Dimensionality of brain networks linked to life-long individual differences in self-control

Marc G. Berman (), Grigori Yourganov, Mary K. Askren, Ozlem Ayduk, B. J. Casey, Ian H. Gotlib, Ethan Kross, Anthony R. McIntosh, Stephen Strother, Nicole L. Wilson, Vivian Zayas, Walter Mischel, Yuichi Shoda and John Jonides
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Marc G. Berman: Rotman Research Institute at Baycrest
Grigori Yourganov: Rotman Research Institute at Baycrest
Mary K. Askren: Integrated Brain Imaging Center, University of Washington
Ozlem Ayduk: University of California, Berkeley
B. J. Casey: Sackler Institute for Developmental Psychobiology, Weill Cornell Medical College
Ian H. Gotlib: Stanford University
Ethan Kross: University of Michigan
Anthony R. McIntosh: Rotman Research Institute at Baycrest
Stephen Strother: Rotman Research Institute at Baycrest
Nicole L. Wilson: University of Washington
Vivian Zayas: Cornell University
Walter Mischel: Columbia University
Yuichi Shoda: University of Washington
John Jonides: University of Michigan

Nature Communications, 2013, vol. 4, issue 1, 1-7

Abstract: Abstract The ability to delay gratification in childhood has been linked to positive outcomes in adolescence and adulthood. Here we examine a subsample of participants from a seminal longitudinal study of self-control throughout a subject’s life span. Self-control, first studied in children at age 4 years, is now re-examined 40 years later, on a task that required control over the contents of working memory. We examine whether patterns of brain activation on this task can reliably distinguish participants with consistently low and high self-control abilities (low versus high delayers). We find that low delayers recruit significantly higher-dimensional neural networks when performing the task compared with high delayers. High delayers are also more homogeneous as a group in their neural patterns compared with low delayers. From these brain patterns, we can predict with 71% accuracy, whether a participant is a high or low delayer. The present results suggest that dimensionality of neural networks is a biological predictor of self-control abilities.

Date: 2013
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DOI: 10.1038/ncomms2374

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