Enhanced Brain-Heart Connectivity as a Precursor of Reduced State Anxiety After Therapeutic Virtual Reality Immersion
bioRxiv (Cold Spring Harbor Laboratory),
Journal Year:
2024,
Volume and Issue:
unknown
Published: Nov. 30, 2024
Abstract
State
anxiety
involves
transient
feelings
of
tension
and
nervousness
in
response
to
threats,
which
can
escalate
into
disorders
if
persistent.
Despite
treatments,
30%-50%
individuals
show
limited
improvement,
neurophysiological
mechanisms
treatment
responsiveness
remain
unclear,
requiring
the
development
objective
biomarkers.
In
this
study,
we
monitored
multimodal
electrophysiological
parameters:
heart
rate
variability
(high-frequency,
low-frequency,
LF/HF
ratio),
EEG
beta
alpha
relative
power,
brain-to-heart
connectivity
participants
with
real-life
state
anxiety.
Participants
underwent
a
therapeutic
intervention
combining
virtual-reality
immersion,
hypnotic
script,
breath
control
exercise.
Real-life
was
captured
using
STAI-Y1
scale
before
after
intervention.
We
observed
reduced
immediately
16
out
27
participants.
While
all
participants,
independently
their
score,
showed
increased
HRV
low
frequency
only
treatment-responders
displayed
overall
autonomic
tone
(high
HRV),
midline
power
connectivity.
Notably,
ratio
significant
linear
relationship
reduction,
higher
ratios
linked
greater
response.
These
findings
suggest
that
cognitive
regulation
could
serve
as
biomarker
for
efficacy,
elevated
facilitating
improved
cardiac
responders.
Significance
Statement
Elevated
debilitating
disorders,
such
generalized
disorder,
yet
efficacy
remains
inconsistent,
reliable
biomarkers
predicting
outcomes
are
lacking.
This
study
identifies
key
neural
physiological
markers
effective
reduction
following
virtual
reality-based
non-pharmacological
healthy
Anxiety
is
associated
heightened
(LF/HF
enhanced
highlight
role
modulation
nervous
system
functioning
By
highlighting
these
biomarkers,
research
aims
at
advancing
our
understanding
offering
insights
biomarker-driven,
scalable
interventions.
Language: Английский
Ongoing Dynamics of Peak Alpha Frequency Characterize Hypnotic Induction in Highly Hypnotic-Susceptible Individuals
Brain Sciences,
Journal Year:
2024,
Volume and Issue:
14(9), P. 883 - 883
Published: Aug. 30, 2024
Hypnotic
phenomena
exhibit
significant
inter-individual
variability,
with
some
individuals
consistently
demonstrating
efficient
responses
to
hypnotic
suggestions,
while
others
show
limited
susceptibility.
Recent
neurophysiological
studies
have
added
a
growing
body
of
research
that
shows
variability
in
susceptibility
is
linked
distinct
neural
characteristics.
Building
on
this
foundation,
our
previous
work
identified
high
and
low
can
be
differentiated
based
the
arrhythmic
activity
observed
resting-state
electrophysiology
(rs-EEG)
outside
hypnosis.
However,
because
has
largely
focused
mean
spectral
characteristics,
understanding
over
time
these
features,
how
they
relate
susceptibility,
still
limited.
Here
we
address
gap
using
time-resolved
assessment
rhythmic
alpha
peaks
components
EEG
spectrum
both
prior
following
induction.
Using
multivariate
pattern
classification,
investigated
whether
features
differ
between
Specifically,
used
classification
investigate
non-stationary
could
distinguish
hypnosis
before
after
Our
analytical
approach
decomposition
capture
intricate
dynamics
oscillations
their
non-oscillatory
counterpart,
as
well
Lempel-Ziv
complexity.
results
variations
center
frequency
are
indicative
but
discrimination
only
evident
during
Highly
hypnotic-susceptible
higher
peak
frequency.
These
findings
underscore
dynamic
changes
states
related
represent
central
feature
Language: Английский
Aperiodic activity as a central neural feature of hypnotic susceptibility outside of hypnosis
bioRxiv (Cold Spring Harbor Laboratory),
Journal Year:
2023,
Volume and Issue:
unknown
Published: Nov. 17, 2023
Abstract
How
well
a
person
responds
to
hypnosis
is
stable
trait,
which
exhibits
considerable
inter-individual
diversity
across
the
general
population.
Yet,
its
neural
underpinning
remains
elusive.
Here,
we
address
this
gap
by
combining
EEG
data,
multivariate
statistics,
and
machine
learning
in
order
identify
brain
patterns
that
differentiate
between
individuals
high
low
susceptibility
hypnosis.
In
particular,
computed
periodic
aperiodic
components
of
power
spectrum,
as
graph
theoretical
measures
derived
from
functional
connectivity,
data
acquired
at
rest
(pre-induction)
under
(post-induction).
We
found
1/f
slope
spectrum
was
best
predictor
hypnotic
susceptibility.
Our
findings
support
idea
trait
linked
balance
cortical
excitation
inhibition
baseline
offers
novel
perspectives
on
foundations
Future
work
can
explore
contribution
background
activity
target
distinguish
responsiveness
clinic.
Significance
Statement
Hypnotic
phenomena
reflect
ability
alter
one’s
subjective
experiences
based
targeted
verbal
suggestions.
This
varies
greatly
The
correlates
explain
variability
remain
Addressing
gap,
our
study
employs
predict
By
recording
electroencephalography
(EEG)
before
after
induction
analyzing
diverse
neurophysiological
features,
were
able
determine
several
features
susceptible
both
during
analysis
revealed
paramount
discriminative
feature
non-oscillatory
induction—a
new
finding
field.
outcome
aligns
with
represents
latent
observable
through
plain
five-minutes
resting-state
EEG.
Language: Английский