Decreased bilateral fdg-pet uptake and inter-hemispheric connectivity in multi-domain amnestic mild cognitive impairment patients: A preliminary study


Luo, X.; Li, K.; Zeng, Q.; Huang, P.; Jiaerken, Y.; Qiu, T.; Xu, X.; Zhou, J.; Xu, J.; Zhang, M.

Frontiers in Aging Neuroscience 10: 161-161

2018


Amnestic mild cognitive impairment (aMCI) is a heterogeneous condition. Based on clinical symptoms, aMCI could be categorized into single-domain aMCI (SD-aMCI, only memory deficit) and multi-domain aMCI (MD-aMCI, one or more cognitive domain deficit). As core intrinsic functional architecture, inter-hemispheric connectivity maintains many cognitive abilities. However, few studies investigated whether SD-aMCI and MD-aMCI have different inter-hemispheric connectivity pattern. We evaluated inter-hemispheric connection pattern using fluorine-18 positron emission tomography - fluorodeoxyglucose (<sup>18</sup>F PET-FDG), resting-state functional MRI and structural T1 in 49 controls, 32 SD-aMCI, and 32 MD-aMCI patients. Specifically, we analyzed the 18<sup>F</sup> PET-FDG (intensity normalized by cerebellar vermis) in a voxel-wise manner. Then, we estimated inter-hemispheric functional and structural connectivity by calculating the voxel-mirrored homotopic connectivity (VMHC) and corpus callosum (CC) subregions volume. Further, we correlated inter-hemispheric indices with the behavioral score and pathological biomarkers. We found that MD-aMCI exhibited more several inter-hemispheric connectivity damages than SD-aMCI. Specifically, MD-aMCI displayed hypometabolism in the bilateral middle temporal gyrus (MTG), inferior parietal lobe, and left precuneus (PCu) (<i>p</i> &lt; 0.001, corrected). Correspondingly, MD-aMCI showed decreased VMHC in MTG, PCu, calcarine gyrus, and postcentral gyrus, as well as smaller mid-posterior CC than the SD-aMCI and controls (<i>p</i> &lt; 0.05, corrected). Contrary to MD-aMCI, there were no neuroimaging indices with significant differences between SD-aMCI and controls, except reduced hypometabolism in bilateral MTG. Within aMCI patients, hypometabolism and reduced inter-hemispheric connectivity correlated with worse executive ability. Moreover, hypometabolism indices correlated to increased amyloid deposition. In conclusion, patients with MD-aMCI exhibited the more severe deficit in inter-hemispheric communication than SD-aMCI. This long-range connectivity deficit may contribute to cognitive profiles and potentially serve as a biomarker to estimate disease progression of aMCI patients.

a
p
frontiers
in
Aging
Neuroscience
ORIGINAL
RESEARCH
published:
05
June
2018
doi:
10.3389/fnagi.2018.00161
OPEN
ACCESS
Edited
by:
Mohammad
Amjad
Kamal,
King
Fahad
Medical
Research
Center,
King
Abdulaziz
University,
Saudi
Arabia
Reviewed
by:
Arun
Bokde,
Trinity
College
Dublin,
Ireland
Panteleimon
Giannakopoulos,
University
de
Geneve,
Switzerland
*Correspondence:
Minming
Zhang
zhangminming@zju.edu.cn
tThese
authors
have
contributed
equally
to
this
work.
orcid.org/0000-0003-1743-7842
'The
data
used
in
the
preparation
of
this
article
were
obtained
from
the
Alzheimer's
Disease
Neuroimaging
Initiative
(ADNI)
database
(adnUoni.usc.edu).
As
such,
the
investigators
within
the
ADNI
contributed
to
the
design
and
implementation
of
ADNI
and
provided
data
but
did
not
participate
in
the
analysis
or
writing
of
this
report.
Received:
22
February
2018
Accepted:
14
May
2018
Published:
05
June
2018
Citation:
Luo
X,
Li
K,
Zeng
Q,
Huang
P,
Jiaerken
Y,
Qiu
T
Xu
X,
Zhou
J,
Xu
J
and
Zhang
M
for
the
Alzheimer's
Disease
Neuroimaging
Initiative
(ADNI)
(2018)
Decreased
Bilateral
FDG-PET
Uptake
and
Inter
-Hemispheric
Connectivity
in
Multi
-Domain
Amnestic
Mild
Cognitive
Impairment
Patients:
A
Preliminary
Study.
Front.
Aging
Neurosci.
10:161.
doi:
10.3389/fnagi.2018.00161
1
11
1
)
Check
for
updates
Decreased
Bilateral
FDG-PET
Uptake
and
Inter
-Hemispheric
Connectivity
in
Multi
-Domain
Amnestic
Mild
Cognitive
Impairment
Patients:
A
Preliminary
Study
Xiao
Luoltt,
Kaicheng
Lilt,
Qingze
Zeng',
Peiyu
Huang',
Yeerfan
Jiaerken',
77antian
Qiu',
Xiaojun
Xu',
Jiong
Zhou
2
,
Jingjing
Xu'
and
Minming
Zhang'*
for
the
Alzheimer's
Disease
Neuroimaging
Initiative
(ADNI)§
'
Department
of
Radiology,
The
Second
Affiliated
Hospital
of
Zhejiang
University
School
of
Medicine,
Hangzhou,
China,
2
Department
of
Neurology
The
Second
Affiliated
Hospital
of
Zhejiang
University
School
of
Medicine,
Hangzhou,
China
Background:
Amnestic
mi
ld
cognitive
impairment
(aMCI)
is
a
heterogeneous
condition.
Based
on
cl
inical
symptoms,
aMCI
could
be
categorized
into
single
-domain
aMCI
(SD-
aMCI,
only
memory
deficit)
and
multi
-domain
aMCI
(MD-aMCI,
one
or
more
cognitive
domain
deficit).
As
core
intrinsic
functional
architecture,
inter
-hemispheric
connectivity
maintains
many
cognitive
abi
lities.
However,
few
studies
investigated
whether
SD-aMCI
and
MD-aMCI
have
different
inter
-hemispheric
connectivity
pattern.
Methods:
We
evaluated
inter
-hemispheric
connection
pattern
using
fluorine
-18
positron
emission
tomography
fluorodeoxyglucose
(
18
F
PET-FDG),
resting
-state
functional
MRI
and
structural
T1
in
49
controls,
32
SD-aMCI,
and
32
MD-aMCI
patients.
Specifically,
we
analyzed
the
18F
PET-FDG
(intensity
normalized
by
cerebel
lar
vermis)
in
a
voxel-wise
manner.
Then,
we
estimated
inter
-hemispheric
functional
and
structural
connectivity
by
calculating
the
voxel-mirrored
homotopic
connectivity
(VMHC)
and
corpus
cal
losum
(CC)
subregions
volume.
Further,
we
correlated
inter
-hemispheric
indices
with
the
behavioral
score
and
pathological
biomarkers.
Results:
We
found
that
MD-aMCI
exhibited
more
several
inter
-hemispheric
connectivity
damages
than
SD-aMCI.
Specifically,
MD-aMCI
displayed
hypometabolism
in
the
bi
lateral
middle
temporal
gyrus
(MTG),
inferior
parietal
lobe,
and
left
precuneus
(PCu)
(p
<
0.001,
corrected).
Correspondingly,
MD-aMCI
showed
decreased
VMHC
in
MTG,
PCu,
calcarine
gyrus,
and
postcentral
gyrus,
as
wel
l
as
smal
ler
mid
-posterior
CC
than
the
SD-aMCI
and
controls
(p
<
0.05,
corrected).
Contrary
to
MD-aMCI
,
there
were
no
neuroimaging
indices
with
significant
differences
between
SD-aMCI
and
controls,
except
reduced
hypometabol
ism
in
bi
lateral
MTG.
Within
aMCI
patients,
hypometabolism
and
reduced
inter
-hemispheric
connectivity
correlated
with
worse
executive
abil
ity.
Moreover,
hypometabol
ism
indices
correlated
to
increased
amyloid
deposition.
Frontiers
in
Aging
Neuroscience
I
www.frontiersin.org
1
June
2018
I
Volume
10
I
Article
161
Luo
et
al.
Divergent
Bi-hemispheric
Connectivity
in
aMCI
Subtype
Conclusion:
In
conclusion,
patients
with
MD-aMCI
exhibited
the
more
severe
deficit
in
inter
-hemispheric
communication
than
SD-aMCI.
This
long-range
connectivity
deficit
may
contribute
to
cognitive
profiles
and
potentially
serve
as
a
biomarker
to
estimate
disease
progression
of
aMCI
patients.
