Functional connectome fingerprinting: Identifying individuals and predicting cognitive functions via autoencoder

Functional network connectivity has been widely acknowledged to characterize brain functions, which can be regarded as “brain fingerprinting” to identify an individual from a pool of subjects. Both common and unique information has been shown to exist in the connectomes across individuals. However,...

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Vydané v:	Human brain mapping Ročník 42; číslo 9; s. 2691 - 2705
Hlavní autori:	Cai, Biao, Zhang, Gemeng, Zhang, Aiying, Xiao, Li, Hu, Wenxing, Stephen, Julia M., Wilson, Tony W., Calhoun, Vince D., Wang, Yu‐Ping
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Hoboken, USA John Wiley & Sons, Inc 15.06.2021
Predmet:	Adult autoencoder network Biological Variation, Individual Brain Brain - diagnostic imaging Brain - physiology Cognition Cognition - physiology Cognitive ability common connectivity patterns Connectome - methods Default Mode Network - diagnostic imaging Default Mode Network - physiology Executive function Fingerprinting functional connectivity Functionals high‐level cognition prediction Humans individual identification Intelligence Machine learning Magnetic Resonance Imaging - methods Nerve Net - diagnostic imaging Nerve Net - physiology Neural networks refined connectomes autoencoder network common connectivity patterns functional connectivity refined connectomes individual identification high-level cognition prediction
ISSN:	1065-9471, 1097-0193, 1097-0193
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Abstract	Functional network connectivity has been widely acknowledged to characterize brain functions, which can be regarded as “brain fingerprinting” to identify an individual from a pool of subjects. Both common and unique information has been shown to exist in the connectomes across individuals. However, very little is known about whether and how this information can be used to predict the individual variability of the brain. In this paper, we propose to enhance the uniqueness of individual connectome based on an autoencoder network. Specifically, we hypothesize that the common neural activities shared across individuals may reduce the individual identification. By removing contributions from shared activities, inter‐subject variability can be enhanced. Our experimental results on HCP data show that the refined connectomes obtained by utilizing autoencoder with sparse dictionary learning can distinguish an individual from the remaining participants with high accuracy (up to 99.5% for the rest–rest pair). Furthermore, high‐level cognitive behaviors (e.g., fluid intelligence, executive function, and language comprehension) can also be better predicted with the obtained refined connectomes. We also find that high‐order association cortices contribute more to both individual discrimination and behavior prediction. In summary, our proposed framework provides a promising way to leverage functional connectivity networks for cognition and behavior study, in addition to a better understanding of brain functions. Our main contribution is to enhance the individual uniqueness based on a framework applying an autoencoder network. Our approach is validated using six modalities of fMRI (resting‐state 1, resting‐state 2, working memory, motor, language, and emotion) from the HCP data set.
