin, Constable, R. Todd, Chun, Marvin M.
Format Journal Article
LanguageEnglish
Published United States Elsevier Inc 15.02.2018
Elsevier Limited
Subjects
Adult
Algorithms
Attention
Attention - physiology
Attention Deficit Disorder with Hyperactivity - diagnostic imaging
Attention Deficit Disorder with Hyperactivity - physiopathology
Attention deficit hyperactivity disorder
Attention task
Behavior
Brain - diagnostic imaging
Brain - physiology
Brain mapping
Cognitive ability
Connectome - methods
Connectome - standards
Connectome - statistics & numerical data
Datasets as Topic
Discordance
Executive Function - physiology
Functional connectivity
Functional magnetic resonance imaging
Humans
Intelligence
Magnetic Resonance Imaging - methods
Magnetic Resonance Imaging - standards
Magnetic Resonance Imaging - statistics & numerical data
Memory
Methylphenidate
Models, Statistical
Neural networks
Noise
Partial least square regression
Predictive model
Principal components analysis
Psychomotor Performance - physiology
Regression analysis
Reproducibility of Results
Statistical analysis
Studies
Synchronization
Young Adult
Functional connectivity
Predictive model
Attention
Partial least square regression
Online AccessGet full text
ISSN1053-8119
1095-9572
1095-9572
DOI10.1016/j.neuroimage.2017.11.010

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Abstract Connectome-based predictive modeling (CPM; Finn et al., 2015; Shen et al., 2017) was recently developed to predict individual differences in traits and behaviors, including fluid intelligence (Finn et al., 2015) and sustained attention (Rosenberg et al., 2016a), from functional brain connectivity (FC) measured with fMRI. Here, using the CPM framework, we compared the predictive power of three different measures of FC (Pearson's correlation, accordance, and discordance) and two different prediction algorithms (linear and partial least square [PLS] regression) for attention function. Accordance and discordance are recently proposed FC measures that respectively track in-phase synchronization and out-of-phase anti-correlation (Meskaldji et al., 2015). We defined connectome-based models using task-based or resting-state FC data, and tested the effects of (1) functional connectivity measure and (2) feature-selection/prediction algorithm on individualized attention predictions. Models were internally validated in a training dataset using leave-one-subject-out cross-validation, and externally validated with three independent datasets. The training dataset included fMRI data collected while participants performed a sustained attention task and rested (N = 25; Rosenberg et al., 2016a). The validation datasets included: 1) data collected during performance of a stop-signal task and at rest (N = 83, including 19 participants who were administered methylphenidate prior to scanning; Farr et al., 2014a; Rosenberg et al., 2016b), 2) data collected during Attention Network Task performance and rest (N = 41, Rosenberg et al., in press), and 3) resting-state data and ADHD symptom severity from the ADHD-200 Consortium (N = 113; Rosenberg et al., 2016a). Models defined using all combinations of functional connectivity measure (Pearson's correlation, accordance, and discordance) and prediction algorithm (linear and PLS regression) predicted attentional abilities, with correlations between predicted and observed measures of attention as high as 0.9 for internal validation, and 0.6 for external validation (all p's < 0.05). Models trained on task data outperformed models trained on rest data. Pearson's correlation and accordance features generally showed a small numerical advantage over discordance features, while PLS regression models were usually better than linear regression models. Overall, in addition to correlation features combined with linear models (Rosenberg et al., 2016a), it is useful to consider accordance features and PLS regression for CPM. •Functional connectivity can predict individual differences in attention.•We compared different connectivity measures and feature selection algorithms.•Four different data sets permitted both internal and external validation.•For rest data, PLS regression models were numerically better than linear regression.•Pearson’s correlation, accordance, and discordance did not meaningfully differ.