Keywords:
mild
cognitive
impairment,
cerebral
metabolism,
corpus
callosum,
resting
-state
functional
MRI,
voxel-
mirrored
homotopic
connectivity
INTRODUCTION
The
amnestic
mild
cognitive
impairment
(aMCI)
represents
an
intermediate
stage
between
normal
aging
and
Alzheimer's
disease
(AD)
(Petersen
et
al.,
2014).
According
to
impaired
cognitive
domains
number,
aMCI
patients
could
be
further
categorized
into
two
subtypes:
single
-domain
aMCI
(SD-aMCI),
characterized
by
relatively
selective
memory
impairment
and
the
multi
-domain
aMCI
(MD-aMCI),
indicating
extensive
deficits
involving
at
least
one
other
domain
(Winblad
et
al.,
2004;
Busse
et
al.,
2006).
Previous
epidemiology
study
shows
that
MD-aMCI
has
a
higher
risk
of
clinical
progression
than
the
SD-aMCI
(Golob
et
al.,
2007).
Therefore,
to
investigate
the
mechanism
underlying
these
aMCI
subtypes
may
efficiently
facilitate
clinical
early
intervention
and
management.
Recently,
neuroimaging
studies
pointed
out
that
the
MD-aMCI
displays
more
diffuse
gray
matter
atrophy
(Haller
et
al.,
2010;
Zhang
et
al.,
2012)
and
lower
brain
activity
than
the
SD-aMCI,
mainly
involving
default
mode
network
(DMN)
and
frontoparietal
regions
(Li
et
al.,
2014).
Despite
these
studies
shedding
light
into
the
aMCI
pathological
mechanism
to
some
extent,
it
remains
unclear
whether
SD-aMCI
and
MD-aMCI
have
different
inter
-hemispheric
connection
pattern.
Compared
to
the
other
neuroimaging
indices,
insufficient
attention
paid
to
the
direct
inter
-hemispheric
connectivity
of
aMCI
patients.
Anatomically,
the
connectivity
between
hemispheres
is
a
core
mode
of
the
brain
intrinsic
functional
architecture.
Moreover,
this
bi-hemispheric
communication
procedure
substantially
affects
many
cognitive
domains,
including
executive
and
memory
functions
(Salvador
et
al.,
2008;
Stark
et
al.,
2008;
Saar-Ashkenazy
et
al.,
2016).
Until
now,
only
two
functional
MRI
studies
directly
explored
the
inter-
hemispheric
functional
connectivity
in
aMCI
patients.
However,
two
studies
drew
the
entirely
different
conclusion.
Specifically,
Wang
et
al.
(2015)
demonstrated
that
MCI
patients
exhibited
an
enhanced
inter
-hemispheric
functional
connectivity
in
the
sensorimotor
cortex
to
resist
the
cognitive
decline;
however,
another
work
reported
the
negative
result
between
aMCI
and
controls
(Qiu
et
al.,
2016).
Given
that
aMCI
is
a
heterogeneous
condition,
we
thus
hypothesized
that
this
inconsistency
might
attribute
to
the
different
damage
pattern
of
inter
-hemispheric
connectivity
between
SD-aMCI
and
MD-aMCI
patients.
Notably,
some
previous
fi
ndings
focusing
on
corpus
callosum
(CC)
supported
this
hypothesis.
Anatomically,
CC
acts
as
the
most
robust
commissural
white
matter
bundle
to
maintain
the
functional
connectivity
between
the
hemispheres
(Roland
et
al.,
2017).
Specifically,
some
studies
demonstrated
that
the
AD
and
MCI
patients
exhibit
the
CC
shape
change,
atrophy,
and
impaired
diffusivity
indices
impairment,
especially
in
the
posterior
part
(Janowsky
et
al.,
1996;
Di
Paola
et
al.,
2010;
Ardekani
et
al.,
2014;
Rasero
et
al.,
2017).
Subsequently,
some
studies
further
demonstrated
patients
with
MD-aMCI,
but not
SD-aMCI,
have
reduced
mean
diffusivity
(MD)
in
the
whole
CC
compared
to
controls;
moreover,
decrease
of
MD
in
the
CC
body
is
associated
with
decreased
general
cognition
and
executive
ability
(Li
et
al.,
2013).
On
the
other
hand,
as
a
valid
diagnostic
biomarker
in
dementia
studies,
fl
uorine
-18
fl
uorodeoxyglucose
(
18
F
FDG)
positron
emission
tomography
(PET)
research
also
shows
more
diffuse
hypometabolism
in
MD-aMCI
than
the
SD-
aMCI. Interestingly,
these
hypometabolism
regions
are
mostly
located
in
the
bilateral
homotopic
precuneus
(PCu)
and
the
temporoparietal
cortex
(Caffarra
et
al.,
2008;
Cerami
et
al.,
2015).
Considering
the
evidence
of
CC
degeneration
and
bilateral
homotopic
hypometabolism,
we
inferred
that
different
inter-
hemispheric
connectivity
damage
pattern
might
exist
between
aMCI
subgroups.
To
test
this
hypothesis,
we
analyzed
the
structural
T1
image,
resting
-state
functional
MRI
(rsfMRI),
18
F
PET-FDG,
neuropsychological
scales,
and
pathological
biomarkers
in
a
relatively
large
aMCI
sample
from
ADNI
database.
Firstly,
we
analyzed
inter
-hemispheric
homotopic
functional
connectivity
by
using
a
voxel-wise
method,
namely
voxel-mirrored
homotopic
connectivity
(VMHC)
(Zuo
et
al.,
2010;
Luo
et
al.,
2018b).
The
potential
inter
-hemispheric
structural
connectivity
was
evaluated
by
estimating
CC
volume.
We
divided
CC
into
fi
ve
part
due
to
its
different
anatomical
connection
[i.e.,
genus
connects
the
frontal
areas,
while
body
and
splenium
part
connects
the
temporal
and
parietal
areas
(Fischl,
2012)]. Moreover,
to
test
whether
regions
with
inter
-hemispheric
disruption
accompanied
by
metabolic
abnormalities,
we
also
analyzed
18
F
FDG-PET
data
in
a
voxel-
wise
manner.
Additionally,
we
also
correlated
neuroimaging
indices
with
cognition
and
pathological
biomarkers
(reflecting
by
CSF
and
amyloid
imaging).
Our
study
aims
to
(i)
compare
the
alteration
of
the
inter
-hemispheric
connectivity
and
the
metabolism
between
aMCI
subtypes;
(ii)
explore
the
possible
interactions
between
different
neuroimaging
modalities;
and
(iii)
explore
the
relationships
between
neuroimaging
indices
and
cognition.
MATERIALS
AND
METHODS
Alzheimer's
Disease
Neuroimaging
Initiative
Data
used
in
this
study
were
obtained
from
the
Alzheimer's
disease
Neuroimaging
Initiative
(ADNI)
database'.
The
ADNI
was
launched in
2003
by
the
National
Institute
on
Aging
(NIA),
1
adni.loni.
usc.edu
Frontiers
in
Aging
Neuroscience
I
www.frontiersin.org
2
June
2018
I
Volume
10
I
Article
161
Luo
et
al.
Divergent
Bi-hemispheric
Connectivity
in
aMCI
Subtype
the
National
Institute
of
Biomedical
Imaging
and
Bioengineering
(NIBIB),
the
Food
and
Drug
Administration
(FDA),
private
pharmaceutical
companies,
and non-profit
organizations,
as
a
$60
million,
5
-year
public
—private
partnership.
The
primary
goal
of
ADNI
has
been
to
test
whether
serial
magnetic
resonance
imaging
(MRI),
PET,
other
biological
markers,
and
clinical
and
neuropsychological
assessment
can
be
combined
to
measure
the
progression
of
mild
cognitive
impairment
(MCI)
and
early
Alzheimer's
disease
(AD).
Determination
of
sensitive
and
specific
markers
of
very
early
AD
progression
is
intended
to
aid
researchers
and
clinicians
in
developing
new
treatments
and
monitor
their
effectiveness,
as
well
as
lessen
the
time
and
cost
of
clinical
trials.
Subjects
All
procedures
performed
in
studies
involving
human
participants
were
following
the
ethical
standards
of
the
institutional
and
national
research
committee
and
with
the
1975
Helsinki
Declaration
and
its
later
amendments
or
comparable
ethical
standards.
The
ADNI
project
was
approved
by
the
Institutional
Review
Boards
of
all
participating
institutions,
and
all
participants
at
each
site
signed
informed
consent.
