Frontiers in Psychiatry,
Год журнала:
2021,
Номер
12
Опубликована: Июнь 2, 2021
Psychiatry
faces
fundamental
challenges
with
regard
to
mechanistically
guided
differential
diagnosis,
as
well
prediction
of
clinical
trajectories
and
treatment
response
individual
patients.
This
has
motivated
the
genesis
two
closely
intertwined
fields:
(i)
Translational
Neuromodeling
(TN),
which
develops
“computational
assays”
for
inferring
patient-specific
disease
processes
from
neuroimaging,
electrophysiological,
behavioral
data;
(ii)
Computational
(CP),
goal
incorporating
computational
assays
into
decision
making
in
everyday
practice.
In
order
serve
objective
reliable
tools
routine,
require
end-to-end
pipelines
raw
data
(input)
clinically
useful
information
(output).
While
these
are
yet
be
established
practice,
components
this
general
pipeline
being
developed
made
openly
available
community
use.
paper,
we
present
T
ranslational
A
lgorithms
P
sychiatry-
dvancing
S
cience
(TAPAS)
software
package,
an
open-source
collection
building
blocks
psychiatry.
Collectively,
TAPAS
presently
cover
several
important
aspects
desired
pipeline,
including:
tailored
experimental
designs
optimization
measurement
strategy
prior
acquisition,
quality
control
during
(iii)
artifact
correction,
statistical
inference,
application
after
acquisition.
Here,
review
different
within
illustrate
how
may
help
provide
a
deeper
understanding
neural
cognitive
mechanisms
disease,
ultimate
establishing
automatized
predictions
about
We
hope
that
will
contribute
further
development
TN/CP
facilitate
translation
advances
neuroscience
relevant
assays.
Biological Psychiatry,
Год журнала:
2018,
Номер
84(9), С. 634 - 643
Опубликована: Май 25, 2018
Fueled
by
developments
in
computational
neuroscience,
there
has
been
increasing
interest
the
underlying
neurocomputational
mechanisms
of
psychosis.
One
successful
approach
involves
predictive
coding
and
Bayesian
inference.
Here,
inferences
regarding
current
state
world
are
made
combining
prior
beliefs
with
incoming
sensory
signals.
Mismatches
between
signals
constitute
prediction
errors
that
drive
new
learning.
Psychosis
suggested
to
result
from
a
decreased
precision
encoding
relative
data,
thereby
garnering
maladaptive
inferences.
we
review
evidence
for
aberrant
discuss
challenges
this
canonical
account
For
example,
hallucinations
delusions
may
relate
distinct
alterations
coding,
despite
their
common
co-occurrence.
More
broadly,
some
studies
implicate
weakened
psychosis,
others
find
stronger
priors.
These
might
be
answered
more
nuanced
view
coding.
Different
priors
specified
different
modalities
integration,
deficits
each
modality
need
not
uniform.
Furthermore,
hierarchical
organization
critical.
Altered
processes
at
lower
levels
hierarchy
linearly
related
higher
(and
vice
versa).
Finally,
theories
do
highlight
active
inference—the
process
through
which
effects
our
actions
on
sensations
anticipated
minimized.
It
is
possible
conflicting
findings
reconciled
considering
these
complexities,
portending
framework
psychosis
equipped
deal
its
many
manifestations.
Neuroscience & Biobehavioral Reviews,
Год журнала:
2016,
Номер
68, С. 862 - 879
Опубликована: Июль 1, 2016
This
paper
offers
an
active
inference
account
of
choice
behaviour
and
learning.
It
focuses
on
the
distinction
between
goal-directed
habitual
how
they
contextualise
each
other.
We
show
that
habits
emerge
naturally
(and
autodidactically)
from
sequential
policy
optimisation
when
agents
are
equipped
with
state-action
policies.
In
inference,
has
explorative
(epistemic)
exploitative
(pragmatic)
aspects
sensitive
to
ambiguity
risk
respectively,
where
epistemic
(ambiguity-resolving)
enables
pragmatic
(reward-seeking)
subsequent
emergence
habits.
Although
policies
usually
associated
model-based
model-free
schemes,
we
find
more
important
is
belief-free
belief-based
schemes.
The
underlying
(variational)
belief
updating
provides
a
comprehensive
(if
metaphorical)
process
theory
for
several
phenomena,
including
transfer
dopamine
responses,
reversal
learning,
habit
formation
devaluation.
