Communication dynamics in complex brain networks
Nature reviews. Neuroscience,
Journal Year:
2017,
Volume and Issue:
19(1), P. 17 - 33
Published: Dec. 14, 2017
Language: Английский
Linking Structure and Function in Macroscale Brain Networks
Trends in Cognitive Sciences,
Journal Year:
2020,
Volume and Issue:
24(4), P. 302 - 315
Published: Feb. 25, 2020
The
emergence
of
network
neuroscience
allows
researchers
to
quantify
the
link
between
organizational
features
neuronal
networks
and
spectrum
cortical
functions.Current
models
indicate
that
structure
function
are
significantly
correlated,
but
correspondence
is
not
perfect
because
reflects
complex
multisynaptic
interactions
in
structural
networks.Function
cannot
be
directly
estimated
from
structure,
must
inferred
by
higher-order
interactions.
Statistical,
communication,
biophysical
have
been
used
translate
brain
function.Structure–function
coupling
regionally
heterogeneous
follows
molecular,
cytoarchitectonic,
functional
hierarchies.
Structure–function
relationships
a
fundamental
principle
many
naturally
occurring
systems.
However,
research
suggests
there
an
imperfect
connectivity
brain.
Here,
we
synthesize
current
state
knowledge
linking
macroscale
discuss
different
types
assess
this
relationship.
We
argue
do
include
requisite
biological
detail
completely
predict
function.
Structural
reconstructions
enriched
with
local
molecular
cellular
metadata,
concert
more
nuanced
representations
functions
properties,
hold
great
potential
for
truly
multiscale
understanding
structure–function
relationship
central
concept
natural
sciences
engineering.
Consider
how
conformation
protein
determines
its
chemical
properties
and,
ultimately,
folding
into
3D
promotes
among
amino
acids,
allowing
chemically
interact
other
molecules
endowing
it
Conversely,
disruption
protein's
results
loss
Tellingly,
said
denatured,
highlighting
idea
changing
has
fundamentally
altered
nervous
system
analogously
shaped
arrangement
neurons
populations.
synaptic
projections
forms
hierarchy
(see
Glossary)
nested
increasingly
polyfunctional
neural
circuits
support
perception,
cognition,
action.
Modern
imaging
technology
permits
high-throughput
reconstruction
across
spatiotemporal
scales
species
(Box
1).
Through
extensive
international
data
sharing
efforts,
detailed
system's
connection
patterns
available
humans
multiple
model
organisms,
including
invertebrate
[1.Chiang
A-S.
et
al.Three-dimensional
brain-wide
wiring
Drosophila
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Full
Text
PDF
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Scopus
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Google
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avian
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M.
al.Large-scale
organization
forebrain:
matrix
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Comput.
Neurosci.
2013;
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89Crossref
(111)
rodent
[3.Oh
S.W.
al.A
mesoscale
connectome
mouse
brain.Nature.
2014;
508:
207Crossref
(777)
Scholar,4.Bota
al.Architecture
cerebral
association
underlying
cognition.Proc.
Natl.
Acad.
Sci.
U.S.A.
2015;
112:
E2093-E2101Crossref
(90)
Scholar,
primate
[5.Markov
N.T.
weighted
directed
interareal
macaque
cortex.Cereb.
Cortex.
2012;
24:
17-36Crossref
(272)
Scholar,6.Majka
P.
al.Towards
comprehensive
atlas
connections
brain:
Mapping
tracer
injection
studies
common
marmoset
reference
digital
template.J.
Com.
Neurol.
2016;
524:
2161-2181Crossref
(33)
Scholar.
These
diagrams
system,
termed
(SC)
or
connectomes,
represent
physical
elements
[7.Sporns
O.
al.The
human
connectome:
description
brain.PLoS
2005;
1:
e42Crossref
(1521)
Scholar].
offers
opportunity
articulate
functions.
SC
possess
distinctive
nonrandom
attributes,
high
clustering
short
path
length,
characteristic
small-world
architecture
[8.Watts
D.J.
Strogatz
S.H.
Collective
dynamics
networks.Nature.
1998;
393:
440Crossref
(0)
Populations
similar
tend
cluster
together,
forming
specialized
modules
crosslinked
hub
nodes
diverse
connectional
fingerprints
[9.Young
M.P.
systems
cortex.J.
Roy.
Soc.
Lond.
B.
1993;
252:
13-18Crossref
Scholar,10.Kötter
R.
al.Connectional
characteristics
areas
Walker's
map
prefrontal
cortex.Neurocomputing.