Based
on
ADNI
GO
and
ADNI
2
database,
we
identified
49
healthy
right-
handed
subjects
(normal
controls,
NC)
and
64
aMCI
patients,
who
had
undergone
structural
scans,
rsfMRI
scans,
FDG-PET
scans,
and
neuropsychological
evaluation.
We
downloaded
the
study
data
from
the
ADNI
publicly
available
database
before
April
15,
2017.
According
to
ADNI
protocol,
to
be
classified
as
healthy
controls,
the
Mini
-Mental
State
Examination
(MMSE)
for
the
subject
should
be
between
24
and
30
(inclusive),
and
clinical
dementia
rating
(CDR)
score
should
be
0.
Besides,
the
subject
has
no
signs
of
depression
(Geriatric
Depression
Scale,
GDS
<6)
or
possible
dementia.
To
be
defined
as
aMCI,
the
subject
had
an
MMSE
score
between
24
and
30
(inclusive),
memory
complaint,
objective
abnormal
memory
evidence,
and
a
CDR
score
of
0.5.
Besides,
aMCI
patients'
general
cognition
preserved
well
(cannot
meet
AD
diagnostic
criteria).
Also,
there
were
no
signs
of
depression
(GDS
score
<6)
in
aMCI
patients.
Followed
by
exclusion
standards:
the
subject
who
has
a
history
of
apparent
head
trauma,
other
neurological
or
major
psychiatric
disorder,
and
alcohol/drug
abuse.
Additionally,
subjects
were
also
excluded
if
they
exhibited
a
significant
vascular
disease
risk
history,
defined
as
Hachinski
Ischemia
Scale
(HIS)
scores
higher
than
4.
Neuropsychological
Evaluation
and
MCI
Subtypes
Diagnosis
All
subjects
underwent
comprehensive
neuropsychological
tests.
Content
includes
general
mental
status
(MMSE
score,
Alzheimer's
Disease
Assessment
Scale,
ADAS)
and
other
cognitive
domain,
including
memory
function
(Auditory
Verbal
Learning
Test,
AVLT;
Wechsler
Memory
Scale
-Logical
Memory,
WMS-LM,
including
immediate
and
delayed
score),
processing
speed
(Trail
-Making
Test,
Part
A,
TMT-A),
visuospatial
function
(Clock
-Drawing
Test,
CDT),
executive
function
(Trail
-Making
Test,
Part
B,
TMT-B),
and
language
ability
(Boston
Naming
Test,
BNT).
According
to
the
previous
studies,
we
used
the
composite
scores
for
executive
functioning
and
memory
(Crane
et
al.,
2012;
Gibbons
et
al.,
2012).
The
aMCI
patients
were
divided
into
two
subtypes
by
the
performance
in
cognitive
domains
(Shu
et
al.,
2012).
Specifically,
SD-aMCI
refers
to
aMCI
patients
having
an
impairment
in
memory
alone;
MD-aMCI
refers
to
aMCI
patients
having
an
impairment
in
memory
and
other
cognitive
domain
(at
least
one).
We
defined
impairment
as
the
presence
of
a
test
scoring
1.5
standard
deviations
(SD)
below
the
average
score,
from
age-
and
education
-matched
healthy
controls
from
ADNI.
Specifically,
the
total
number
of
healthy
controls
from
ADNI
is
198,
mean
age
is
73.03
±
6.20,
and
mean
education
attainment
is
16.47
±
2.45.
No
significant
difference
existed
in
terms
of
age
and
education
level
(p
>
0.05)
between
ADNI
controls
and
aMCI
subjects
(mean
age:
73.33
±
5.69;
education
level:
15.84
±
2.45).
Finally,
we
enrolled
49
out
of
198
ADNI
controls
who
undergone
structural
scans,
rsfMRI
scans,
and
neuropsychological
evaluation
for
subsequent
analyses.
Meanwhile,
32
MD-aMCI
and
32
SD-aMCI
patients
entered
the
subsequent
analyses.
CSF
Data
We
downloaded
the
CSF
dataset
from
the
ADNI
database.
Based
on
CSF
samples,
AB
1
_
42
,
total
tau
(t
-tau)
and
phosphorylated
tau
(p-tau
181
)
were
measured
(Bittner
et
al.,
2016).
These
CSF
biomarkers,
including
AB
1
_
42
,
t
-tau,
and
p-tau181,
are
also
useful
candidates
as
they
are
intimately
related
to
amyloid
plaques,
neuronal
death,
and
accumulation
of
tangles, respectively
(Jovicich
et
al.,
2016).
Moreover,
the
ratio
of
p-tau/AB
1
_
42
was
also
calculated
due
to
its
association
with
cognition
and
disease
progression
(Landau
et
al.,
2010).
Notably,
not
all
subjects
in
the
present
study
had
the
CSF
samples
due
to
the
invasive
procedure
of
lumbar
puncture.
Thus,
the
fi
nal
CSF
sample
for
analyses
included
26
out
of
the
49
NC,
30
out
of
the
32
SD-aMCI,
and
28
out
of
the
32
MD-aMCI
patients.
Amyloid
PET
Data
Given
that
amyloid
PET
shows
more
potent
than
CSF
markers
for
MCI
prognosis,
we
further
downloaded
the
results
of
38
F-florbetapir
PET
data
from
the
ADNI
database,
namely
UCBERKELEYAV45_11_14_17.csv
(Bouallegue
et
al.,
2017).
The
detailed
processing
procedure
was
described
previously
(Landau
et
al.,
2013).
In
the
following
analysis,
we
used
the
results
of
composite
SUVR
value
(intensity
-normalized
by
a
whole
cerebellum
region).
Image
Acquisition
The
3.0-Tesla
Philips
MRI
scanner
was
used
to
scan
all
participants.
Based
on
MPRAGE
T1
-weighted
sequence,
structural
images
were
acquired
with
the
following
parameters:
repetition
time
(TR)
=
2300
ms;
echo
time
(TE)
=
2.98
ms;
inversion
time
(TI)
=
900
ms;
170
sagittal
slices;
within
plane
FOV
=
256
x
240
mm
2
;
voxel
size
=
1.1
x
1.1
x
1.2
mm
3
;
fl
ip
angle
=
9';
and
bandwidth
=
240
Hz/pix.
Based
on
echo
-
planar
imaging
sequence,
rsfMRI
images
were
acquired
with
the
following
parameters:
time
points
=
140;
TR
=
3000
ms;
TE
=
30
ms;
fl
ip
angle
=
80';
the
number
of
slices
=
48;
slice
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Luo
et
al.
Divergent
Bi-hemispheric
Connectivity
in
aMCI
Subtype
thickness
=
3.3
mm;
spatial
resolution
=
3.31
x
3.31
x
3.31
mm
3
;
and
matrix
=
64
x
64.
Meanwhile,
PET
images
were
obtained
by
either
a
30
-min
six
frame
scan
acquired
30
-60
-min
post
-injection
or
a
static
30
-min
single
-frame
scan
acquired
30
-60
-min
post
-injection.
Image
Preprocessing
The
image
data
were
preprocessed
by
using
the
Data
Processing
Assistant
for
rsfMRI,
DPASF
2
,
based
on
the
Statistical
Parametric
Mapping
(SPM
12)
3
and
rsfMRI
Data
Analysis
Toolkit,
REST
4
.
Briefly,
we
removed
the
fi
rst
10
image
volumes
of
rsfMRI
scans
were
for
the
signal
equilibrium
and
the
subjects'
adaptation
to
the
machine
noise.
The
remaining
130
image
volumes
were
corrected
for
both
timing
differences
and
head
motion
(24
Friston;
Friston
et
al.,
1996).
We
excluded
the
data
with
more
than
2.0
mm
maximum
displacement
in
any
directions
(X-,
Y-,
and
Z-axis)
and
2.0
degrees
of
any
angular
motion.
Subsequently,
the
T1
-weighted
images
were
co
-registered
to
the
mean
rsfMRI
image
based
on
a
thorough
rigid
-body
transformation
and
normalized
to
the
Montreal
Neurological
Institute
(MNI)
space,
subsequent
resliced
into
of
3
mm
x
3
mm
x
3
mm
cubic
voxels.
Besides,
we
performed
linear
trends
and
temporal
fi
lter
(0.01
Hz
<
f
<
0.08
Hz).
To
remove
any
motion
residual
effects
and
other
non
-neuronal
factors,
we
corrected
covariates
(including
six
head
motion
parameters,
WM
signal,
CSF)
in
the
following
functional
connectivity
analysis.
