Endophenotype trait domains for advancing gene discovery in autism spectrum disorder
Journal of Neurodevelopmental Disorders,
Год журнала:
2023,
Номер
15(1)
Опубликована: Ноя. 22, 2023
Abstract
Autism
spectrum
disorder
(ASD)
is
associated
with
a
diverse
range
of
etiological
processes,
including
both
genetic
and
non-genetic
causes.
For
plurality
individuals
ASD,
it
likely
that
the
primary
causes
involve
multiple
common
inherited
variants
individually
account
for
only
small
levels
variation
in
phenotypic
outcomes.
This
landscape
creates
major
challenge
detecting
but
important
pathogenic
effects
ASD.
To
address
similar
challenges,
separate
fields
medicine
have
identified
endophenotypes,
or
discrete,
quantitative
traits
reflect
likelihood
particular
clinical
condition
leveraged
study
these
to
map
polygenic
mechanisms
advance
more
personalized
therapeutic
strategies
complex
diseases.
Endophenotypes
represent
distinct
class
biomarkers
useful
understanding
contributions
psychiatric
developmental
disorders
because
they
are
embedded
within
causal
chain
between
genotype
phenotype,
proximal
action
gene(s)
than
behavioral
traits.
Despite
their
demonstrated
power
guiding
new
structures
conditions,
few
endophenotypes
ASD
been
integrated
into
family
studies.
In
this
review,
we
argue
advancing
knowledge
processes
contribute
can
be
accelerated
by
refocusing
attention
toward
identifying
endophenotypic
reflective
mechanisms.
pivot
requires
renewed
emphasis
on
designs
measurement
familial
co-variation
infant
sibling
studies,
trio
quad
designs,
analysis
monozygotic
dizygotic
twin
concordance
select
trait
dimensions.
We
also
emphasize
clarification
necessarily
will
integration
transdiagnostic
approaches
as
candidate
liability
conditions
often
agnostic
diagnostic
boundaries.
Multiple
described,
propose
focus
“endophenotype
domains”
(ETDs),
measured
across
(e.g.,
molecular,
cellular,
neural
system,
neuropsychological)
along
pathway
from
genes
behavior.
inform
our
central
argument
research
efforts
ETD
discovery,
first
provide
brief
review
concept
application
psychiatry.
Next,
highlight
key
criteria
determining
value
unique
considerations
Descriptions
different
assessing
then
offered,
how
patterns
results
may
help
prioritize
future
research.
present
ETDs
collectively
cover
breadth
phenomena
social,
language/communication,
cognitive
control,
sensorimotor
processes.
These
described
promising
targets
gene
discovery
related
autistic
traits,
serve
models
domains
well
overlapping
neurodevelopmental
disorders.
Язык: Английский
Systematic Review and Meta-Analysis of the Effect of Motor Intervention on Cognition, Communication, and Social Interaction in Children with Autism Spectrum Disorder
Physical & Occupational Therapy In Pediatrics,
Год журнала:
2025,
Номер
unknown, С. 1 - 23
Опубликована: Май 4, 2025
Conduct
a
systematic
review
and
meta-analysis
on
the
effects
of
motor
intervention
social,
communication,
cognitive
skills
in
individuals
(0-21
years)
with
autism
spectrum
disorder
(ASD).
Seven
databases
were
used
to
search
for
randomized
control
trials
(RCT)
implementing
children
ASD;
measured
outcomes.
Twenty-three
RCTs
selected
66
outcomes
636
participants
(range
mean
age:
4.3
-
12.3
years).
Motor
interventions
had
significant,
positive
effect
(1)
all
combined
(i.e.
cognitive)
(SSMD:
0.41,
p
=
.01),
(2)
social
0.46,
.012)
(3)
social/communication
0.47,
.01)
domains,
but
not
domain
0.45,
.25)
or
alone
0.22,
.18).
In
above
age
nine,
1-year
increase
corresponded
0.29
decrease
SSMD
(less
effective).
have
impact
should
be
considered
when
planning
ASD.
Язык: Английский
Aggression; Angelman syndrome; NLGN3
The Transmitter,
Год журнала:
2024,
Номер
unknown
Опубликована: Янв. 1, 2024
Limbic/paralimbic connection weakening in preschool autism‐spectrum disorder based on diffusion basis spectrum imaging
European Journal of Neuroscience,
Год журнала:
2024,
Номер
unknown
Опубликована: Дек. 9, 2024
Abstract
This
study
aims
to
investigate
the
value
of
basal
ganglia
and
limbic/paralimbic
networks
alteration
in
identifying
preschool
children
with
ASD
normal
controls
using
diffusion
basis
spectrum
imaging
(DBSI).
DBSI
data
from
31
patients
30
NC
were
collected
Hunan
Children's
Hospital.
All
imported
into
post‐processing
server.
The
most
discriminative
features
extracted
connection,
global
nodal
metrics
separately
two‐sample
t
‐test.
To
effectively
integrate
multimodal
information,
we
employed
multi‐kernel
learning
support
vector
machine
(MKL‐SVM).
In
group,
efficiency,
local
clustering
coefficient
synchronization
lower
than
while
modularity
score,
hierarchy,
normalized
coefficient,
characteristic
path
length,
small‐world,
length
assortativity
higher.
Significant
weaker
connections
are
mainly
distributed
networks.
model
combining
consensus
graph
can
achieve
best
performance
patients,
an
accuracy
96.72%.The
specific
brain
regions
connection
weakening
associated
predominantly
located
networks,
suggesting
their
involvement
abnormal
development
processes.
effective
combination
information
by
MKL‐SVM
distinguish
ASD.
Язык: Английский