Rare Diseases Symptoms Automatic Extraction
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A random Abstract
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Identifying the default-mode component in spatial IC analyses of patients with disorders of consciousness.
[locked-in syndrome]
Recent
fMRI
studies
have
shown
that
it
is
possible
to
reliably
identify
the
default-mode
network
(
DMN
)
in
the
absence
of
any
task
,
by
resting-
state
connectivity
analyses
in
healthy
volunteers
.
We
here
aimed
to
identify
the
DMN
in
the
challenging
patient
population
of
disorders
of
consciousness
encountered
following
coma
.
A
spatial
independent
component
analysis-based
methodology
permitted
DMN
assessment
,
decomposing
connectivity
in
all
its
different
sources
either
neuronal
or
artifactual
.
Three
different
selection
criteria
were
introduced
assessing
anticorrelation-corrected
connectivity
with
or
without
an
automatic
masking
procedure
and
calculating
connectivity
scores
encompassing
both
spatial
and
temporal
properties
.
These
three
methods
were
validated
on
10
healthy
controls
and
applied
to
an
independent
group
of
8
healthy
controls
and
11
severely
brain
-damaged
patients
[
locked-
in
syndrome
(
n
=
2
)
,
minimally
conscious
(
n
=
1
)
,
and
vegetative
state
(
n
=
8
)
]
.
All
vegetative
patients
showed
fewer
connections
in
the
default-mode
areas
,
when
compared
with
controls
,
contrary
to
locked-
in
patients
who
showed
near-normal
connectivity
.
In
the
minimally
conscious-
state
patient
,
only
the
two
selection
criteria
considering
both
spatial
and
temporal
properties
were
able
to
identify
an
intact
right
lateralized
BOLD
connectivity
pattern
,
and
metabolic
PET
data
suggested
its
neuronal
origin
.
W
hen
assessing
resting-
state
connectivity
in
patients
with
disorders
of
consciousness
,
it
is
important
to
use
a
methodology
excluding
non-
neuronal
contributions
caused
by
head
motion
,
respiration
,
and
heart
rate
artifacts
encountered
in
all
studied
patients
.
Diseases
Validation
Diseases presenting
"head motion"
symptom
locked-in syndrome
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