After
preprocessing,
we
obtained
each
subject's
4D
residual
functional
volume
in
native
functional
space.
These
4D
data
were
registered
to
the
MNI152
space
with
2
mm
resolution
(affine
transformation).
Due
to
the
dispute
of
removing
the
global
signal,
we
omit
to
regress
out
the
global
signal.
Given
the
effect
of
the
micro
-motion
artifact,
we
evaluated
the
frame
-
wise
displacement
(FD)
value
for
each
subject.
Subjects
were
screened
and
excluded
if
FD
value
>0.5
mm
on
more
than
35
volumes.
Voxel-Mirrored
Homotopic
Connectivity
Analysis
For
VMHC
computation,
fi
rstly,
the
mean
T1
image
was
generated
from
the
average
of
113
spatially
normalized
T1
images.
Then,
we
fl
ipped
the
left
hemisphere
along
the
midline
of
the
x-axis
to
obtain
the
symmetric
brain
template,
further,
to
create
the
fi
nal
template.
Subsequently,
the
each
subject's
T1
image
was
co
-registered
nonlinearly
to
the
customized
symmetric
template.
The
same
deformation
fi
eld
subsequently
applied
to
the
rsfMRI.
More
details
regarding
VMHC
data
processing
are
available
in
the
literature
(Anderson
et
al.,
2010;
Zuo
et
al.,
2010).
Based
on
Pearson's
correlation,
the
homotopic
RSFC
between
any
pair
of
symmetrical
inter
-hemispheric
voxels
was
estimated,
then
transformed
to
Fisher's
Z
map.
Finally,
we
defined
these
resultant
values
as
the
VMHC
and
used
it
in
following
analyses.
2
http://www.rfmri.org/DPARSF
3
http://www.fil.ion.ucl.ac.uk/spm/
4
http://www.resting-
fmri.sourceforge.net/
Corpus
Callosum
Volume
We
evaluated
the
CC
volume
and
its
subregions
based
on
FreeSurfer
software
package
(Version
5.1
5
)
as
described
previously
(Fischl,
2012;
Reuter
et
al.,
2012).
In
detail,
FreeSurfer
automatically
segmented
CC
into
fi
ve
sections,
with
each
section
representing
a
fi
ft
h
of
the
total
area.
We
defined
fi
ve
segments
as
anterior
(CC
-A),
mid
-anterior
(CC
-MA),
central
(CC
-C),
mid
-
posterior
(CC
-MP),
and
posterior
CC
part
(CC
-P).
FDG-PET
Voxel-Wise
Analysis
We
downloaded
the
38
F
FDG-PET
data
from
the
ADNI
database
in
their
most
processed
formats
(PET
Pre-processing
protocol
6
).
Specifically,
pre-processed
scans
were
generated
following
co
-registration
dynamic
(to
the
fi
rst
acquired
frame),
averaging
frames,
spatial
re
-orientation
(AC
—PC
line),
intensity
normalization
(within
subject
-specific
mask),
and
smoothing
(uniform
isotropic
spatial
resolution
8
mm
full
width
at
half
maximum
kernel).
The
subsequent
processing
procedures
of
FDG-PET
were
conducted
by
combining
T1
MRI
images,
which
were
similar
to
VMHC
processing
mentioned
above.
Firstly,
each
FDG-PET
scan
was
co
-registered
to
the
individual's
corresponding
T1
MRI
image
in
native
space.
Then,
these
structural
scans
were
segmented,
and
the
resulting
GM
partitions
were
spatially
normalized
using
the
SPM
12.
The
spatial
normalization
parameters
(deformation
fi
elds)
that
had
been
estimated
from
the
gray
matter
segment
normalization
were
subsequently
applied
to
the
co
-registered
FDG-PET
images
so
that
both
the
GM,
VMHC,
and
the
FDG-PET
images
were
in
MNI
space.
To
remove
inter
-
individual
nuisance
variability
in
tracer
metabolism,
we
intensity
-
normalized
FDG-PET
image
via
dividing
it
by
the
average
FDG-
PET
value
of
the
reference
region
(cerebellar
vermis,
defined
by
the
AAL
regions
within
the
MNI
atlas,
manually
checked
by
experienced
radiologists,
MMZ).
Finally,
we
created
the
standardized
uptake
value
ratio
(SUVR)
images
for
the
following
analysis.
Statistics
All
statistical
analyses
were
performed
using
IBM
SPSS
statistical
software.
The
TMT-A,
TMT-B,
and
ADAS-cog
were
log
-
transformed
because
of
a
positively
skewed
distribution.
We
used
ANOVA
to
detect
group
differences
in
terms
of
age,
education
level,
cognitive
ability,
and
AD
-related
pathological
results.
We
used
the
post
hoc
pairwise
t
-tests
if
ANOVA
was
significant
(p
<
0.05,
corrected
by
Bonferroni).
Regarding
VMHC
results,
the
ANCOVA
was
used
to
explore
different
brain
regions
among
the
three
groups,
with
controlling
for
age,
education
level,
and
gender.
For
the
objective
to
explore,
the
threshold
set
at
height
p
<
0.05
and
cluster
level
p
<
0.05
(Gaussian
random
fi
eld,
GRF
corrected).
We
performed
the
post
hoc
pairwise
t
-tests
within
the
brain
regions
identified
by
the
ANCOVA
(p
<
0.005
Bonferroni
corrected).
Regarding
the
FDG-PET
SUVR
image,
we
performed
ANCOVA
to
explore
metabolic
differences,
with
controlling
for
age,
education
level,
5
http://surfer.nmr.mgh.haryard.edu/
6
http://adni.loni.usc.edu/methods/pet-
analysis/pre-
processing/
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Luo
et
al.
Divergent
Bi-hemispheric
Connectivity
in
aMCI
Subtype
and
gender
[p
<
0.001
at
height
and
p
<
0.05
at
the
cluster
level
(GRF
corrected)].
Similarly,
we
performed
the
post
hoc
pairwise
(-tests
if
ANCOVA
was
significant
(p
<
0.005,
Bonferroni
corrected).
Also,
we
calculated
the
between
-group
CC
subregions
volume
difference
by
using
ANCOVA
corrected
by
age,
education
level,
gender,
and
total
intracranial
volume
(TIV).
We
performed
post
hoc
pairwise
t
-test
if
ANOVA
was
significant
(p
<
0.05,
corrected
by
the
least
significant
difference,
LSD).
Further,
we
correlated
imaging
measures
to
cognitive
abilities
and
also
explored
the
possible
interactions
between
neuroimaging
modalities.
To
achieve
the
best
visual
effect,
we
overlapped
between
-group
difference
results
of
VMHC
and
FDG-PET
SUVR
image
by
the
same
statistical
standard
(ANCOVA,
corrected
by
age,
education
level,
and
gender,
p
<
0.05
at
height,
p
<
0.05
at
the
cluster
level,
GRF
corrected).
Meanwhile,
to
explore
the
possible
interaction
between
CC
subregions
and
other
modalities,
we
performed
voxel-wise
regression
analysis
(p
<
0.05
at
height,
p
<
0.05
at
the
cluster
level,
GRF
corrected).
Notably,
we
calculated
correlations
limited
to
those
indices
having
significant
between
-
group
differences.
RESULTS
Patient
Characteristics
We
displayed
quantitative
variables
as
the
mean
and
its
SD,
and
the
categorical
variable
as
absolute
and
relative
frequency.
Table
1
shows
the
demographic
characteristics,
cognitive
abilities,
and
pathological
biomarkers
for
each
group.
There
were
no
significant
differences
in
terms
of
age,
education
level,
and
gender
composition
among
the
three
groups.
Regarding
the
general
cognitive
ability,
we
found
that
three
groups
exhibited
the
significant
differences
in
the
score
of
MMSE
and
ADAS-cog,
with
MD-aMCI
displaying
the
worst
performance.
Regarding
other
cognitive
domains,
group
effects
were
significant,
with
the
best
performance
in
NC
and
also
the
worst
performance
in
MD-aMCI
patients.
Regarding
AD
-related
pathological
biomarkers,
MD-aMCI
patients
exhibited
a
significantly
higher
composite
PET
PiB
SUVR
value
than
NC
(p
<
0.01).
Meanwhile,
although
MD-
aMCI
group
presented
a
trend
of
the
increased
level
of
t
-tau
and
p-tau
mi
as
well
as
decreased
AN
_42,
no
significant
differences
existed
among
groups
(p
>
0.05).
Also,
we
also
failed
to
detect
the
difference
in
the
p-tau
mi
/AN
_
42
ratio
among
groups.
