BMJ Mental Health,
Год журнала:
2023,
Номер
26(1), С. e300803 - e300803
Опубликована: Окт. 1, 2023
The
importance
of
improving
brain
and
mental
health
developing
sustainable
environments
is
increasingly
recognised.
Understanding
the
syndemic
interactions
between
these
processes
can
help
address
contemporary
societal
challenges
foster
global
innovation.
Here,
we
propose
a
green
capital
model
that
integrates
environmental
drivers
skills
necessary
for
long-term
sustainability
discuss
role
interdisciplinary
approaches
in
promoting
individual
collective
behavioural
changes.
We
draw
on
existing
literature
research
to
highlight
connections
health,
factors
skills.
Environmental
exposome
have
long-lasting
adverse
effects
particularly
vulnerable
populations.
Investing
prepare
societies
crises.
Green
skills,
including
creativity,
ecological
intelligence
digital
literacy,
are
critical
environments.
Access
nature
improves
fields
such
as
neurourbanism
inform
urban
planning
benefit
citizens'
well-being.
Building
requires
increasing
future
generations'
awareness,
education
training.
A
comprehensive
approach
enable
greater
scaling,
synergistically
protecting
sustainability.
Brain,
Год журнала:
2024,
Номер
147(6), С. 1953 - 1966
Опубликована: Фев. 9, 2024
Abstract
Impaired
social
cognition
is
a
core
deficit
in
frontotemporal
dementia
(FTD).
It
most
commonly
associated
with
the
behavioural-variant
of
FTD,
atrophy
orbitofrontal
and
ventromedial
prefrontal
cortex.
Social
cognitive
changes
are
also
common
semantic
dementia,
centred
on
anterior
temporal
lobes.
The
impairment
behaviour
FTD
has
typically
been
attributed
to
damage
cortex
and/or
poles
uncinate
fasciculus
that
connects
them.
However,
relative
contributions
each
region
unresolved.
In
this
review,
we
present
unified
neurocognitive
model
controlled
not
only
explains
observed
behaviours
but
assimilates
both
consistent
potentially
contradictory
findings
from
other
patient
groups,
comparative
neurology
normative
neuroscience.
We
propose
impaired
results
two
cognitively-
anatomically-distinct
components.
first
component
social-semantic
knowledge,
part
general
semantic-conceptual
system
supported
by
lobes
bilaterally.
second
control,
cortex,
medial
frontal
ventrolateral
which
interacts
knowledge
guide
shape
behaviour.
The Lancet Regional Health - Americas,
Год журнала:
2022,
Номер
17, С. 100387 - 100387
Опубликована: Ноя. 3, 2022
Global
brain
health
initiatives
call
for
improving
methods
the
diagnosis
of
Alzheimer's
disease
(AD)
and
frontotemporal
dementia
(FTD)
in
underrepresented
populations.
However,
diagnostic
procedures
upper-middle-income
countries
(UMICs)
lower-middle
income
(LMICs),
such
as
Latin
American
(LAC),
face
multiple
challenges.
These
include
heterogeneity
methods,
lack
clinical
harmonisation,
limited
access
to
biomarkers.
Journal of Neuroscience,
Год журнала:
2023,
Номер
43(9), С. 1643 - 1656
Опубликована: Фев. 2, 2023
Healthy
brain
dynamics
can
be
understood
as
the
emergence
of
a
complex
system
far
from
thermodynamic
equilibrium.
Brain
are
temporally
irreversible
and
thus
establish
preferred
direction
in
time
(i.e.,
arrow
time).
However,
little
is
known
about
how
time-reversal
symmetry
spontaneous
activity
affected
by
Alzheimer's
disease
(AD).
We
hypothesized
that
level
irreversibility
would
compromised
AD,
signaling
fundamental
shift
collective
properties
toward
equilibrium
dynamics.
investigated
resting-state
fMRI
EEG
data
male
female
human
patients
with
AD
elderly
healthy
control
subjects
(HCs).
quantified
and,
thus,
proximity
to
nonequilibrium
comparing
forward
backward
series
through
time-shifted
correlations.
was
associated
breakdown
temporal
at
global,
local,
network
levels,
multiple
oscillatory
frequency
bands.
At
local
level,
temporoparietal
frontal
regions
were
AD.
The
limbic,
frontoparietal,
default
mode,
salience
networks
most
level.
reversibility
cognitive
decline
gray
matter
volume
HCs.
provided
higher
accuracy
more
distinctive
information
than
classical
neurocognitive
measures
when
differentiating
subjects.
Findings
validated
using
an
out-of-sample
cohort.
Present
results
offer
new
evidence
regarding
pathophysiological
links
between
entropy
generation
rate
clinical
presentation
opening
avenues
for
dementia
characterization
different
levels.
SIGNIFICANCE
STATEMENT
By
assessing
large-scale
across
signals,
we
provide
precise
signature
capable
distinguishing
Alzheimer’s
(AD)
levels
regimes.