AbstractList Connectome-based predictive modeling (CPM; Finn et al., 2015; Shen et al., 2017) was recently developed to predict individual differences in traits and behaviors, including fluid intelligence (Finn et al., 2015) and sustained attention (Rosenberg et al., 2016a), from functional brain connectivity (FC) measured with fMRI. Here, using the CPM framework, we compared the predictive power of three different measures of FC (Pearson's correlation, accordance, and discordance) and two different prediction algorithms (linear and partial least square [PLS] regression) for attention function. Accordance and discordance are recently proposed FC measures that respectively track in-phase synchronization and out-of-phase anti-correlation (Meskaldji et al., 2015). We defined connectome-based models using task-based or resting-state FC data, and tested the effects of (1) functional connectivity measure and (2) feature-selection/prediction algorithm on individualized attention predictions. Models were internally validated in a training dataset using leave-one-subject-out cross-validation, and externally validated with three independent datasets. The training dataset included fMRI data collected while participants performed a sustained attention task and rested (N = 25; Rosenberg et al., 2016a). The validation datasets included: 1) data collected during performance of a stop-signal task and at rest (N = 83, including 19 participants who were administered methylphenidate prior to scanning; Farr et al., 2014a; Rosenberg et al., 2016b), 2) data collected during Attention Network Task performance and rest (N = 41, Rosenberg et al., in press), and 3) resting-state data and ADHD symptom severity from the ADHD-200 Consortium (N = 113; Rosenberg et al., 2016a). Models defined using all combinations of functional connectivity measure (Pearson's correlation, accordance, and discordance) and prediction algorithm (linear and PLS regression) predicted attentional abilities, with correlations between predicted and observed measures of attention as high as 0.9 for internal validation, and 0.6 for external validation (all p's < 0.05). Models trained on task data outperformed models trained on rest data. Pearson's correlation and accordance features generally showed a small numerical advantage over discordance features, while PLS regression models were usually better than linear regression models. Overall, in addition to correlation features combined with linear models (Rosenberg et al., 2016a), it is useful to consider accordance features and PLS regression for CPM.
Connectome-based predictive modeling (CPM; Finn et al., 2015; Shen et al., 2017) was recently developed to predict individual differences in traits and behaviors, including fluid intelligence (Finn et al., 2015) and sustained attention (Rosenberg et al., 2016a), from functional brain connectivity (FC) measured with fMRI. Here, using the CPM framework, we compared the predictive power of three different measures of FC (Pearson's correlation, accordance, and discordance) and two different prediction algorithms (linear and partial least square [PLS] regression) for attention function. Accordance and discordance are recently proposed FC measures that respectively track in-phase synchronization and out-of-phase anti-correlation (Meskaldji et al., 2015). We defined connectome-based models using task-based or resting-state FC data, and tested the effects of (1) functional connectivity measure and (2) feature-selection/prediction algorithm on individualized attention predictions. Models were internally validated in a training dataset using leave-one-subject-out cross-validation, and externally validated with three independent datasets. The training dataset included fMRI data collected while participants performed a sustained attention task and rested (N = 25; Rosenberg et al., 2016a). The validation datasets included: 1) data collected during performance of a stop-signal task and at rest (N = 83, including 19 participants who were administered methylphenidate prior to scanning; Farr et al., 2014a; Rosenberg et al., 2016b), 2) data collected during Attention Network Task performance and rest (N = 41, Rosenberg et al., in press), and 3) resting-state data and ADHD symptom severity from the ADHD-200 Consortium (N = 113; Rosenberg et al., 2016a). Models defined using all combinations of functional connectivity measure (Pearson's correlation, accordance, and discordance) and prediction algorithm (linear and PLS regression) predicted attentional abilities, with correlations between predicted and observed measures of attention as high as 0.9 for internal validation, and 0.6 for external validation (all p's < 0.05). Models trained on task data outperformed models trained on rest data. Pearson's correlation and accordance features generally showed a small numerical advantage over discordance features, while PLS regression models were usually better than linear regression models. Overall, in addition to correlation features combined with linear models (Rosenberg et al., 2016a), it is useful to consider accordance features and PLS regression for CPM. •Functional connectivity can predict individual differences in attention.•We compared different connectivity measures and feature selection algorithms.•Four different data sets permitted both internal and external validation.•For rest data, PLS regression models were numerically better than linear regression.•Pearson’s correlation, accordance, and discordance did not meaningfully differ.
