Early brain functional segregation and integration predict later cognitive performance

Han Zhang, Weiyan Yin, Weili Lin, Dinggang Shen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

The human brain in the first 2 years of life is fascinating yet mysterious. Whether its connectivity pattern is genetically predefined for neonates and predictive to the later cognitive performance is unknown. Numerous neurological/psychiatric diseases in adults with impaired cognitive functions have been linked with deteriorated “triple networks” that govern the high-level cognition. The triple networks are referred to salience network for salient event monitoring and emotion processing, default mode network for self-cognition and episodic memory, and executive control network for attention control, set maintenance and task executions. We investigate the infancy “triple networks” and their development in the pivotal period of the first two years of life with longitudinal resting-state fMRI from 52 term infants (24 having cognitive performance scores tested at 4 years old). We found that the triple networks harbor at the medial prefrontal cortex, an ideal brain region for unveiling early development of the high-level functions. Further parcellation of this area indicates consistent subdivisions from 0 to 2 years old, indicating largely predefined functional segregation in this highly heterogeneous region. Interconnectivity among the mediofrontal subdivisions reveals a significant invert U-shape curve for modularity, with the inter-network functional connectivity (FC) peaking at 6–9 months, manifesting a developing functional integration within the frontal region. Through long-range FC, we found the development of the high-level functions starts from salience monitoring, followed by self-cognition, then to executive control. We extract both within-frontal modularity index (reflecting short-distance FC), and outreaching index (measuring long-distance FC) for the newborns. Interestingly, these connectomics features for the newborns well predict their later cognitive performance at 4 years old. These results converge to favoring a predefined genetic dominance in the development of triple networks’ FC, which is essential for understanding early high-level neuro-cognitive development and promising for early abnormality detection.

Original languageEnglish
Title of host publicationConnectomics in NeuroImaging - 1st International Workshop, CNI 2017 Held in Conjunction with MICCAI 2017, Proceedings
PublisherSpringer Verlag
Pages116-124
Number of pages9
Volume10511 LNCS
ISBN (Print)9783319671581
DOIs
Publication statusPublished - 2017
Event1st International Workshop on Connectomics in NeuroImaging, CNI 2017 held in conjunction with the 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017 - Quebec City, Canada
Duration: 2017 Sep 142017 Sep 14

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10511 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other1st International Workshop on Connectomics in NeuroImaging, CNI 2017 held in conjunction with the 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017
CountryCanada
CityQuebec City
Period17/9/1417/9/14

Keywords

  • Brain networks
  • Development
  • Functional connectivity
  • functional Magnetic Resonance Imaging (fMRI)
  • Infant
  • Modularity
  • Resting-state fMRI

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Zhang, H., Yin, W., Lin, W., & Shen, D. (2017). Early brain functional segregation and integration predict later cognitive performance. In Connectomics in NeuroImaging - 1st International Workshop, CNI 2017 Held in Conjunction with MICCAI 2017, Proceedings (Vol. 10511 LNCS, pp. 116-124). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10511 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-67159-8_14