Format

Send to

Choose Destination
Hum Brain Mapp. 2017 Sep;38(9):4744-4759. doi: 10.1002/hbm.23699. Epub 2017 Jun 24.

Cohesive network reconfiguration accompanies extended training.

Author information

1
Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, 19104.
2
Human Research and Engineering Directorate, U.S. Army Research Laboratory, Aberdeen, Maryland, 21001.
3
Department of Neurology, Johns Hopkins University, Baltimore, Maryland, 21218.
4
Department of Psychological and Brain Sciences, University of California, Santa Barbara, California, 93106.
5
Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, Pennsylvania, 19104.

Abstract

Human behavior is supported by flexible neurophysiological processes that enable the fine-scale manipulation of information across distributed neural circuits. Yet, approaches for understanding the dynamics of these circuit interactions have been limited. One promising avenue for quantifying and describing these dynamics lies in multilayer network models. Here, networks are composed of nodes (which represent brain regions) and time-dependent edges (which represent statistical similarities in activity time series). We use this approach to examine functional connectivity measured by non-invasive neuroimaging techniques. These multilayer network models facilitate the examination of changes in the pattern of statistical interactions between large-scale brain regions that might facilitate behavior. In this study, we define and exercise two novel measures of network reconfiguration, and demonstrate their utility in neuroimaging data acquired as healthy adult human subjects learn a new motor skill. In particular, we identify putative functional modules in multilayer networks and characterize the degree to which nodes switch between modules. Next, we define cohesive switches, in which a set of nodes moves between modules together as a group, and we define disjoint switches, in which a single node moves between modules independently from other nodes. Together, these two concepts offer complementary yet distinct insights into the changes in functional connectivity that accompany motor learning. More generally, our work offers statistical tools that other researchers can use to better understand the reconfiguration patterns of functional connectivity over time. Hum Brain Mapp 38:4744-4759, 2017.

KEYWORDS:

connectomics; dynamic networks; functional magnetic resonance imaging; graph theory; motor learning

PMID:
28646563
PMCID:
PMC5554863
DOI:
10.1002/hbm.23699
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Wiley Icon for PubMed Central
Loading ...
Support Center