Network Neuroscience,
Journal Year:
2021,
Volume and Issue:
5(2), P. 322 - 336
Published: Jan. 1, 2021
Abstract
The
application
of
graph
theory
to
model
the
complex
structure
and
function
brain
has
shed
new
light
on
its
organization,
prompting
emergence
network
neuroscience.
Despite
tremendous
progress
that
been
achieved
in
this
field,
still
relatively
few
methods
exploit
topology
networks
analyze
activity.
Recent
attempts
direction
have
leveraged
one
hand
spectral
analysis
(to
decompose
connectivity
into
eigenmodes
or
gradients)
other
signal
processing
activity
“coupled
to”
an
underlying
Fourier
modes).
These
studies
used
a
variety
imaging
techniques
(e.g.,
fMRI,
electroencephalography,
diffusion-weighted
myelin-sensitive
imaging)
estimators
networks.
Results
are
promising
terms
interpretability
functional
relevance,
but
methodologies
terminology
variable.
goals
paper
twofold.
First,
we
summarize
recent
contributions
related
gradients
processing,
attempt
clarification
while
pointing
out
current
methodological
limitations.
Second,
discuss
perspective
relevance
could
be
fruitfully
exploited
by
considering
them
as
bases
The
human
cerebral
cortex
is
symmetrically
organized
along
large-scale
axes
but
also
presents
inter-hemispheric
differences
in
structure
and
function.
quantified
contralateral
homologous
difference,
that
asymmetry,
a
key
feature
of
the
brain
left-right
axis
supporting
functional
processes,
such
as
language.
Here,
we
assessed
whether
asymmetry
cortical
organization
heritable
phylogenetically
conserved
between
humans
macaques.
Our
findings
indicate
asymmetric
an
describing
trajectory
from
perceptual/action
to
abstract
cognition.
Whereas
language
network
showed
leftward
organization,
frontoparietal
rightward
humans.
These
asymmetries
were
similar
spatial
distribution
with
macaques,
case
intra-hemispheric
hierarchy.
This
suggests
(phylo)genetic
conservation.
However,
both
networks
qualitatively
larger
relative
Overall,
our
suggest
genetic
basis
for
intrinsic
linked
higher
order
cognitive
functions
uniquely
developed
Scientific Reports,
Journal Year:
2023,
Volume and Issue:
13(1)
Published: Dec. 7, 2023
Cognitive
neuroscience
has
gained
insight
into
covert
states
using
experience
sampling.
Traditionally,
this
approach
focused
on
off-task
states.
However,
task-relevant
are
also
maintained
via
processes.
Our
study
examined
whether
sampling
can
provide
insights
goal-relevant
that
support
task
performance.
To
address
question,
we
developed
a
neural
state
space,
dimensions
of
brain
function
variation,
allows
correlates
overt
and
to
be
in
common
analytic
space.
We
use
describe
activity
during
performance,
its
relation
identified
sampling,
links
between
individual
variation
established
deliberate
focus
was
linked
faster
target
detection,
underlying
experience-and
detection-were
associated
with
patterns
emphasizing
the
fronto-parietal
network.
In
contrast,
experiences-and
vigilance
periods-were
default
mode
shows
not
only
unrelated
at
hand,
but
used
highlight
role
regions
play
maintenance
NeuroImage,
Journal Year:
2022,
Volume and Issue:
261, P. 119526 - 119526
Published: July 30, 2022
The
human
brain
exhibits
a
diverse
yet
constrained
range
of
activity
states.
While
these
states
can
be
faithfully
represented
in
low-dimensional
latent
space,
our
understanding
the
constitutive
functional
anatomy
is
still
evolving.
Here
we
applied
dimensionality
reduction
to
task-free
and
task
fMRI
data
address
whether
dimensions
reflect
intrinsic
systems
if
so,
how
may
interact
generate
different
We
find
that
each
dimension
represents
dynamic
gradient,
including
primary
unipolar
sensory-association
gradient
underlying
global
signal.
gradients
appear
stable
across
individuals
cognitive
states,
while
recapitulating
key
connectivity
properties
anticorrelation,
modularity,
regional
hubness.
then
use
dynamical
modeling
show
causally
via
state-specific
coupling
parameters
create
distinct
patterns.
Together,
findings
indicate
set
dynamic,
spatial
determine
repertoire
possible
NeuroImage Clinical,
Journal Year:
2022,
Volume and Issue:
36, P. 103176 - 103176
Published: Jan. 1, 2022
High
co-morbidity
and
substantial
overlap
across
psychiatric
disorders
encourage
a
transition
in
psychiatry
research
from
categorical
to
dimensional
approaches
that
integrate
neuroscience
psychopathology.
Converging
evidence
suggests
the
cerebellum
is
involved
wide
range
of
cognitive
functions
mental
disorders.
An
important
question
thus
centers
on
extent
which
cerebellar
function
can
be
linked
transdiagnostic
dimensions
To
address
this
question,
we
used
multivariate
data-driven
statistical
technique
(partial
least
squares)
identify
latent
linking
human
connectome
as
assessed
by
functional
MRI
large
set
clinical,
cognitive,
trait
measures
198
participants,
including
healthy
controls
(n
=
92)
well
patients
diagnosed
with
attention-deficit/hyperactivity
disorder
35),
bipolar
36),
schizophrenia
35).
Macroscale
spatial
gradients
connectivity
at
voxel
level
were
characterize
properties,
provide
low-dimensional
representation
connectivity,
i.e.,
sensorimotor-supramodal
hierarchical
organization.
This
analysis
revealed
significant
correlated
patterns
behavioral
could
represented
into
four
dimensions:
general
psychopathology,
impulsivity
mood,
internalizing
symptoms
executive
dysfunction.
Each
dimension
was
associated
unique
pattern
all
participants.
Multiple
control
analyses
10-fold
cross-validation
confirmed
robustness
generalizability
yielded
dimensions.
These
findings
highlight
relevance
necessity
for
study
classification
psychopathology
call
researcher
pay
more
attention
role
not
just
within
cerebral
cortex.
Network Neuroscience,
Journal Year:
2021,
Volume and Issue:
5(2), P. 322 - 336
Published: Jan. 1, 2021
Abstract
The
application
of
graph
theory
to
model
the
complex
structure
and
function
brain
has
shed
new
light
on
its
organization,
prompting
emergence
network
neuroscience.
Despite
tremendous
progress
that
been
achieved
in
this
field,
still
relatively
few
methods
exploit
topology
networks
analyze
activity.
Recent
attempts
direction
have
leveraged
one
hand
spectral
analysis
(to
decompose
connectivity
into
eigenmodes
or
gradients)
other
signal
processing
activity
“coupled
to”
an
underlying
Fourier
modes).
These
studies
used
a
variety
imaging
techniques
(e.g.,
fMRI,
electroencephalography,
diffusion-weighted
myelin-sensitive
imaging)
estimators
networks.
Results
are
promising
terms
interpretability
functional
relevance,
but
methodologies
terminology
variable.
goals
paper
twofold.
First,
we
summarize
recent
contributions
related
gradients
processing,
attempt
clarification
while
pointing
out
current
methodological
limitations.
Second,
discuss
perspective
relevance
could
be
fruitfully
exploited
by
considering
them
as
bases