Neural Correlates of Social Touch Processing: An fMRI Study on Brain Functional Connectivity
Journal of Integrative Neuroscience,
Год журнала:
2025,
Номер
24(1)
Опубликована: Янв. 20, 2025
Background:
The
significance
of
tactile
stimulation
in
human
social
development
and
personal
interaction
is
well
documented;
however,
the
underlying
cerebral
processes
remain
under-researched.
This
study
employed
functional
magnetic
resonance
imaging
(fMRI)
to
investigate
neural
correlates
touch
processing,
with
a
particular
focus
on
connectivity
associated
aftereffects
touch.
Methods:
A
total
27
experimental
subjects
were
recruited
for
study,
all
whom
underwent
5-minute
calf
foot
massage
prior
undergoing
resting-state
fMRI.
Additionally,
11
healthy
controls
participated
solely
fMRI
recording.
network
analysis
was
conducted
examine
alterations
connections
between
different
brain
regions
following
massage.
Results:
findings
indicated
involvement
discrete
networks
processing
touch,
notable
discrepancies
observed
control
groups.
revealed
that
group
exhibited
higher
degree
within
subnetwork
comprising
25
23
nodes
than
intervention.
showed
hypoactivation
this
left
anterior
pulvinar
thalamus
right
pregenual
cingulate
cortex,
which
serve
as
key
hubs
subnetwork,
clustering
increased
node
strength
group.
Relatively
small
unequal
sample
sizes
are
limitations
may
affect
generalizability
results.
Conclusions:
These
elucidate
underpinnings
experiences
their
potential
impact
behavior
emotional
state.
Gaining
insight
into
these
mechanisms
could
inform
therapeutic
approaches
utilize
mitigate
stress
enhance
mental
health.
From
practical
standpoint,
our
results
have
significant
implications
sensory
strategies
patients
prolonged
disorders
consciousness,
loss,
autism
spectrum
disorders,
or
limited
access
upper
extremities.
Язык: Английский
Disruptions in segregation mechanisms in fMRI-based brain functional network predict the major depressive disorder condition
Chaos Solitons & Fractals,
Год журнала:
2024,
Номер
188, С. 115566 - 115566
Опубликована: Окт. 1, 2024
Язык: Английский
Abnormal changes of dynamic topological characteristics in patients with major depressive disorder
Journal of Affective Disorders,
Год журнала:
2023,
Номер
345, С. 349 - 357
Опубликована: Окт. 25, 2023
Язык: Английский
Characteristics of brain functional networks specific for different types of tactile perception
The European Physical Journal Special Topics,
Год журнала:
2023,
Номер
233(3), С. 499 - 504
Опубликована: Дек. 1, 2023
Язык: Английский
Methodology of collection, recording and markup of biophysical multimodal data in the study of human psychoemotional states
Izvestiya of Saratov University Physics,
Год журнала:
2024,
Номер
24(3), С. 239 - 249
Опубликована: Авг. 22, 2024
Аннотация.Цель
настоящей
работы
-проанализировать
требования
к
методике
сбора
биофизических
данных
на
основе
открытых
наборов
определения
психоэмоционального
состояния,
аппаратному
и
программному
обеспечению
для
их
первичной
обработки.Сформулировать
методику
формирования
мультимодальных
данных,
пригодную
исследования
психических
состояний
изменений,
в
том
числе
с
использованием
алгоритмов
машинного
обучения.Описать
возможный
метод
реализации
этих
требований
аппаратно-программных
комплексах.Методы.Для
анализа
основных
особенностей
характеризующих
психические
были
выбраны
открытые
наборы
пациентов
депрессивными
расстройствами.Основные
сформулированы
изучения
публикаций
об
особенностях
применения
диагностики
депрессивных
расстройств.Результатом
являются
набор
мультимодальным
данным
биопотенциалов
психоэмоциональных
состояний,
методика
функциональная
концепция
аппаратно-программного
комплекса
регистрации,
синхронизации
записи
аннотированном
виде.Заключение.На
примере
депрессивного
расстройства
показана
целесообразность
возможность
регистрации
мультимодальных,
синхронизированных
между
собой
аннотированных
о
психоэмоциональном
состоянии
испытуемого
исследовательских,
диагностических
целей
качестве
обучающей
выборки
алгоритмах
обучения.Предложенная
программно-аппаратного
позволяют
Язык: Русский
Recurrency time entropy of brain wave rhythms as an indicator of performance on visual search tasks in schoolchildren
The European Physical Journal Special Topics,
Год журнала:
2024,
Номер
unknown
Опубликована: Окт. 2, 2024
Язык: Английский
Intermediary-guided windowed attention Aggregation network for fine-grained characterization of Major Depressive Disorder fMRI
Biomedical Signal Processing and Control,
Год журнала:
2024,
Номер
100, С. 107166 - 107166
Опубликована: Ноя. 6, 2024
Язык: Английский
Regime switching in coupled nonlinear systems: Sources, prediction, and control—Minireview and perspective on the Focus Issue
Chaos An Interdisciplinary Journal of Nonlinear Science,
Год журнала:
2024,
Номер
34(12)
Опубликована: Дек. 1, 2024
Regime
switching,
the
process
where
complex
systems
undergo
transitions
between
qualitatively
different
dynamical
states
due
to
changes
in
their
conditions,
is
a
widespread
phenomenon,
from
climate
and
ocean
circulation,
ecosystems,
power
grids,
brain.
Capturing
mechanisms
that
give
rise
isolated
or
sequential
switching
dynamics,
as
well
developing
generic
robust
methods
for
forecasting,
detecting,
controlling
them
essential
maintaining
optimal
performance
preventing
dysfunctions
even
collapses
systems.
This
Focus
Issue
provides
new
insights
into
regime
covering
recent
advances
theoretical
analysis
harnessing
reduction
approaches,
data-driven
detection
non-feedback
control
strategies.
Some
of
key
challenges
addressed
include
development
techniques
coupled
stochastic
adaptive
systems,
influence
multiple
timescale
dynamics
on
chaotic
structures
cyclic
patterns
forced
role
saddles
heteroclinic
cycles
pattern
oscillators.
The
contributions
further
highlight
deep
learning
applications
predicting
grid
failures,
use
blinking
networks
enhance
synchronization,
creating
strategies
epidemic
spreading,
suppress
epileptic
seizures.
These
developments
are
intended
catalyze
dialog
branches
complexity.
Язык: Английский
Classification of MDD patients with using network measures
Опубликована: Сен. 18, 2023
Major
depressive
disorder
(MDD)
is
a
common
and
debilitating
psychiatric
illness
that
affects
millions
of
people
worldwide.
Despite
advancements
in
the
understanding
its
underlying
mechanisms,
diagnosis
treatment
MDD
remain
significant
challenge.
In
this
paper,
we
present
an
approach
for
classification
patients
with
based
on
their
functional
network
measures.
Our
results
demonstrate
simple
Linear
Discriminant
Analysis
achieves
high
accuracy
(83
%)
two
cases:
when
use
all
network's
couplings
or
only
strongest
ones.
Язык: Английский