Neuroanatomical correlates of behavioural phenotypes in behavioural variant of frontotemporal dementia

Abstract

Behavioural variant of frontotemporal dementia (bvFTD) frequently presents complex behavioural changes, that rarely occur in isolation. Targeting behavioural phenotypes instead of single behavioural symptoms may potentially provide a disease model in which to investigate brain substrates of behavioural abnormalities.
To identify behavioural phenotypes and to assess the associated brain correlates in a cohort of patients with bvFTD.
Two hundred and seven consecutive individuals fulfilling clinical criteria for bvFTD were enrolled. Each participant's caregiver completed frontal behavioural inventory on 24 key behavioural disturbances. Confirmatory factor analysis (CFA) models were applied, and behavioural phenotypes identified. For each phenotype, a score was derived based on the "best" CFA model (Bifactor CFA). One hundred two participants underwent SPECT scan. A regression analysis between scores for each factor and regional cerebral blood flow was carried out (P<0.001).
One "general" behavioural phenotype and four factors were identified, that were termed "disinhibited", "apathetic", "aggressive", and "language" phenotypes. The most robust brain correlate was identified for "disinhibited" phenotype, in the region of the anterior cingulated and anterior temporal cortex, bilaterally, and for apathetic phenotype in the left dorsolateral frontal cortex. As expected, language phenotype correlated with greater hypoperfusion in the left frontotemporal lobes. No significant correlation between aggressive phenotype and regional cerebral blood flow was found. Moreover, the "general" behavioural severity was associated with greater damage in the right frontal lobe.
Behavioural phenotypes are associated with specific brain damage in bvFTD, involving distinct cerebral networks.

Full text

Behavioural
Brain
Research
235 (2012) 124–
129
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lists
available
at
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ScienceDirect
Behavioural
Brain
Research
j
ourna
l
ho
me
pa
ge:
www.elsevier.com/locate/bbr
Research
report
Neuroanatomical
correlates
of
behavioural
phenotypes
in
behavioural
variant
of
frontotemporal
dementia
B.
Borronia,∗,
M.
Grassib,
E.
Premia,
S.
Gazzinaa,
A.
Albericia,
M.
Cosseddua,
B.
Pagherac,
A.
Padovania
aCenter
for
Aging
Brain
and
Dementia,
Department
of
Neurology,
University
of
Brescia,
Brescia,
Italy
bDepartment
of
Health
Sciences,
Section
of
Medical
Statistics
&
Epidemiology,
University
of
Pavia,
Pavia,
Italy
cNuclear
Medicine
Unit,
University
of
Brescia,
Brescia,
Italy
h
i
g
h
l
i
g
h
t
s
Confirmatory
factor
analysis
identified
4
behavioural
phenotypes
in
FTD
sample.
Disinhibition
correlates
with
in
anterior
cingulate
and
anterior
temporal
gyri.
Apathy
correlates
in
the
left
dorsolateral
cortex.
Language
correlates
with
left
frontotemporal
lobes.
a
r
t
i
c
l
e
i
n
f
o
Article
history:
Received
4
June
2012
Received
in
revised
form
28
July
2012
Accepted
2
August
2012
Available online 10 August 2012
Keywords:
Frontotemporal
dementia
Behaviour
SPECT
Confirmatory
factor
analysis
a
b
s
t
r
a
c
t
Background:
Behavioural
variant
of
frontotemporal
dementia
(bvFTD)
frequently
presents
complex
behavioural
changes,
that
rarely
occur
in
isolation.
Targeting
behavioural
phenotypes
instead
of
single
behavioural
symptoms
may
potentially
provide
a
disease
model
in
which
to
investigate
brain
substrates
of
behavioural
abnormalities.
Objective:
To
identify
behavioural
phenotypes
and
to
assess
the
associated
brain
correlates
in
a
cohort
of
patients
with
bvFTD.
Methods:
Two
hundred
and
seven
consecutive
individuals
fulfilling
clinical
criteria
for
bvFTD
were
enrolled.
Each
participant’s
caregiver
completed
frontal
behavioural
inventory
on
24
key
behavioural
disturbances.
Confirmatory
factor
analysis
(CFA)
models
were
applied,
and
behavioural
phenotypes
iden-
tified.
For
each
phenotype,
a
score
was
derived
based
on
the
“best”
CFA
model
(Bifactor
CFA).
One
hundred
two
participants
underwent
SPECT
scan.
A
regression
analysis
between
scores
for
each
factor
and
regional
cerebral
blood
flow
was
carried
out
(P
<
0.001).
Results:
One
“general”
behavioural
phenotype
and
four
factors
were
identified,
that
were
termed
“dis-
inhibited”,
“apathetic”,
“aggressive”,
and
“language”
phenotypes.
