Graph‐matching distance between individuals' functional connectomes varies with relatedness, age, and cognitive score
Functional connectomes (FCs), represented by networks or graphs that summarize coactivation patterns between pairs of brain regions, have been related at a population level to age, sex, cognitive/behavioral scores, life experience, genetics, and disease/disorders. However, quantifying FC differences...
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| Published in | Human brain mapping Vol. 44; no. 9; pp. 3541 - 3554 |
|---|---|
| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Hoboken, USA
John Wiley & Sons, Inc
15.06.2023
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| Subjects | |
| Online Access | Get full text |
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| Abstract | Functional connectomes (FCs), represented by networks or graphs that summarize coactivation patterns between pairs of brain regions, have been related at a population level to age, sex, cognitive/behavioral scores, life experience, genetics, and disease/disorders. However, quantifying FC differences between individuals also provides a rich source of information with which to map to differences in those individuals' biology, experience, genetics or behavior. In this study, graph matching is used to create a novel inter‐individual FC metric, called swap distance, that quantifies the distance between pairs of individuals' partial FCs, with a smaller swap distance indicating the individuals have more similar FC. We apply graph matching to align FCs between individuals from the the Human Connectome Project N=997 and find that swap distance (i) increases with increasing familial distance, (ii) increases with subjects' ages, (iii) is smaller for pairs of females compared to pairs of males, and (iv) is larger for females with lower cognitive scores compared to females with larger cognitive scores. Regions that contributed most to individuals' swap distances were in higher‐order networks, that is, default‐mode and fronto‐parietal, that underlie executive function and memory. These higher‐order networks' regions also had swap frequencies that varied monotonically with familial relatedness of the individuals in question. We posit that the proposed graph matching technique provides a novel way to study inter‐subject differences in FC and enables quantification of how FC may vary with age, relatedness, sex, and behavior.
We use a novel graph‐matching metric, swap distance, to quantify differences between FCs of pairs of individuals and look at how this pairwise metric varies with age, sex, cognitive scores, and familial relationships. This metric highlights similarity of FCs between pairs of individuals, increases monotonically along with familial distance, increases with subjects' ages, is smaller for pairs of females compared to pairs of males, and is larger for females with lower cognitive scores compared to females with higher cognitive scores. Furthermore, higher‐order association regions like those in the frontoparietal and default mode network show more variability across individuals compared to lower regions belong to lower order networks. |
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| AbstractList | Functional connectomes (FCs), represented by networks or graphs that summarize coactivation patterns between pairs of brain regions, have been related at a population level to age, sex, cognitive/behavioral scores, life experience, genetics, and disease/disorders. However, quantifying FC differences between individuals also provides a rich source of information with which to map to differences in those individuals' biology, experience, genetics or behavior. In this study, graph matching is used to create a novel inter‐individual FC metric, called swap distance, that quantifies the distance between pairs of individuals' partial FCs, with a smaller swap distance indicating the individuals have more similar FC. We apply graph matching to align FCs between individuals from the the Human Connectome Project
N
=
997
and find that swap distance (i) increases with increasing familial distance, (ii) increases with subjects' ages, (iii) is smaller for pairs of females compared to pairs of males, and (iv) is larger for females with lower cognitive scores compared to females with larger cognitive scores. Regions that contributed most to individuals' swap distances were in higher‐order networks, that is, default‐mode and fronto‐parietal, that underlie executive function and memory. These higher‐order networks' regions also had swap frequencies that varied monotonically with familial relatedness of the individuals in question. We posit that the proposed graph matching technique provides a novel way to study inter‐subject differences in FC and enables quantification of how FC may vary with age, relatedness, sex, and behavior.
