Changing brain connectivity dynamics: From early childhood to adulthood
Brain maturation through adolescence has been the topic of recent studies. Previous works have evaluated changes in morphometry and also changes in functional connectivity. However, most resting‐state fMRI studies have focused on static connectivity. Here we examine the relationship between age/matu...
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| Published in | Human brain mapping Vol. 39; no. 3; pp. 1108 - 1117 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
John Wiley & Sons, Inc
01.03.2018
John Wiley and Sons Inc |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1065-9471 1097-0193 1097-0193 |
| DOI | 10.1002/hbm.23896 |
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| Abstract | Brain maturation through adolescence has been the topic of recent studies. Previous works have evaluated changes in morphometry and also changes in functional connectivity. However, most resting‐state fMRI studies have focused on static connectivity. Here we examine the relationship between age/maturity and the dynamics of brain functional connectivity. Utilizing a resting fMRI dataset comprised 421 subjects ages 3–22 from the PING study, we first performed group ICA to extract independent components and their time courses. Next, dynamic functional network connectivity (dFNC) was calculated via a sliding window followed by clustering of connectivity patterns into 5 states. Finally, we evaluated the relationship between age and the amount of time each participant spent in each state as well as the transitions among different states. Results showed that older participants tend to spend more time in states which reflect overall stronger connectivity patterns throughout the brain. In addition, the relationship between age and state transition is symmetric. This can mean individuals change functional connectivity through time within a specific set of states. On the whole, results indicated that dynamic functional connectivity is an important factor to consider when examining brain development across childhood. |
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| AbstractList | Brain maturation through adolescence has been the topic of recent studies. Previous works have evaluated changes in morphometry and also changes in functional connectivity. However, most resting-state fMRI studies have focused on static connectivity. Here we examine the relationship between age/maturity and the dynamics of brain functional connectivity. Utilizing a resting fMRI dataset comprised 421 subjects ages 3-22 from the PING study, we first performed group ICA to extract independent components and their time courses. Next, dynamic functional network connectivity (dFNC) was calculated via a sliding window followed by clustering of connectivity patterns into 5 states. Finally, we evaluated the relationship between age and the amount of time each participant spent in each state as well as the transitions among different states. Results showed that older participants tend to spend more time in states which reflect overall stronger connectivity patterns throughout the brain. In addition, the relationship between age and state transition is symmetric. This can mean individuals change functional connectivity through time within a specific set of states. On the whole, results indicated that dynamic functional connectivity is an important factor to consider when examining brain development across childhood.Brain maturation through adolescence has been the topic of recent studies. Previous works have evaluated changes in morphometry and also changes in functional connectivity. However, most resting-state fMRI studies have focused on static connectivity. Here we examine the relationship between age/maturity and the dynamics of brain functional connectivity. Utilizing a resting fMRI dataset comprised 421 subjects ages 3-22 from the PING study, we first performed group ICA to extract independent components and their time courses. Next, dynamic functional network connectivity (dFNC) was calculated via a sliding window followed by clustering of connectivity patterns into 5 states. Finally, we evaluated the relationship between age and the amount of time each participant spent in each state as well as the transitions among different states. Results showed that older participants tend to spend more time in states which reflect overall stronger connectivity patterns throughout the brain. In addition, the relationship between age and state transition is symmetric. This can mean individuals change functional connectivity through time within a specific set of states. On the whole, results indicated that dynamic functional connectivity is an important factor to consider when examining brain development across childhood. Brain maturation through adolescence has been the topic of recent studies. Previous works have evaluated changes in morphometry and also changes in functional connectivity. However, most resting‐state fMRI studies have