Comparative Analysis of Brain Age Prediction Using Structural and Diffusion MRIs in Neonates
Zhicong Fang,
No information about this author
Ningning Pan,
No information about this author
Shujuan Liu
No information about this author
et al.
NeuroImage,
Journal Year:
2024,
Volume and Issue:
299, P. 120815 - 120815
Published: Aug. 25, 2024
Using
machine
learning
techniques
to
predict
brain
age
from
multimodal
data
has
become
a
crucial
biomarker
for
assessing
development.
Among
various
types
of
imaging
data,
structural
magnetic
resonance
(sMRI)
and
diffusion
(dMRI)
are
the
most
commonly
used
modalities.
sMRI
focuses
on
depicting
macrostructural
features
brain,
while
dMRI
reveals
orientation
major
white
matter
fibers
changes
in
tissue
microstructure.
However,
their
differential
capabilities
reflecting
newborn
clinical
implications
have
not
been
systematically
studied.
This
study
aims
explore
impact
prediction.
Comparing
predictions
based
T2-weighted(T2w)
fractional
anisotropy
(FA)
images,
we
found
mean
absolute
errors
(MAE)
predicting
infant
be
similar.
Exploratory
analysis
revealed
T2w
areas
such
as
cerebral
cortex
ventricles
contribute
significantly
prediction,
whereas
FA
images
highlight
regions
main
tracts.
Despite
both
modalities
focusing
cortex,
they
exhibit
significant
region-wise
differences,
developmental
disparities
macro-
microstructural
aspects
cortex.
Additionally,
examined
effects
prematurity,
gender,
hemispherical
asymmetry
prediction
Results
showed
differences
(p<0.05)
biases
across
gender
asymmetry,
no
were
observed
with
images.
underscores
between
age,
offering
new
perspectives
studying
development
aiding
more
effective
assessment
tracking
Language: Английский
LSTGINet: Local Attention Spatio-Temporal Graph Inference Network for Age Prediction
Yi Lei,
No information about this author
Xin Wen,
No information about this author
Yanrong Hao
No information about this author
et al.
Algorithms,
Journal Year:
2025,
Volume and Issue:
18(3), P. 138 - 138
Published: March 3, 2025
There
is
a
close
correlation
between
brain
aging
and
age.
However,
traditional
neural
networks
cannot
fully
capture
the
potential
age
due
to
limited
receptive
field.
Furthermore,
they
are
more
concerned
with
deep
spatial
semantics,
ignoring
fact
that
effective
temporal
information
can
enrich
representation
of
low-level
semantics.
To
address
these
limitations,
local
attention
spatio-temporal
graph
inference
network
(LSTGINet)
was
developed
explore
details
association
aging,
taking
into
account
both
perspectives.
First,
multi-scale
branches
used
increase
field
model
simultaneously,
achieving
perception
static
correlation.
Second,
feature
graphs
reconstructed,
large
topographies
constructed.
The
node
aggregation
transfer
functions
hidden
dynamic
A
new
module
embedded
in
component
global
context
establish
dependencies
interactivity
different
features,
balance
differences
distribution
We
use
newly
designed
weighted
loss
function
supervise
learning
entire
prediction
framework
strengthen
process
final
experimental
results
show
MAE
on
baseline
datasets
such
as
CamCAN
NKI
6.33
6.28,
respectively,
better
than
current
state-of-the-art
methods,
provides
basis
for
assessing
state
adults.
Language: Английский
Prompt-guided orthogonal multimodal fusion for cancer survival prediction
Lan Huang,
No information about this author
Shuyu Guo,
No information about this author
Tian Bai
No information about this author
et al.
Information Sciences,
Journal Year:
2025,
Volume and Issue:
unknown, P. 122242 - 122242
Published: April 1, 2025
Language: Английский
Mapping brain development against neurological disorder using contrastive sharing
Expert Systems with Applications,
Journal Year:
2024,
Volume and Issue:
256, P. 124893 - 124893
Published: Aug. 2, 2024
Language: Английский
MFCA: Collaborative prediction algorithm of brain age based on multimodal fuzzy feature fusion
Information Sciences,
Journal Year:
2024,
Volume and Issue:
687, P. 121376 - 121376
Published: Aug. 19, 2024
Language: Английский
Enhancing perinatal brain maturity estimation using surface deep learning and cross-modal relationship inference technology
Ziyi Yang,
No information about this author
Runtao He,
No information about this author
Yucen Sheng
No information about this author
et al.
Research Square (Research Square),
Journal Year:
2024,
Volume and Issue:
unknown
Published: Dec. 23, 2024
Abstract
Neonates
with
marked
brain
developmental
delays
are
at
increased
risk
of
neurodevelopmental
disorders.
Brain
chronological
age
is
a
valuable
biomarker
for
assessing
abnormal
maturation
in
developing
brains;
however,
accurately
estimating
birth
remains
challenging.
In
this
study,
we
introduce
cross-modal
relationship
inference
network
(CMRINet)
that
integrates
structural
and
diffusion
magnetic
resonance
imaging
data
to
improve
the
accuracy
neonatal
estimation.
The
CMRINet
employs
Transformer
encoder
relational
module
capture
both
long-
short-range
dependencies
multimodal
features
among
cortical
parcels.
Our
model
outperformed
others
predicting
age,
achieving
mean
squared
error
0.51
absolute
0.55
on
test
set.
By
applying
trained
full-term
neonates
preterm
infants
term-equivalent
found
predicted
was
significantly
lower
than
suggesting
delayed
development
brains.
Furthermore,
deviation
associated
long-term
motor
infants.
These
findings
highlight
effectiveness
estimation,
potential
clinical
utility
early
detection
risks
during
perinatal
period.
Language: Английский
Do transformers and CNNs learn different concepts of brain age?
bioRxiv (Cold Spring Harbor Laboratory),
Journal Year:
2024,
Volume and Issue:
unknown
Published: Aug. 9, 2024
Abstract
“Predicted
brain
age”
refers
to
a
biomarker
of
structural
health
derived
from
machine
learning
analysis
T1-weighted
magnetic
resonance
(MR)
images.
A
range
methods
have
been
used
predict
age,
with
convolutional
neural
networks
(CNNs)
currently
yielding
state-of-the-art
accuracies.
Recent
advances
in
deep
introduced
transformers,
which
are
conceptually
distinct
CNNs,
and
appear
set
new
benchmarks
various
domains
computer
vision.
However,
transformers
not
yet
applied
age
prediction.
Thus,
we
address
two
research
questions:
First,
superior
CNNs
predicting
age?
Second,
do
different
model
architectures
learn
similar
or
“concepts
age”?
We
adapted
Simple
Vision
Transformer
(sViT)
Shifted
Window
(SwinT)
compared
both
models
ResNet50
on
46,381
MR
images
the
UK
Biobank.
found
that
SwinT
ResNet
performed
par,
while
additional
training
samples
will
most
likely
give
edge
prediction
accuracy.
identified
may
characterize
(sub-)sets
aging
effects,
representing
diverging
concepts
age.
systematically
tested
whether
sViT,
focus
by
examining
variations
their
predictions
clinical
utility
for
indicating
deviations
neurological
psychiatric
disorders.
Reassuringly,
did
find
substantial
differences
structure
between
architectures.
Based
our
results,
choice
architecture
does
confounding
effect
studies.
Language: Английский