Glucose
Metabolism
Differences
Three
groups
demonstrated
the
significant
metabolic
differences
in
the
following
regions:
namely
bilateral
middle
temporal
gyrus
(MTG),
bilateral
inferior
parietal
lobe
(IPL),
and
left
PCu.
Subsequent
pairwise
group
comparisons
carried
out
on
these
clusters
revealed
that
both
aMCI
patients
exhibited
a
lower
metabolism
in
bilateral MTG.
Also,
the
MD-aMCI
demonstrated
a
reduced
metabolism
in
left
PCu
and
bilateral
IPL
compared
to
the
SD-aMCI
and
NC
(Figure
1
and
Table
2).
Inter
-Hemispheric
Connection
Differences
Among
the
three
groups,
we
established
the
significant
VMHC
difference
in
these
regions:
namely
middle
MTG,
PCu,
postcentral
gyrus
(PCG),
and
calcarine
gyrus
(CG).
As
expected,
subsequent
pair
-wise
group
comparisons
suggested
that
MD-
aMCI
exhibited
a
decreased
VMHC
in
MTG,
CG,
PCu,
and
PCG
relative
to
SD-aMCI
and
NC
(p
<
0.005,
corrected
by
Bonferroni);
however,
there
were
no
significant
differences
of
VMHC
between
SD-aMCI
and
NC
(Figure
2
and
Table
3).
We
observed
the
between
-group
effect
on
CC
-MP
subregion
(Figure
1).
Then,
the
post
hoc
t
-test
revealed
that
MD-aMCI
exhibited
the
smallest
CC
-MP
volume
among
groups,
and
no
significant
volume
difference
existed
between
SD-aMCI
and
NC
(p
<
0.05,
corrected
by
LSD).
Neuroimaging
Indices
Correlate
Cognitive
Abilities
and
Pathological
Biomarkers
All
the
correlation
analyses
we
performed
were
within
aMCI
patients
(Figure
3).
We
observed
that
reduced
left
IPL
metabolism
(r
=
0.27)
was
related
to
worse
executive
ability
(p
<
0.05).
In
addition,
regarding
functional
connectivity,
we
identified
that
reduced
inter
-hemispheric
connection
in
PCu
(r
=
0.39),
PCG
(r
=
0.40),
and
CG
(r
=
0.54)
were
related
to
worse
composite
score
of
executive
functioning
(p
<
0.001).
Also,
the
CC
-MP
volume
was
related
to
the
composite
score
of
executive
functioning
(r
=
0.40,
p
<
0.001).
Regarding
AD
-related
pathological
biomarkers,
we
analyzed
the
correlation
only
within
the
indices
having
the
between
-group
difference.
Our
results
revealed
that
hypometabolism
of
left
PCu
(r
=
—0.25,
p
<
0.05)
and
right
IPL
(r
=
—0.25,
p
<
0.05)
was
related
to
the
increased
amyloid
accumulation
reflecting
composite
18
F-florbetapir
PET
value.
Interaction
Between
Modalities
As
expected,
we
noted
that
VMHC
and
PET-FDG
SUVR
between
-group
difference
results
overlapped
well.
Specifically,
these
overlapping
included
bilateral
the
MTG,
the
PCu,
the
IPL,
and
the
PCG
(Supplementary
Material
1).
Within
these
overlapping
regions,
we
further
observed
that
VMHC
value
related
to
SUVR
uptake
value
within
aMCI
patient
groups
(r
=
0.27,
p
<0.05).
Within
the
aMCI
patients,
the
mid
-posterior
CC
volume
was
related
to
the
bilateral
MTG,
PCu,
CG,
and
insula
(p
<
0.05
at
height
and
p
<
0.05
at
the
cluster
level,
GRF-corrected
Supplementary
Material
2).
However,
we
found
no
significance
between
mid
-posterior
CC
volume
and
FDG
SUVR
uptake
value
(p
>
0.05).
DISCUSSION
This
study
provides
evidence
that
the
MD-aMCI
displayed
simultaneous
decreased
bilateral
metabolism
and
reduced
inter
-hemispheric
functional
connectivity
in
MTG,
PCu,
and
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Luo
et
al.
Divergent
Bi-hemispheric
Connectivity
in
aMCI
Subtype
TABLE
1
I
Comparison
of
demographic
information,
behavioral
data,
and
pathological
biomarkers.
NC
SD-aMCI
MD-aMCI
F
(x
2
)
P
-value
Number
Age
Female
49
73.33
+
4.60
31
32
72.43
+
4.25
15
32
74.91
+
5.27
15
2.29
3.00
0.11
0.22
Education
16.24
+
2.60
16.47
+
2.24
15.25
+
2.65
2.20
0.12
General
mental
status
MMSE
29.02
+
1.20
28.34
+
1.68
27.16
+
1.71
15.03
<0.001
bc
Log
-transformed
ADAS
0.91
+
0.17
1
.07
+
0.19
1.21
+
0.20
24.21
<0.001
abc
Spatial
processing
CDT
4.76
+
0.48
4.75
+
0.44
3.78
+
1.18
19.69
<0.001
bc
Language
BNT
28.57
+
1.44
28.09
+
2.07
27.09
+
11.36
0.55 0.58
Memory
WMS-LM
immediate
14.73
+
2.76
9.88
+
3.17
7.88
+
2.83
60.49
<0.001
abc
WMS-LM
delayed
13.96
+
2.99
7.97
+
2.78
5.75
+
3.06
84.93
<0.001
abc
AVLT-Total
43.22
+
9.01
37.34
+
9.63
31
.25
+
8.78
16.85
<0.001
abc
Processing
speed
Log
-transformed
TMTA
1
.51
+0.12
1
.47
+
0.08
1.66
+
0.15
23.82
<0.001
bc
Executive
function
Log
-transformed
TMTB
1.86
+
0.16
1.86
+
0.13
2.18
+
0.16
51.75
<0.001
bc
Composite
memory
1.05
+
0.51
0.44
+
0.59
0.06
+
0.61
31.99
<0.001
abc
Composite
executive
0.80
+
0.65
0.87
+
0.51
-0.35
+
0.51
47.82
<0.001
bc
Pathological
results
Alit
-
42
(pg/ml)
1222.19
+
552.35
1132.06
+
616.57
963.00
+
438.94
1.85
0.16
t
-tau
(pg/ml)
248.52
+
84.62
275.76
+
90.51
300.33
+
134.34
2.18
0.12
P-tamai
(pg/ml)
23.43
+
9.46
25.69
+
9.30
29.34
+
15.01
2.30
0.11
p-tau/Alii
-42
0.03
+
0.02
0.03
+
0.02
0.04
+
0.02
2.83
0.06
Composite
PET-PiB
1
.14
+
0.19
1
.20
+
0.20
1.29
+
0.25
4.54
0.01
b
Data
are
presented
as
means
+
SD.
Notably,
the
mean
levels
of
Ali,
_42,
t
-tau,
and
p-taui8i
in
Table
1
only
represent
the
subjects
who
had
CSF
samples.
a
Post
hoc
paired
comparisons
showed
significant
group
differences
between
NC
and
SD-aMCI,
after
Bonferroni
correction
(p
<
0.05).
bPost
hoc
paired
comparisons
showed
significant
group
differences
between
NC
and
MD-aMCI,
after
Bonferroni
correction
(p
<
0.05).
a
Post
hoc
paired
comparisons
showed
significant
group
differences
between
SD-aMCI
and
MD-aMCI,
after
Bonferroni
correction
(p
<
0.05).
MMSE,
Mini
-Mental
State
Examination;
ADAS,
the
Alzheimer's Disease
Assessment
Scale
-
Cognitive
Subscale;
WMS-LM,
Wechsler
Memory
Scale
-Logical
Memory;
TMT,
Trail
-Making
Test;
CSF,
cerebrospinal
fluid;
CDT
Clock
-Drawing
Test;
AVLT,
Auditory
Verbal
Learning
Test;
BNT,
Boston
Naming
Test.
parietal
regions,
accompanying
with
mid
-posterior
CC
atrophy.
However,
except
hypometabolism
in
bilateral
MTG,
no
other
differences
existed
between
SD-aMCI
and
NC.
Subsequently,
the
correlation
analyses
showed
that
these
neuroimaging
indices
related
to
executive
function
across
aMCI
patients.
Moreover,
hypometabolism
indices
related
to
increased
amyloid
deposition.
In
summary,
compared
to
SD-aMCI,
the
MD-aMCI
exhibited
the
more
severe
deficit
in
inter
-hemispheric
communication.