Irreversibility
default-mode,
compared
sensory–motor
networks.
Moreover,
time-irreversibility
atrophy
outperformed
complemented
markers
predictive
classification
performance.
generalized
replicated
validation
procedure.
novel
multilevel
reduced
has
potential
open
understating
neurodegeneration
terms
asymmetry
EBioMedicine,
Год журнала:
2023,
Номер
90, С. 104540 - 104540
Опубликована: Март 25, 2023
Dementia's
diagnostic
protocols
are
mostly
based
on
standardised
neuroimaging
data
collected
in
the
Global
North
from
homogeneous
samples.
In
other
non-stereotypical
samples
(participants
with
diverse
admixture,
genetics,
demographics,
MRI
signals,
or
cultural
origins),
classifications
of
disease
difficult
due
to
demographic
and
region-specific
sample
heterogeneities,
lower
quality
scanners,
non-harmonised
pipelines.We
implemented
a
fully
automatic
computer-vision
classifier
using
deep
learning
neural
networks.
A
DenseNet
was
applied
raw
(unpreprocessed)
3000
participants
(behavioural
variant
frontotemporal
dementia-bvFTD,
Alzheimer's
disease-AD,
healthy
controls;
both
male
female
as
self-reported
by
participants).
We
tested
our
results
demographically
matched
unmatched
discard
possible
biases
performed
multiple
out-of-sample
validations.Robust
classification
across
all
groups
were
achieved
3T
North,
which
also
generalised
Latin
America.
Moreover,
non-standardised,
routine
1.5T
clinical
images
These
generalisations
robust
heterogenous
recordings
not
confounded
demographics
(i.e.,
samples,
when
incorporating
variables
multifeatured
model).
Model
interpretability
analysis
occlusion
sensitivity
evidenced
core
pathophysiological
regions
for
each
(mainly
hippocampus
AD,
insula
bvFTD)
demonstrating
biological
specificity
plausibility.The
generalisable
approach
outlined
here
could
be
used
future
aid
clinician
decision-making
samples.The
specific
funding
this
article
is
provided
acknowledgements
section.
Neurobiology of Disease,
Год журнала:
2023,
Номер
179, С. 106047 - 106047
Опубликована: Фев. 23, 2023
Brain
functional
connectivity
in
dementia
has
been
assessed
with
dissimilar
EEG
metrics
and
estimation
procedures,
thereby
increasing
results'
heterogeneity.
In
this
scenario,
joint
analyses
integrating
information
from
different
may
allow
for
a
more
comprehensive
characterization
of
brain
interactions
subtypes.
To
test
hypothesis,
resting-state
electroencephalogram
(rsEEG)
was
recorded
individuals
Alzheimer's
Disease
(AD),
behavioral
variant
frontotemporal
(bvFTD),
healthy
controls
(HCs).
Whole-brain
estimated
the
source
space
using
101
types
connectivity,
capturing
linear
nonlinear
both
time
frequency-domains.
Multivariate
machine
learning
progressive
feature
elimination
run
to
discriminate
AD
HCs,
bvFTD
based
on
i)
frequency
bands,
ii)
complementary
frequency-domain
(e.g.,
instantaneous,
lagged,
total
connectivity),
iii)
time-domain
linearity
assumption
Pearson
correlation
coefficient
mutual
information).
<10%
all
possible
connections
were
responsible
differences
between
patients
controls,
atypical
never
captured
by
>1/4
measures.
Joint
revealed
patterns
hypoconnectivity
(patientsHCs)
groups
mainly
identified
regions.
These
atypicalities
differently
frequency-
metrics,
bandwidth-specific
fashion.
The
multi-metric
representation
whole-brain
evidenced
inadequacy
single-metric
approaches,
resulted
valid
alternative
selection
problem
connectivity.
reveal
interdependence
that
are
overlooked
single
contributing
reliable
interpretable
description
neurodegeneration.
The
treatment
of
neurodegenerative
diseases
is
hindered
by
lack
interventions
capable
steering
multimodal
whole-brain
dynamics
towards
patterns
indicative
preserved
brain
health.
To
address
this
problem,
we
combined
deep
learning
with
a
model
reproducing
functional
connectivity
in
patients
diagnosed
Alzheimer’s
disease
(AD)
and
behavioral
variant
frontotemporal
dementia
(bvFTD).
These
models
included
disease-specific
atrophy
maps
as
priors
to
modulate
local
parameters,
revealing
increased
stability
hippocampal
insular
signatures
AD
bvFTD,
respectively.
Using
variational
autoencoders,
visualized
different
pathologies
their
severity
the
evolution
trajectories
low-dimensional
latent
space.
Finally,
perturbed
reveal
key
AD-
bvFTD-specific
regions
induce
transitions
from
pathological
healthy
states.
Overall,
obtained
novel
insights
on
progression
control
means
external
stimulation,
while
identifying
dynamical
mechanisms
that
underlie
alterations
neurodegeneration.
