Psychological Medicine,
Journal Year:
2024,
Volume and Issue:
unknown, P. 1 - 19
Published: Nov. 26, 2024
Abstract
Background
In
contemporary
neuroimaging
studies,
it
has
been
observed
that
patients
with
major
depressive
disorder
(MDD)
exhibit
aberrant
spontaneous
neural
activity,
commonly
quantified
through
the
amplitude
of
low-frequency
fluctuations
(ALFF).
However,
substantial
individual
heterogeneity
among
poses
a
challenge
to
reaching
unified
conclusion.
Methods
To
address
this
variability,
our
study
adopts
novel
framework
parse
individualized
ALFF
abnormalities.
We
hypothesize
abnormalities
can
be
portrayed
as
unique
linear
combination
shared
differential
factors.
Our
involved
two
large
multi-center
datasets,
comprising
2424
MDD
and
2183
healthy
controls.
patients,
were
derived
normative
modeling
further
deconstructed
into
factors
using
non-negative
matrix
factorization.
Results
Two
positive
negative
identified.
These
closely
linked
clinical
characteristics
explained
group-level
in
datasets.
Moreover,
these
exhibited
distinct
associations
distribution
neurotransmitter
receptors/transporters,
transcriptional
profiles
inflammation-related
genes,
connectome-informed
epicenters,
underscoring
their
neurobiological
relevance.
Additionally,
factor
compositions
facilitated
identification
four
subtypes,
each
characterized
by
abnormal
patterns
features.
Importantly,
findings
successfully
replicated
another
dataset
different
acquisition
equipment,
protocols,
preprocessing
strategies,
medication
statuses,
validating
robustness
generalizability.
Conclusions
This
research
identifies
underlying
activity
contributes
insights
MDD.
Brain‐X,
Journal Year:
2024,
Volume and Issue:
2(3)
Published: Sept. 1, 2024
Abstract
The
incidence
of
affective
disorders,
which
major
depression
disorder
(MDD)
and
bipolar
(BD)
are
two
main
types,
has
increased
rapidly
in
recent
years.
They
significantly
impact
patients,
their
families,
society.
However,
while
disorders
have
become
a
issue
worldwide,
pathogenesis
remains
unclear.
In
the
last
6
years,
research
using
magnetic
resonance
imaging
(MRI)
genetic
data
gained
prominence
understanding
pathophysiology
etiology.
This
systematic
review
collected
studies
MDD
BD
published
between
January
1,
2018,
February
2024,
focusing
on
MRI
indexed
Web
Science
PubMed
database.
It
aims
to
investigate
similarities
differences
phenotypes
underlying
molecular
bases.
After
exclusions,
total
80
articles
were
included
this
review.
Research
reveals
critical
role
epigenetic
modifications,
such
as
DNA
methylation,
brain
structure
function
changes.
genes
pathways
implicated
directly
associated
with
depressive
symptoms.
contrast,
those
mood
regulation
cognitive
functions.
addition,
functional
revealed
that
abnormalities
frequently
concentrated
regions
involved
emotion
stress
response.
neural
circuits
related
reward
processing
emotional
stability.
Further
multimodal
multiscale
needed
advance
field
research.
Frontiers in Aging Neuroscience,
Journal Year:
2024,
Volume and Issue:
16
Published: Nov. 29, 2024
Brain
structural
abnormalities
have
been
associated
with
cognitive
impairment
in
individuals
small
cerebral
vascular
disease
(CSVD).
However,
the
molecular
and
cellular
factors
making
different
brain
regions
more
vulnerable
to
CSVD-related
remain
largely
unknown.
Psychological Medicine,
Journal Year:
2024,
Volume and Issue:
unknown, P. 1 - 19
Published: Nov. 26, 2024
Abstract
Background
In
contemporary
neuroimaging
studies,
it
has
been
observed
that
patients
with
major
depressive
disorder
(MDD)
exhibit
aberrant
spontaneous
neural
activity,
commonly
quantified
through
the
amplitude
of
low-frequency
fluctuations
(ALFF).
However,
substantial
individual
heterogeneity
among
poses
a
challenge
to
reaching
unified
conclusion.
Methods
To
address
this
variability,
our
study
adopts
novel
framework
parse
individualized
ALFF
abnormalities.
We
hypothesize
abnormalities
can
be
portrayed
as
unique
linear
combination
shared
differential
factors.
Our
involved
two
large
multi-center
datasets,
comprising
2424
MDD
and
2183
healthy
controls.
patients,
were
derived
normative
modeling
further
deconstructed
into
factors
using
non-negative
matrix
factorization.
Results
Two
positive
negative
identified.
These
closely
linked
clinical
characteristics
explained
group-level
in
datasets.
Moreover,
these
exhibited
distinct
associations
distribution
neurotransmitter
receptors/transporters,
transcriptional
profiles
inflammation-related
genes,
connectome-informed
epicenters,
underscoring
their
neurobiological
relevance.
Additionally,
factor
compositions
facilitated
identification
four
subtypes,
each
characterized
by
abnormal
patterns
features.
Importantly,
findings
successfully
replicated
another
dataset
different
acquisition
equipment,
protocols,
preprocessing
strategies,
medication
statuses,
validating
robustness
generalizability.
Conclusions
This
research
identifies
underlying
activity
contributes
insights
MDD.