We use a novel graph‐matching metric, swap distance, to quantify differences between FCs of pairs of individuals and look at how this pairwise metric varies with age, sex, cognitive scores, and familial relationships. This metric highlights similarity of FCs between pairs of individuals, increases monotonically along with familial distance, increases with subjects' ages, is smaller for pairs of females compared to pairs of males, and is larger for females with lower cognitive scores compared to females with higher cognitive scores. Furthermore, higher‐order association regions like those in the frontoparietal and default mode network show more variability across individuals compared to lower regions belong to lower order networks. Functional connectomes (FCs), represented by networks or graphs that summarize coactivation patterns between pairs of brain regions, have been related at a population level to age, sex, cognitive/behavioral scores, life experience, genetics, and disease/disorders. However, quantifying FC differences between individuals also provides a rich source of information with which to map to differences in those individuals' biology, experience, genetics or behavior. In this study, graph matching is used to create a novel inter-individual FC metric, called swap distance, that quantifies the distance between pairs of individuals' partial FCs, with a smaller swap distance indicating the individuals have more similar FC. We apply graph matching to align FCs between individuals from the the Human Connectome Project N = 997 and find that swap distance (i) increases with increasing familial distance, (ii) increases with subjects' ages, (iii) is smaller for pairs of females compared to pairs of males, and (iv) is larger for females with lower cognitive scores compared to females with larger cognitive scores. Regions that contributed most to individuals' swap distances were in higher-order networks, that is, default-mode and fronto-parietal, that underlie executive function and memory. These higher-order networks' regions also had swap frequencies that varied monotonically with familial relatedness of the individuals in question. We posit that the proposed graph matching technique provides a novel way to study inter-subject differences in FC and enables quantification of how FC may vary with age, relatedness, sex, and behavior.Functional connectomes (FCs), represented by networks or graphs that summarize coactivation patterns between pairs of brain regions, have been related at a population level to age, sex, cognitive/behavioral scores, life experience, genetics, and disease/disorders. However, quantifying FC differences between individuals also provides a rich source of information with which to map to differences in those individuals' biology, experience, genetics or behavior. In this study, graph matching is used to create a novel inter-individual FC metric, called swap distance, that quantifies the distance between pairs of individuals' partial FCs, with a smaller swap distance indicating the individuals have more similar FC. We apply graph matching to align FCs between individuals from the the Human Connectome Project N = 997 and find that swap distance (i) increases with increasing familial distance, (ii) increases with subjects' ages, (iii) is smaller for pairs of females compared to pairs of males, and (iv) is larger for females with lower cognitive scores compared to females with larger cognitive scores. Regions that contributed most to individuals' swap distances were in higher-order networks, that is, default-mode and fronto-parietal, that underlie executive function and memory. These higher-order networks' regions also had swap frequencies that varied monotonically with familial relatedness of the individuals in question. We posit that the proposed graph matching technique provides a novel way to study inter-subject differences in FC and enables quantification of how FC may vary with age, relatedness, sex, and behavior. Functional connectomes (FCs), represented by networks or graphs that summarize coactivation patterns between pairs of brain regions, have been related at a population level to age, sex, cognitive/behavioral scores, life experience, genetics, and disease/disorders. However, quantifying FC differences between individuals also provides a rich source of information with which to map to differences in those individuals' biology, experience, genetics or behavior. In this study, graph matching is used to create a novel inter‐individual FC metric, called swap distance, that quantifies the distance between pairs of individuals' partial FCs, with a smaller swap distance indicating the individuals have more similar FC. We apply graph matching to align FCs between individuals from the the Human Connectome Project N=997 and find that swap distance (i) increases with increasing familial distance, (ii) increases with subjects' ages, (iii) is smaller for pairs of females compared to pairs of males, and (iv) is larger for females with lower cognitive scores compared to females with larger cognitive scores. Regions that contributed most to individuals' swap distances were in higher‐order networks, that is, default‐mode and fronto‐parietal, that underlie executive function and memory. These higher‐order networks' regions also had swap frequencies that varied monotonically with familial relatedness of the individuals in question. We posit that the proposed graph matching technique provides a novel way to study inter‐subject differences in FC and enables quantification of how FC may vary with age, relatedness, sex, and behavior. Functional connectomes (FCs), represented by networks or graphs that summarize coactivation patterns between pairs of brain regions, have been related at a population level to age, sex, cognitive/behavioral scores, life experience, genetics, and disease/disorders. However, quantifying FC differences between individuals also provides a rich source of information with which to map to differences in those individuals' biology, experience, genetics or behavior. In this study, graph matching is used to create a novel inter‐individual FC metric, called swap distance, that quantifies the distance between pairs of individuals' partial FCs, with a smaller swap distance indicating the individuals have more similar FC. We apply graph matching to align FCs between individuals from the the Human Connectome Project and find that swap distance (i) increases with increasing familial distance, (ii) increases with subjects' ages, (iii) is smaller for pairs of females compared to pairs of males, and (iv) is larger for females with lower cognitive scores compared to females with larger cognitive scores. Regions that contributed most to individuals' swap distances were in higher‐order networks, that is, default‐mode and fronto‐parietal, that underlie executive function and memory. These higher‐order networks' regions also had swap frequencies that varied monotonically with familial relatedness of the