AbstractList	Functional network connectivity has been widely acknowledged to characterize brain functions, which can be regarded as “brain fingerprinting” to identify an individual from a pool of subjects. Both common and unique information has been shown to exist in the connectomes across individuals. However, very little is known about whether and how this information can be used to predict the individual variability of the brain. In this paper, we propose to enhance the uniqueness of individual connectome based on an autoencoder network. Specifically, we hypothesize that the common neural activities shared across individuals may reduce the individual identification. By removing contributions from shared activities, inter‐subject variability can be enhanced. Our experimental results on HCP data show that the refined connectomes obtained by utilizing autoencoder with sparse dictionary learning can distinguish an individual from the remaining participants with high accuracy (up to 99.5% for the rest–rest pair). Furthermore, high‐level cognitive behaviors (e.g., fluid intelligence, executive function, and language comprehension) can also be better predicted with the obtained refined connectomes. We also find that high‐order association cortices contribute more to both individual discrimination and behavior prediction. In summary, our proposed framework provides a promising way to leverage functional connectivity networks for cognition and behavior study, in addition to a better understanding of brain functions. Functional network connectivity has been widely acknowledged to characterize brain functions, which can be regarded as "brain fingerprinting" to identify an individual from a pool of subjects. Both common and unique information has been shown to exist in the connectomes across individuals. However, very little is known about whether and how this information can be used to predict the individual variability of the brain. In this paper, we propose to enhance the uniqueness of individual connectome based on an autoencoder network. Specifically, we hypothesize that the common neural activities shared across individuals may reduce the individual identification. By removing contributions from shared activities, inter-subject variability can be enhanced. Our experimental results on HCP data show that the refined connectomes obtained by utilizing autoencoder with sparse dictionary learning can distinguish an individual from the remaining participants with high accuracy (up to 99.5% for the rest-rest pair). Furthermore, high-level cognitive behaviors (e.g., fluid intelligence, executive function, and language comprehension) can also be better predicted with the obtained refined connectomes. We also find that high-order association cortices contribute more to both individual discrimination and behavior prediction. In summary, our proposed framework provides a promising way to leverage functional connectivity networks for cognition and behavior study, in addition to a better understanding of brain functions.Functional network connectivity has been widely acknowledged to characterize brain functions, which can be regarded as "brain fingerprinting" to identify an individual from a pool of subjects. Both common and unique information has been shown to exist in the connectomes across individuals. However, very little is known about whether and how this information can be used to predict the individual variability of the brain. In this paper, we propose to enhance the uniqueness of individual connectome based on an autoencoder network. Specifically, we hypothesize that the common neural activities shared across individuals may reduce the individual identification. By removing contributions from shared activities, inter-subject variability can be enhanced. Our experimental results on HCP data show that the refined connectomes obtained by utilizing autoencoder with sparse dictionary learning can distinguish an individual from the remaining participants with high accuracy (up to 99.5% for the rest-rest pair). Furthermore, high-level cognitive behaviors (e.g., fluid intelligence, executive function, and language comprehension) can also be better predicted with the obtained refined connectomes. We also find that high-order association cortices contribute more to both individual discrimination and behavior prediction. In summary, our proposed framework provides a promising way to leverage functional connectivity networks for cognition and behavior study, in addition to a better understanding of brain functions. Functional network connectivity has been widely acknowledged to characterize brain functions, which can be regarded as “brain fingerprinting” to identify an individual from a pool of subjects. Both common and unique information has been shown to exist in the connectomes across individuals. However, very little is known about whether and how this information can be used to predict the individual variability of the brain. In this paper, we propose to enhance the uniqueness of individual connectome based on an autoencoder network. Specifically, we hypothesize that the common neural activities shared across individuals may reduce the individual identification. By removing contributions from shared activities, inter‐subject variability can be enhanced. Our experimental results on HCP data show that the refined connectomes obtained by utilizing autoencoder with sparse dictionary learning can distinguish an individual from the remaining participants with high accuracy (up to 99.5% for the rest–rest pair). Furthermore, high‐level cognitive behaviors (e.g., fluid intelligence, executive function, and language comprehension) can also be better predicted with the obtained refined connectomes. We also find that high‐order association cortices contribute more to both individual discrimination and behavior prediction. In summary, our proposed framework provides a promising way to leverage functional connectivity networks for cognition and behavior study, in addition to a better understanding of brain functions. Our main contribution is to enhance the individual uniqueness based on a framework applying an autoencoder network. Our approach is validated using six modalities of fMRI (resting‐state 1, resting‐state 2, working memory, motor, language, and emotion) from the HCP data set. Functional network connectivity has been widely acknowledged to characterize brain functions, which can be regarded as “brain fingerprinting” to identify an individual from a pool of subjects. Both common and unique information has been shown to exist in the connectomes across individuals. However, very little is known about whether and how this information can be used to predict the individual variability of the brain. In this paper, we propose to enhance the uniqueness of individual connectome based on an autoencoder network. Specifically, we hypothesize that the common neural activities shared across individuals may reduce the individual identification. By removing contributions from shared activities, inter‐subject variability can be enhanced. Our experimental results on HCP data show that the refined connectomes obtained by utilizing autoencoder with sparse dictionary learning can distinguish an individual from the remaining participants with high accuracy (up to 99.5% for the rest–rest pair). Furthermore, high‐level cognitive behaviors (e.g., fluid intelligence, executive function, and language comprehension) can also be better predicted with the obtained refined connectomes. We also find that high‐order association cortices contribute more to both individual discrimination and behavior prediction. In summary, our proposed framework provides a promising way to leverage functional connectivity networks for cognition and behavior study, in addition to a better understanding of brain functions. Our main contribution is to enhance the individual uniqueness based on a framework applying an autoencoder network. Our approach is validated using six modalities of fMRI (resting‐state 1, resting‐state 2, working memory, motor, language, and emotion) from the HCP data set.