focused on static connectivity. Here we examine the relationship between age/maturity and the dynamics of brain functional connectivity. Utilizing a resting fMRI dataset comprised 421 subjects ages 3–22 from the PING study, we first performed group ICA to extract independent components and their time courses. Next, dynamic functional network connectivity (dFNC) was calculated via a sliding window followed by clustering of connectivity patterns into 5 states. Finally, we evaluated the relationship between age and the amount of time each participant spent in each state as well as the transitions among different states. Results showed that older participants tend to spend more time in states which reflect overall stronger connectivity patterns throughout the brain. In addition, the relationship between age and state transition is symmetric. This can mean individuals change functional connectivity through time within a specific set of states. On the whole, results indicated that dynamic functional connectivity is an important factor to consider when examining brain development across childhood. Brain maturation through adolescence has been the topic of recent studies. Previous works have evaluated changes in morphometry and also changes in functional connectivity. However, most resting‐state fMRI studies have focused on static connectivity. Here we examine the relationship between age/maturity and the dynamics of brain functional connectivity. Utilizing a resting fMRI dataset comprised 421 subjects ages 3–22 from the PING study, we first performed group ICA to extract independent components and their time courses. Next, dynamic functional network connectivity (dFNC) was calculated via a sliding window followed by clustering of connectivity patterns into 5 states. Finally, we evaluated the relationship between age and the amount of time each participant spent in each state as well as the transitions among different states. Results showed that older participants tend to spend more time in states which reflect overall stronger connectivity patterns throughout the brain. In addition, the relationship between age and state transition is symmetric. This can mean individuals change functional connectivity through time within a specific set of states. On the whole, results indicated that dynamic functional connectivity is an important factor to consider when examining brain development across childhood. |
| Author | Faghiri, Ashkan Wang, Yu‐Ping Wilson, Tony W. Stephen, Julia M. Calhoun, Vince D. |
| AuthorAffiliation | 5 Department of Neurological Sciences University of Nebraska Medical Center Omaha Nebraska 3 Biomedical Engineering Department Tulane University New Orleans Louisiana 1 The Mind Research Network, 1101 Yale Blvd NE Albuquerque New Mexico 6 Center for Magnetoencephalography University of Nebraska Medical Center Omaha Nebraska 2 Electrical and Computer Engineering Department University of New Mexico Albuquerque New Mexico 4 Center of Genomics and Bioinformatics Tulane University New Orleans Louisiana |
| AuthorAffiliation_xml | – name: 1 The Mind Research Network, 1101 Yale Blvd NE Albuquerque New Mexico – name: 5 Department of Neurological Sciences University of Nebraska Medical Center Omaha Nebraska – name: 6 Center for Magnetoencephalography University of Nebraska Medical Center Omaha Nebraska – name: 3 Biomedical Engineering Department Tulane University New Orleans Louisiana – name: 4 Center of Genomics and Bioinformatics Tulane University New Orleans Louisiana – name: 2 Electrical and Computer Engineering Department University of New Mexico Albuquerque New Mexico |
| Author_xml | – sequence: 1 givenname: Ashkan orcidid: 0000-0003-1807-6815 surname: Faghiri fullname: Faghiri, Ashkan email: ashkanfa.shirazu@gmail.com organization: University of New Mexico – sequence: 2 givenname: Julia M. surname: Stephen fullname: Stephen, Julia M. organization: The Mind Research Network, 1101 Yale Blvd NE – sequence: 3 givenname: Yu‐Ping surname: Wang fullname: Wang, Yu‐Ping organization: Tulane University – sequence: 4 givenname: Tony W. orcidid: 0000-0002-5053-8306 surname: Wilson fullname: Wilson, Tony W. organization: University of Nebraska Medical Center – sequence: 5 givenname: Vince D. surname: Calhoun fullname: Calhoun, Vince D. organization: University of New Mexico |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/29205692$$D View this record in MEDLINE/PubMed |
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| SubjectTerms | Adolescent Adolescents Age Brain Brain - diagnostic imaging Brain - growth & development Brain - physiology Brain Mapping Child Child, Preschool Children Clustering Female Functional magnetic resonance imaging Humans Magnetic Resonance Imaging Male Morphometry Neural networks Neural Pathways - diagnostic imaging Neural Pathways - growth & development Neural Pathways - physiology Rest Young Adult |
| Title | Changing brain connectivity dynamics: From early childhood to adulthood |
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