Finally,
reduces
classical
(Bellman)
scheme,
in
absence
ambiguity.
Biological Psychiatry,
Год журнала:
2015,
Номер
77(12), С. 1089 - 1097
Опубликована: Май 2, 2015
Perception,
cognition,
and
social
interaction
depend
upon
coordinated
neural
activity.
This
coordination
operates
within
noisy,
overlapping,
distributed
networks
operating
at
multiple
timescales.
These
are
built
a
structural
scaffolding
with
intrinsic
neuroplasticity
that
changes
development,
aging,
disease,
personal
experience.
In
this
article,
we
begin
from
the
perspective
successful
interregional
communication
relies
transient
synchronization
between
distinct
low-frequency
(<80
Hz)
oscillations,
allowing
for
brief
windows
of
via
phase-coordinated
local
neuronal
spiking.
From
this,
construct
theoretical
framework
dynamic
network
communication,
arguing
these
reflect
balance
oscillatory
coupling
population
spiking
activity
two
levels
interact.
We
theorize
when
is
too
strong,
spike
timing
becomes
synchronous;
weak,
disorganized.
Each
results
in
specific
disruptions
to
communication.
alterations
dynamics
may
underlie
cognitive
associated
healthy
development
addition
neurological
psychiatric
disorders.
A
number
disorders—including
Parkinson's
autism,
depression,
schizophrenia,
anxiety—are
abnormalities
Although
experience
differ
biological
gray
or
white
matter,
neurotransmission,
gene
expression,
our
suggests
any
resultant
behavioral
normal
disordered
states
their
treatment
product
how
physical
processes
affect
Abstract
Noradrenaline
is
believed
to
support
cognitive
flexibility
through
the
alpha
2A
noradrenergic
receptor
(a2A-NAR)
acting
in
prefrontal
cortex.
Enhanced
has
been
inferred
from
improved
working
memory
with
a2A-NA
agonist
Guanfacine.
But
it
unclear
whether
Guanfacine
improves
specific
attention
and
learning
mechanisms
beyond
memory,
drug
effects
can
be
formalized
computationally
allow
single
subject
predictions.
We
tested
confirmed
these
suggestions
a
case
study
healthy
nonhuman
primate
performing
feature-based
reversal
task
evaluating
performance
using
Bayesian
Reinforcement
models.
In
an
initial
dose-testing
phase
we
found
dose
that
increased
accuracy,
decreased
distractibility
learning.
second
experimental
only
examined
faster
single-subject
computational
modeling.
Parameter
estimation
suggested
not
accounted
for
by
varying
reinforcement
mechanism,
but
changing
set
of
parameter
values
higher
rates
stronger
suppression
non-chosen
over
chosen
feature
information.
These
findings
provide
important
starting
point
developing
models
discern
synaptic
functions
within
context
neuropsychiatry
framework.
Trends in Cognitive Sciences,
Год журнала:
2015,
Номер
20(1), С. 15 - 24
Опубликована: Ноя. 4, 2015
TrendsWith
increasing
use
of
computational
models
to
understand
human
behavior,
scientists
have
begun
model
the
dynamics
subjective
states
such
as
mood.Recent
data
suggest
that
mood
reflects
cumulative
impact
differences
between
reward
outcomes
and
expectations.Behavioral
neural
findings
biases
perception
are
perceived
better
when
one
is
in
a
good
relative
bad
mood.These
two
lines
research
establish
bidirectional
interaction
reinforcement
learning,
which
may
play
an
important
adaptive
role
healthy
whose
dysfunction
might
contribute
psychiatric
disorders.AbstractExperiences
affect
mood,
turn
affects
subsequent
experiences.
Recent
studies
specific
principles.
First,
depends
on
how
recent
differ
from
expectations.
Second,
way
we
perceive
(e.g.,
rewards),
this
bias
learning
about
those
outcomes.
We
propose
two-way
serves
mitigate
inefficiencies
application
real-world
problems.
Specifically,
represents
overall
momentum
outcomes,
its
biasing
influence
'corrects'
account
for
environmental
dependencies.
describe
potential
dysfunctions
mechanism
symptoms
disorders.
Computational Psychiatry,
Год журнала:
2017,
Номер
1(0), С. 24 - 24
Опубликована: Авг. 25, 2017
Reinforcement
learning
and
decision-making
(RLDM)
provide
a
quantitative
framework
computational
theories
with
which
we
can
disentangle
psychiatric
conditions
into
the
basic
dimensions
of
neurocognitive
functioning.
