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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| Veröffentlicht in: | Human brain mapping 2018-03, Vol.39 (3), p.1108-1117 |
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| creator | Faghiri, Ashkan Stephen, Julia M. Wang, Yu‐Ping Wilson, Tony W. Calhoun, Vince D. |
| description | 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. |
| doi_str_mv | 10.1002/hbm.23896 |
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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. 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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. 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Stephen, Julia M. ; Wang, Yu‐Ping ; Wilson, Tony W. ; Calhoun, Vince D.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c5096-bd8b59b3e90a10bf841323c506f0283ffbe86db0d61d39755482b052b0d6dab43</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Adolescent</topic><topic>Adolescents</topic><topic>Age</topic><topic>Brain</topic><topic>Brain - diagnostic imaging</topic><topic>Brain - growth & development</topic><topic>Brain - physiology</topic><topic>Brain Mapping</topic><topic>Child</topic><topic>Child, Preschool</topic><topic>Children</topic><topic>Clustering</topic><topic>Female</topic><topic>Functional magnetic resonance imaging</topic><topic>Humans</topic><topic>Magnetic Resonance Imaging</topic><topic>Male</topic><topic>Morphometry</topic><topic>Neural networks</topic><topic>Neural Pathways - diagnostic imaging</topic><topic>Neural Pathways - growth & development</topic><topic>Neural Pathways - physiology</topic><topic>Rest</topic><topic>Young Adult</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Faghiri, Ashkan</creatorcontrib><creatorcontrib>Stephen, Julia M.</creatorcontrib><creatorcontrib>Wang, Yu‐Ping</creatorcontrib><creatorcontrib>Wilson, Tony W.</creatorcontrib><creatorcontrib>Calhoun, Vince D.</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Chemoreception Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Toxicology Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Human brain mapping</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Faghiri, Ashkan</au><au>Stephen, Julia M.</au><au>Wang, Yu‐Ping</au><au>Wilson, Tony W.</au><au>Calhoun, Vince D.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Changing brain connectivity dynamics: From early childhood to adulthood</atitle><jtitle>Human brain mapping</jtitle><addtitle>Hum Brain Mapp</addtitle><date>2018-03</date><risdate>2018</risdate><volume>39</volume><issue>3</issue><spage>1108</spage><epage>1117</epage><pages>1108-1117</pages><issn>1065-9471</issn><eissn>1097-0193</eissn><abstract>Brain maturation through adolescence has been the topic of recent studies. 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| subjects | 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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