In
this
study,
we
examined
reservoir
computing
(RC)as
a
tool
for
predicting
the
macroscopic
dynamics
of
subset
oscillators
in
network
based
on
other
parts
it.
As
model
network,
utilized
300
Kuramoto
with
adaptation.
Our
results
demonstrate
that
effectively
addresses
task.
Additionally,
similar
was
applied
to
experimental
neurovisualization
data
and
exhibited
high
accuracy
reconstructing
damaged
EEG
channels
compared
classical
methods
like
spatial
interpolation.
NeuroImage,
Journal Year:
2024,
Volume and Issue:
292, P. 120594 - 120594
Published: April 1, 2024
Converging
evidence
increasingly
suggests
that
psychiatric
disorders,
such
as
major
depressive
disorder
(MDD)
and
autism
spectrum
(ASD),
are
not
unitary
diseases,
but
rather
heterogeneous
syndromes
involve
diverse,
co-occurring
symptoms
divergent
responses
to
treatment.
This
clinical
heterogeneity
has
hindered
the
progress
of
precision
diagnosis
treatment
effectiveness
in
disorders.
In
this
study,
we
propose
BPI-GNN,
a
new
interpretable
graph
neural
network
(GNN)
framework
for
analyzing
functional
magnetic
resonance
images
(fMRI),
by
leveraging
famed
prototype
learning.
addition,
introduce
novel
generation
process
subgraph
discover
essential
edges
distinct
prototypes
employ
total
correlation
(TC)
ensure
independence
patterns.
BPI-GNN
can
effectively
discriminate
patients
healthy
controls
(HC),
identify
biological
meaningful
subtypes
We
evaluate
performance
against
11
popular
brain
classification
methods
on
three
datasets
observe
our
always
achieves
highest
accuracy.
More
importantly,
examine
differences
symptom
profiles
gene
expression
among
identified
brain-based
have
relevance.
It
also
discovers
subtype
biomarkers
align
with
current
neuro-scientific
knowledge.
Research Square (Research Square),
Journal Year:
2025,
Volume and Issue:
unknown
Published: March 25, 2025
Abstract
Background
Post-Traumatic
Stress
Disorder
(PTSD)
is
associated
with
neurobiological
alterations,
which
can
be
examined
using
surface-based
morphology
(SBM).
While
machine
learning
(ML)
approaches
have
shown
potential
in
classifying
PTSD
based
on
SBM
features,
further
exploration
needed
to
improve
interpretability
and
clinical
relevance.
Objectives
This
study
seeks
integrate
ML-based
classification
of
SHAP
analysis
identify
important
features
their
associations
symptomatology,
providing
insights
into
the
structural
changes
underlying
PTSD.
Methods
High-resolution
T1-weighted
MRI
data
from
101
participants
(62
PTSD,
39
healthy
controls)
were
analyzed
FreeSurfer’s
pipeline,
extracting
cortical
thickness,
surface
area,
curvature
aparc.a2009s
atlas.
Several
ML
models,
including
Random
Forest,
SVM,
XGBoost,
trained
evaluated
ten-fold
cross-validation.
was
applied
determine
feature
importance,
correlation
analyses
conducted
examine
relationships
between
key
symptom
severity.
Results
Sixteen
regions
identified
significant
differences
reduced
thickness
left
lingual
gyrus
increased
bilateral
central
sulcus.
The
Forest
model
achieved
highest
accuracy
(91%)
classification.
highlighted
parahippocampal
as
features.
Correlation
suggested
links
these
specific
clusters.
Conclusion
integration
interpretable
methods
provides
a
promising
approach
for
investigating
brain
validation
needed,
findings
contribute
better
understanding
neurobiology
may
support
future
research
diagnostic
therapeutic
strategies.
Chaos An Interdisciplinary Journal of Nonlinear Science,
Journal Year:
2023,
Volume and Issue:
33(9)
Published: Sept. 1, 2023
We
present
a
novel
method
for
analyzing
brain
functional
networks
using
magnetic
resonance
imaging
data,
which
involves
utilizing
consensus
networks.
