Altered microstate C and D dynamics in high social anxiety: a resting-state EEG study
Frontiers in Psychology,
Journal Year:
2025,
Volume and Issue:
16
Published: May 8, 2025
Introduction
Social
anxiety
is
characterized
by
excessive
fear
of
negative
evaluation
and
avoidance
in
social
situations.
While
its
neural
processing
patterns
are
well-documented,
the
millisecond-level
temporal
dynamics
brain
functional
networks
remain
poorly
understood.
This
study
used
EEG
microstate
analysis
to
explore
dynamic
mechanisms
underlying
anxiety.
Methods
Eyes-closed
resting-state
data
were
collected
from
41
participants,
divided
into
high
(
n
=
23)
low
18)
groups
based
on
their
Liebowitz
Anxiety
Scale
(LSAS)
scores.
parameters,
including
duration,
occurrence
frequency,
time
coverage,
transition
probabilities,
analyzed.
Correlation
analyses
conducted
between
LSAS
scores
dynamics.
Results
The
group
exhibited
significantly
increased
duration
coverage
C
(associated
with
personally
significant
information
self-reflection)
decreased
D
executive
functioning).
Transition
probabilities
involving
(A
↔
C,
B
C)
higher,
while
those
D)
lower
group.
In
group,
probability
showed
correlations
total
subscale
Discussion
These
findings
reveal
distinct
anxiety,
heightened
self-referential
(microstate
impaired
functioning
D).
altered
suggest
a
predisposition
for
self-focus
reduced
coordination
control
individuals.
results
provide
new
insights
offer
potential
directions
clinical
interventions
early
detection.
Language: Английский
Abnormal large‐scale brain functional network dynamics in social anxiety disorder
CNS Neuroscience & Therapeutics,
Journal Year:
2024,
Volume and Issue:
30(8)
Published: Aug. 1, 2024
Abstract
Aims
Although
static
abnormalities
of
functional
brain
networks
have
been
observed
in
patients
with
social
anxiety
disorder
(SAD),
the
connectome
dynamics
at
macroscale
network
level
remain
obscure.
We
therefore
used
a
multivariate
data‐driven
method
to
search
for
dynamic
connectivity
(dFNC)
alterations
SAD.
Methods
conducted
spatial
independent
component
analysis,
and
sliding‐window
approach
k‐means
clustering
algorithm,
characterize
recurring
states
resting‐state
networks;
then
state
transition
metrics
FNC
strength
different
were
compared
between
SAD
healthy
controls
(HC),
relationship
clinical
characteristics
was
explored.
Results
Four
distinct
identified.
Compared
HC,
demonstrated
higher
fractional
windows
mean
dwelling
time
highest‐frequency
State
3,
representing
“widely
weaker”
FNC,
but
lower
States
2
4,
“locally
stronger”
respectively.
In
1,
moderate”
showed
decreased
mainly
default
mode
attention
perceptual
networks.
Some
aberrant
dFNC
signatures
correlated
illness
duration.
Conclusion
These
patterns
synchronization
among
large‐scale
may
provide
new
insights
into
neuro‐functional
underpinnings
Language: Английский