2001;
38:
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(20)
hubs
disproportionately
interconnected
each
other,
putative
core
[11.Hagmann
al.Mapping
cortex.PLoS
2008;
6:
e159Crossref
(2384)
Scholar]
'rich
club'
[12.van
den
Heuvel
al.High-cost,
high-capacity
backbone
global
communication.Proc.
109:
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architectural
feature
potentially
signals
sampled
integrated
[13.Zamora-López
G.
al.Cortical
form
module
multisensory
integration
on
top
networks.Front.
Neuroinform.
2010;
4:
1PubMed
Finally,
spatially
embedded,
finite
metabolic
material
resources
[14.Bullmore
E.
Sporns
economy
organization.Nat.
Rev.
13:
336Crossref
(1282)
resulting
increased
prevalence
shorter,
low-cost
[15.Horvát
S.
al.Spatial
embedding
cost
constrain
layout
rodents
primates.PLoS
14:
e1002512Crossref
Scholar,16.Roberts
J.A.
contribution
geometry
connectome.NeuroImage.
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379-393Crossref
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attributes
replicated
range
tracing
techniques,
suggesting
principles
phylogeny
[17.Van
al.Comparative
connectomics.Trends.
Cogn.
20:
345-361Abstract
(118)
imparts
distinct
signature
coactivation
patterns.
Inter-regional
promote
signaling
synchrony
distant
populations,
giving
rise
coherent
dynamics,
measured
as
regional
time
series
electromagnetic
hemodynamic
activity.
Systematic
pairs
regions
can
(FC)
networks.
Over
past
decade,
these
recorded
without
task
instruction
stimulation;
'intrinsic'
'
resting-state'
FC
thought
reflect
spontaneous
activity
[18.Biswal
al.Functional
motor
cortex
resting
using
echo-planar
MRI.Magn.
Reson.
Med.
1995;
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(5503)
Intrinsic
highly
organized
[19.Damoiseaux
J.
al.Consistent
resting-state
healthy
subjects.Proc.
2006;
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al.Multi-level
bootstrap
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stable
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fMRI.NeuroImage.
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21.Thomas
Yeo
intrinsic
connectivity.J.
Neurophysiol.
106:
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reproducible
[22.Gordon
E.M.
al.Precision
mapping
individual
brains.Neuron.
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Scholar,23.Noble
decade
test-retest
reliability
connectivity:
systematic
review
meta-analysis.NeuroImage.
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(5)
comparable
task-driven
[24.Smith
S.M.
al.Correspondence
brain's
during
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rest.Proc.
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Scholar,25.Cole
M.W.
al.Intrinsic
task-evoked
architectures
brain.Neuron.
83:
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(516)
persistent
nature
rest
makes
ideal
starting
point
study
[26.Honey
C.J.
al.Can
brain?.NeuroImage.
52:
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(291)
Scholar,27.Damoiseaux
J.S.
Greicius
M.D.
Greater
than
sum
parts:
combining
connectivity.Brain
Struct.
Funct.
213:
525-533Crossref
Here
first
show
direct
one-to-one
links
limited
inherently
obscured
networked
survey
modern
quantitative
methods
move
away
correlations
conceptualizing
emerging
focus
strengths,
limitations,
commonalities.
posit
next
steps
network-level
take
account
heterogeneity
enriching
microscale
transcriptomic,
neuromodulatory
information.
close
theories
uniform
brain,
vary
parallel
cytoarchitectonic
representational
Early
emphasized
weights.
weights
correlated
[28.Honey
C.
al.Predicting
connectivity.Proc.
2035-2040Crossref
(1543)
also
Furthermore,
structurally
connected
display
greater
unconnected
Scholar,29.Shen
K.
al.Information
processing
functionally
defined
32:
17465-17476Crossref
(63)
Scholar
(Figure
1A).
More
globally,
networks,
particularly
visual
somatomotor
circumscribed
dense
anatomical
[29.Shen
30.Van
Den
al.Functionally
linked
brain.Hum.
Brain
Mapp.
30:
3127-3141Crossref
(625)
31.Alves
P.N.
al.An
improved
neuroanatomical
default-mode
reconciles
previous
neuroimaging
neuropathological
findings.Commun.
2:
1-14Crossref
While
perfect.
Even
best-case
estimates
place
correlation
R2
≈
0.5
which
means
considerable
variance
(at
least
half)
unexplained
simple
1:1
structure.
discrepancy
widens
case
1B
).
A
salient
example
homotopic
corresponding
structures
two
hemispheres.
typically
strongest
subset
[32.Mišić
landscape
One.
