Multi-layer Temporal Network Analysis Reveals Increasing Temporal Reachability and Spreadability in the First Two Years of Life

for UNC/UMN Baby Connectome Project Consortium

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

1 Citation (Scopus)

Abstract

Spatiotemporal dynamics analysis of the human brain functional connectome and its early development in the first few years of life is tremendously essential for grasping such a shining jewel in life science as such a knowledge shed light on the long-standing mysteries of emerging and fast developing of various high-level cognitive abilities in such a pivotal stage. Most of the existing developmental neuroscience studies with resting-state functional MRI (fMRI) failed in correctly modeling information flow, exchanges, and spreads across space and time via the dedicatedly designed and continuously rewiring complex brain networks. We propose a novel multi-layer temporal network analysis with two intuitive and intriguing metrics, reachability and spreadability, measuring the extent of a certain brain region getting in touch with other regions in a short period of time through temporal linkage (inter-layer connections between corresponding regions across time). We applied this method on a large-scale, high-quality, high-resolution sleeping state fMRI of normally developed neonates/infants without sedating them. We unravel a first-ever picture of how the human brain facilitates more and more efficient information exchange and integration. The early and fast maturation of the ventral visual “what” pathway with the highest developing velocity over all other regions during 0–6 months may underpin the rapid developing consciousness and all other aspects of complex cognitive functions.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings
EditorsDinggang Shen, Pew-Thian Yap, Tianming Liu, Terry M. Peters, Ali Khan, Lawrence H. Staib, Caroline Essert, Sean Zhou
PublisherSpringer
Pages665-672
Number of pages8
ISBN (Print)9783030322472
DOIs
Publication statusPublished - 2019 Jan 1
Externally publishedYes
Event22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 - Shenzhen, China
Duration: 2019 Oct 132019 Oct 17

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11766 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
CountryChina
CityShenzhen
Period19/10/1319/10/17

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'Multi-layer Temporal Network Analysis Reveals Increasing Temporal Reachability and Spreadability in the First Two Years of Life'. Together they form a unique fingerprint.

  • Cite this

    for UNC/UMN Baby Connectome Project Consortium (2019). Multi-layer Temporal Network Analysis Reveals Increasing Temporal Reachability and Spreadability in the First Two Years of Life. In D. Shen, P-T. Yap, T. Liu, T. M. Peters, A. Khan, L. H. Staib, C. Essert, & S. Zhou (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings (pp. 665-672). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11766 LNCS). Springer. https://doi.org/10.1007/978-3-030-32248-9_74