Literature DB >> 32216874

Optimization of energy state transition trajectory supports the development of executive function during youth.

Danielle S Bassett1,2,3,4,5, Theodore D Satterthwaite6, Zaixu Cui6, Jennifer Stiso1, Graham L Baum6, Jason Z Kim1, David R Roalf6, Richard F Betzel7, Shi Gu8, Zhixin Lu1, Cedric H Xia6, Xiaosong He1, Rastko Ciric6, Desmond J Oathes6, Tyler M Moore6, Russell T Shinohara9, Kosha Ruparel6, Christos Davatzikos1,2, Fabio Pasqualetti10, Raquel E Gur6, Ruben C Gur6.   

Abstract

Executive function develops during adolescence, yet it remains unknown how structural brain networks mature to facilitate activation of the fronto-parietal system, which is critical for executive function. In a sample of 946 human youths (ages 8-23y) who completed diffusion imaging, we capitalized upon recent advances in linear dynamical network control theory to calculate the energetic cost necessary to activate the fronto-parietal system through the control of multiple brain regions given existing structural network topology. We found that the energy required to activate the fronto-parietal system declined with development, and the pattern of regional energetic cost predicts unseen individuals' brain maturity. Finally, energetic requirements of the cingulate cortex were negatively correlated with executive performance, and partially mediated the development of executive performance with age. Our results reveal a mechanism by which structural networks develop during adolescence to reduce the theoretical energetic costs of transitions to activation states necessary for executive function.
© 2020, Cui et al.

Entities:  

Keywords:  adolescence; development; diffusion MRI; energy; human; network; neuroscience

Mesh:

Year:  2020        PMID: 32216874      PMCID: PMC7162657          DOI: 10.7554/eLife.53060

Source DB:  PubMed          Journal:  Elife        ISSN: 2050-084X            Impact factor:   8.140


  91 in total

1.  Predicting human resting-state functional connectivity from structural connectivity.

Authors:  C J Honey; O Sporns; L Cammoun; X Gigandet; J P Thiran; R Meuli; P Hagmann
Journal:  Proc Natl Acad Sci U S A       Date:  2009-02-02       Impact factor: 11.205

2.  Temporal dynamics of brain activation during a working memory task.

Authors:  J D Cohen; W M Perlstein; T S Braver; L E Nystrom; D C Noll; J Jonides; E E Smith
Journal:  Nature       Date:  1997-04-10       Impact factor: 49.962

3.  Topologically dissociable patterns of development of the human cerebral cortex.

Authors:  Simon N Vandekar; Russell T Shinohara; Armin Raznahan; David R Roalf; Michelle Ross; Nicholas DeLeo; Kosha Ruparel; Ragini Verma; Daniel H Wolf; Ruben C Gur; Raquel E Gur; Theodore D Satterthwaite
Journal:  J Neurosci       Date:  2015-01-14       Impact factor: 6.167

4.  The effect of machine learning regression algorithms and sample size on individualized behavioral prediction with functional connectivity features.

Authors:  Zaixu Cui; Gaolang Gong
Journal:  Neuroimage       Date:  2018-06-02       Impact factor: 6.556

Review 5.  Midcingulate cortex: Structure, connections, homologies, functions and diseases.

Authors:  Brent A Vogt
Journal:  J Chem Neuroanat       Date:  2016-03-15       Impact factor: 3.052

6.  Modular Segregation of Structural Brain Networks Supports the Development of Executive Function in Youth.

Authors:  Graham L Baum; Rastko Ciric; David R Roalf; Richard F Betzel; Tyler M Moore; Russell T Shinohara; Ari E Kahn; Simon N Vandekar; Petra E Rupert; Megan Quarmley; Philip A Cook; Mark A Elliott; Kosha Ruparel; Raquel E Gur; Ruben C Gur; Danielle S Bassett; Theodore D Satterthwaite
Journal:  Curr Biol       Date:  2017-05-25       Impact factor: 10.834

Review 7.  Neuroimaging of the Philadelphia neurodevelopmental cohort.