This
long-range
connectivity
deficit
may
further
contribute
to
cognitive
abilities
deficits
and
potentially
serve
as
a
biomarker
to
monitor
the
disease
progression
in
aMCI
patients.
Firstly,
our
fi
ndings
suggested
that
both
aMCI
patients
displayed
hypometabolism
in
bilateral
MTG,
which
is
a
region
involving
memory
-related
network
(Ward
et
al.,
2014;
Munoz
-
Lopez
et
al.,
2015).
These
deficits
thus
may
contribute
to
the
memory
deficit
in
these
patients.
However,
only
the
MD-
aMCI
simultaneously
displayed
inter
-hemispheric
functional
connectivity
decrease
in
MTG,
supporting
the
notion
that
the
MD-aMCI
is
a
more
advanced
disease
form
than
the
SD-aMCI.
Notably,
one
study
pointed
out
that
SD-aMCI
displayed
decreased
regional
activity
in
right
MTG
relative
to
NC
(Luo
et
al.,
2018a).
Accordingly,
we
speculated
that
the
intact
inter
-hemispheric
connectivity
in
SD-aMCI
might
compensate
its
unilateral
MTL
impairment
to
some
extent.
Our
neuropsychological
results
could
support
this
interpretation,
showing
that
MD-aMCI
exhibited
worse
memory
performance
than
SD-aMCI.
Besides,
other
support
evidence
comes
result
from
"
F-florbetapir
PET
indicating
that
only
the
MD-aMCI
has
increased
amyloid
deposition
than
NC.
Meanwhile,
our
fi
ndings
validated
previous
studies,
which
reported
that
only
MD-aMCI
patients
have
cortical
thinning,
gray
matter
atrophy,
and
white
matter
tract
deficit
in
MTG
regions
(Seo
et
al.,
2007;
Whitwell
et
al.,
2007).
Many
evidence
suggests
that
amyloid
accumulation
preferentially
starts
in
PCu,
especially
in
the
left
side
(Janke
et
al.,
2001;
Braak
et
al.,
2006;
Palmqvist
et
al.,
2017).
As
expected,
our
results
showed
the
MD-aMCI,
but
not
the
SD-aMCI,
exhibited
hypometabolism
(left
side)
and
reduced
inter
-hemispheric
functional
connection
in
PCu
regions
simultaneously.
Moreover,
we
found
that
the
reduced
PCu
inter
-hemispheric
functional
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2018
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Article
161
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et
al.
Divergent
Bi-hemispheric
Connectivity
in
aMCI
Subtype
A
-18mm -15mm -12mm
j
L
MT
-28mm -34mm
40mm
L
PCu
MTG
-9mm -6mm
-46mm -52mm
L
IPL
IPL
B
1200
-
900-
E
E
ru
600-
>
300-
0
1H
1.8
1.6
0
***
Left
MTG
Right
MTO
Left
P01
Left
IPL
Right
IPL
+12
0
NC
SD-aMCI
MD-aMCI
FIGURE
1
I
Upper
(A)
shows
the
voxel-wise comparison
result:
brain
areas
with
the
significant
difference
of
metabolism
(normalized
by
cerebellum
vermis)
among
NC,
single
-domain
aMCI
(SD-aMCI),
and
multi
-domain
aMCI
(MD-aMCI),
by
controlling
for
age,
education
level,
and
gender.
The
threshold
set
p
<
0.001
at
height
and
p
<
0.05
at
the
cluster
level,
GFR
corrected.
Lower
figures
show
the
histogram
for
MD-aMCI
(red),
SD-aMCI
(green),
and
NCs
(blue).
Error
bars
represent
SD.
*
represents
p
<
0.05
while
***
represents
p
<
0.001
,
corrected
for
multiple
comparisons.
(B)
The
post
hoc
analyses
were
performed
within
the
areas
identified
by
the
ANCOVA
(p
<
0.05).
The
MD-aMCI
patients
displayed
the
significantly
decreased
volume
of
the
CC
-MP
compared
to
the
other
two
groups.
(C)
We
performed
the
post
hoc
analyses
within
the
areas
identified
by
the
ANCOVA
(p
<
0.005).
Among
the
three
groups,
the
MD-aMCI
patients
exhibited
the
lowest
metabolism
in
bilateral
MTG,
bilateral
inferior
parietal
lobe
(IPL),
and
left
precuneus
(L-PCu).
Other
abbreviation:
A,
anterior;
MA,
mid
-anterior;
C,
central;
MP,
mid
-posterior;
and
P,
posterior.
connection
associated
with
the
worse
executive
function
across
aMCI
patients.
This
relationship
agrees
with
clinical
manifestation in
MD-aMCI
patients,
manifested
by
more
than
one
cognitive
domain
impairment.
Meanwhile,
previous
work
also
documented
that
MD-aMCI
displayed
impaired
microstructure
in
PCu
(Li
et
al.,
2013).
Specifically,
by
utilizing
rsfMRI,
one
study
demonstrated
that
the
MD-aMCI
exhibited
lower
regional
activity
in
PCu
than
the
SD-aMCI
and
NC.
Coincidentally,
their
work
also
found
that
reduced
PCu
activity
was
related
to
worse
executive
ability
(Li
et
al.,
2014).
As
a
central
role
in
the
primary
executive
networks,
studies
of
the
AD
and
MCI
frequently
reported
that
regional
PCu
deficit
associated
with
the
decreased
executive
ability
(Sridharan
et
al.,
2008;
Chen
et
al.,
2013;
Luo
et
al.,
2017a,b).
Furthermore,
we
found
that
left
PCu
hypometabolism
was
related
to
amyloid
deposition.
Consequently,
we
speculated
that
amyloid-related
neurotoxicity
in
unilateral
PCu
region
might
lead
to
the
inter
-hemispheric
connectivity
damage
in
corresponding
areas.
Regarding
the
parietal
cortex,
the
MD-aMCI
exhibited
hypometabolism
in
bilateral
IPL
and
reduced
inter
-hemispheric
PCG
functional
connectivity;
additionally,
we
found
that
IPL
hypometabolism
was
related
to
increased
amyloid
deposition.
Therefore,
we
again
interpreted
that
the
hypometabolism
in
bilateral
IPL
may
attribute
to
synaptic dysfunction
caused
by
amyloid
(Palop
and
Mucke,
2010;
Tonnies
and
Trushina,
2017).
Moreover,
we
observed
that
parietal
cortex
hypometabolism
and
inter
-hemispheric
dysfunction
associated
with
worse
executive
ability.
This
relationship
could
attribute
to
the
reason
that
both
the
IPL
and
PCG
have
extensive
connections
with
the
frontal
region
(i.e.,
frontoparietal
circuit).
Functionally,
this
circuit
could
send
rich
sensory
information
for
processing
speed
and
affect
executive
ability
(Sullivan
et
al.,
2001;
Buckner
et
al.,
2008;
Luo
et
al.,
2017b).
Furthermore,
some
structural
MRI
studies
support
our
results.
One
VBM
study
pointed
out
that
patients
with
MD-
aMCI
exhibited
GM
reduction
and
increased
mean
diffusion
in
PCG
(Li
et
al.,
2013).
Meanwhile,
using
diffusion
spectrum
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June
2018
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Article
161
Luo
et
al.
Divergent
Bi-hemispheric
Connectivity
in
aMCI
Subtype
TABLE
2
I
Brain
areas
with
the
significant
difference
of
VMHC
and
PET-FDG
among
NCs,
SD-aMCI
,
and
MD-aMCI
patients.
Regions
MNI
coordinates
Cluster
Peak
voxels
intensity
VMHC
Middle
temporal
gyrus
+51
—54
—3
128
8.30
Precuneus
+3
—57
60
133
7.60
Postcentral
gyrus
+60
—21
24
317
8.86
Calcarine
gyrus
+3
—75
9
195
9.60
PET-FDG
L
middle
temporal
gyrus
—60
—32
—10
59
11.08
—44
—50
12
14
8.48
R
middle
temporal
gyrus
62
—28
—14
52
9.37
L
precuneus
—6
—54
38 38
8.86
L
inferior
parietal
lobe
—38 —68
48
60
11.61
—38
—42
48
24
9.46
R
inferior
parietal
lobe
48
—58
44
13
8.60
Regarding
VMHC
results,
the
ANCOVA
was
used
to
explore
differential
brain
regions
among
groups
[controlling
for
age,
education
level,
and
gender,
p
<
0.05
at
height
and
p
<
0.05
at
the
cluster
level,
Gaussian
random
field
(GRF)
corrected].
