Developmental Cognitive Neuroscience,
Год журнала:
2024,
Номер
70, С. 101459 - 101459
Опубликована: Окт. 12, 2024
Trial-by-trial
neural
variability,
a
measure
of
response
stability,
has
been
examined
in
relation
to
behavioral
indicators
using
summary
measures,
but
these
methods
do
not
characterize
meaningful
processes
underlying
variability.
Mixed-effects
location
scale
models
(MELSMs)
overcome
limitations
by
accounting
for
predictors
and
covariates
variability
have
rarely
used
developmental
studies.
Here,
we
applied
MELSMs
the
ERP
auditory
mismatch
negativity
(MMN),
often
related
language
psychopathology.
84
toddlers
76
mothers
completed
speech-syllable
MMN
paradigm.
We
extracted
early
late
mean
amplitudes
from
trial-level
waveforms.
first
characterized
our
sample's
psychometric
properties
found
wide
range
subject-level
internal
consistency.
Next,
between
toddler
MMNs
with
theoretically
relevant
child
maternal
variables.
offered
better
model
fit
than
analyses
that
assumed
constant
significant
individual
differences
trial-by-trial
no
associations
their
language,
irritability,
or
mother
indices.
Overall,
illustrate
how
can
answer
questions
about
provide
recommendations
resources
as
well
example
code
analyzing
future
Neuroscience & Biobehavioral Reviews,
Год журнала:
2024,
Номер
164, С. 105795 - 105795
Опубликована: Июль 6, 2024
Multivariate
pattern
analysis
(MVPA)
of
electroencephalographic
(EEG)
data
represents
a
revolutionary
approach
to
investigate
how
the
brain
encodes
information.
By
considering
complex
interactions
among
spatio-temporal
features
at
individual
level,
MVPA
overcomes
limitations
univariate
techniques,
which
often
fail
account
for
significant
inter-
and
intra-individual
neural
variability.
This
is
particularly
relevant
when
studying
clinical
populations,
therefore
EEG
has
recently
started
be
employed
as
tool
study
cognition
in
disorders.
Here,
we
review
insights
offered
by
this
methodology
anomalous
patterns
activity
conditions
such
autism,
ADHD,
schizophrenia,
dyslexia,
neurological
neurodegenerative
disorders,
within
different
cognitive
domains
(perception,
attention,
memory,
consciousness).
Despite
potential
drawbacks
that
should
attentively
addressed,
these
studies
reveal
peculiar
sensitivity
unveiling
dysfunctional
compensatory
neurocognitive
dynamics
information
processing,
remain
blind
traditional
approaches.
Such
higher
characterizing
profiles
can
provide
unique
opportunities
optimise
assessment
promote
personalised
interventions.
Brain Topography,
Год журнала:
2024,
Номер
37(4), С. 496 - 513
Опубликована: Март 2, 2024
Abstract
Microstate
analysis
of
resting-state
EEG
is
a
unique
data-driven
method
for
identifying
patterns
scalp
potential
topographies,
or
microstates,
that
reflect
stable
but
transient
periods
synchronized
neural
activity
evolving
dynamically
over
time.
During
infancy
–
critical
period
rapid
brain
development
and
plasticity
microstate
offers
opportunity
characterizing
the
spatial
temporal
dynamics
activity.
However,
whether
measurements
derived
from
this
approach
(e.g.,
properties,
transition
probabilities,
sources)
show
strong
psychometric
properties
(i.e.,
reliability)
during
unknown
key
information
advancing
our
understanding
how
microstates
are
shaped
by
early
life
experiences
they
relate
to
individual
differences
in
infant
abilities.
A
lack
methodological
resources
performing
has
further
hindered
adoption
cutting-edge
researchers.
As
result,
current
study,
we
systematically
addressed
these
knowledge
gaps
report
most
microstate-based
organization
functioning
except
probabilities
were
with
four
minutes
video-watching
data
highly
internally
consistent
just
one
minute.
In
addition
results,
provide
step-by-step
tutorial,
accompanying
website,
open-access
using
free,
user-friendly
software
called
Cartool.
Taken
together,
study
supports
reliability
feasibility
increases
accessibility
field
developmental
neuroscience.
Neuropsychopharmacology,
Год журнала:
2024,
Номер
50(1), С. 258 - 268
Опубликована: Авг. 21, 2024
Abstract
Neuroimaging,
across
positron
emission
tomography
(PET),
electroencephalography
(EEG),
and
magnetic
resonance
imaging
(MRI),
has
been
a
mainstay
of
clinical
neuroscience
research
for
decades,
yet
penetrated
little
into
psychiatric
drug
development
beyond
often
underpowered
phase
1
studies,
or
care.
Simultaneously,
there
is
pressing
need
to
improve
the
probability
success
in
development,
increase
mechanistic
diversity,
enhance
efficacy.
These
goals
can
be
achieved
by
leveraging
neuroimaging
precision
psychiatry
framework,
wherein
effects
drugs
on
brain
are
measured
early
understand
dosing
indication,
then
later-stage
trials
identify
likely
responders
enrich
trials,
ultimately
improving
outcomes.
Here
we
examine
key
variables
important
using
from
lens
biotechnology
pharmaceutical
companies
developing
deploying
new
psychiatry.
We
argue
that
clear
paths
incorporating
different
modalities
de-risk
subsequent
phases
near
intermediate
term,
culminating
use
select
care
prescription
drugs.
Better
outcomes
through
biomarkers,
however,
require
wholesale
commitment
approach
will
necessitate
cultural
shift
align
biopharma
orientation
already
routine
other
areas
medicine.
