Format

Send to

Choose Destination
Sci Rep. 2015 Jan 5;5:7622. doi: 10.1038/srep07622.

Predicting learning plateau of working memory from whole-brain intrinsic network connectivity patterns.

Author information

1
1] Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International (ATR), Kyoto 619-0288, Japan [2] Graduate School of Information Science, Nara Institute of Science and Technology (NAIST), Nara 630-0192, Japan.
2
1] Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International (ATR), Kyoto 619-0288, Japan [2] Graduate School of Information Science, Nara Institute of Science and Technology (NAIST), Nara 630-0192, Japan [3] Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology and Osaka University, Osaka 565-0871, Japan.
3
1] Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International (ATR), Kyoto 619-0288, Japan [2] Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology and Osaka University, Osaka 565-0871, Japan.

Abstract

Individual learning performance of cognitive function is related to functional connections within 'task-activated' regions where activities increase during the corresponding cognitive tasks. On the other hand, since any brain region is connected with other regions and brain-wide networks, learning is characterized by modulations in connectivity between networks with different functions. Therefore, we hypothesized that learning performance is determined by functional connections among intrinsic networks that include both task-activated and less-activated networks. Subjects underwent resting-state functional MRI and a short period of training (80-90 min) in a working memory task on separate days. We calculated functional connectivity patterns of whole-brain intrinsic networks and examined whether a sparse linear regression model predicts a performance plateau from the individual patterns. The model resulted in highly accurate predictions (R(2) = 0.73, p = 0.003). Positive connections within task-activated networks, including the left fronto-parietal network, accounted for nearly half (48%) of the contribution ratio to the prediction. Moreover, consistent with our hypothesis, connections of the task-activated networks with less-activated networks showed a comparable contribution (44%). Our findings suggest that learning performance is potentially constrained by system-level interactions within task-activated networks as well as those between task-activated and less-activated networks.

PMID:
25557398
PMCID:
PMC5154600
DOI:
10.1038/srep07622
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Nature Publishing Group Icon for PubMed Central
Loading ...
Support Center