bioRxiv (Cold Spring Harbor Laboratory),
Год журнала:
2024,
Номер
unknown
Опубликована: Сен. 13, 2024
ABSTRACT
The
intricate
link
between
brain
functional
connectivity
(FC)
and
structural
(SC)
is
explored
through
models
performing
diffusion
on
SC
to
derive
FC,
using
varied
methodologies
from
single
multiple
graph
kernels.
However,
existing
studies
have
not
correlated
scales
with
specific
regions
of
interest
(RoIs),
limiting
the
applicability
diffusion.
We
propose
a
novel
approach
heat
wavelets
learn
appropriate
scale
for
each
RoI
accurately
estimate
SC-FC
mapping.
Using
open
HCP
dataset,
we
achieve
an
average
Pearson’s
correlation
value
0.833,
surpassing
state-of-the-art
methods
prediction
FC.
It
important
note
that
proposed
architecture
entirely
linear,
computationally
efficient,
notably
demonstrates
power-law
distribution
scales.
Our
results
show
bilateral
frontal
pole,
by
virtue
it
having
large
scale,
forms
community
structure.
finding
in
line
current
literature
role
pole
resting-state
networks.
Overall,
underscore
potential
wavelet
framework
understanding
how
structure
leads
connectivity.
AUTHOR
SUMMARY
In
network
paradigm
structure-to-function
mapping,
noticed
limitations
such
as
manually
decided
absence
RoI-level
analysis.
addressed
this
problem
independently
developing
multiscale
multiresolution
property.
Each
region
associated
defines
extent
spatial
communication.
wavelets,
are
able
predict
connectome
(SoTA)
results.
observe
follow
degree
distribution,
which
indicative
scale-free
process
brain.
dominant
member
various
networks,
our
model
associate
higher
region.
method
only
excels
downstream
task
but
also
provides
insights
into
structure-function
relation.
bioRxiv (Cold Spring Harbor Laboratory),
Год журнала:
2024,
Номер
unknown
Опубликована: Май 8, 2024
The
networked
architecture
of
the
brain
promotes
synchrony
among
neuronal
populations
and
emergence
coherent
dynamics.
These
communication
patterns
can
be
comprehensively
mapped
using
noninvasive
functional
imaging,
resulting
in
connectivity
(FC)
networks.
Despite
its
popularity,
FC
is
a
statistical
construct
operational
definition
arbitrary.
While
most
studies
use
zero-lag
Pearson's
correlations
by
default,
there
exist
hundreds
pairwise
interaction
statistics
broader
scientific
literature
that
used
to
estimate
FC.
How
organization
matrix
varies
with
choice
statistic
fundamental
methodological
question
affects
all
this
rapidly
growing
field.
Here
we
benchmark
topological
geometric
organization,
neurobiological
associations,
cognitive-behavioral
relevance
matrices
computed
large
library
239
statistics.
We
investigate
how
canonical
features
networks
vary
statistic,
including
(1)
hub
mapping,
(2)
weight-distance
trade-offs,
(3)
structure-function
coupling,
(4)
correspondence
other
neurophysiological
networks,
(5)
individual
fingerprinting,
(6)
brain-behavior
prediction.
find
substantial
quantitative
qualitative
variation
across
methods.
Throughout,
observe
measures
such
as
covariance
(full
correlation),
precision
(partial
correlation)
distance
display
multiple
desirable
properties,
close
structural
connectivity,
capacity
differentiate
individuals
predict
differences
behavior.
Using
information
flow
decomposition,
methods
may
arise
from
differential
sensitivity
underlying
mechanisms
inter-regional
communication,
some
more
sensitive
redundant
synergistic
flow.
In
summary,
our
report
highlights
importance
tailoring
specific
mechanism
research
question,
providing
blueprint
for
future
optimize
their
method.
The European Physical Journal B,
Год журнала:
2024,
Номер
97(6)
Опубликована: Июнь 1, 2024
Abstract
In
functionally
complex
systems,
higher
order
connectivity
is
often
revealed
in
the
underlying
geometry
of
networked
units.
Furthermore,
such
systems
show
signatures
self-organised
criticality,
a
specific
type
non-equilibrium
collective
behaviour
associated
with
an
attractor
internal
dynamics
long-range
correlations
and
scale
invariance,
which
ensures
robust
functioning
as
brain.