Connectome-based predictive modeling (CPM; Finn et al., 2015; Shen et al., 2017) was recently developed to predict individual differences in traits and behaviors, including fluid intelligence (Finn et al., 2015) and sustained attention (Rosenberg et al., 2016a), from functional brain connectivity (FC) measured with fMRI. Here, using the CPM framework, we compared the predictive power of three different measures of FC (Pearson's correlation, accordance, and discordance) and two different prediction algorithms (linear and partial least square [PLS] regression) for attention function. Accordance and discordance are recently proposed FC measures that respectively track in-phase synchronization and out-of-phase anti-correlation (Meskaldji et al., 2015). We defined connectome-based models using task-based or resting-state FC data, and tested the effects of (1) functional connectivity measure and (2) feature-selection/prediction algorithm on individualized attention predictions. Models were internally validated in a training dataset using leave-one-subject-out cross-validation, and externally validated with three independent datasets. The training dataset included fMRI data collected while participants performed a sustained attention task and rested (N = 25; Rosenberg et al., 2016a). The validation datasets included: 1) data collected during performance of a stop-signal task and at rest (N = 83, including 19 participants who were administered methylphenidate prior to scanning; Farr et al., 2014a; Rosenberg et al., 2016b), 2) data collected during Attention Network Task performance and rest (N = 41, Rosenberg et al., in press), and 3) resting-state data and ADHD symptom severity from the ADHD-200 Consortium (N = 113; Rosenberg et al., 2016a). Models defined using all combinations of functional connectivity measure (Pearson's correlation, accordance, and discordance) and prediction algorithm (linear and PLS regression) predicted attentional abilities, with correlations between predicted and observed measures of attention as high as 0.9 for internal validation, and 0.6 for external validation (all p's < 0.05). Models trained on task data outperformed models trained on rest data. Pearson's correlation and accordance features generally showed a small numerical advantage over discordance features, while PLS regression models were usually better than linear regression models. Overall, in addition to correlation features combined with linear models (Rosenberg et al., 2016a), it is useful to consider accordance features and PLS regression for CPM.Connectome-based predictive modeling (CPM; Finn et al., 2015; Shen et al., 2017) was recently developed to predict individual differences in traits and behaviors, including fluid intelligence (Finn et al., 2015) and sustained attention (Rosenberg et al., 2016a), from functional brain connectivity (FC) measured with fMRI. Here, using the CPM framework, we compared the predictive power of three different measures of FC (Pearson's correlation, accordance, and discordance) and two different prediction algorithms (linear and partial least square [PLS] regression) for attention function. Accordance and discordance are recently proposed FC measures that respectively track in-phase synchronization and out-of-phase anti-correlation (Meskaldji et al., 2015). We defined connectome-based models using task-based or resting-state FC data, and tested the effects of (1) functional connectivity measure and (2) feature-selection/prediction algorithm on individualized attention predictions. Models were internally validated in a training dataset using leave-one-subject-out cross-validation, and externally validated with three independent datasets. The training dataset included fMRI data collected while participants performed a sustained attention task and rested (N = 25; Rosenberg et al., 2016a). The validation datasets included: 1) data collected during performance of a stop-signal task and at rest (N = 83, including 19 participants who were administered methylphenidate prior to scanning; Farr et al., 2014a; Rosenberg et al., 2016b), 2) data collected during Attention Network Task performance and rest (N = 41, Rosenberg et al., in press), and 3) resting-state data and ADHD symptom severity from the ADHD-200 Consortium (N = 113; Rosenberg et al., 2016a). Models defined using all combinations of functional connectivity measure (Pearson's correlation, accordance, and discordance) and prediction algorithm (linear and PLS regression) predicted attentional abilities, with correlations between predicted and observed measures of attention as high as 0.9 for internal validation, and 0.6 for external validation (all p's < 0.05). Models trained on task data outperformed models trained on rest data. Pearson's correlation and accordance features generally showed a small numerical advantage over discordance features, while PLS regression models were usually better than linear regression models. Overall, in addition to correlation features combined with linear models (Rosenberg et al., 2016a), it is useful to consider accordance features and PLS regression for CPM.
