Neuroimaging
stands
to
benefit
from
emerging
ultrahigh-resolution
3D
histological
atlases
of
the
human
brain;
first
which
is
‘BigBrain’.
Here,
we
review
recent
methodological
advances
for
integration
BigBrain
with
multi-modal
neuroimaging
and
introduce
a
toolbox,
’BigBrainWarp’,
that
combines
these
developments.
The
aim
BigBrainWarp
simplify
workflows
support
adoption
best
practices.
This
accomplished
simple
wrapper
function
allows
users
easily
map
data
between
standard
MRI
spaces.
automatically
pulls
specialised
transformation
procedures,
based
on
ongoing
research
wide
collaborative
network
researchers.
Additionally,
toolbox
improves
accessibility
information
through
dissemination
ready-to-use
cytoarchitectural
features.
Finally,
demonstrate
utility
three
tutorials
discuss
potential
multi-scale
investigations
brain
organisation.
NeuroImage,
Journal Year:
2020,
Volume and Issue:
220, P. 117038 - 117038
Published: June 22, 2020
Studies
of
large-scale
brain
organization
have
revealed
interesting
relationships
between
spatial
gradients
in
maps
across
multiple
modalities.
Evaluating
the
significance
these
findings
requires
establishing
statistical
expectations
under
a
null
hypothesis
interest.
Through
generative
modeling
synthetic
data
that
instantiate
specific
hypothesis,
quantitative
benchmarks
can
be
derived
for
arbitrarily
complex
measures.
Here,
we
present
model,
provided
as
an
open-access
software
platform,
generates
surrogate
with
autocorrelation
(SA)
matched
to
SA
target
map.
is
prominent
and
ubiquitous
property
violates
assumptions
independence
conventional
tests.
Our
method
simulate
maps,
constrained
by
empirical
data,
preserve
cortical,
subcortical,
parcellated,
dense
maps.
We
characterize
how
impacts
p-values
pairwise
map
comparisons.
Furthermore,
demonstrate
SA-preserving
used
gene
set
enrichment
analyses
test
hypotheses
interest
related
topography.
utility
testing
analyses,
underscore
need
disambiguate
meaningful
from
chance
associations
studies
organization.
Proceedings of the National Academy of Sciences,
Journal Year:
2020,
Volume and Issue:
117(38), P. 23242 - 23251
Published: June 5, 2020
Brain
plasticity
is
dynamically
regulated
across
the
life
span,
peaking
during
windows
of
early
life.
Typically
assessed
in
physiological
range
milliseconds
(real
time),
these
trajectories
are
also
influenced
on
longer
timescales
developmental
time
(nurture)
and
evolutionary
(nature),
which
shape
neural
architectures
that
support
plasticity.
Properly
sequenced
critical
periods
circuit
refinement
build
up
complex
cognitive
functions,
such
as
language,
from
more
primary
modalities.
Here,
we
consider
recent
progress
biological
basis
a
unifying
rubric
for
understanding
multiple
timescales.
Notably,
maturation
parvalbumin-positive
(PV)
inhibitory
neurons
pivotal.
These
fast-spiking
cells
generate
gamma
oscillations
associated
with
period
plasticity,
sensitive
to
circadian
gene
manipulation,
emerge
at
different
rates
brain
regions,
acquire
perineuronal
nets
age,
may
be
by
epigenetic
factors
over
generations.
features
provide
further
novel
insight
into
impact
adversity
neurodevelopmental
risk
mental
disorders.
Complex
cognitive
functions
such
as
working
memory
and
decision-making
require
information
maintenance
over
seconds
to
years,
from
transient
sensory
stimuli
long-term
contextual
cues.
While
theoretical
accounts
predict
the
emergence
of
a
corresponding
hierarchy
neuronal
timescales,
direct
electrophysiological
evidence
across
human
cortex
is
lacking.
Here,
we
infer
timescales
invasive
intracranial
recordings.
Timescales
increase
along
principal
sensorimotor-to-association
axis
entire
cortex,
scale
with
single-unit
within
macaques.
Cortex-wide
transcriptomic
analysis
shows
alignment
between
expression
excitation-
inhibition-related
genes,
well
genes
specific
voltage-gated
transmembrane
ion
transporters.
Finally,
are
functionally
dynamic:
prefrontal
expand
during
individual
performance,
while
cortex-wide
compress
aging.
Thus,
follow
cytoarchitectonic
gradients
relevant
for
cognition
in
both
short
long
terms,
bridging
microcircuit
physiology
macroscale
dynamics
behavior.
Proceedings of the National Academy of Sciences,
Journal Year:
2020,
Volume and Issue:
117(34), P. 20890 - 20897
Published: Aug. 12, 2020
Multimodal
evidence
suggests
that
brain
regions
accumulate
information
over
timescales
vary
according
to
anatomical
hierarchy.
Thus,
these
experimentally
defined
"temporal
receptive
windows"
are
longest
in
cortical
distant
from
sensory
input.
Interestingly,
spontaneous
activity
also
plays
out
relatively
slow
(i.e.,
exhibits
slower
temporal
autocorrelation
decay).
These
findings
raise
the
possibility
hierarchical
represent
an
intrinsic
organizing
principle
of
function.
Here,
using
resting-state
functional
MRI,
we
show
timescale
ongoing
dynamics
follows
spatial
gradients
throughout
human
cerebral
cortex.
give
rise
systematic
frequency
differences
among
large-scale
networks
and
predict
individual-specific
features
connectivity.
Whole-brain
coverage
permitted
us
further
investigate
organization
subcortical
dynamics.
We
topographically
mirrored
striatum,
thalamus,
cerebellum.
Finally,
hippocampus
followed
a
posterior-to-anterior
gradient,
corresponding
longitudinal
axis
increasing
representational
scale.
emerge
as
global
mammalian
brains.
Neural
activity
underlying
working
memory
is
not
a
local
phenomenon
but
distributed
across
multiple
brain
regions.
To
elucidate
the
circuit
mechanism
of
such
activity,
we
developed
an
anatomically
constrained
computational
model
large-scale
macaque
cortex.
We
found
that
mnemonic
internal
states
may
emerge
from
inter-areal
reverberation,
even
in
regime
where
none
isolated
areas
capable
generating
self-sustained
activity.
The
pattern
along
cortical
hierarchy
indicates
transition
space,
separating
engaged
and
those
which
do
not.
A
host
spatially
distinct
attractor
found,
potentially
subserving
various
processes.
yields
testable
predictions,
including
idea
counterstream
inhibitory
bias,
role
prefrontal
controlling
attractors,
resilience
to
lesions
or
inactivation.
This
work
provides
theoretical
framework
for
identifying
mechanisms
principles
cognitive