Regarding
the
FDG-PET
standardized
uptake
value
ratio
(SUVR)
image,
the
ANCOVA
was
used
to
explore
differential
brain
regions
among
the
three
groups
(controlling
for
age,
education
level,
and
gender,
p
<
0.001
at
height
and
p
<
0.05
at
cluster
level,
GRF
corrected).
The
X,
Y,
and
Z
coordinates
of
the
primary
peak
locations
in
the
MNI
space.
MD-aMCI,
multiple
-domain
amnestic
mild
cognitive
impairment;
SD-aMCI,
single
-domain
amnestic
mild
cognitive
impairment;
MNI,
Montreal
Neurological
Institute.
imaging
(DSI),
Chang
et
al.
(2015)
highlighted
that
the
MD-
aMCI
groups
have
more
impairment
in
the
inferior
cingulum
bundle
than
the
SD-aMCI
groups,
which
anatomically
connected
with
the
parietal
cortex.
Interestingly,
MD-aMCI
patients
displayed
reduced
inter-
hemispheric
RSFC
in
CG,
which
is
part
of
the
extra
-striate
visual
network
and
involved
with
the
perception
created
by
visual
stimuli.
Similarly,
one
rsfMRI
study
also
illustrated
that
the
MD-
aMCI
displayed
decreased
brain
activity
here
(Li
et
al.,
2014).
On
the
other
hand,
most
previous
aMCI
studies
(without
dividing
into
SD-aMCI
and
MD-aMCI)
concluded
that
aMCI
patients
displayed
increased
intrinsic
activity
in
CG
(Han
et
al.,
2011;
Liu
et
al.,
2012;
Lou
et
al.,
2016).
This
inconsistency
may
attribute
to
the
aMCI
heterogeneity.
Besides,
we
found
that
decreased
inter
-hemispheric
functional
connectivity
between
bilateral
CG
was
related
to
worse
executive
ability.
Conceptually,
the
reaction
time
includes
not
only
visual
processing
but
also
the
time
required
for
response
execution
(Thorpe
et
al.,
1996).
Moreover,
optical
encoding
impairment
could
contribute
to
the
reduction
of
processing
speed
(Brebion
et
al.,
2015).
Consequently,
we
interpreted
this
correlation
as
that
the
MD-aMCI
patients
were
difficult
to
receive
the
information
fl
ows
from
visual
sensors,
further,
exerting
a
negative
influence
on
executive
ability.
As
the
dominant
white
matter
pathways,
the
CC
links
cortical
hubs
of
the
left
and
right
hemispheres
together.
Here,
we
observed
that
MD-aMCI
exhibited
a
smaller
mid
-
posterior
CC
volume
than
the
SD-aMCI
and
NC.
Moreover,
we
found
the
selective
mid
-posterior
CC
degeneration
in
MD-aMCI
corresponded
well
with
the
fMRI
results
discussed
above.
Specifically,
regression
analysis
results
showed
that
mid
-
posterior
CC
was
significantly
related
to
VMHC
results,
involving
the
bilateral
MTG,
PCu,
CG,
and
PCG.
In
accord
with
our
results,
anatomical
evidence
showed
that
the
bilateral
MTG
and
PCu
are
strongly
connected
through
the
posterior
CC
(Cavanna
and
Trimble,
2006;
van
der
Knaap
and
van
der
Ham,
2011;
Wang
et
al.,
2014).
Additionally,
we
noted
that
smaller
mid
-posterior
CC
volume
was
related
to
worse
executive
function.
This
relationship
suggests
that
CC
degeneration
might
contribute
to
or
even
accelerate
cognitive
deficit
(Agosta
et
al.,
2015;
Farrar
et
al.,
2017;
Luo
et
al.,
2017b).
Combined
with
the
results
of
bilateral
hypometabolism
and
inter-
hemispheric
disconnection,
we
hypothesized
that
mid
-posterior
CC
atrophy
may
reflect
the
Wallerian
degeneration
secondary
to
neuronal
loss
caused
by
amyloid
deposition
(Bozzali
et
al.,
2002).
Without
the
histological
data,
it
was
difficult
to
describe
the
exact
mechanism
underlying
inter
-hemispheric
connectivity
deficits
in
MD-aMCI
patients.
There
are
two
reasons
that
may
interpret
the
possible
mechanism.
Firstly,
these
deficits
may
result
from
the
amyloid-related
pathological
process.
Specifically,
we
found
that
the
between
-group
difference
results
of
PET-
FDG
and
VMHC
overlapped
well,
including
the
bilateral
MTG,
PCu,
IPL,
and
PCG
regions.
Further,
within
these
overlapping
regions,
the
level
of
hypometabolism
was
significantly
related
to
inter
-hemispheric
decrease.
Nevertheless,
only
the
metabolism
indices
were
related
to
amyloid
deposition.
We,
therefore,
speculated
that
accumulated
amyloid
plaques
in
MD-aMCI
patients
might
lead
to
synapse
loss
and
hypometabolism,
further,
result
in
functional
connectivity
disruption
with
its
contralateral
regions
(Tunnies
and
Trushina,
2017).
Second,
decreased
inter
-hemispheric
functional
connectivity
may
also
result
from
the
widespread
white
matter
integrity
disruption
(White
et
al.,
2009;
Luo
et
al.,
2016).
Conclusively,
these
interpretations
should
be
taken
with
caution
due
to
the
lack
of
animal
study
and
diffusion
tensor
imaging
(DTI)
method,
respectively.
Some
limitations
existed
in
our
study.
First,
this
cross-sectional
study
was
failed
to
assess
the
long-term
effects
of
hypometabolism
and
inter
-hemispheric
disconnection
in
the
subsequent
disease
progression.
Therefore,
longitudinal
studies
are
needed
to
explore
the
inter
-hemispheric
connection
pattern
following
AD
continuum,
from
healthy
aging
to
clinical
stages.
Second,
in
accord
with
the
inter
-hemispheric
functional
connectivity
results,
correspondingly,
MD-aMCI
patients
demonstrated
selective
mid
-posterior
CC
degeneration.
However,
CC
volume
only
identified
the
main
inter
-hemispheric
connective
tracts,
rather
than
illustrating
the
precise
definition
of
"inter
-hemispheric
structural
connection."
Therefore,
future
DTI
studies
by
using
tractography
or
network
analysis
are
needed
to
provide
more
detailed
information
about
inter
-hemispheric
white
matter
pathways
in
aMCI
patients
(Cavanna
and
Trimble,
2006;
Rasero
et
al.,
2017;
Tucholka
et
al.,
2018).
In
the
current
study,
based
on
the
ADNI
2
database,
we
noticed
that
subjects
do
not
have
both
the
DTI
and
the
rsfMRI
Frontiers
in
Aging
Neuroscience
I
www.frontiersin.org
8
June
2018
I
Volume
10
I
Article
161
Luo
et
al.
Divergent
Bi-hemispheric
Connectivity
in
aMCI
Subtype
mm
+4mm
+9mm
P
24mm
+29mm +34mm
+14mm +19mm
+39mm +44mm
1.5
1.0
0.5
0.0
-0.5
MTG
*
T
NC
SD
MD
1.5
1.0
0.5
0.0
-0.5
PCG
*
*
NC
SD
MD
2.0-
1.5-
1
0-
0.5-
0.0
PCu
*
IIIIIIIIIIIII
IIIIIIIIIIIII
NC
SD
MD
2.5-
2.0-
(..)
1.5-
2
>
1.0-
0.5-
0.0
CAL
NC
SD
MD
8.51
7.42
6.34
5.25
4.17
3.08
0.00
0.00
0.00
0.00
0.00
0.00
FIGURE
2
I
Upper:
brain
areas
with
the
significant
difference
of
voxel-mirrored
homotopic
connectivity
(VMHC)
among
normal
control
(NC),
single
-domain
aMCI
(SD-aMCI),
and
multi
-domain
aMCI
(MD-aMCI),
by
controlling
for
age,
education
level,
and
gender.
The
threshold
set
p
<
0.05
at
height
and
cluster
p
<
0.05
at
the
cluster
level,
GFR
corrected.
Lower:
we
performed
the
post
hoc
analyses
within
the
areas
identified
by
the
ANCOVA
(corrected
by
Bonferroni).
MD-aMCI
patients
displayed
significantly
decreased
inter
-hemispheric
functional
connectivity
in
bilateral
middle
temporal
gyrus,
precuneus
(PCu),
postcentral
gyrus
(PCG),
and
CG
compared
to
NC
and
SD-aMCI
patients
(p
<
0.005).
