Title |
Integration and segregation of large-scale brain networks during short-term task automatization
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Published in |
Nature Communications, November 2016
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DOI | 10.1038/ncomms13217 |
Pubmed ID | |
Authors |
Holger Mohr, Uta Wolfensteller, Richard F. Betzel, Bratislav Mišić, Olaf Sporns, Jonas Richiardi, Hannes Ruge |
Abstract |
The human brain is organized into large-scale functional networks that can flexibly reconfigure their connectivity patterns, supporting both rapid adaptive control and long-term learning processes. However, it has remained unclear how short-term network dynamics support the rapid transformation of instructions into fluent behaviour. Comparing fMRI data of a learning sample (N=70) with a control sample (N=67), we find that increasingly efficient task processing during short-term practice is associated with a reorganization of large-scale network interactions. Practice-related efficiency gains are facilitated by enhanced coupling between the cingulo-opercular network and the dorsal attention network. Simultaneously, short-term task automatization is accompanied by decreasing activation of the fronto-parietal network, indicating a release of high-level cognitive control, and a segregation of the default mode network from task-related networks. These findings suggest that short-term task automatization is enabled by the brain's ability to rapidly reconfigure its large-scale network organization involving complementary integration and segregation processes. |
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Country | Count | As % |
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United States | 14 | 34% |
Australia | 4 | 10% |
Canada | 2 | 5% |
China | 2 | 5% |
Japan | 2 | 5% |
United Kingdom | 2 | 5% |
Italy | 1 | 2% |
France | 1 | 2% |
Mexico | 1 | 2% |
Other | 2 | 5% |
Unknown | 10 | 24% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 27 | 66% |
Scientists | 13 | 32% |
Practitioners (doctors, other healthcare professionals) | 1 | 2% |
Mendeley readers
Geographical breakdown
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---|---|---|
Germany | 2 | <1% |
France | 1 | <1% |
United States | 1 | <1% |
Unknown | 227 | 98% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 64 | 28% |
Researcher | 36 | 16% |
Student > Master | 29 | 13% |
Student > Doctoral Student | 19 | 8% |
Student > Bachelor | 15 | 6% |
Other | 40 | 17% |
Unknown | 28 | 12% |
Readers by discipline | Count | As % |
---|---|---|
Neuroscience | 61 | 26% |
Psychology | 49 | 21% |
Agricultural and Biological Sciences | 18 | 8% |
Medicine and Dentistry | 11 | 5% |
Computer Science | 10 | 4% |
Other | 31 | 13% |
Unknown | 51 | 22% |