Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2010-1-7
pubmed:abstractText
Previous studies have supported the concept that the default network is an intrinsic brain system that participates in internal modes of cognition. Neural activity and connectivity within the default network, which are correlated with cognitive ability even at rest, may be plausible intermediate phenotypes that will enable us to understand the genetic mechanisms of individuals' cognitive function or the risk for genetic brain diseases. Using resting functional magnetic resonance imaging and imaging genetic paradigms, we investigated whether individual default network connectivity was modulated by COMT val(158)met in 57 healthy young subjects. Compared with COMT heterozygous individuals, homozygous val individuals showed significantly decreased prefrontal-related connectivities, which primarily occurred between prefrontal regions and the posterior cingulate/restrosplenial cortices. Further analyses of the topological characteristics of the default network showed homozygous val individuals had significantly fewer node degrees in the prefrontal regions. This finding may partially elucidate previous reports that the COMT val variant is associated with inefficient prefrontal information processing and poor cognitive performance. Our findings suggest that default network connectivity that involves the prefrontal cortex is modulated by COMT val(158)met through differential effects on prefrontal dopamine levels.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Jan
pubmed:issn
1529-2401
pubmed:author
pubmed:issnType
Electronic
pubmed:day
6
pubmed:volume
30
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
64-9
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
Prefrontal-related functional connectivities within the default network are modulated by COMT val158met in healthy young adults.
pubmed:affiliation
LIAMA Center for Computational Medicine, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, People's Republic of China.
pubmed:publicationType
Journal Article, Comparative Study, Research Support, Non-U.S. Gov't