bioRxiv (Cold Spring Harbor Laboratory),
Год журнала:
2023,
Номер
unknown
Опубликована: Авг. 22, 2023
Abstract
Video
games
are
a
valuable
tool
for
studying
the
effects
of
training
and
neural
plasticity
on
brain.
However,
underlaying
mechanisms
related
to
plasticity-induced
brain
structural
changes
their
impact
in
dynamics
unknown.
Here,
we
used
semi-empirical
whole-brain
model
study
linked
video
game
expertise.
We
hypothesized
that
expertise
is
associated
with
plasticity-mediated
connectivity
manifest
at
meso-scale
level,
resulting
more
segregated
functional
network
topology.
To
test
this
hypothesis,
combined
data
StarCraft
II
players
(VGPs,
n
=
31)
non-players
(NVGPs,
31),
generic
fMRI
from
Human
Connectome
Project
computational
models,
aim
generating
simulated
recordings.
Graph
theory
analysis
was
performed
during
both
resting-state
conditions
external
stimulation.
VGPs’
characterized
by
integration,
increased
local
frontal,
parietal
occipital
regions.
The
same
analyses
level
showed
no
differences
between
VGPs
NVGPs.
Regions
strength
known
be
involved
cognitive
processes
crucial
task
performance
such
as
attention,
reasoning,
inference.
In-silico
stimulation
suggested
FC
NVGPs
emerge
noisy
contexts,
specifically
when
increased.
This
indicates
connectomes
may
facilitate
filtering
noise
stimuli.
These
alterations
drive
observed
individuals
gaming
Overall,
our
work
sheds
light
into
underlying
triggered
experiences.
bioRxiv (Cold Spring Harbor Laboratory),
Год журнала:
2025,
Номер
unknown
Опубликована: Апрель 30, 2025
Abstract
Predicting
repetitive
transcranial
magnetic
stimulation
(rTMS)
effects
on
whole-brain
dynamics
in
clinical
populations
is
crucial
for
developing
personalized
therapies
and
advancing
precision
medicine
brain
disorders.
This
study
provides
the
first
proof-of-concept
demonstrating
that
Digital
Twin
Brain
(DTB)
can
forecast
rTMS
state
individuals
with
disorders
(chronic
tinnitus).
First,
we
identified
two
aberrant
states
predominantly
overlapped
somatomotor
default
mode
networks,
respectively.
Subsequently,
developed
DTB
patients
derived
regional
responses
each
region,
revealing
distinct
roles
of
parieto-occipital
frontal
regions.
Mechanistically,
examined
biological
plausibility
using
tinnitus-specific
risk
genes
investigated
multi-scale
neurobiological
relevance.
Clinically,
found
predict
an
independent,
longitudinal
dataset
(all
r
>
0.78).
Particularly,
predictive
capacity
exhibits
a
state-specific
nature.
Overall,
this
work
proposes
novel
DTB-based
framework
predicting
empirical
evidence
supporting
its
utility.
approach
may
be
generalizable
to
other
neuromodulation
techniques,
promoting
broader
advancements
health.
Abstract
Network
neuroscience
has
proven
essential
for
understanding
the
principles
and
mechanisms
underlying
complex
brain
(dys)function
cognition.
In
this
context,
whole-brain
network
modeling–also
known
as
virtual
modeling–combines
computational
models
of
dynamics
(placed
at
each
node)
with
individual
imaging
data
(to
coordinate
connect
nodes),
advancing
our
its
neurobiological
underpinnings.
However,
there
remains
a
critical
need
automated
model
inversion
tools
to
estimate
control
(bifurcation)
parameters
large
scales
across
neuroimaging
modalities,
given
their
varying
spatio-temporal
resolutions.
This
study
aims
address
gap
by
introducing
flexible
integrative
toolkit
efficient
Bayesian
inference
on
models,
called
Virtual
Brain
Inference
(<monospace>VBI</monospace>).
open-source
provides
fast
simulations,
taxonomy
feature
extraction,
storage
loading,
probabilistic
machine
learning
algorithms,
enabling
biophysically
interpretable
from
non-invasive
invasive
recordings.