IEEE Transactions on Neural Systems and Rehabilitation Engineering,
Год журнала:
2024,
Номер
32, С. 587 - 596
Опубликована: Янв. 1, 2024
Resting
state
electroencephalography
(rsEEG)
is
widely
used
to
investigate
intrinsic
brain
activity,
with
the
potential
for
detecting
neurophysiological
abnormalities
in
clinical
conditions
from
neurodegenerative
disease
developmental
disorders.
When
interpreting
quantitative
rsEEG
changes,
a
key
question
is:
how
much
deviation
healthy
normal
indicates
clinically
significant
change?
Here,
we
build
on
existing
variability
literature
by
quantifying
this
baseline
range
can
be
attributed
common
but
underinvestigated
sources
of
variability:
experiment
day,
time
and
pre-recording
exercise
level.
We
found
that
even
within
individuals,
frequency
band
powers
entropy
measures
vary
6%
(sample
relative
alpha
power)
25%
(absolute
delta
power).
Absolute
power
increased
significantly
after
running,
while
theta
decreased
significantly.
Relative
beta
gamma
were
higher
afternoon
compared
morning
trials.
Sample
relatively
consistent.
The
coefficients
are
similar
some
effect
sizes
identified
prior
literature,
bringing
into
significance
these
sizes.
Furthermore,
day
activity
level
accounted
more
than
indicating
reduce
controlling
factors
repeated-measures
studies.
Importance
Advancing
precision
psychiatry,
where
treatments
are
based
on
an
individual’s
biology
rather
than
solely
their
clinical
presentation,
requires
attention
to
several
key
attributes
for
any
candidate
biomarker.
These
include
test-retest
reliability,
sensitivity
relevant
neurophysiology,
cost-effectiveness,
and
scalability.
Unfortunately,
these
issues
have
not
been
systematically
addressed
by
biomarker
development
efforts
that
use
common
neuroimaging
tools
like
magnetic
resonance
imaging
(MRI)
electroencephalography
(EEG).
Here,
the
critical
barriers
methods
will
need
overcome
achieve
relevance
in
near
intermediate
term
examined.
Observations
Reliability
is
often
overlooked,
which
together
with
aspects
of
neurophysiology
replicated
predictive
utility,
favors
EEG-based
methods.
The
principal
barrier
EEG
has
lack
large-scale
data
collection
among
multisite
psychiatric
consortia.
By
contrast,
despite
its
high
structural
MRI
demonstrated
utility
may
be
due
limited
psychiatry-relevant
neurophysiology.
Given
prevalence
MRIs,
establishment
a
compelling
case
remains
barrier.
low
reliability
difficulty
standardizing
functional
MRI,
along
demonstration
superior
spatial
resolution
over
ability
directly
image
subcortical
regions
fact
provide
unique
value.
Often
missing,
moreover,
consideration
how
various
scientific
can
balanced
against
practical
economic
realities
health
care
delivery
today,
embedding
modeling
into
help
direct
research
efforts.
Conclusions
Relevance
seems
most
ripe
near-
intermediate-term
impact,
especially
considering
scalability
cost-effectiveness.
Recent
broaden
collection,
as
well
low-cost
turnkey
systems,
suggest
promising
pathway
impact
care.
Continued
focused
hold
promise
longer-horizon
utility.
Clinical EEG and Neuroscience,
Год журнала:
2025,
Номер
unknown
Опубликована: Фев. 3, 2025
Quantitative
electroencephalogram
(QEEG)
is
a
technology
which
has
grown
exponentially
since
the
foundational
publication
by
in
Science
1997,
introducing
use
of
age-regressed
metrics
to
quantify
characteristics
EEG
signal,
enhancing
clinical
utility
neuropsychiatry.
Essential
validity
and
reliability
QEEG
standardization
multi-channel
data
acquisition
follows
standards
set
forth
American
Clinical
Neurophysiology
Society
including
accurate
management
artifact
facilitation
proper
visual
inspection
paroxysmal
events
both
are
expanded
this
guideline.
Additional
requirements
on
selection
EEG,
quality
reporting,
submission
spectral,
statistical,
topographic
analysis
proposed.
While
there
thousands
features
that
can
be
mathematically
derived
using
QEEG,
common
have
been
most
recognized
validated
these
along
with
other
mathematical
tools,
such
as
low
resolution
electromagnetic
tomographic
analyses
(LORETA)
classifier
functions,
reviewed
cautions
noted.
The
efficacy
applications
depends
strongly
acquired
correctness
subsequent
inspection,
selection,
processing.
These
recommendations
described
following
sections
minimum
for
supported
International
Certification
Board
(IQCB).
Abstract
The
balance
between
potential
gains
and
losses
under
risk,
the
stability
of
risk
propensity,
associated
reward
processing,
prediction
subsequent
behaviors
over
time
have
become
increasingly
important
topics
in
recent
years.
In
this
study,
we
asked
participants
to
carry
out
2
tasks
with
outcome
evaluation—the
monetary
gambling
task
mixed
lottery
twice,
simultaneous
recording
behavioral
electroencephalography
data.
Regarding
behavior,
observed
both
individual-specific
attitudes
outcome-contingent
risky
inclination
following
a
loss
outcome,
which
remained
stable
across
sessions.
terms
event-related
(ERP)
results,
low
outcomes,
compared
high
induced
larger
feedback-related
negativity,
was
modulated
by
magnitude
outcome.
Similarly,
outcomes
evoked
deflection
P300
amplitude
also
being
sensitive
magnitude.
Intraclass
correlation
coefficient
analyses
indicated
that
negativity
exhibited
modest
good
test–retest
reliability
tasks.
choice
prediction,
found
neural
responses—especially
those
outcome—predicted
risk-taking
behavior
at
single-trial
level
for
Therefore,
study
extends
our
understanding
preferences
gain-loss
trade-offs.