Connectome-based predictive modeling (CPM; Finn et al., 2015 ; Shen et al., 2017 ) was recently developed to predict individual differences in traits and behaviors, including fluid intelligence ( Finn et al., 2015 ) and sustained attention ( Rosenberg et al., 2016a ), from functional brain connectivity (FC) measured with fMRI. Here, using the CPM framework, we compared the predictive power of three different measures of FC (Pearson’s correlation, accordance, and discordance) and two different prediction algorithms (linear and partial least square [PLS] regression) for attention function. Accordance and discordance are recently proposed FC measures that respectively track in-phase synchronization and out-of-phase anti-correlation ( Meskaldji et al., 2016 ). We defined connectome-based models using task-based or resting-state FC data, and tested the effects of (1) functional connectivity measure and (2) feature-selection/prediction algorithm on individualized attention predictions. Models were internally validated in a training dataset using leave-one-subject-out cross-validation, and externally validated with three independent datasets. The training dataset included fMRI data collected while participants performed a sustained attention task and rested (N=25; Rosenberg et al., 2016a ). The validation datasets included: 1) data collected during performance of a stop-signal task and at rest (N=83, including 19 participants who were administered methylphenidate prior to scanning; Rosenberg et al., 2016b ; f al., 2014a), 2) data collected during Attention Network Task performance and rest (N=41, Rosenberg et al., in press), and 3) resting-state data and ADHD symptom severity from the ADHD-200 Consortium (N=113; Rosenberg et al., 2016a ). Models defined using all combinations of functional connectivity measure (Pearson’s correlation, accordance, and discordance) and prediction algorithm (linear and PLS regression) predicted attentional abilities, with correlations between predicted and observed measures of attention as high as 0.9 for internal validation, and 0.6 for external validation (all p’s < 0.05). Models trained on task data outperformed models trained on rest data. Pearson’s correlation and accordance features generally showed a small numerical advantage over discordance features, while PLS regression models were usually better than linear regression models. Overall, in addition to correlation features combined with linear models ( Rosenberg et al., 2016a ), it is useful to consider accordance features and PLS regression for CPM.
Connectome-based predictive modeling (CPM; Finn et al., 2015; Shen et al., 2017) was recently developed to predict individual differences in traits and behaviors, including fluid intelligence (Finn et al., 2015) and sustained attention (Rosenberg et al., 2016a), from functional brain connectivity (FC) measured with fMRI. Here, using the CPM framework, we compared the predictive power of three different measures of FC (Pearson's correlation, accordance, and discordance) and two different prediction algorithms (linear and partial least square [PLS] regression) for attention function. Accordance and discordance are recently proposed FC measures that respectively track in-phase synchronization and out-of-phase anti-correlation (Meskaldji et al., 2015). We defined connectome-based models using task-based or resting-state FC data, and tested the effects of (1) functional connectivity measure and (2) feature-selection/prediction algorithm on individualized attention predictions. Models were internally validated in a training dataset using leave-one-subject-out cross-validation, and externally validated with three independent datasets. The training dataset included fMRI data collected while participants performed a sustained attention task and rested (N = 25; Rosenberg et al., 2016a). The validation datasets included: 1) data collected during performance of a stop-signal task and at rest (N = 83, including 19 participants who were administered methylphenidate prior to scanning; Farr et al., 2014a; Rosenberg et al., 2016b), 2) data collected during Attention Network Task performance and rest (N = 41, Rosenberg et al., in press), and 3) resting-state data and ADHD symptom severity from the ADHD-200 Consortium (N = 113; Rosenberg et al., 2016a). Models defined using all combinations of functional connectivity measure (Pearson's correlation, accordance, and discordance) and prediction algorithm (linear and PLS regression) predicted attentional abilities, with correlations between predicted and observed measures of attention as high as 0.9 for internal validation, and 0.6 for external validation (all p's < 0.05). Models trained on task data outperformed models trained on rest data. Pearson's correlation and accordance features generally showed a small numerical advantage over discordance features, while PLS regression models were usually better than linear regression models. Overall, in addition to correlation features combined with linear models (Rosenberg et al., 2016a), it is useful to consider accordance features and PLS regression for CPM.
Author Rosenberg, Monica D.
Yoo, Kwangsun
Zhang, Sheng
Hsu, Wei-Ting
Li, Chiang-Shan R.