Through
in-silico
testing,
we
demonstrate
accuracy
reliability
commonly
used
associated
data.
<monospace>VBI</monospace>
shows
potential
improve
hypothesis
evaluation
in
through
uncertainty
quantification,
contribute
advances
precision
medicine
enhancing
predictive
power
models.
Abstract
Network
neuroscience
has
proven
essential
for
understanding
the
principles
and
mechanisms
underlying
complex
brain
(dys)function
cognition.
In
this
context,
whole-brain
network
modeling–also
known
as
virtual
modeling–combines
computational
models
of
dynamics
(placed
at
each
node)
with
individual
imaging
data
(to
coordinate
connect
nodes),
advancing
our
its
neurobiological
underpinnings.
However,
there
remains
a
critical
need
automated
model
inversion
tools
to
estimate
control
(bifurcation)
parameters
large
scales
across
neuroimaging
modalities,
given
their
varying
spatio-temporal
resolutions.
This
study
aims
address
gap
by
introducing
flexible
integrative
toolkit
efficient
Bayesian
inference
on
models,
called
Virtual
Brain
Inference
(<monospace>VBI</monospace>).
open-source
provides
fast
simulations,
taxonomy
feature
extraction,
storage
loading,
probabilistic
machine
learning
algorithms,
enabling
biophysically
interpretable
from
non-invasive
invasive
recordings.
Through
in-silico
testing,
we
demonstrate
accuracy
reliability
commonly
used
associated
data.
<monospace>VBI</monospace>
shows
potential
improve
hypothesis
evaluation
in
through
uncertainty
quantification,
contribute
advances
precision
medicine
enhancing
predictive
power
models.
Major
Depressive
Disorder
(MDD)
is
a
complex,
heterogeneous
condition
affecting
millions
worldwide.
Computational
neuropsychiatry
offers
potential
breakthroughs
through
mechanistic
modeling
of
this
disorder.
Using
the
Kolmogorov
Theory
consciousness
(KT),
we
develop
foundational
model
where
algorithmic
agents
interact
with
world
to
maximize
an
Objective
Function
evaluating
affective
\textit{valence}.
Depression,
defined
in
context
by
state
persistently
low
valence,
may
arise
from
various
factors---including
inaccurate
models
(cognitive
biases),
dysfunctional
(anhedonia,
anxiety),
deficient
planning
(executive
deficits),
or
unfavorable
environments.
Integrating
algorithmic,
dynamical
systems,
and
neurobiological
concepts,
map
agent
brain
circuits
functional
networks,
framing
etiological
routes
linking
depression
biotypes.
Finally,
explore
how
stimulation,
psychotherapy,
plasticity-enhancing
compounds
such
as
psychedelics
can
synergistically
repair
neural
optimize
therapies
using
personalized
computational
models.
NeuroImage,
Год журнала:
2024,
Номер
293, С. 120633 - 120633
Опубликована: Май 3, 2024
Video
games
are
a
valuable
tool
for
studying
the
effects
of
training
and
neural
plasticity
on
brain.
However,
underlying
mechanisms
related
to
plasticity-associated
brain
structural
changes
their
impact
dynamics
unknown.
Here,
we
used
semi-empirical
whole-brain
model
study
linked
video
game
expertise.
We
hypothesized
that
expertise
is
associated
with
plasticity-mediated
in
connectivity
manifest
at
meso‑scale
level,
resulting
more
segregated
functional
network
topology.
To
test
this
hypothesis,
combined
data
StarCraft
II
players
(VGPs,
n
=
31)
non-players
(NVGPs,
31),
generic
fMRI
from
Human
Connectome
Project
computational
models,
generate
simulated
recordings.
Graph
theory
analysis
was
performed
during
both
resting-state
conditions
external
stimulation.
VGPs'
characterized
by
integration,
increased
local
frontal,
parietal,
occipital
regions.
The
same
analyses
level
showed
no
differences
between
VGPs
NVGPs.
Regions
strength
known
be
involved
cognitive
processes
crucial
task
performance
such
as
attention,
reasoning,
inference.