The
most
robust
brain
correlate
was
identified
for
“disinhibited”
phenotype,
in
the
region
of
the
anterior
cingulated
and
anterior
temporal
cortex,
bilaterally,
and
for
apathetic
phenotype
in
the
left
dorsolateral
frontal
cortex.
As
expected,
lan-
guage
phenotype
correlated
with
greater
hypoperfusion
in
the
left
frontotemporal
lobes.
No
significant
correlation
between
aggressive
phenotype
and
regional
cerebral
blood
flow
was
found.
Moreover,
the
“general”
behavioural
severity
was
associated
with
greater
damage
in
the
right
frontal
lobe.
Conclusions:
Behavioural
phenotypes
are
associated
with
specific
brain
damage
in
bvFTD,
involving
dis-
tinct
cerebral
networks.
© 2012 Elsevier B.V. All rights reserved.
1.
Introduction
The
behavioural
variant
of
frontotemporal
dementia
(bvFTD)
is
one
of
the
most
frequent
causes
of
young-onset
dementia
that
manifests
with
progressive
behavioural
abnormalities,
personality
∗Corresponding
author
at:
Clinica
Neurologica,
Università
degli
Studi
di
Brescia,
Pza
Spedali
Civili,
1
–
25100
Brescia,
Italy.
Tel.:
+39
0303995632;
fax:
+39
0303995027.
E-mail
address:
bborroni@inwind.it
(B.
Borroni).
changes
and
social
dysfunction.
While
bvFTD
is
considered
a
single
entity,
there
is
considerable
variability
in
its
clinical
presentation,
and
either
apathetic
or
disinhibited
behaviours
may
be
frequently
recognised
[1–3].
Furthermore,
bvFTD
can
exhibit
changes
in
lan-
guage
[4],
the
extend
of
these
varying
between
subjects
[5].
Behavioural
disturbances
occur
in
concert
with
focal
neurode-
generation
of
frontal
and
temporal
lobes
that
can
be
quantified
with
modern
structural
and
functional
imaging
methods.
From
a
neurobiological
perspective,
bvFTD
therefore
presents
an
opportu-
nity
to
assess
the
critical
brain
substrates
that
lead
to
personality
changes.
0166-4328/$
–
see
front
matter ©
2012 Elsevier B.V. All rights reserved.
http://dx.doi.org/10.1016/j.bbr.2012.08.003

B.
Borroni
et
al.
/
Behavioural
Brain
Research
235 (2012) 124–
129 125
Specific
standardised
behavioural
scales,
usually
assessed
by
carer-based
questionnaires,
have
been
developed.
In
the
case
of
bvFTD,
frontal
behavioural
inventory
(FBI)
is
one
of
the
most
help-
ful
assessments
to
detect
the
pattern
and
severity
of
behavioural
abnormalities
[6,7].
A
number
of
studies
have
investigated
the
neural
correlates
of
behavioural
symptoms
in
bvFTD,
even
though
these
rarely
present
in
isolation
[3,8].
Since
bvFTD
is
associated
with
multi-
ple
behavioural
abnormalities,
it
has
been
hard
to
conclude
that
the
relationship
between
a
single
behavioural
feature
and
the
identified
brain
damage
is
unique.
As
standardised
questionnaires
include
overlapping
behavioural
disturbances,
the
analysis
of
neu-
ral
correlates
of
undergoing
behavioural
clusters
should
be
assessed
to
overcome
potential
biases
and
to
further
clarify
whether
distinct
neural
networks
are
selectively
involved.
Thus,
the
goals
of
the
present
study
conducted
in
a
large
cohort
of
bvFTD
patients
were:
(a)
to
carefully
characterize
behavioural
disturbances;
(b)
to
identify
consistent
behavioural
phenotypes
(by
using
FBI)
as
the
results
of
confirmatory
factor
analysis
mod-
elling
approaches;
(c)
to
find
out
the
functional
SPECT
correlates
of
behavioural
severity
in
bvFTD;
and
(d)
to
investigate
functional
brain
correlates
of
the
identified
behavioural
phenotypes,
using
statistical
parametric
mapping
on
SPECT
scans.
We
hypothesized
that
the
single
behavioural
disturbances
might
be
clustered
into
behavioural
phenotypes,
and
these
latter
would
correlate
with
distinct
profiles
of
brain
hypoperfusion.
2.
Methods
2.1.
Subjects
Patients
attending
the
tertiary
cognitive
disorders
clinic
at
Neurology
Depart-
ment,
University
of
Brescia,
Italy,
with
clinical
diagnoses
of
bvFTD
according
to
current
consensus
criteria
[9,10]
entered
the
study.