Audience	Academic
Author	Hu, Wenxing Zhang, Aiying Cai, Biao Wang, Yu‐Ping Stephen, Julia M. Xiao, Li Wilson, Tony W. Calhoun, Vince D. Zhang, Gemeng
AuthorAffiliation	4 Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University, Georgia Institute of Technology, Emory University Atlanta Georgia USA 3 Department of Neurological Sciences University of Nebraska Medical Center (UNMC) Omaha Nebraska USA 2 The Mind Research Network Albuquerque New Mexico USA 1 Biomedical Engineering Department Tulane University New Orleans Louisiana USA
AuthorAffiliation_xml	– name: 1 Biomedical Engineering Department Tulane University New Orleans Louisiana USA – name: 3 Department of Neurological Sciences University of Nebraska Medical Center (UNMC) Omaha Nebraska USA – name: 2 The Mind Research Network Albuquerque New Mexico USA – name: 4 Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University, Georgia Institute of Technology, Emory University Atlanta Georgia USA
Author_xml	– sequence: 1 givenname: Biao orcidid: 0000-0002-2706-4982 surname: Cai fullname: Cai, Biao organization: Tulane University – sequence: 2 givenname: Gemeng surname: Zhang fullname: Zhang, Gemeng organization: Tulane University – sequence: 3 givenname: Aiying orcidid: 0000-0001-9623-3922 surname: Zhang fullname: Zhang, Aiying organization: Tulane University – sequence: 4 givenname: Li surname: Xiao fullname: Xiao, Li organization: Tulane University – sequence: 5 givenname: Wenxing surname: Hu fullname: Hu, Wenxing organization: Tulane University – sequence: 6 givenname: Julia M. surname: Stephen fullname: Stephen, Julia M. organization: The Mind Research Network – sequence: 7 givenname: Tony W. orcidid: 0000-0002-5053-8306 surname: Wilson fullname: Wilson, Tony W. organization: University of Nebraska Medical Center (UNMC) – sequence: 8 givenname: Vince D. surname: Calhoun fullname: Calhoun, Vince D. organization: Georgia State University, Georgia Institute of Technology, Emory University – sequence: 9 givenname: Yu‐Ping surname: Wang fullname: Wang, Yu‐Ping email: wyp@tulane.edu organization: Tulane University
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Snippet	Functional network connectivity has been widely acknowledged to characterize brain functions, which can be regarded as “brain fingerprinting” to identify an... Functional network connectivity has been widely acknowledged to characterize brain functions, which can be regarded as "brain fingerprinting" to identify an...
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SubjectTerms	Adult autoencoder network Biological Variation, Individual Brain Brain - diagnostic imaging Brain - physiology Cognition Cognition - physiology Cognitive ability common connectivity patterns Connectome - methods Default Mode Network - diagnostic imaging Default Mode Network - physiology Executive function Fingerprinting functional connectivity Functionals high‐level cognition prediction Humans individual identification Intelligence Machine learning Magnetic Resonance Imaging - methods Nerve Net - diagnostic imaging Nerve Net - physiology Neural networks refined connectomes
Title	Functional connectome fingerprinting: Identifying individuals and predicting cognitive functions via autoencoder
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