From phenomenological to biophysical models of seizures
Neurobiology of Disease,
Год журнала:
2023,
Номер
182, С. 106131 - 106131
Опубликована: Апрель 21, 2023
Epilepsy
is
a
complex
disease
that
requires
various
approaches
for
its
study.
This
short
review
discusses
the
contribution
of
theoretical
and
computational
models.
The
presents
frameworks
underlie
understanding
certain
seizure
properties
their
classification
based
on
dynamical
at
onset
offset
seizures.
Dynamical
system
tools
are
valuable
resources
in
study
These
can
provide
insights
into
mechanisms
offer
framework
classification,
by
analyzing
complex,
dynamic
behavior
Additionally,
models
have
high
potential
clinical
applications,
as
they
be
used
to
develop
more
accurate
diagnostic
personalized
medicine
tools.
We
discuss
modeling
span
different
scales
levels,
while
also
questioning
neurocentric
view,
emphasizing
importance
considering
glial
cells.
Finally,
we
explore
epistemic
value
provided
this
type
approach.
Язык: Английский
Editorial: What AI and Neuroscience Can Learn from Each Other—Open Problems in Models and Theories
Cognitive Computation,
Год журнала:
2024,
Номер
16(5), С. 2331 - 2333
Опубликована: Июль 23, 2024
Язык: Английский
Would you publish unrealistic model?
Authorea (Authorea),
Год журнала:
2024,
Номер
unknown
Опубликована: Авг. 29, 2024
Theoretical
neurosciences
research
community
produces
many
models
of
different
natures
to
capture
activities
or
functions
the
brain.Some
these
are
some
presented
as
"realistic"
models,
often
because
variable
and
parameters
have
biophysical
units,
but
not
always.In
this
short
technical
spotlight,
I
explain
why
term
can
be
misleading
propose
elements
that
useful
characterize
a
model.
Язык: Английский
BrainWave Diagnostics: An Extensive Examination of Determinants of Multiple Neurological Disease from EEG Signals
Research Square (Research Square),
Год журнала:
2023,
Номер
unknown
Опубликована: Дек. 20, 2023
Abstract
BrainWave
Diagnostics,
an
emerging
field,
leverages
electroencephalography
(EEG)
data
for
cost-effective
and
resource-efficient
neurological
disorder
detection.
Although
EEGs
are
commonly
used
disease
detection,
their
low
signal
intensity
nonlinear
features
pose
analytical
challenges.
This
review
explores
the
use
of
high-performance
computational
tools,
machine
learning,
deep
learning
methods
in
diagnosing
a
range
disorders,
including
epilepsy,
Parkinson's
disease,
autism,
ADHD,
stroke,
tumors,
schizophrenia,
Alzheimer's,
depression,
alcohol
disorder.
The
increasing
prevalence
disorders
resource
burden
underscores
urgency
these
diagnostic
advancements.
Future
research
can
consider
multi-modal
approaches,
providing
practical
solutions
detection
beyond
EEGs,
with
potential
applications
diverse
analysis
domains.
Язык: Английский