The
diagnosis
was
supported
by
detailed
neuropsychological
testing,
as
previously
reported
[11],
and
careful
behavioural
assessment
with
patient’s
carer.
For
each
patient,
a
structural
brain
MRI
excluded
major
causes
of
cerebrovascular
disease
and
white
matter
lesions.
bvFTD
patients
underwent
SPECT
imaging
on
the
same
scanner
for
studying
cere-
bral
blood
flow
perfusion.
Patients
with
potentially
confounding
neurological
and
psychiatric
disorders,
a
past
history
of
alcohol
abuse,
psychosis,
or
major
depres-
sion,
were
excluded
from
the
study.
The
use
of
medication
that
could
interfere
with
behavioural
disturbances
and
the
absence
of
informant
proxy
carer
were
considered
as
further
exclusion
criteria.
The
study
was
conducted
in
accordance
with
the
Declaration
of
Helsinki
and
ethics
approval
was
obtained
from
the
Ethics
Committee
of
the
Brescia
Hospital,
Italy.
2.2.
Assessment
of
behavioural
disturbances
Behavioural
disturbances
were
assessed
by
FBI
[6],
which
has
been
recently
vali-
dated
for
Italian
language
[7].
In
the
present
work,
we
used
the
latter
version.
The
FBI,
a
24-point
behavioural
assessment
tool,
was
completed
by
a
caregiver
respondent
for
each
participant
[12].
In
this
scale,
12
items
assess
negative
behaviours
(apathy,
aspontaneity,
indifference,
inflexibility,
personal
neglect,
disorganisation,
inatten-
tion,
loss
of
insight,
logopenia,
verbal
apraxia,
semantic
deficit,
alien
hand),
and
12
items
assess
positive
behaviours
(perseverations/obsessions,
irritability,
excessive
jocularity,
social
inappropriateness,
impulsivity,
restlessness,
aggression,
hyperoral-
ity,
hypersexuality,
utilisation
behaviour,
and
incontinence).
Some
of
the
negative
items
are
aimed
at
language
and
motor
behaviours.
For
the
purposes
of
this
study,
the
caregiver
respondent
was
a
first-degree
rela-
tive
with
excellent
personal
knowledge
of
the
participant.
The
respondent
was
asked
to
rate
each
item,
and
a
score
from
0
(absence)
to
3
(highly
present)
was
assigned.
2.3.
Statistical
analysis
(CFA)
Confirmatory
factor
analysis
(CFA)
modelling
was
used
to
determine
the
num-
ber
and
the
behavioural
phenotypes
(dimensions)
underling
the
FBI
instrument.
As
the
FBI
items
were
developed
and
standardised
with
reference
to
Kertesz
et
al.
[6],
CFA
was
carried
out
in
order
to
build
up
a
model
that
was
clinically
meaningful
and
comparable
to
the
original
one,
and
satisfying
the
goodness
of
fitting
index
require-
ment.
We
performed
three
CFA
models
with
different
dimensions
across
different
model
classes,
as
follows
[13,14]:
Fig.
1.
Path
diagram
of
the
fitted
confirmatory
factor
analysis
(CFA)
models.
Panel
A.
Correlated
CFA
models;
Panel
B.
Hierarchical
CFA
models;
Panel
C.
Bifactor
CFA
models.
See
Section
2
for
details.
(a)
Correlated
CFA
models
(Fig.
1a)
with
one
to
four
dimensions
extracted
by
a
prelim-
inary
exploratory
factor
analysis
(EFA)
with
oblique
rotation;
we
hypothesized
that
unidimensionality
(one
dimension)
of
the
original
FBI
instrument
was
not
adequate
for
representing
the
behavioural
construct,
but
different
highly
related
behavioural
phenotypes
might
be
the
“true”
dimension.
(b)
Hierarchical
CFA
models
(Fig.
1b)
with
two
to
four
first-order
dimensions
and
one
second-order
dimension
underling
these
first-order
behavioural
phenotypes;
we
hypothesized
an
overall
behavioural
factor,
as
the
original
FBI
instrument,
and
this
factor
was
comprised
by
at
least
four
highly
related
specific
behavioural
phenotypes
that
account
for
the
communality
of
the
items.
(c)
Bifactor
CFA
models
(Fig.
1c)
with
one
general
factor
that
account
for
the
commu-
nality
of
the
items,
and
two
to
four
factors
that
account
for
the
unique
influence
of
the
specific
behavioural
phenotypes;
we
hypothesized
that
this
general
fac-
tor
was
the
focal
interest
of
the
original
FBI
instrument,
and
the
relations
among
the
general
and
specific
behavioural
phenotypes
were
assumed
to
be
orthogo-
nal
(non-correlated),
as
these
phenotypes
were
related
to
the
item
contribution
that
was
over
and
above
the
general
factor.