But
no
significant
difference
existed
between
SD-aMCI
patients
and
NC.
Box
graph
displays
mean
VMHC
value
for
middle
temporal
gyrus
(MTG)
(red),
PCu
(yellow),
PCG
(blue),
and
CG
(black).
*p
<
0.005,
Bonferroni
corrected.
data.
Finally,
future
studies
with
larger
sample
sizes
are
urgently
required.
CONCLUSION
By
using
multi
-modal
neuroimaging methods,
this
study
initially
explored
the
mechanism
underlying
inter
-hemispheric
connection
pattern
in
SD-aMCI
and
MD-aMCI
patients.
Firstly,
our
results
support
the
notion
that
aMCI
is
heterogeneous.
Specifically,
MD-aMCI
displayed
more
severe
inter
-hemispheric
communication
impairment
while
the
SD-aMCI
relatively
preserved
it.
Moreover,
our
results
demonstrate
that
different
inter
-hemispheric
connectivity
damage
pattern
contributes
to
distinct
clinical
symptoms
in
these
two
aMCI
subtypes.
Finally,
our
study
shows
that
inter
-hemispheric
connectivity
may
serve
as
a
potential
biomarker
to
monitor
disease
progression
in
aMCI
patients.
Frontiers
in
Aging
Neuroscience
I
www.frontiersin.org
9
June
2018IVolume
10
I
Article
161
Luo
et
al.
Divergent
Bi-hemispheric
Connectivity
in
aMCI
Subtype
TABLE
3
I
Comparisons
of
VMHC,
FDG-PET
SUVR,
and
corpus
callosum
subregions
volume
among
groups.
NC
SD-aMCI
MD-aMCI
F
-value
P
-value
Bi-hemispheric
functional
connectivity
Middle
temporal
gyrus
0.48
+
0.26 0.48
+
0.17
0.28
+
0.23
8.93
<0.001
bc
Precuneus
1.23
+
0.26
1.21
+
0.17
1.02
+
0.24
10.92
<0.001
bc
Postcentral
gyrus
0.54
+
0.22
0.60
+
0.18
0.37
+
0.20
12.86
<0.001
bc
Calcarine
gyrus
1.25
+
0.24
1.33
+
0.20
1.06
+
0.20
8.71
<0.001
bc
Cerebral
metabolism
L
Middle
temporal
gyrus
1.26
+
0.12
1.14
+
0.14
1.11
+
0.16
11
.72
<0.001
ab
R
Middle
temporal
gyrus
1.31
+0.14
1.22
+
0.14
1.20
+
0.15
6.94
<0.001
ab
L
Precuneus
1.55
+
0.15
1.50
+
0.20
1.39
+
0.13
9.50
<0.001
bc
L
Inferior
parietal
lobe
1.42
+
0.13
1.37
+
0.13
1.26
+
0.16
13.32
<0.001
bc
R
Inferior
parietal
lobe
1.48
+
0.12
1.40
+
0.16
1.34
+
0.17
8.05
<0.001
b
CC
subregions
volume
CC
-A
771.08
+
113.95
781.06
+
183.62
756.16
+
120.71
0.26
0.77
CC
-AP
374.65
+
62.76
380.88
+
87.48
343.75
+
65.36
2.59
0.08
CC
-C
367.82
+
63.85
356.59
+
77.61
337.59
+
59.22
1.98
0.14
CC
-MP
338.27
+
61
.17
342.06
+
75.19
303.84
+
56.78
3.61
0.03*
CC
-P
921.65
+
121
.69
921.34
+
157.03
904.50
+
149.93
0.17
0.85
a
Post
hoc
paired
comparisons
showed
significant
group
differences
between
NC
and
SD-aMCI,
after
Bonferroni
correction
(p
<
0.05).
b
Post
hoc
paired
comparisons
showed
significant
group
differences
between
NC
and
MD-aMCI,
after
Bonferroni
correction
(p
<
0.05).
a
Post
hoc
paired
comparisons
showed
significant
group
differences
between
SD-aMCI
and
MD-aMCI,
after
Bonferroni
correction
(p
<
0.05).
*Post
hoc
paired
comparisons
showed
significant
group
differences
between
NC
and
MD-aMCI
as
well
as
SD-aMCI
and
MD-aMCI,
corrected
by
the
LSD
(p
<
0.05).
Data
are
presented
as
means
+
SD.
CC,
corpus
callosum;
A,
anterior;
MA,
mid
-anterior;
C,
central;
MP,
mid
-posterior;
P,
posterior
SD-MICI
.
MD
-ma
D
tse
VMHC
FRP..
VMHC
of
POD
snea
it
1.4
Volume
MCC
-MP
FDO
DCVO
Value
IN
L4P1.
VMHC
NCO
as
aw
MO
SUMMING
of
1.-PCo
FOG
SIND
Value
NLJPL
FIGURE
3
I
It
shows
the
scatter
plot
association
between
neuroimaging
indices
and
behavioral/pathological
data
across
aMCI
patients.
The
blue
and
red
dot
represents
the
SD-aMCI
and
MD-aMCI
patients,
respectively.
(A
-C)
Regarding
functional
connectivity,
reduced
inter
-hemispheric
functional
connectivity
in
PCu
(r
=
0.39,
p
<
0.001),
PCG
(r
=
0.40,
p
<
0.001),
and
CG
(r
=
0.54,
p
<
0.001)
were
related
to
the
executive
composite
score.
(D)
The
CC
-MP
volume
was
related
to
the
executive
composite
score
(r
=
0.40,
p
<
0.001).
(E)
The
PET-FDG
SUVR
uptake
value
in
the
left
IPL
(r
=
0.27,
p
<
0.05)
was
related
to
executive
function.
(F,
G)
The
PET-FDG
SUVR
uptake
value
in
left
PCu
(r
=
-0.25,
p
<
0.05)
and
left
IPL
(r
=
-0.25,
p
<
0.05)
were
related
to
composite
amyloid
value
reflecting
by
18
F-florbetapir
PET.
Abbreviation:
aMCI,
amnestic
mild
cognitive
impairment;
PCu,
precuneus;
ITG,
inferior
temporal
gyrus;
AG,
angular
gyrus;
CG,
calcarine
gyrus;
PCG,
postcentral
gyrus;
IPL,
inferior
parietal
lobe;
CC
-MP
mid
-posterior
corpus
callosum.
Frontiers
in
Aging
Neuroscience
I
www.frontiersin.org
10
June
2018
I
Volume
10
I
Article
161
Luo
et
al.
Divergent
Bi-hemispheric
Connectivity
in
aMCI
Subtype
AUTHOR
CONTRIBUTIONS
XL
study
design,
analysis,
interpretation,
and
writing.
KL,
QZ,
and
TQ
analysis
and
interpretation
of
data,
study
concept,
and
design.
PH,
XX,
MZ,
YJ,
JX,
and
JZ
manuscript
revision
and
statistical
analysis.
FUNDING
This
study
was
funded
by
National
Key
Research
and
Development
Program
of
China
(Grant
No.
2016YFC1306600),
Zhejiang
Provincial
Natural
Science
Foundation
of
China
(Grant
Nos.
LZ14H180001
and
Y16H090026),
Young
Research
Talents
Fund,
Chinese
Medicine
Science,
and
Technology
Project
of
Zhejiang
Province
(Grant
No.
2018ZQ035).
The
data
collection
and
sharing
for
this
project
were
funded
by
the
ADNI
(National
Institutes
of
Health
Grant
U01
AG024904)
and
DOD
ADNI
(Department
of
Defense
Award
No.
W81XWH-12-2-0012).
ADNI
was
funded
by
the
NIA,
the
NIBIB,
and
through
generous
contributions
from
the
following:
AbbVie,
Alzheimer's
Association;
Alzheimer's
Drug
Discovery
Foundation;
Araclon
Biotech;
Bio
Clinica,
Inc.;
Biogen;
Bristol-Myers
Squibb
Company;
Cere
Spir,
Inc.;
Eisai
Inc.;
Elan
Pharmaceuticals,
Inc.;
Eli
Lilly
and
Company;
Euro
Immun;
F.
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Statement:
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Copyright
©
2018
Luo,
Li,
Zeng,
Huang,
Jiaerken,
Qiu,
Xu,
Zhou,
Xu
and
Zhang
for
the
Alzheimer's
Disease
Neuroimaging
Initiative
(ADNI).
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Frontiers
in
Aging
Neuroscience
I
www.frontiersin.org
13
June
2018
I
Volume
10
I
Article
161