TY - JOUR
T1 - Large-scale cortical network properties predict future sound-to-word learning success
AU - Sheppard, John Patrick
AU - Wang, Ji Ping
AU - Wong, Patrick C.M.
PY - 2012/5
Y1 - 2012/5
N2 - The human brain possesses a remarkable capacity to interpret and recall novel sounds as spoken language. These linguistic abilities arise from complex processing spanning a widely distributed cortical network and are characterized by marked individual variation. Recently, graph theoretical analysis has facilitated the exploration of how such aspects of large-scale brain functional organization may underlie cognitive performance. Brain functional networks are known to possess small-world topologies characterized by efficient global and local information transfer, but whether these properties relate to language learning abilities remains unknown. Here we applied graph theory to construct large-scale cortical functional networks from cerebral hemodynamic (fMRI) responses acquired during an auditory pitch discrimination task and found that such network properties were associated with participants' future success in learning words of an artificial spoken language. Successful learners possessed networks with reduced local efficiency but increased global efficiency relative to less successful learners and had a more cost-efficient network organization. Regionally, successful and less successful learners exhibited differences in these network properties spanning bilateral prefrontal, parietal, and right temporal cortex, overlapping a core network of auditory language areas. These results suggest that efficient cortical network organization is associated with sound-toword learning abilities among healthy, younger adults.
AB - The human brain possesses a remarkable capacity to interpret and recall novel sounds as spoken language. These linguistic abilities arise from complex processing spanning a widely distributed cortical network and are characterized by marked individual variation. Recently, graph theoretical analysis has facilitated the exploration of how such aspects of large-scale brain functional organization may underlie cognitive performance. Brain functional networks are known to possess small-world topologies characterized by efficient global and local information transfer, but whether these properties relate to language learning abilities remains unknown. Here we applied graph theory to construct large-scale cortical functional networks from cerebral hemodynamic (fMRI) responses acquired during an auditory pitch discrimination task and found that such network properties were associated with participants' future success in learning words of an artificial spoken language. Successful learners possessed networks with reduced local efficiency but increased global efficiency relative to less successful learners and had a more cost-efficient network organization. Regionally, successful and less successful learners exhibited differences in these network properties spanning bilateral prefrontal, parietal, and right temporal cortex, overlapping a core network of auditory language areas. These results suggest that efficient cortical network organization is associated with sound-toword learning abilities among healthy, younger adults.
UR - http://www.scopus.com/inward/record.url?scp=84859151158&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84859151158&partnerID=8YFLogxK
U2 - 10.1162/jocn_a_00210
DO - 10.1162/jocn_a_00210
M3 - Article
C2 - 22360625
AN - SCOPUS:84859151158
SN - 0898-929X
VL - 24
SP - 1087
EP - 1103
JO - Journal of cognitive neuroscience
JF - Journal of cognitive neuroscience
IS - 5
ER -