The
number
of
dimensions
and
the
item
loading
structure
of
EFA
with
oblique
rotation
(Oblim
method)
was
conducted
on
the
polychoric
correlation
matrix
to
adjust
for
the
ordinal
nature
of
the
FBI
items.
Four
classical
criteria
from
EFA
were
used:
(1)
eigenvalue
rule
(i.e.,
number
of
factors
with
eigenvalue
>1);
(2)
scree
plot
(i.e.,
number
of
factors
before
the
break
in
the
scree
plot);
(3)
Horn’s
paral-
lel
analysis
(i.e.,
number
of
factors
with
eigenvalue
>average
random
eigenvalues);
(4)
factor
loading
rule
(i.e.,
item-factor
correlations
>0.32
suggested
for
behavioural
phenotypes
interpretation).
Parameters
of
CFA
models
were
obtained
by
maxi-
mum
likelihood
estimation
(MLE),
and
several
goodness-of-fit
criteria
were
used
for
model
selection
(see
legend
of
Table
2).
Factor
scores
of
the
selected
(best)
CFA
model
for
each
subject
were
computed
summing
the
original
FBI
items
per
factor
weights
derived
by
BLUP
(“Best
Linear
Unbiased
Predictor”)
method.
EFA/CFA
mod-
els
were
processed
with
R
software
(version
2.13.2
for
Windows),
and
full
details
of
the
EFA/CFA
approaches
can
be
found
in
several
references
[15].

126 B.
Borroni
et
al.
/
Behavioural
Brain
Research
235 (2012) 124–
129
2.4.
99mTc-bicisate
(ECD)
SPECT
acquisition
protocol
and
image
analysis
Patients
were
administered
an
intravenous
injection
of
1110
MBq
of
99mTc-
bicisate
(ECD)
(Neurolite,
Lantheus
Medical
Imaging)
in
a
rest
condition,
lying
supine
in
a
quiet,
dimly
lit
room.
All
individuals
were
imaged
using
a
dual-head
rotating
gamma
camera
(GE
Millenium
VG)
fitted
with
a
low-energy,
high-resolution
col-
limator,
30
min
after
intravenous
injection
of
99mTc-bicisate
(ECD).
A
128
×
128
pixel
matrix
was
used
for
images
acquisition
with
120
views
over
a
360◦orbit
(in
3◦step)
with
a
pixel
size
of
4.02
mm,
in
27
min
or
more
to
collect
at
least
5
×
106
total
counts.
Images
reconstruction
were
performed
by
a
filtered
back
projection
and
three-dimensionally
smoothed
with
a
Butterworth
filter
(cut
off
0.5
cycles/cm,
order
15).
The
reconstructed
images
were
corrected
for
gamma
ray
attenuation
using
the
Chang
method
(attenuation
coefficient:
0.11
cm−1).
Statistical
Parametric
Mapping
(SPM8,
Welcome
Department
of
Cognitive
Neurology,
University
College,
London),
and
Matlab
7.1
(Mathworks
Inc.,
Sherborn,
MA)
were
used
for
images
pre-processing.
Images
were
spatially
normalised
to
a
reference
stereotactic
tem-
plate
(Montreal
Neurological
Institute,
MNI)
and
smoothed
by
a
Gaussian
kernel
of
8
mm
×8
mm
×8
mm
FWHM.
Regression
analysis
between
factor
scores
of
the
selected
CFA
and
regional
cerebral
blood
flow
was
carried
out
with
age,
gender
and
all
the
others
cluster
scores
considered
as
nuisance
variables.
Findings
meeting
a
height
threshold
of
P
<
0.005
uncorrected
were
considered
significant.
The
extension
threshold
was
set
at
25
voxels.
3.
Results
3.1.
Subjects
Two-hundred
and
seven
patients
(mean
age
66.5
±
7.4
years;
44.5%
females;
age
at
onset
63.7
±
7.4
years)
fulfilling
inclusion
and
exclusion
criteria
were
considered
in
the
present
study
(see
Table
1),
and
FBI
was
carefully
administered
to
the
proxy
carer.
As
shown
in
Fig.
2,
apathy
was
the
most
common
behavioural
disorder,
present
in
68.6%
of
patients,
followed
by
negative
symptoms
such
as
inattention,
aspontaneity,
and
disorganisation.
The
less
common
were
hypersexuality
(7.7%)
and
utilisation
behaviour
(11.6%).
3.2.