9:
e111007Crossref
(14)
all
supported
callosal
projection
[33.Shen
al.Stable
long-range
interhemispheric
coordination
projections.Proc.
6473-6478Crossref
(52)
strong
may
observed
even
individuals
no
[34.Uddin
L.Q.
al.Residual
split-brain
revealed
fMRI.Neuroreport.
19:
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(96)
35.O'Reilly
J.X.
al.Causal
effect
disconnection
lesions
rhesus
monkeys.Proc.
110:
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(106)
36.Layden
E.A.
al.Interhemispheric
zebra
finch
absent
corpus
callosum
normal
ontogeny.NeuroImage.
Crossref
(1)
examples
illustrate
sustained
communication
via
indirect
manifest
FC.
discordance
pronounced
mesoscopic
scale.
commonly
meta-analytic
recovered
[37.Mišić
al.Cooperative
competitive
spreading
connectome.Neuron.
86:
1518-1529Abstract
Scholar,38.Betzel
R.F.
al.Diversity
meso-scale
non-human
connectomes.Nat.
Commun.
2018;
346Crossref
(21)
1C).
reproducibly
independent
component
community
detection
[39.Power
J.D.
72:
665-678Abstract
(1499)
data-driven
[20.Bellec
Scholar,21.Thomas
both
recordings
application
diffusion-weighted
covariance
yields
contiguous
Scholar,40.Betzel
modular
networks:
accounting
wiring.Net.
42-68Crossref
For
example,
fail
identify
default
mode-like
network,
perhaps
parts
anatomically
inter-connected
differences.
evidence
assortative
mixing,
whereby
(e.g.,
degrees)
likely
connected,
whereas
same
true
[50.Lim
al.Discordant
two-layer
multiplex
network.Sci.
Rep.
2885Crossref
(2)
At
scale,
communities
assortative,
while
disassortative
[38.Betzel
In
words,
affinity
dissimilar
attributes.
As
result,
tuning
algorithms
sensitive
improves
match
Altogether,
rich
body
work
demonstrates
spans
scales,
edges
their
arrangement.
Why
FC?
Functional
arise
connections,
courses
synapses
removed
other.
propensity
correlate
driven
only
them,
inputs
they
receive
sensory
organs
entire
[27.Damoiseaux
Scholar,51.Bettinardi
R.G.
al.How
sculpts
function:
unveiling
structure.Chaos.
27:
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(12)
corollary
much
less
distance-dependent
connections.
Anatomical
subject
material,
spatial,
constraints
Scholar];
pressures
reduced
probability
weight
increasing
spatial
separation
Although
distance-dependence
FC,
weaker,
ensuring
differences
configurations.
section
consider
emergent
property
links.
seen
so
far,
exists
nontrivial
perfectly
aligned.
number
emerged
embody
link,
statistical
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al.Network-level
structure-function
neocortex.Cereb.
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A.
al.Relating
relative
contributions
anatomy,
stationary
non-stationarities.PLoS.
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D.
Rockmore
packet
switching
brain.J.
23:
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al.Resting-brain
predicted
analytic
measures
111:
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J.J.
Higham
communicability
measure
applied
networks.J.
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411-414Crossref
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shapes
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activity.Nat.
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al.Mathematical
framework
modeling
Virtual
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385-430Crossref
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al.Key
role
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delay,
noise
fluctuations.Proc.
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Though
implementation
assumptions,
emphasize
collective,
transcends
geometric
dependence
dyadic
relationships.
briefly
strategies,
interpretation
predictive
utility,
most
importantly,
what
teach
us
about
Perhaps
simplest
way
statistically.
Varying
rank
regression
useful,
canonical
[52.Deligianni
F.
al.NODDI
tensor-based
microstructural
indices
predictors
connectivity.PLoS
11:
e0153404Crossref
(13)
partial
squares
objective
simultaneously
combinations
maximally
[53.McIntosh
A.R.
Mišić
Multivariate
analyses
data.Annu.
Psychol.
64:
499-525Crossref
(73)
2).
An
appealing
such
modes.
particular
configuration
subnetwork
give
Taking
further,
artificial
learn
recent
variant
word2vec
algorithm
build
low-dimensional
representation
train
deep
edge-wise
[54.Rosenthal
relations
embedded
vector
2178Crossref
(3)
offer
associate
assuming
specific
mode
interaction
Communication
science
telecommunication
engineering
conceptualize
superposition
elementary
events
[43.Graham
Scholar,55.Avena-Koenigsberger
al.Communication
networks.Nat.
