The Journal of Physiology,
Год журнала:
2023,
Номер
602(2), С. 373 - 395
Опубликована: Дек. 12, 2023
Abstract
Parkinson's
disease
is
characterized
by
exaggerated
beta
activity
(13–35
Hz)
in
cortico‐basal
ganglia
motor
loops.
Beta
includes
both
periodic
fluctuations
(i.e.
oscillatory
activity)
and
aperiodic
reflecting
spiking
excitation/inhibition
balance
non‐oscillatory
activity).
However,
the
relative
contribution,
dopamine
dependency
clinical
correlations
of
vs
.
remain
unclear.
We
recorded,
modelled
analysed
subthalamic
local
field
potentials
parkinsonian
patients
at
rest
while
off
or
on
medication.
Autoregressive
modelling
with
additive
1/
f
noise
clarified
relationships
between
measures
time
domain
amplitude
duration
bursts)
frequency
power
sharpness
spectral
peak)
activity:
burst
are
specifically
sensitive
to
activity,
whereas
ambiguously
activity.
Our
experimental
data
confirmed
model
predictions
assumptions.
subsequently
effect
levodopa,
obtaining
strong‐to‐extreme
Bayesian
evidence
that
reduced
medication,
moderate
for
absence
modulation
component.
Finally,
component
correlated
rate
progression
disease.
Methodologically,
these
results
provide
an
integrative
understanding
beta‐based
biomarkers
relevant
adaptive
deep
brain
stimulation.
Biologically,
they
suggest
primarily
dependent
may
play
a
role
not
only
pathophysiology
but
also
image
Key
points
true
synaptic
The
Burst
Only
dopamine‐dependent.
Stronger
correlates
faster
bioRxiv (Cold Spring Harbor Laboratory),
Год журнала:
2024,
Номер
unknown
Опубликована: Авг. 6, 2024
Neurophysiological
brain
activity
comprises
rhythmic
(periodic)
and
arrhythmic
(aperiodic)
signal
elements,
which
are
increasingly
studied
in
relation
to
behavioral
traits
clinical
symptoms.
Current
methods
for
spectral
parameterization
of
neural
recordings
rely
on
user-dependent
parameter
selection,
challenges
the
replicability
robustness
findings.
Here,
we
introduce
a
principled
approach
model
relying
Bayesian
information
criterion,
static
time-resolved
neurophysiological
data.
We
present
extensive
tests
with
ground-truth
empirical
magnetoencephalography
recordings.
Data-driven
selection
enhances
both
specificity
sensitivity
spectrogram
decompositions,
even
non-stationary
contexts.
Overall,
proposed
decomposition
data-driven
minimizes
reliance
user
expertise
subjective
choices,
enabling
more
robust,
reproducible,
interpretable
research
bioRxiv (Cold Spring Harbor Laboratory),
Год журнала:
2023,
Номер
unknown
Опубликована: Окт. 10, 2023
Abstract
Cognitive
symptoms
in
Parkinson’s
disease
(PD)
are
common
and
can
significantly
affect
patients’
quality
of
life.
Therefore,
there
is
an
urgent
clinical
need
to
identify
a
signature
derived
from
behavioral
and/or
neuroimaging
indicators
that
could
predict
which
patients
at
increased
risk
for
early
rapid
cognitive
decline.
Recently,
converging
evidence
identified
electroencephalogram
(EEG)
aperiodic
activity
as
meaningful
physiological
information
associated
with
age,
development,
perceptual
states
or
pathologies.
In
this
study,
we
aimed
investigate
PD
during
control
characterize
its
possible
association
behavior.
Here,
recorded
high-density
EEG
(HD-EEG)
30
healthy
controls
Simon
task.
We
analyzed
task-related
data
the
context
activation-suppression
model
extracted
parameters
(offset,
exponent)
both
scalp
source
levels.
Our
results
showed
alterations
well
higher
offsets
parieto-occipital
areas,
suggesting
excitability
PD.
A
small
congruence
effect
on
pre-
post-central
brain
areas
was
also
found,
possibly
task
execution.
Significant
differences
between
resting
state,
post-stimulus
phases
all
across
cortex
confirmed
observed
changes
linked
No
correlation
found
behavior
features.
findings
provide
characterized
by
greater
offsets,
differ
depending
arousal
state.
However,
our
do
not
support
hypothesis
behavior-related
related
changes.
Overall,
study
highlights
importance
considering
contributions
disorders
further
investigating
relationship
The Journal of Physiology,
Год журнала:
2023,
Номер
602(2), С. 373 - 395
Опубликована: Дек. 12, 2023
Abstract
Parkinson's
disease
is
characterized
by
exaggerated
beta
activity
(13–35
Hz)
in
cortico‐basal
ganglia
motor
loops.
Beta
includes
both
periodic
fluctuations
(i.e.
oscillatory
activity)
and
aperiodic
reflecting
spiking
excitation/inhibition
balance
non‐oscillatory
activity).
However,
the
relative
contribution,
dopamine
dependency
clinical
correlations
of
vs
.
remain
unclear.
We
recorded,
modelled
analysed
subthalamic
local
field
potentials
parkinsonian
patients
at
rest
while
off
or
on
medication.
Autoregressive
modelling
with
additive
1/
f
noise
clarified
relationships
between
measures
time
domain
amplitude
duration
bursts)
frequency
power
sharpness
spectral
peak)
activity:
burst
are
specifically
sensitive
to
activity,
whereas
ambiguously
activity.
Our
experimental
data
confirmed
model
predictions
assumptions.
subsequently
effect
levodopa,
obtaining
strong‐to‐extreme
Bayesian
evidence
that
reduced
medication,
moderate
for
absence
modulation
component.
Finally,
component
correlated
rate
progression
disease.
Methodologically,
these
results
provide
an
integrative
understanding
beta‐based
biomarkers
relevant
adaptive
deep
brain
stimulation.
Biologically,
they
suggest
primarily
dependent
may
play
a
role
not
only
pathophysiology
but
also
image
Key
points
true
synaptic
The
Burst
Only
dopamine‐dependent.
Stronger
correlates
faster