RLDM
offer
novel
approach
to
assessing
potentially
diagnosing
patients,
there
is
growing
enthusiasm
for
both
psychiatry
among
clinical
researchers.
Such
also
insights
brain
substrates
particular
processes,
as
exemplified
by
model-based
analysis
data
from
functional
magnetic
resonance
imaging
(fMRI)
or
electroencephalography
(EEG).
However,
researchers
often
find
too
technical
have
difficulty
adopting
it
their
research.
Thus,
critical
need
remains
develop
user-friendly
tool
wide
dissemination
methods.
We
introduce
an
R
package
called
hBayesDM
(hierarchical
Bayesian
modeling
underline;">Decision-Making
tasks),
offers
array
tasks
social
exchange
games.
The
state-of-the-art
hierarchical
Bayesian
modeling,
in
individual
group
parameters
(i.e.,
posterior
distributions)
are
estimated
simultaneously
mutually
constraining
fashion.
At
same
time,
extremely
user-friendly:
users
perform
output
visualization,
model
comparisons,
each
single
line
coding.
Users
extract
trial-by-trial
latent
variables
(e.g.,
prediction
errors)
required
fMRI/EEG.
With
package,
anticipate
that
anyone
minimal
knowledge
programming
take
advantage
cutting-edge
computational-modeling
approaches
investigate
underlying
processes
interactions
between
multiple
goal-directed,
habitual,
Pavlovian)
systems.
In
this
way,
expect
will
contribute
advanced
enable
range
easily
research
within
different
populations.
Nature Communications,
Год журнала:
2016,
Номер
7(1)
Опубликована: Март 29, 2016
The
effects
of
stress
are
frequently
studied,
yet
its
proximal
causes
remain
unclear.
Here
we
demonstrate
that
subjective
estimates
uncertainty
predict
the
dynamics
and
physiological
responses.
Subjects
learned
a
probabilistic
mapping
between
visual
stimuli
electric
shocks.
Salivary
cortisol
confirmed
our
stressor
elicited
changes
in
endocrine
activity.
Using
hierarchical
Bayesian
learning
model,
quantified
relationship
different
forms
task
acute
Subjective
stress,
pupil
diameter
skin
conductance
all
tracked
evolution
irreducible
uncertainty.
We
observed
coupling
emotional
somatic
state,
with
tuning
to
tightly
correlated.
Furthermore,
predicted
individual
performance,
consistent
an
adaptive
role
for
under
uncertain
threat.
Our
finding
responses
tuned
environmental
provides
new
insight
into
their
generation
likely
function.
Journal of Neurology Neurosurgery & Psychiatry,
Год журнала:
2015,
Номер
unknown, С. jnnp - 310737
Опубликована: Июль 8, 2015
Computational
Psychiatry
aims
to
describe
the
relationship
between
brain9s
neurobiology,
its
environment
and
mental
symptoms
in
computational
terms.
In
so
doing,
it
may
improve
psychiatric
classification
diagnosis
treatment
of
illness.
It
can
unite
many
levels
description
a
mechanistic
rigorous
fashion,
while
avoiding
biological
reductionism
artificial
categorisation.
We
how
models
cognition
infer
current
state
weigh
up
future
actions,
these
provide
new
perspectives
on
two
example
disorders,
depression
schizophrenia.
Reinforcement
learning
describes
brain
choose
value
courses
actions
according
their
long-term
value.
Some
depressive
result
from
aberrant
valuations,
which
could
arise
prior
beliefs
about
loss
agency
(‘helplessness’),
or
an
inability
inhibit
exploration
aversive
events.
Predictive
coding
explains
might
perform
Bayesian
inference
by
combining
sensory
data
with
beliefs,
each
weighted
certainty
(or
precision).
Several
cortical
abnormalities
schizophrenia
reduce
precision
at
higher
inferential
hierarchy,
biasing
towards
away
beliefs.
discuss
whether
striatal
hyperdopaminergia
have
adaptive
function
this
context,
also
reinforcement
incentive
salience
shed
light
disorder.
Finally,
we
review
some
Psychiatry9s
applications
neurological
such
as
Parkinson9s
disease,
pitfalls
avoid
when
applying
methods.