17Crossref
(92)
By
explicitly
formulating
inter-regional
signaling,
open
important
questions,
namely:
biologically
realistic
model,
well
does
fit
network?
focused
centralized
shortest
routing,
discrete
travel
set
source
node
prespecified
target
node.
recently,
attention
shifted
decentralized
mechanisms
where
diffuse
through
[56.Mišić
convergence
zone
hippocampus.PLoS.
e1003982Crossref
Scholar,57.Atasoy
al.Human
connectome-specific
harmonic
waves.Nat.
10340Crossref
often
broadcast
fronts
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diffusion
accurately
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Scholar,59.Worrell
J.C.
al.Optimized
sensory-motor
integration.Net.
415-430Crossref
Others
considered
neither
fully
nor
decentralized,
ensembles
[45.Crofts
Scholar,60.Avena-Koenigsberger
al.Path
tradeoff
efficiency
resilience
connectome.Brain
222:
603-618Crossref
(17)
multiplexed
strategies
involving
[44.Goñi
Scholar,61.Avena-Koenigsberger
routing
networks.PLoS
15:
e1006833Crossref
62.Betzel
al.Structural,
genetic
factors
interregional
probed
electrocorticography.Nat.
Biomed.
Eng.
63.Vazquez-Rodriguez
al.Gradients
tethering
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116:
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(6)
consensus
that,
given
topological
proximity
possible
utilize
either
al.Re
Language: Английский
A cross-disorder connectome landscape of brain dysconnectivity
Nature reviews. Neuroscience,
Journal Year:
2019,
Volume and Issue:
20(7), P. 435 - 446
Published: May 24, 2019
Language: Английский
Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature
David S. Grayson,
No information about this author
Damien A. Fair
No information about this author
NeuroImage,
Journal Year:
2017,
Volume and Issue:
160, P. 15 - 31
Published: Feb. 2, 2017
Language: Английский
A Network Model of the Emotional Brain
Trends in Cognitive Sciences,
Journal Year:
2017,
Volume and Issue:
21(5), P. 357 - 371
Published: March 28, 2017
Language: Английский
Deschloroclozapine, a potent and selective chemogenetic actuator enables rapid neuronal and behavioral modulations in mice and monkeys
Nature Neuroscience,
Journal Year:
2020,
Volume and Issue:
23(9), P. 1157 - 1167
Published: July 6, 2020
Language: Английский
Chemogenetic Interrogation of a Brain-wide Fear Memory Network in Mice
Neuron,
Journal Year:
2017,
Volume and Issue:
94(2), P. 363 - 374.e4
Published: April 1, 2017
Language: Английский
An Open Resource for Non-human Primate Imaging
Neuron,
Journal Year:
2018,
Volume and Issue:
100(1), P. 61 - 74.e2
Published: Sept. 27, 2018
Non-human
primate
neuroimaging
is
a
rapidly
growing
area
of
research
that
promises
to
transform
and
scale
translational
cross-species
comparative
neuroscience.
Unfortunately,
the
technological
methodological
advances
past
two
decades
have
outpaced
accrual
data,
which
particularly
challenging
given
relatively
few
centers
necessary
facilities
capabilities.
The
PRIMatE
Data
Exchange
(PRIME-DE)
addresses
this
challenge
by
aggregating
independently
acquired
non-human
magnetic
resonance
imaging
(MRI)
datasets
openly
sharing
them
via
International
Neuroimaging
Data-sharing
Initiative
(INDI).
Here,
we
present
rationale,
design,
procedures
for
PRIME-DE
consortium,
as
well
initial
release,
consisting
25
independent
data
collections
aggregated
across
22
sites
(total
=
217
primates).
We
also
outline
unique
pitfalls
challenges
should
be
considered
in
analysis
MRI
datasets,
including
providing
automated
quality
assessment
contributed
datasets.
Language: Английский
The Basal Forebrain Regulates Global Resting-State fMRI Fluctuations
Janita Turchi,
No information about this author
Catie Chang,
No information about this author
Frank Q. Ye
No information about this author
et al.
Neuron,
Journal Year:
2018,
Volume and Issue:
97(4), P. 940 - 952.e4
Published: Feb. 1, 2018
Language: Английский
The central extended amygdala in fear and anxiety: Closing the gap between mechanistic and neuroimaging research
Neuroscience Letters,
Journal Year:
2017,
Volume and Issue:
693, P. 58 - 67
Published: Nov. 30, 2017
Language: Английский