Behavioural
phenotypes
EFA
identified
four
factors
with
eigenvalues
9.44
to
1.55
>
1,
that
explained
almost
the
65.2%
and
94.4%
of
observed
total
variance,
and
of
the
observed
inter-item
correlations
matrix,
respectively;
the
Scree
plot
and
Parallel
Analysis
also
pointed
out
three
or
four
factors.
Thus,
the
Oblim
oblique
rotation
was
performed
on
two,
three
and
four
factors,
and
appearing
factor
structure
were
tested
with
CFA
modelling.
As
shown
in
Table
2,
the
best
CFA
fitting,
repre-
sented
by
the
lowest
AIC
and
BIC
scores,
and
adequate
SRMR
index,
was
the
bifactor
model
with
1
general
factor
and
4
specific
factors.
As
shown
in
Fig.
3,
according
to
bCFA
output,
a
general
behavioural
phenotype
(named
g
=
“overall
behavioural
severity”),
and
over
and
above
this
general
construct,
four
specific
behavioural
phenotypes
Table
1
Demographic
and
clinical
characteristics
of
FTLD
patients.
Variable
bvFTD
(all)
bvFTD
with
SPECT
Pa
N
207
102
–
Age
at
evaluation,
years 66.47
±
7.39
65.14
±
7.31
0.14
Gender,
males% 55.6%
(115)
52%(53)
0.55b
Age
at
onset,
years
63.66
±
7.45
62.7
±
7.25
0.29
Education,
years 7.43
±
3.69
7.34
±
3.38
0.83
FH,
%
42.4%
(84)
39.2%
(40)
0.62b
MMSE
21.56
±
6.34
22.16
±
5.38
0.42
FTD-modified
CDR
5.79
±
4.46
4.90
±
3.55
0.09
NPI 17.05
±
12.44
17.55
±
12.34
0.74
FBI-A
10.47
±
7.70
10.40
±
7.00
0.94
FBI-B 5.74
±
5.83
5.95
±
5.94
0.77
FBI-AB
16.23
±
12.01
16.35
±
11.58
0.93
bvFTD:
behavioural
variant
frontotemporal
dementia;
FH:
family
history;
MMSE:
mini-mental
state
examination;
FTD-modified
CDR:
frontotemporal
dementia-
modified
clinical
dementia
rating
scale;
NPI:
neuropsychiatry
inventory;
FBI:
frontal
behavioural
inventory.
Number
of
subjects
between
brackets.
at-test,
otherwise
specified.
bChi-Square
test.
were
identified.
The
first
(9
items)
was
determined
by
lack
of
judg-
ment,
personal
neglect,
perseverations,
hyperorality,
utilisation
behaviours,
hoarding,
euphoria,
and
social
inappropriateness,
thus
being
termed
f1
=
“disinhibited
phenotype”,
the
second
(2
items)
by
apathy
and
aspontaneity,
thus
being
named
f2
=
“apathetic
phenotype”;
the
third
(3
items)
by
inflexibility,
irritability
and
aggressiveness,
and
named
f3
=
“aggressive
phenotype”,
the
latter
(4
items)
by
logopenia,
aphasia,
semantic
dementia
and
alien
limb,
and
then
called
f4
=
“language
phenotype”.
Seven
items
had
not
any
specific
factor,
and
two
items
(hypersexuality
and
alien
hand)
were
not
related
to
the
general
factor.
A
score
of
each
behavioural
phenotype
(see
method
section,
sta-
tistical
analysis)
for
each
patient
was
computed;
score
correlations
are
appropriate
with
the
bifactor
model:
g
correlation
with
f1–f4
ranged
from
0.03
to
0.13.
g
and
f1–f4
high
scores
indicate
high
dis-
turbance
behavioural,
and
vice
versa.
The
measurement
reliability
(omega
index)
of
the
selected
bifactor
model
was
of
0.91.
3.3.
Brain
correlates
of
behavioural
severity
and
behavioural
phenotypes
Demographic
and
clinical
characteristics
of
this
subgroup
were
comparable
to
those
of
the
all
bvFTD
group
(see
Table
1).
A
regres-
sion
analysis
of
g
and
each
f1–f4
phenotype
scores
with
rCBF
was
carried
out.
One
hundred
two
bvFTD
patients
underwent
SPECT
scan
and
were
considered
for
further
analysis.
Fig.
2.
Behavioural
disturbances
in
bvFTD
according
to
frontal
behavioural
inventory
scale.

B.
Borroni
et
al.
/
Behavioural
Brain
Research
235 (2012) 124–
129 127
Table
2
Goodness-of-fit
indices
of
the
different
confirmatory
factor
analysis
(CFA)
models.