individuals in question. We posit that the proposed graph matching technique provides a novel way to study inter‐subject differences in FC and enables quantification of how FC may vary with age, relatedness, sex, and behavior. Functional connectomes (FCs), represented by networks or graphs that summarize coactivation patterns between pairs of brain regions, have been related at a population level to age, sex, cognitive/behavioral scores, life experience, genetics, and disease/disorders. However, quantifying FC differences between individuals also provides a rich source of information with which to map to differences in those individuals' biology, experience, genetics or behavior. In this study, graph matching is used to create a novel inter‐individual FC metric, called swap distance, that quantifies the distance between pairs of individuals' partial FCs, with a smaller swap distance indicating the individuals have more similar FC. We apply graph matching to align FCs between individuals from the the Human Connectome Project N=997 and find that swap distance (i) increases with increasing familial distance, (ii) increases with subjects' ages, (iii) is smaller for pairs of females compared to pairs of males, and (iv) is larger for females with lower cognitive scores compared to females with larger cognitive scores. Regions that contributed most to individuals' swap distances were in higher‐order networks, that is, default‐mode and fronto‐parietal, that underlie executive function and memory. These higher‐order networks' regions also had swap frequencies that varied monotonically with familial relatedness of the individuals in question. We posit that the proposed graph matching technique provides a novel way to study inter‐subject differences in FC and enables quantification of how FC may vary with age, relatedness, sex, and behavior. We use a novel graph‐matching metric, swap distance, to quantify differences between FCs of pairs of individuals and look at how this pairwise metric varies with age, sex, cognitive scores, and familial relationships. This metric highlights similarity of FCs between pairs of individuals, increases monotonically along with familial distance, increases with subjects' ages, is smaller for pairs of females compared to pairs of males, and is larger for females with lower cognitive scores compared to females with higher cognitive scores. Furthermore, higher‐order association regions like those in the frontoparietal and default mode network show more variability across individuals compared to lower regions belong to lower order networks. Functional connectomes (FCs), represented by networks or graphs that summarize coactivation patterns between pairs of brain regions, have been related at a population level to age, sex, cognitive/behavioral scores, life experience, genetics, and disease/disorders. However, quantifying FC differences between individuals also provides a rich source of information with which to map to differences in those individuals' biology, experience, genetics or behavior. In this study, graph matching is used to create a novel inter-individual FC metric, called swap distance, that quantifies the distance between pairs of individuals' partial FCs, with a smaller swap distance indicating the individuals have more similar FC. We apply graph matching to align FCs between individuals from the the Human Connectome Project and find that swap distance (i) increases with increasing familial distance, (ii) increases with subjects' ages, (iii) is smaller for pairs of females compared to pairs of males, and (iv) is larger for females with lower cognitive scores compared to females with larger cognitive scores. Regions that contributed most to individuals' swap distances were in higher-order networks, that is, default-mode and fronto-parietal, that underlie executive function and memory. These higher-order networks' regions also had swap frequencies that varied monotonically with familial relatedness of the individuals in question. We posit that the proposed graph matching technique provides a novel way to study inter-subject differences in FC and enables quantification of how FC may vary with age, relatedness, sex, and behavior. |
| Author | Su, Chang Gu, Zijin Kuceyeski, Amy Jamison, Keith Bukhari, Hussain Dhamala, Elvisha |
| AuthorAffiliation | 1 Department of Neuroscience Weill Cornell Medicine New York New York USA 4 Department of Electrical and Computer Engineering Cornell University Ithaca New York USA 3 Department of Psychology Yale University New Haven Connecticut USA 5 Department of Radiology Weill Cornell Medicine New York New York USA 2 Department of Biostatistics Yale University New Haven Connecticut USA |
| AuthorAffiliation_xml | – name: 3 Department of Psychology Yale University New Haven Connecticut USA – name: 2 Department of Biostatistics Yale University New Haven Connecticut USA – name: 5 Department of Radiology Weill Cornell Medicine New York New York USA – name: 1 Department of Neuroscience Weill Cornell Medicine New York New York USA – name: 4 Department of Electrical and Computer Engineering Cornell University Ithaca New York USA |
| Author_xml | – sequence: 1 givenname: Hussain orcidid: 0000-0001-7730-6399 surname: Bukhari fullname: Bukhari, Hussain organization: Weill Cornell Medicine – sequence: 2 givenname: Chang surname: Su fullname: Su, Chang organization: Yale University – sequence: 3 givenname: Elvisha surname: Dhamala fullname: Dhamala, Elvisha organization: Yale University – sequence: 4 givenname: Zijin orcidid: 0000-0002-0385-2677 surname: Gu fullname: Gu, Zijin organization: Cornell University – sequence: 5 givenname: Keith surname: Jamison fullname: Jamison, Keith organization: Weill Cornell Medicine – sequence: 6 givenname: Amy orcidid: 0000-0002-5050-8342 surname: Kuceyeski fullname: Kuceyeski, Amy email: amk2012@med.cornell.edu organization: Weill Cornell Medicine |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/37042411$$D View this record in MEDLINE/PubMed |
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| Keywords | functional MRI functional connectome inter-individual variability graph-matching |
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| Snippet | Functional connectomes (FCs), represented by networks or graphs that summarize coactivation patterns between pairs of brain regions, have been related at a... |
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| SubjectTerms | Age Anatomy & physiology Brain Brain - physiology Cognition - physiology Cognitive ability Connectome - methods Executive Function Female Females functional connectome functional MRI Genetics Graph matching Graphical representations Humans inter‐individual variability Magnetic Resonance Imaging - methods Male Networks Physiology Sex Sexual behavior Time series Young adults |
| Title | Graph‐matching distance between individuals' functional connectomes varies with relatedness, age, and cognitive score |
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