In
this
study,
we
compare
our
approach
to
standard
group-based
patients
diagnosed
with
major
depressive
disorder
(MDD)
and
healthy
control
group,
taking
into
account
different
levels
of
connectivity.
Our
findings
demonstrate
that
the
network
uncovers
distinct
characteristics
in
measures
degree
distributions
when
considering
connection
strengths.
as
strengths
increase,
observe
transition
topology
from
combination
scale-free
random
topologies
small-world
topology.
Conversely,
MDD
group
exhibits
uncertainty
weak
connections,
while
strong
connections
display
properties.
contrast,
does
not
exhibit
significant
differences
behavior
between
two
groups.
However,
it
indicate
scale-free-like
structure
topologies.
The
use
also
holds
immense
potential
classification
patients,
unveils
substantial
distinctions
Chaos An Interdisciplinary Journal of Nonlinear Science,
Journal Year:
2024,
Volume and Issue:
34(10)
Published: Oct. 1, 2024
This
Focus
Issue
covers
recent
developments
in
the
broad
areas
of
nonlinear
dynamics,
synchronization,
and
emergent
behavior
dynamical
networks.
It
targets
current
progress
on
issues
such
as
time
series
analysis
data-driven
modeling
from
real
data
climate,
brain,
social
dynamics.
Predicting
detecting
early
warning
signals
extreme
climate
conditions,
epileptic
seizures,
or
other
catastrophic
conditions
are
primary
tasks
experimental
data.
Exploring
machine-based
learning
for
purpose
prediction
is
an
emerging
area.
Application
evolutionary
game
theory
biological
systems
(eco-evolutionary
theory)
a
developing
direction
future
research
understanding
interactions
between
species.
Recent
bifurcations,
analysis,
control,
time-delay
also
discussed.
International Journal of Molecular Sciences,
Journal Year:
2024,
Volume and Issue:
25(13), P. 7049 - 7049
Published: June 27, 2024
Neuropeptides
are
biomolecules
with
crucial
physiological
functions.
Accurate
identification
of
neuropeptides
is
essential
for
understanding
nervous
system
regulatory
mechanisms.
However,
traditional
analysis
methods
expensive
and
laborious,
the
development
effective
machine
learning
models
continues
to
be
a
subject
current
research.
Hence,
in
this
research,
we
constructed
an
SVM-based
neuropeptide
predictor,
iNP_ESM,
by
integrating
protein
language
Evolutionary
Scale
Modeling
(ESM)
Unified
Representation
(UniRep)
first
time.
Our
model
utilized
feature
fusion
selection
strategies
improve
prediction
accuracy
during
optimization.
In
addition,
validated
effectiveness
optimization
strategy
UMAP
(Uniform
Manifold
Approximation
Projection)
visualization.
iNP_ESM
outperforms
existing
on
variety
evaluation
metrics,
up
0.937
cross-validation
0.928
independent
testing,
demonstrating
optimal
recognition
capabilities.
We
anticipate
improved
data
future,
believe
that
will
have
broader
applications
research
clinical
treatment
neurological
diseases.
The
purpose
of
this
paper
is
to
comparatively
analyse
the
efficiency
using
artificial
neural
networks
with
different
convolutional
and
recurrent
architectures
in
task
depression
diagnosis
based
on
electroencephalogram
(EEG)
data.
Open
datasets
were
chosen
as
objects
study
own
EEG
data
real
patients
collected.
Methods.
To
solve
problem
identifying
biomarkers
depressive
disorder
from
data,
we
used
two-dimensional
or
one-dimensional
convolution
operation,
well
hybrid
models
networks.
test
developed
networks,
selected
open
sets,
performed
an
experiment
collect
our
depressed
patients,
merged
prepared
sets.
result
work
analysis
comparison
performance
classifiers
network
models.
Conclusion.
We
show
that
average
accuracy
classification
a
sample
cross-validation
was
0.68.
results
are
consistent
known
literature
for
small
patient-disaggregated
datasets.
Although
obtained
insufficient
practical
application
model,
it
can
be
argued
further
research
improve
model
promising,
need
create
sufficiently
large
representative
dataset
which
important
scientific
construction
biophysical
disorders.