Model
X2
df
AIC
BIC
GFI
CFI
RMSEA
SRMR
CFA1
1306
252
801.5
−117.1
0.722
0.588
0.1215
0.102
cCFA2 1109 253
602.8
−319.5
0.753
0.665
0.1093
0.107
cCFA3 1046
252
541.9
−376.8
0.764
0.689
0.1055
0.128
cCFA4
851.3
247
357.3
−543.2
0.799
0.764
0.0930
0.117
bCFA2
816.7
228
360.7
−470.5
0.806
0.770
0.0955
0.073
bCFA3
781
228
325.0
−506.1
0.812
0.784
0.0926
0.073
bCFA4
670.6
228
214.6
−616.6
0.835
0.827
0.0828
0.072
hCFA2 1084 250 583.7
−327.7
0.754
0.674
0.1088
0.092
hCFA3 1015 249
517.4
−390.3
0.769
0.700
0.1043
0.093
hCFA4 917.7
248
421.7
−482.3
0.787
0.738
0.0977
0.094
X2:
model
log-likelihood
chi-square
statistic;
df:
model
degree
of
freedom
(df
=
number
of
observations
−
number
of
model
parameters);
AIC:
Akaike’s
information
criterion
(AIC
=
−2
×
model
log-likelihood
+
2
×
number
of
model
parameters);
BIC:
Bayesian
information
criterion
(BIC
=
−2
×
model
log-likelihood
+
log(n)
×
number
of
model
param-
eters),
GFI:
goodness
of
fit
index
(GFI
is
an
estimate
of
the
proportion
of
the
total
model
“variance”
that
is
free
of
“error”
variance);
CFI:
comparative
fit
index
(CFI
estimate
the
difference
of
the
fitted
model
and
a
hypothetical
(null)
model
of
zero-association);
RMSEA:
root-mean-square
error
of
approximation
(RMSEA
is
based
on
the
difference
between
the
fitted
model
and
a
hypothetical
“true”
model);
SRMR:
standardised
root-mean-square
residual
(SRMR
is
based
on
the
differences
between
observed
and
model
values
of
the
correlation
matrix).
The
recommended
criteria
for
good
fit
were
offered
by:
minimum
AIC
or
BIC;
GFI
>
0.90;
CFI
>
0.95;
RMSEA
<
0.06;
SRMR
<
0.08
[25].
As
shown
in
Fig.
4
and
Table
3,
disinhibited
phenotype
was
asso-
ciated
with
significant
greater
hypoperfusion
in
anterior
cingulate
cortex,
orbitofrontal
cortex
and
right
anterior
temporal
lobe.
Apa-
thetic
phenotype
was
related
to
greater
hypoperfusion
in
the
left
dorsolateral
frontal
cortex
and
anterior
cingulate
cortex,
bilaterally.
As
expected,
language
phenotype
correlated
with
greater
hypop-
erfusion
in
the
left
frontotemporal
lobes.
No
significant
correlation
between
aggressive
phenotype
and
rCBF
was
found.
Fig.
3.
Path
diagram
of
the
selected
(best)
bifactor
model
with
1
general
behavioural
phenotype
r
(g)
and
4
specific
behavioural
phenotypes
(f1–f4);
g
and
f1–f4
are
assumed
orthogonal
(non-correlated),
as
the
f1–f4
are
related
to
the
residual
con-
tribution
that
is
over
and
above
g.
The
inverse
correlation
between
each
phenotype
and
rCBF
did
not
show
any
significant
voxel
above
the
pre-established
statistical
threshold.
Significant
negative
correlation
was
observed
between
the
g
score,
i.e.
a
measure
of
behavioural
severity,
and
rCBF
in
right
infe-
rior
frontal
gyrus
(see
Fig.
4
and
Table
3).
The
same
pattern
was
observed
when
regression
analysis
between
total
FBI
score
and
rCBF
was
carried
out
(data
not
shown).
4.
Discussion
The
bvFTD
is
traditionally
considered
a
unitary
disease,
but
a
wide
range
of
clinical
presentations
has
been
recognised.
Accord-
ingly,
either
selective
damage
of
frontal
lobes
or
predominant
involvement
of
temporal
regions
has
been
described
in
bvFTD
[15].
In
the
present
work,
we
have
carefully
characterised
the
behavioural
disturbances
and
how
these
are
grouped
by
using
FBI
questionnaire.
The
spectrum
of
behavioural
abnormalities
was
large
and
heterogeneous,
and
consistent
with
previous
studies,
the
most
common
behaviour
was
apathy,
occurring
in
over
60%
of
subjects
[15–17].