Authors:  Theodore D Satterthwaite; Mark A Elliott; Kosha Ruparel; James Loughead; Karthik Prabhakaran; Monica E Calkins; Ryan Hopson; Chad Jackson; Jack Keefe; Marisa Riley; Frank D Mentch; Patrick Sleiman; Ragini Verma; Christos Davatzikos; Hakon Hakonarson; Ruben C Gur; Raquel E Gur
Journal:  Neuroimage       Date:  2013-08-03       Impact factor: 6.556

8.  Age- and gender-related differences in the cortical anatomical network.

Authors:  Gaolang Gong; Pedro Rosa-Neto; Felix Carbonell; Zhang J Chen; Yong He; Alan C Evans
Journal:  J Neurosci       Date:  2009-12-16       Impact factor: 6.167

9.  The impact of in-scanner head motion on structural connectivity derived from diffusion MRI.

Authors:  Graham L Baum; David R Roalf; Philip A Cook; Rastko Ciric; Adon F G Rosen; Cedric Xia; Mark A Elliott; Kosha Ruparel; Ragini Verma; Birkan Tunç; Ruben C Gur; Raquel E Gur; Danielle S Bassett; Theodore D Satterthwaite
Journal:  Neuroimage       Date:  2018-02-24       Impact factor: 6.556

10.  A practical guide to methodological considerations in the controllability of structural brain networks.

Authors:  Teresa M Karrer; Jason Z Kim; Jennifer Stiso; Ari E Kahn; Fabio Pasqualetti; Ute Habel; Danielle S Bassett
Journal:  J Neural Eng       Date:  2020-04-09       Impact factor: 5.379

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  13 in total

1.  The impact of aging on human brain network target controllability.

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2.  A practical guide to methodological considerations in the controllability of structural brain networks.

Authors:  Teresa M Karrer; Jason Z Kim; Jennifer Stiso; Ari E Kahn; Fabio Pasqualetti; Ute Habel; Danielle S Bassett
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3.  Brain network dynamics during working memory are modulated by dopamine and diminished in schizophrenia.

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4.  Control of brain network dynamics across diverse scales of space and time.

Authors:  Evelyn Tang; Harang Ju; Graham L Baum; David R Roalf; Theodore D Satterthwaite; Fabio Pasqualetti; Danielle S Bassett
Journal:  Phys Rev E       Date:  2020-06       Impact factor: 2.529

5.  Temporal sequences of brain activity at rest are constrained by white matter structure and modulated by cognitive demands.

Authors:  Eli J Cornblath; Arian Ashourvan; Jason Z Kim; Richard F Betzel; Rastko Ciric; Azeez Adebimpe; Graham L Baum; Xiaosong He; Kosha Ruparel; Tyler M Moore; Ruben C Gur; Raquel E Gur; Russell T Shinohara; David R Roalf; Theodore D Satterthwaite; Danielle S Bassett
Journal:  Commun Biol       Date:  2020-05-22

6.  On the Nature of Explanations Offered by Network Science: A Perspective From and for Practicing Neuroscientists.

Authors:  Maxwell A Bertolero; Danielle S Bassett
Journal:  Top Cogn Sci       Date:  2020-05-22

7.  Age-associated network controllability changes in first episode drug-naïve schizophrenia.

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8.  Network Controllability in Transmodal Cortex Predicts Positive Psychosis Spectrum Symptoms.

Authors:  Linden Parkes; Tyler M Moore; Monica E Calkins; Matthew Cieslak; David R Roalf; Daniel H Wolf; Ruben C Gur; Raquel E Gur; Theodore D Satterthwaite; Danielle S Bassett
Journal:  Biol Psychiatry       Date:  2021-03-21       Impact factor: 12.810

9.  Path-dependent connectivity, not modularity, consistently predicts controllability of structural brain networks.

Authors:  Shubhankar P Patankar; Jason Z Kim; Fabio Pasqualetti; Danielle S Bassett
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10.  Structural control energy of resting-state functional brain states reveals less cost-effective brain dynamics in psychosis vulnerability.

Authors:  Daniela Zöller; Corrado Sandini; Marie Schaer; Stephan Eliez; Danielle S Bassett; Dimitri Van De Ville
Journal:  Hum Brain Mapp       Date:  2021-02-10       Impact factor: 5.038

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