Research Square (Research Square),
Год журнала:
2024,
Номер
unknown
Опубликована: Янв. 3, 2024
Abstract
Neurological
tremors,
prevalent
among
a
large
population,
are
one
of
the
most
rampant
movement
disorders.
Biomechanical
loading
and
utilization
exoskeletons
have
been
demonstrated
to
enhance
everyday
life
patients.
However
reliance
on
traditional
control
algorithms
limit
use
this
treatment
in
dynamic
movements
diseases
where
model
underlying
condition
is
not
available
or
susceptible
change.
Here
we
present
novel
deep
reinforcement
learning
based
strategy
capable
handling
wide
range
tremors
across
multiple
movements.
Our
approach
incorporates
encoder
networks,
modified
buffers
heavily
shaped
rewards
create
actuation
forces
for
exoskeleton.
We
find
that
efficiently
suppressing
with
different
frequencies
originating
from
concurrent
joint
axes,
multitude
Journal of Clinical Medicine,
Год журнала:
2025,
Номер
14(2), С. 550 - 550
Опубликована: Янв. 16, 2025
The
convergence
of
Artificial
Intelligence
(AI)
and
neuroscience
is
redefining
our
understanding
the
brain,
unlocking
new
possibilities
in
research,
diagnosis,
therapy.
This
review
explores
how
AI’s
cutting-edge
algorithms—ranging
from
deep
learning
to
neuromorphic
computing—are
revolutionizing
by
enabling
analysis
complex
neural
datasets,
neuroimaging
electrophysiology
genomic
profiling.
These
advancements
are
transforming
early
detection
neurological
disorders,
enhancing
brain–computer
interfaces,
driving
personalized
medicine,
paving
way
for
more
precise
adaptive
treatments.
Beyond
applications,
itself
has
inspired
AI
innovations,
with
architectures
brain-like
processes
shaping
advances
algorithms
explainable
models.
bidirectional
exchange
fueled
breakthroughs
such
as
dynamic
connectivity
mapping,
real-time
decoding,
closed-loop
systems
that
adaptively
respond
states.
However,
challenges
persist,
including
issues
data
integration,
ethical
considerations,
“black-box”
nature
many
systems,
underscoring
need
transparent,
equitable,
interdisciplinary
approaches.
By
synthesizing
latest
identifying
future
opportunities,
this
charts
a
path
forward
integration
neuroscience.
From
harnessing
multimodal
cognitive
augmentation,
fusion
these
fields
not
just
brain
science,
it
reimagining
human
potential.
partnership
promises
where
mysteries
unlocked,
offering
unprecedented
healthcare,
technology,
beyond.
Journal of Neurology,
Год журнала:
2025,
Номер
272(4)
Опубликована: Март 12, 2025
Abstract
Next-generation
neurostimulators
capable
of
running
closed-loop
adaptive
deep
brain
stimulation
(aDBS)
are
about
to
enter
the
clinical
landscape
for
treatment
Parkinson’s
disease.
Already
promising
results
using
aDBS
have
been
achieved
symptoms
such
as
bradykinesia,
rigidity
and
motor
fluctuations.
However,
heterogeneity
freezing
gait
(FoG)
with
its
wide
range
presentations
exacerbation
cognitive
emotional
load
make
it
more
difficult
predict
treat.
Currently,
a
successful
strategy
ameliorate
FoG
lacks
robust
oscillatory
biomarker.
Furthermore,
technical
implementation
suppressing
an
upcoming
episode
in
real-time
represents
significant
challenge.
This
review
describes
neurophysiological
signals
underpinning
explains
how
is
currently
being
implemented.
we
offer
discussion
addressing
both
theoretical
practical
areas
that
will
need
be
resolved
if
going
able
unlock
full
potential
treat
FoG.
Artificial Intelligence Review,
Год журнала:
2024,
Номер
57(12)
Опубликована: Окт. 10, 2024
Abstract
The
emergence
of
neuromorphic
computing,
inspired
by
the
structure
and
function
human
brain,
presents
a
transformative
framework
for
modelling
neurological
disorders
in
drug
development.
This
article
investigates
implications
applying
computing
to
simulate
comprehend
complex
neural
systems
affected
conditions
like
Alzheimer’s,
Parkinson’s,
epilepsy,
drawing
from
extensive
literature.
It
explores
intersection
with
neurology
pharmaceutical
development,
emphasizing
significance
understanding
processes
integrating
deep
learning
techniques.
Technical
considerations,
such
as
circuits
into
CMOS
technology
employing
memristive
devices
synaptic
emulation,
are
discussed.
review
evaluates
how
optimizes
discovery
improves
clinical
trials
precisely
simulating
biological
systems.
also
examines
role
models
comprehending
disorders,
facilitating
targeted
treatment
Recent
progress
is
highlighted,
indicating
potential
therapeutic
interventions.
As
advances,
synergy
between
neuroscience
holds
promise
revolutionizing
study
brain’s
complexities
addressing
challenges.
npj Parkinson s Disease,
Год журнала:
2025,
Номер
11(1)
Опубликована: Май 5, 2025
While
conventional
deep
brain
stimulation
(cDBS)
treatment
delivers
continuous
electrical
stimuli,
new
adaptive
DBS
(aDBS)
technology
provides
dynamic
symptom-related
stimulation.
Research
data
are
promising,
and
devices
already
available,
but
we
ready
for
it?
We
asked
leading
experts
worldwide
(n
=
21)
to
discuss
a
research
agenda
aDBS
in
the
near
future
allow
full
adoption.
A
5-point
Likert
scale
questionnaire,
along
with
Delphi
method,
was
employed.
In
next
10
years,
will
be
clinical
routine,
is
needed
define
which
patients
would
benefit
more
from
treatment;
second,
implantation
programming
procedures
should
simplified
actual
generalized
adoption;
third,
algorithms,
integration
of
paradigm
technologies,
improve
control
complex
symptoms.
Since
years
crucial
implementation,
focus
on
improving
precision
making
accessible.
BMJ Open,
Год журнала:
2025,
Номер
15(5), С. e091563 - e091563
Опубликована: Май 1, 2025
Deep
brain
stimulation
(DBS)
is
a
proven
effective
treatment
for
Parkinson's
disease
(PD).
However,
titrating
DBS
parameters
labourious
process
and
requires
frequent
hospital
visits.
Additionally,
its
current
application
uses
continuous
high-frequency
at
constant
intensity,
which
may
reduce
efficacy
cause
side
effects.
The
objective
of
the
AI-DBS
study
to
identify
patient-specific
patterns
neuronal
activity
that
are
associated
with
severity
motor
symptoms
PD.
This
information
essential
development
advanced
responsive
algorithms,
improve
DBS.
longitudinal
prospective
observational
cohort
will
enrol
100
patients
PD
who
bilaterally
implanted
sensing-enabled
system
(Percept
PC,
Medtronic)
in
subthalamic
nucleus
as
part
standard
clinical
care.
Local
activity,
specifically
local
field
potential
(LFP)
signals,
be
recorded
during
first
6
months
after
implantation.
Correlations
tested
between
spectral
features
LFP
data
symptom
severity,
assessed
using
(1)
inertial
sensor
from
wearable
smartwatch,
(2)
rating
scales
(3)
patient
diaries
analysed
conventional
descriptive
statistics
artificial
intelligence
algorithms.
primary
profiles
presence
symptoms,
forming
'neuronal
fingerprint'.
Ethical
approval
was
granted
by
ethics
committee
Amsterdam
UMC
(registration
number
2022.0368).
Study
findings
disseminated
through
scientific
journals
presented
national
international
conferences.