However,
specific
phenotypes
were
iden-
tified,
namely
apathetic,
disinhibited,
aggressive
and
language
behaviours,
as
the
results
of
clustering
of
single
behavioural
symp-
toms.
Different
confirmatory
factor
analysis
models
have
been
applied
to
reach
these
results,
and
the
best
fitting
model
suggested
that
the
score
of
overall
behavioural
disturbances
(i.e.
g-score)
is
consistent
with
FBI
A
plus
FBI
B
scores,
while
subgrouping
FBI
A
and
FBI
B
as
currently
used
(i.e.,
positive
and
negative
symptoms)
seems
to
not
well
describe
the
different
behavioural
phenotypes,
these
latter
clustering
independently
from
the
two
subscales.
Furthermore,
by
functional
neuroimaging,
we
reported
that
right
frontal
lobe
was
correlated
to
g-score,
thus
suggesting
that
behavioural
severity
in
bvFTD
is
related
to
hypoperfusion
of
this
hemisphere.
In
addition,
three
out
of
four
behavioural
clusters
were
associated
with
selective
hypoperfusion
in
specific
brain
regions,
independent
of
the
effect
of
other
behaviours.
Disinhibited
phe-
notype
was
associated
with
significant
greater
hypoperfusion
in
anterior
cingulate
cortex,
left
orbitofrontal
cortex
and
right
anterior
temporal
lobe,
while
apathetic
phenotype
was
related
to
greater
hypoperfusion
in
the
left
dorsolateral
frontal
cortex
and
anterior
cingulate
cortex.
As
expected,
language
phenotype
correlated
with
greater
hypoperfusion
in
the
left
frontotemporal
lobes.
In
this
phe-
notype,
beyond
aphasia,
the
unexpected
alien
hand
symptom
was
observed;
however,
only
a
few
patients
scored
positively
to
this
FBI
item.

128 B.
Borroni
et
al.
/
Behavioural
Brain
Research
235 (2012) 124–
129
Fig.
4.
Results
of
linear
regression
analysis
between
rCBF
and
behavioural
phenotypes,
i.e.
disinhibited
(a),
apathetic
(b),
language
(c),
and
total
behavioural
score
(d)
superimposed
on
a
3D
brain
template.
A
=
anterior
view;
I
=
inferior
view;
S
=
superior
view;
R
=
right;
L
=
left.
P
<
0.005
uncorrected,
threshold
=
25
voxels.
L
=
left.
No
significant
correlation
between
aggressive
phenotype
and
rCBF
was
found.
These
findings
provide
evidence
that
specific
neuroanatomical-
behavioural
relationships
can
be
delineated
in
bvFTD
patients.
Some
studies
have
investigated
the
neuronal
correlates
of
individ-
ual
behavioural
disturbances
in
bvFTD
[18–20]
by
using
different
carer
questionnaire,
with
some
discrepancies.
The
contrasting
results
might
be
partially
due
to
the
selection
of
individual
items
instead
of
behavioural
clusters
that
may
be
not
uniquely
associated
with
brain
damage.
In
fact,
the
utilisation
of
questionnaires,
such
as
Neuropsychiatric
Inventory
or
FBI,
originally
studied
to
discrim-
inate
between
FTD
and
other
types
of
dementia
[21]
do
not
allow
the
detailed
definition
of
different
behavioural
presentations.
At
this
purpose,
confirmatory
factor
analysis
overcomes
the
structural
Table
3
Location
of
the
peaks
of
regional
reduction
of
regional
cerebral
perfusion
in
FTLD
patients
according
to
the
clinical
pattern.
For
aggressive
score
(f3)
none
region
were
statistical
significant
(P
>
0.05).
Region
x
Y
z
T
P
Cluster
size
Disinhibited
score
(f1)
R
superior
temporal
gyrus 46
14
−26
5.15
<0.001
1496
R
anterior
cingulate
gyrus
4
20
−10
4.63
<0.001
2406
L
orbitofrontal
cortex
0
38
−10
4.51
<0.001
–
L
middle
temporal
gyrus
−40
6
−34
3.56
<0.001
225
L
inferior
frontal
gyrus
−40
28
−18
3.02
<0.005
84
Apathetic
score
(f2)
L
superior
frontal
gyrus
−20
28
50
3.82
<0.001
1157
R
anterior
cingulate
gyrus
4
22
−10
3.30
<0.005
59
L
anterior
cingulate
gyrus
−2
16
−10
3.12
<0.005
50
Language
score(f4)
L
superior
frontal
gyrus −4
8
66
4.54
<0.001
2178
L
middle
temporal
gyrus
−64
−22
−4
3.86
<0.001
1162
Overall
behavioural
score
(g)
R
inferior
frontal
gyrus
42
22
−12
4.20
<0.001
2206
R
insula
48
4
6
4.05
<0.001
–
R
middle
temporal
gyrus
46
10
−30
3.72
<0.001
–
L
uncus −16
4
−24
3.62
<0.001
197
Talairach
coordinates
of
significant
voxels,
at
P
<
0.005
uncorrected.
