Psychiatry Research,
Год журнала:
2022,
Номер
317, С. 114866 - 114866
Опубликована: Сен. 29, 2022
One
of
my
first
placements
in
psychiatry
training
was
with
the
early
intervention
psychosis
services
Birmingham,
late
1990's.
It
this
context
that
I
became
aware
frequency
and
importance
affective
co-
modbidity
lack
diagnostic
certianty
stages
develping
severe
mental
illness.
This
challenged
established
dichotomy
between
non-affective
illnesses,
has
driven
work
thinking
ever
since-
including
embracing
presence
symptoms
schizophrenia
may
open
door
for
new
treatments.
Understanding
dysfunction
as
a
potential
intrisic
component
developing
psychotic
disorders
also
shown
transdiagnostic
shared
underlying
biological
processess,
immune
dysfunction,
related
to
remission
functional
outcomes.
Currently
focuses
on
targeting
system
improve
recovery
clinical
trials,
further
mechanistic
studies
reach
beyond
traditional
catagories.
arXiv (Cornell University),
Год журнала:
2024,
Номер
unknown
Опубликована: Янв. 1, 2024
Machine
learning
has
been
increasingly
used
to
obtain
individualized
neuroimaging
signatures
for
disease
diagnosis,
prognosis,
and
response
treatment
in
neuropsychiatric
neurodegenerative
disorders.
Therefore,
it
contributed
a
better
understanding
of
heterogeneity
by
identifying
subtypes
that
present
significant
differences
various
brain
phenotypic
measures.
In
this
review,
we
first
systematic
literature
overview
studies
using
machine
multimodal
MRI
unravel
disorders,
including
Alzheimer
disease,
schizophrenia,
major
depressive
disorder,
autism
spectrum
multiple
sclerosis,
as
well
their
potential
transdiagnostic
settings.
Subsequently,
summarize
relevant
methodologies
discuss
an
emerging
paradigm
which
call
dimensional
endophenotype
(DNE).
DNE
dissects
the
neurobiological
disorders
into
low
yet
informative,
quantitative
representation,
serving
robust
intermediate
phenotype
(i.e.,
endophenotype)
largely
reflecting
underlying
genetics
etiology.
Finally,
clinical
implications
current
findings
envision
future
research
avenues.
Research Square (Research Square),
Год журнала:
2024,
Номер
unknown
Опубликована: Апрель 18, 2024
Abstract
Major
depressive
disorder
(MDD)
is
highly
heterogeneous
in
terms
of
responses
to
treatment,
which
hinders
the
improvement
treatment
effectiveness
and
outcomes
for
MDD.
Identifying
MDD
subtypes
associated
with
could
inform
interventions
facilitate
personalized
treatment.
Here,
we
sought
identify
reproducible
characterized
by
distinct
neurofunctional
(i.e.,
neuroimaging)
patterns
delineate
heterogeneity
explored
relationship
between
antidepressant
response.
We
used
contrastive
variational
autoencoders
(CVAEs)
two
REST-meta-MDD
II
dataset
(1660
participants,
1340
HCs).
Subtype
1
exhibited
increased
functional
activity
occipital,
parietal,
temporal,
frontal
areas,
while
subtype
2
showed
decreased
these
areas.
The
number
were
validated
a
further
large
multi-center
(1276
1104
Notably,
patients
be
considered
"treatment-sensitive"
group,
response
rate
over
50%
all
antidepressants
better
repetitive
transcranial
magnetic
stimulation
(rTMS)
compared
2.
In
contrast,
as
"treatment-resistant"
below
most
medications.
ensuing
MDD-specific
features
from
CVAEs
may
serve
neuroimaging
biomarker
predicting
both
medication
rTMS
treatments.
Our
study
shows
that
learning
can
establish
predictive
validity
brain
signatures
—
offering
potential
new
targets
optimizing
strategies
treatment-resistant
depression,
lay
path
toward
higher
outcomes.
One
of
psychiatry's
significant
challenges
lies
in
differentiating
certain
mental
illnesses,
given
the
overlapping
characteristics
exhibited
by
many
them.
The
identification
a
biomarker
or
panel
biomarkers
serving
this
purpose
would
represent
clinical
revolution,
motivating
scientific
community
to
intensify
their
search.
This
chapter
provides
concise
historical
overview
quest
for
molecular
schizophrenia
spectrum
disorders
(SSD).
Additionally,
it
outlines
various
classification
patterns
and
attempts
categorize
them
based
on
hypotheses
related
pathophysiology
SSD.
To
help
reader
comprehend
process
discovery,
introduces
development
diagnostic
models
using
machine
learning.
It
delves
into
some
multivariate
analyses
conducted
explains
evaluation
model
performance
through
receiver
operating
characteristic
(ROC)
curve,
providing
detailed
insights
result
interpretation.
Consortium Psychiatricum,
Год журнала:
2024,
Номер
5(3), С. 31 - 41
Опубликована: Окт. 4, 2024
Depressive
symptoms
in
patients
with
schizophrenia
lead
to
more
frequent
exacerbations
of
the
underlying
disease,
worsen
prognosis,
and
increase
risk
suicide.
Clinical
practitioners
continue
face
challenges
diagnosing
this
disorder.
Psychiatry Research,
Год журнала:
2022,
Номер
317, С. 114866 - 114866
Опубликована: Сен. 29, 2022
One
of
my
first
placements
in
psychiatry
training
was
with
the
early
intervention
psychosis
services
Birmingham,
late
1990's.
It
this
context
that
I
became
aware
frequency
and
importance
affective
co-
modbidity
lack
diagnostic
certianty
stages
develping
severe
mental
illness.
This
challenged
established
dichotomy
between
non-affective
illnesses,
has
driven
work
thinking
ever
since-
including
embracing
presence
symptoms
schizophrenia
may
open
door
for
new
treatments.
Understanding
dysfunction
as
a
potential
intrisic
component
developing
psychotic
disorders
also
shown
transdiagnostic
shared
underlying
biological
processess,
immune
dysfunction,
related
to
remission
functional
outcomes.
Currently
focuses
on
targeting
system
improve
recovery
clinical
trials,
further
mechanistic
studies
reach
beyond
traditional
catagories.
