Structural connectome topology relates to regional BOLD signal dynamics in the mouse brain
File(s)
Published version
Author(s)
Sethi, SS
Zerbi, V
Wenderoth, N
Fornito, A
Fulcher, BD
Type
Journal Article
Abstract
Brain dynamics are thought to unfold on a network determined by the pattern of axonal
connections linking pairs of neuronal elements; the so-called connectome. Prior work has indicated
that structural brain connectivity constrains pairwise correlations of brain dynamics
(“functional connectivity”), but it is not known whether inter-regional axonal connectivity is
related to the intrinsic dynamics of individual brain areas. Here we investigate this relationship
using a weighted, directed mesoscale mouse connectome from the Allen Mouse Brain
Connectivity Atlas and resting state functional MRI (rs-fMRI) time-series data measured in 184
brain regions in eighteen anesthetized mice. For each brain region, we measured degree,
betweenness, and clustering coefficient from weighted and unweighted, and directed and undirected
versions of the connectome. We then characterized the univariate rs-fMRI dynamics in
each brain region by computing 6930 time-series properties using the time-series analysis toolbox,
hctsa. After correcting for regional volume variations, strong and robust correlations
between structural connectivity properties and rs-fMRI dynamics were found only when edge
weights were accounted for, and were associated with variations in the autocorrelation properties
of the rs-fMRI signal. The strongest relationships were found for weighted in-degree, which was
positively correlated to the autocorrelation of fMRI time series at time lag s ¼ 34 s (partial
Spearman correlation q ¼ 0:58), as well as a range of related measures such as relative high frequency
power (f > 0.4 Hz: q ¼ 0:43). Our results indicate that the topology of inter-regional
axonal connections of the mouse brain is closely related to intrinsic, spontaneous dynamics such
that regions with a greater aggregate strength of incoming projections display longer timescales
of activity fluctuations.
connections linking pairs of neuronal elements; the so-called connectome. Prior work has indicated
that structural brain connectivity constrains pairwise correlations of brain dynamics
(“functional connectivity”), but it is not known whether inter-regional axonal connectivity is
related to the intrinsic dynamics of individual brain areas. Here we investigate this relationship
using a weighted, directed mesoscale mouse connectome from the Allen Mouse Brain
Connectivity Atlas and resting state functional MRI (rs-fMRI) time-series data measured in 184
brain regions in eighteen anesthetized mice. For each brain region, we measured degree,
betweenness, and clustering coefficient from weighted and unweighted, and directed and undirected
versions of the connectome. We then characterized the univariate rs-fMRI dynamics in
each brain region by computing 6930 time-series properties using the time-series analysis toolbox,
hctsa. After correcting for regional volume variations, strong and robust correlations
between structural connectivity properties and rs-fMRI dynamics were found only when edge
weights were accounted for, and were associated with variations in the autocorrelation properties
of the rs-fMRI signal. The strongest relationships were found for weighted in-degree, which was
positively correlated to the autocorrelation of fMRI time series at time lag s ¼ 34 s (partial
Spearman correlation q ¼ 0:58), as well as a range of related measures such as relative high frequency
power (f > 0.4 Hz: q ¼ 0:43). Our results indicate that the topology of inter-regional
axonal connections of the mouse brain is closely related to intrinsic, spontaneous dynamics such
that regions with a greater aggregate strength of incoming projections display longer timescales
of activity fluctuations.
Date Issued
2017-04-04
Date Acceptance
2017-03-08
Citation
Chaos, 2017, 27 (4)
ISSN
1054-1500
Publisher
AIP Publishing
Journal / Book Title
Chaos
Volume
27
Issue
4
Copyright Statement
© 2017 American Institute of Physics. This article may be downloaded for personal use only. Any other use requires prior permission of the author and the American Institute of Physics. The following article appeared in Chaos: An Interdisciplinary Journal of Nonlinear Science 2017 27:4 and may be found at https://dx.doi.org/10.1063/1.4979281
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000399154600015&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Subjects
Science & Technology
Physical Sciences
Mathematics, Applied
Physics, Mathematical
Mathematics
Physics
STATE FUNCTIONAL CONNECTIVITY
PRIMATE CORTEX
CEREBRAL-CORTEX
FMRI
TIME
TIMESCALES
ANESTHESIA
MICE
IDENTIFICATION
OPTIMIZATION
0102 Applied Mathematics
0103 Numerical And Computational Mathematics
0299 Other Physical Sciences
Fluids & Plasmas
Publication Status
Published
Article Number
ARTN 047405