Threshold
value
=
25.
R:
right
hemisphere;
L:
left
hemisphere.

B.
Borroni
et
al.
/
Behavioural
Brain
Research
235 (2012) 124–
129 129
limit
of
the
FBI
scale,
grouping
functional
correlated
FBI
subitems
without
a
priori
hypothesis.
Prior
studies
have
found
that
patients
with
apathy
showed
dorsolateral
and
medial
frontal
changes,
while
patients
with
disinhibition
had
orbitofrontal
and
temporal
changes
[3,22].
Apathy
has
been
associated
also
with
brain
damage
in
medial
frontal
cortex
and
cingulate
cortex
in
FTD
[23,24],
again
consid-
ered
as
individual
symptom.
Indeed,
if
disinhibition
is
easy
to
detect,
apathy
may
be
associated
and
confounded
with
depressive
symptoms
in
bvFTD
and
more
specific
scales
developed
for
apathy
diagnosis
are
of
help
for
a
better
definition
of
this
symptom.
This
may
further
explain
the
contrasting
results
obtained
on
this
issue.
On
the
other
hand,
disinhibition
is
a
common
behavioural
symp-
tom
in
bvFTD
but
its
neural
correlates
are
still
debated.
A
recent
work
has
evaluated
the
neural
correlates
of
disinhibition
in
a
sam-
ple
of
bvFTD
and
Alzheimer
disease
patients
[18].
As
in
the
present
work,
the
authors
showed
that
this
was
associated
with
damage
of
orbitofrontal
cortex
and
temporal
pole
brain
regions.
Finally,
language
disturbances
are
commonly
detected
in
patients
with
bvFTD
[4],
beyond
being
present
in
the
well-known
temporal
variants
of
FTD,
i.e.
semantic
dementia
and
progressive
non-fluent
aphasia.
Language
deficits
are
frequently
neglected
in
bvFTD,
as
patients
may
be
less
compliant
to
evaluation
because
of
the
present
of
prominent
behavioural
disturbances.
However,
according
to
previous
anatomic
studies
[25],
the
presence
of
lan-
guage
disturbances
in
bvFTD
patients
was
associated
with
greater
damage
in
left
frontotemporal
lobe.
Unlike
the
other
behaviours
included
in
the
analysis,
aggressive
cluster
was
not
uniquely
associated
with
any
specific
brain
region.
From
a
statistical
point
of
view,
this
indicates
that
this
cluster
was
associated
with
a
variance
of
hypoperfusion
common
to
the
other
three
behaviours.
Taking
all
these
findings
into
account,
it
would
appear
that
behavioural
severity
is
associated
with
brain
damage
of
right
frontal
regions,
but
specific
and
distinct
neuronal
networks
in
the
different
phenotypes
may
be
identified.
Indeed,
a
more
strin-
gent
statistical
threshold
for
imaging
analysis
should
be
used
to
have
more
confidence
in
the
findings.
Moreover,
these
results
suggest
that
carer-based
scales
are
helpful
in
carefully
describ-
ing
behavioural
disturbances
in
bvFTD
patients,
even
though
we
acknowledge
that
we
used
a
carer-based
questionnaire
with
the
inherent
problems
of
employing
subjective
information.
From
this
perspective,
the
utilisation
of
a
factor
analysis
modelling
encom-
passes
the
intrinsic
variability
of
such
carer-based
tools,
grouping
the
single
disturbances
in
high-level
clusters,
that
gives
a
more
reli-
able
description
of
the
behavioural
disturbances
in
bvFTD
patients.
In
a
still
orphan
disease,
the
careful
focus
on
the
link
between
complex
behavioural
patterns
and
anatomical
brain
regions
could
be
of
help,
especially
at
the
disease
onset,
to
trace
the
clinical
course
and
in
the
choice
of
the
better
symptomatic
pharmacological
treat-
ment
[26].
The
strengths
of
this
study
were
the
large
sample
size
prospec-
tively
recruited
and
evaluated
by
a
tertiary
referral
centre,
the
standardised
assessment,
and
the
multiple
different
types
of
sta-
tistical
analyses
performed.
Future
confirmatory
studies
are
warranted
and
the
associa-
tion
between
neuropathological
features
and
obtained
behavioural
phenotypes
will
be
of
interest.
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Frontotemporal
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Snowden
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Neary
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Mann
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