Chun, Marvin M.
Scheinost, Dustin
Constable, R. Todd
AuthorAffiliation 1 Department of Psychology, Yale University, New Haven, CT 06520, USA
5 Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06520, USA
2 Department of Psychiatry, Yale School of Medicine, New Haven, CT 06520, USA
6 Department of Neurosurgery, Yale School of Medicine, New Haven, CT 06520, USA
4 Interdepartmental Neuroscience Program, Yale University, New Haven, CT 06520, USA
3 Department of Neuroscience, Yale School of Medicine, New Haven, CT 06520, USA
AuthorAffiliation_xml – name: 5 Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06520, USA
– name: 1 Department of Psychology, Yale University, New Haven, CT 06520, USA
– name: 6 Department of Neurosurgery, Yale School of Medicine, New Haven, CT 06520, USA
– name: 3 Department of Neuroscience, Yale School of Medicine, New Haven, CT 06520, USA
– name: 4 Interdepartmental Neuroscience Program, Yale University, New Haven, CT 06520, USA
– name: 2 Department of Psychiatry, Yale School of Medicine, New Haven, CT 06520, USA
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  email: kwangsun.yoo@yale.edu
  organization: Department of Psychology, Yale University, New Haven, CT, USA
– sequence: 2
  givenname: Monica D.
  surname: Rosenberg
  fullname: Rosenberg, Monica D.
  organization: Department of Psychology, Yale University, New Haven, CT, USA
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  givenname: Wei-Ting
  surname: Hsu
  fullname: Hsu, Wei-Ting
  organization: Department of Psychology, Yale University, New Haven, CT, USA
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  surname: Zhang
  fullname: Zhang, Sheng
  organization: Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
– sequence: 5
  givenname: Chiang-Shan R.
  surname: Li
  fullname: Li, Chiang-Shan R.
  organization: Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
– sequence: 6
  givenname: Dustin
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  organization: Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
– sequence: 8
  givenname: Marvin M.
  surname: Chun
  fullname: Chun, Marvin M.
  organization: Department of Psychology, Yale University, New Haven, CT, USA
BackLink https://www.ncbi.nlm.nih.gov/pubmed/29122720$$D View this record in MEDLINE/PubMed
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Predictive model
Attention
Partial least square regression
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Snippet Connectome-based predictive modeling (CPM; Finn et al., 2015; Shen et al., 2017) was recently developed to predict individual differences in traits and...
Connectome-based predictive modeling (CPM; Finn et al., 2015; Shen et al., 2017) was recently developed to predict individual differences in traits and...
Connectome-based predictive modeling (CPM; Finn et al., 2015 ; Shen et al., 2017 ) was recently developed to predict individual differences in traits and...
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StartPage 11
SubjectTerms Adult
Algorithms
Attention
Attention - physiology
Attention Deficit Disorder with Hyperactivity - diagnostic imaging
Attention Deficit Disorder with Hyperactivity - physiopathology
Attention deficit hyperactivity disorder
Attention task
Behavior
Brain - diagnostic imaging
Brain - physiology
Brain mapping
Cognitive ability
Connectome - methods
Connectome - standards
Connectome - statistics & numerical data
Datasets as Topic
Discordance
Executive Function - physiology
Functional connectivity
Functional magnetic resonance imaging
Humans
Intelligence
Magnetic Resonance Imaging - methods
Magnetic Resonance Imaging - standards
Magnetic Resonance Imaging - statistics & numerical data
Memory
Methylphenidate
Models, Statistical
Neural networks
Noise
Partial least square regression
Predictive model
Principal components analysis
Psychomotor Performance - physiology
Regression analysis
Reproducibility of Results
Statistical analysis
Studies
Synchronization
Young Adult
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Title Connectome-based predictive modeling of attention: Comparing different functional connectivity features and prediction methods across datasets
URI https://www.clinicalkey.com/#!/content/1-s2.0-S1053811917309175
https://dx.doi.org/10.1016/j.neuroimage.2017.11.010
https://www.ncbi.nlm.nih.gov/pubmed/29122720
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https://pubmed.ncbi.nlm.nih.gov/PMC5845789
Volume 167
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