Here,
we
highlight
intertwining
features
critical
plausible
mechanism
for
emergence
new
properties
on
larger
scale,
representing
central
paradigm
physical
notion
complexity.
Considering
time-scale
structural
evolution
known
separation
i.e.,
external
driving,
distinguish
three
classes
geometries
that
can
shape
them
differently.
We
provide
overview
current
trends
study
phenomena,
synchronisation
phase
oscillators
discrete
spin
couplings
embedded
faces
simplicial
complexes.
For
representative
example
induced
by
structures,
present
more
detailed
analysis
field-driven
reversal
hysteresis
loops
complexes
composed
triangles.
These
numerical
results
suggest
two
fundamental
interactions
edge-embedded
triangle-embedded
must
be
taken
into
account
theoretical
models
to
describe
influence
dynamics.
Graphical
abstract
Physical review. E,
Год журнала:
2024,
Номер
110(3)
Опубликована: Сен. 26, 2024
The
structure
of
a
complex
network
plays
crucial
role
in
determining
its
dynamical
properties.
In
this
paper
,
we
show
that
the
degree
to
which
is
directed
and
hierarchically
organized
closely
associated
with
dynamics
break
detailed
balance
produce
entropy.
We
consider
range
processes
how
different
features
affect
their
entropy
production
rate.
begin
an
analytical
treatment
two-node
followed
by
numerical
simulations
synthetic
networks
using
preferential
attachment
Erdös-Renyi
algorithms.
Next,
analyze
collection
97
empirical
determine
effect
real-world
topologies.
Finally,
present
simple
method
for
inferring
broken
from
multivariate
time
series
apply
our
identify
non-equilibrium
hierarchical
organisation
both
human
neuroimaging
financial
series.
Overall,
results
shed
light
on
consequences
highlight
importance
ubiquity
systems.
Published
American
Physical
Society
2024
Entropy,
Год журнала:
2025,
Номер
27(2), С. 113 - 113
Опубликована: Янв. 24, 2025
We
propose
using
the
ordinal
pattern
transition
(OPT)
entropy
measured
at
sentinel
central
nodes
as
a
potential
predictor
of
explosive
transitions
to
synchronization
in
networks
various
dynamical
systems
with
increasing
complexity.
Our
results
demonstrate
that
OPT
entropic
measure
surpasses
traditional
early
warning
signal
(EWS)
measures
and
could
be
valuable
tools
available
for
predicting
critical
transitions.
In
particular,
we
investigate
diffusively
coupled
phase
oscillators
chaotic
Rössler
systems.
As
maps,
consider
neural
network
Chialvo
maps
star
scale-free
configurations.
Furthermore,
apply
this
time
series
data
obtained
from
electronic
circuits
operating
regime.
Journal of Statistical Mechanics Theory and Experiment,
Год журнала:
2025,
Номер
2025(4), С. 043402 - 043402
Опубликована: Апрель 1, 2025
Abstract
In
this
paper,
we
present
a
detailed
statistical
analysis
related
to
the
characterization
of
spatial
and
temporal
fluctuations
in
rainfall
patterns
North-East
region
(
∘N
–26.95
,
88.05E
–94.95
E
)
India
using
half
hourly
data
over
last
two
decades
from
2001–2020.
We
analyze
nature
rain
distribution
by
computing
mean,
second
moment
fluctuation,
skewness
kurtosis
that
indicate
presence
heavy
tails
right
skewed
distribution,
typical
feature
rare
events.
find
follows
multiplicative
Log-Normal
probability
distribution.
Further,
compute
correlation
region,
indicating
events
are
correlated
direction
within
about
70
km.
The
power
spectral
density
shows
law
behavior
with
frequency
an
exponent
∼−1.5
close
Kolmogorov
1.67
exhibited
turbulent
passive
scalar
driven
mean
flow.
Our
wavelet
reveals
evidence
multiple
frequencies
which
can
be
attributed
different
short
long
range
factors
responsible
for
rainfall.
have
also
used
Hilbert
Huang
transformation
identify
corresponding
fluctuating
quasi-periodic
parts
time
series.
Finally,
report
multifractal
detrended
fluctuation
width
spectrum
identified
low
high
dominated
region.