In-silico
stimulation
suggested
FC
NVGPs
emerge
noisy
contexts,
specifically
when
increased.
This
indicates
connectomes
may
facilitate
filtering
noise
stimuli.
These
alterations
drive
observed
individuals
gaming
Overall,
our
work
sheds
light
triggered
experiences.
bioRxiv (Cold Spring Harbor Laboratory),
Год журнала:
2024,
Номер
unknown
Опубликована: Окт. 22, 2024
Adaptive
cognition
relies
on
cooperation
across
anatomically
distributed
brain
circuits.
However,
specialised
neural
systems
are
also
in
constant
competition
for
limited
processing
resources.
How
does
the
brain's
network
architecture
enable
it
to
balance
these
cooperative
and
competitive
tendencies?
Here
we
use
computational
whole-brain
modelling
examine
dynamical
relevance
of
interactions
mammalian
connectome.
Across
human,
macaque,
mouse
show
that
models
most
faithfully
reproduce
activity,
consistently
combines
modular
with
diffuse,
long-range
interactions.
The
model
outperforms
cooperative-only
model,
excellent
fit
both
spatial
properties
living
brain,
which
were
not
explicitly
optimised
but
rather
emerge
spontaneously.
Competitive
effective
connectivity
produce
greater
levels
synergistic
information
local-global
hierarchy,
lead
superior
capacity
when
used
neuromorphic
computing.
Altogether,
this
work
provides
a
mechanistic
link
between
architecture,
properties,
computation
brain.
bioRxiv (Cold Spring Harbor Laboratory),
Год журнала:
2023,
Номер
unknown
Опубликована: Авг. 17, 2023
Abstract
We
explore
the
intersection
of
neural
dynamics
and
effects
psychedelics
in
light
distinct
timescales
a
framework
integrating
concepts
from
dynamics,
complexity,
plasticity.
call
this
geometrodynamics
for
its
parallels
with
general
relativity’s
description
interplay
spacetime
matter.
The
geometry
trajectories
within
dynamical
landscape
“fast
time”
are
shaped
by
structure
differential
equation
connectivity
parameters,
which
themselves
evolve
over
“slow
driven
state-dependent
state-independent
plasticity
mechanisms.
Finally,
adjustment
processes
(metaplasticity)
takes
place
an
“ultraslow”
time
scale.
Psychedelics
flatten
landscape,
leading
to
heightened
entropy
complexity
as
observed
neuroimaging
modeling
studies
linking
increases
disruption
functional
integration.
highlight
relationship
between
criticality,
fast
synaptic
Pathological,
rigid,
or
“canalized”
result
ultrastable
confined
repertoire,
allowing
slower
plastic
changes
consolidate
them
further.
However,
under
influence
psychedelics,
destabilizing
emergence
complex
leads
more
fluid
adaptable
state
process
that
is
amplified
plasticity-enhancing
psychedelics.
This
shift
manifests
acute
systemic
increase
disorder
possibly
longer-lasting
affecting
both
short-term
long-term
processes.
Our
offers
holistic
perspective
these
substances
their
potential
impacts
on
function.
Brain Sciences,
Год журнала:
2023,
Номер
13(8), С. 1133 - 1133
Опубликована: Июль 28, 2023
Alzheimer's
disease
(AD)
is
a
degenerative
brain
disease,
and
the
condition
difficult
to
assess.
In
past,
numerous
dynamics
models
have
made
remarkable
contributions
neuroscience
from
microcosmic
macroscopic
scale.
Recently,
large-scale
been
developed
based
on
dual-driven
multimodal
neuroimaging
data
neurodynamics
theory.
These
bridge
gap
between
anatomical
structure
functional
played
an
important
role
in
assisting
understanding
of
mechanism.
Large-scale
widely
used
explain
how
macroscale
biomarkers
emerge
potential
neuronal
population
level
disturbances
associated
with
AD.
this
review,
we
describe
emerging
approach
studying
AD
that
utilizes
biophysically
model.
particular,
focus
application
model
discuss
directions
for
future
development
analysis
models.
This
will
facilitate
virtual
field
diagnosis
treatment
add
new
opportunities
advancing
clinical
neuroscience.