Content uploaded by Francesca Trojsi
Author content
All content in this area was uploaded by Francesca Trojsi
Content may be subject to copyright.
Correspondence: G. Tedeschi, Second University of Naples, Department of Medica l, Surgical, Neurological, Metabolic and Aging Sciences, Piazza
Miraglia 2, 80138 Naples, Italy. Fax: 39 0815665095. E -mail: g ioacchino.tedeschi @u nina2.it
(Rece ived 16 January 2013 ; accepte d 10 March 2013 )
Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration, 2013; Early Online: 1–9
ISSN 2167-8421 print/ISSN 2167-9223 online © 2013 In forma Healthcare
DOI : 10.3109 /21678421.2013.785569
other than those comprising the primary corticospi-
nal tracts (CSTs) (5). These fi ndings support the
concept of motor neuron degeneration being part of
a wider progressive process that spreads contigu-
ously through multiple networks, which potentially
explains the heterogeneity in clinical features fre-
quently including variable association of motor
and extramotor symptoms (6,7). In this regard,
ALS and frontotemporal dementia (FTD), both
multisystem neurodegenerative disorders, overlap at
clinical, pathological and genetic levels (1) and these
evidences have been reinforced by the fi nding that
Introduction
Amyotrophic lateral sclerosis (ALS) is a neurodegene-
rative disorder predominantly characterized by pro-
gressive motor involvement in limb, bulbar, and
respiratory functions (1). On the other hand, it also
shows symptoms affecting extramotor systems, such
as frontotemporal dysfunction associated with cogni-
tive decline and behavioural impairment (2).
From a histopathological viewpoint (3), and as
also highlighted in vivo by recent neuroimaging fi nd-
ings (4), it is recognized that ALS brain pathology is
widespread and involves several cerebral networks
ORIGINAL ARTICLE
Motor and extramotor neurodegeneration in amyotrophic lateral
sclerosis: A 3T high angular resolution diffusion imaging (HARDI)
study
FRANCESCA TROJSI
1,2 , DANIELE CORBO
2,3 , GIUSEPPINA CAIAZZO
2,3 ,
GIOVANNI PICCIRILLO
1,2 , MARIA ROSARIA MONSURR Ò
1,2 , SOSSIO CIRILLO
2,4 ,
FABRIZIO ESPOSITO
2,5,6 & GIOACCHINO TEDESCHI
1 – 3
1 Department of Medical, Surgical, Neurological, Metabolic and Aging Sciences, Second University of Naples,
2 MRI Research Centre SUN-FISM – Second University of Naples,
3 Neurological Institute for Diagnosis and
Care ‘ Hermitage Capodimonte ’ , Naples,
4 Department of Medical-Surgical, Clinical and Experimental Inter nistics
‘ F. Magrassi – A. Lanzara ’ , Second University of Naples, Naples,
5 Department of Medicine and Surger y, University
of Salerno, Baronissi, Italy, and
6 Department of Cognitive Neuroscience, Maastricht University, Maastricht, The
Netherlands
Abstract
In amyotrophic lateral sclerosis (ALS), diffusion weighted magnetic resonance imaging (DW-MRI) has produced mount-
ing evidence of a widespread white matter (WM) damage within motor and extramotor pathways. To provide novel infor-
mation about the degenerative process in ALS, overcoming some of the limitations imposed by diffusion tensor imaging
(DTI), we performed a high angular resolution diffusion imaging (HARDI) analysis of DW-MRI data. Generalized frac-
tional anisotropy (GFA) was evaluated in 19 patients with ALS and 19 matched control subjects, and was correlated with
clinical scores of disability, pyramidal impairment by upper motor neuron (UMN) score and frontal dysfunction by the
Frontal Systems Behaviour (FrSBe) scale.
Results demonstrated that ALS patients showed a signifi cant decrease of GFA in the WM tracts underneath the left
and right precentral gyri and the body of the corpus callosum ( p ⬍ 0.05, corrected), where GFA was signifi cantly related
to UMN scores ( p ⬍ 0.001, uncorrected); and in the left superior longitudinal fasciculus ( p ⬍ 0.05, corrected), where GFA
was signifi cantly related to FrSBe scale scores ( p ⬍ 0.01, uncorrected). In conclusion, this study revealed a pattern of motor
and extramotor frontal diffusivity abnormalities (probably related to behavioural and cognitive dysfunctions) showing a
spatial distribution similar to what was previously described in ALS ⫺ frontotemporal dementia continuum.
Key words: Amyotrophic lateral sclerosis , diffusion weighted magnetic resonance imaging , high angular resolution diffusion imaging ,
Q-ball imaging , white matter impairment
Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration Downloaded from informahealthcare.com by 89.97.220.66 on 04/17/13
For personal use only.
2 F. Trojsi et al.
TAR DNA binding protein 43 kD (TDP-43) is the
principal protein inclusion in ALS and in a subgroup
of FTD cases (8). Moreover, a common expansion
in the intron of C9orf72 on chromosome 9 (9p21)
has been identifi ed in families affected by ALS, FTD
or ALS-FTD (1).
Central to the advanced neuroimaging fi ndings
has been the use of diffusion weighted magnetic
resonance imaging (DW-MRI), which has rapidly
become an important non-invasive tool for charac-
terization of white matter (WM) structures through
the visualization and evaluation of fi bre tracts
connecting cortical and subcortical regions.
Specifi cally, the most commonly used technique,
diffusion tensor imaging (DTI), enables the assess-
ment of WM pathways integrity by measuring
water diffusion properties and is represented by
three eigenvectors and corresponding eigenvalues
from which fractional anisotropy (FA) and axial
(AD), radial (RD) and mean (MD) diffusivity
derive (9,10). DTI has already produced promis-
ing results in assessing widespread WM pathology
in patients with ALS (11 – 20), most of which
mainly refer to changes in FA and MD (11,13 – 20)
refl ecting, respectively, directional changes in water
diffusivity and the average of diffusion in all
directions (9,10). Conversely, only few studies have
shown a signifi cant increase of AD (12,13), which
refl ects axonal damage (9), and/or RD (12 – 15,19),
the most accepted myelin related measure (21,22),
along both motor and extramotor fi bre pathways
in ALS.
Most of these studies have reported changes of
FA mainly in the CSTs, but also in the corpus cal-
losum (CC) (12,14 – 16,18,19) and the frontal and
temporal lobes (14 – 16,18 – 20), detecting signifi cant
correlations of FA with duration, progression, and
severity of the disease within and also beyond motor
areas (11,13 – 15,17,18,20).
Recently, insights into corticomotor connectivity
changes in ALS have been derived by acquiring high
angular resolution diffusion imaging (HARDI) scans
along with high-resolution structural images (sMRI),
confi rming and magnifying previous DTI results
(23). As an alternative DW-MRI approach, the
HARDI technique allows to overcome DTI limita-
tions in the analysis of crossing fi bres (24). In par-
ticular, the major shortcoming of DTI is the limitation
in resolving individual fi bre orientations within voxels
containing fi bres with more than one orientation.
Conversely, HARDI allows to evaluate diffusion
attenuation in many more angular directions, dozens
or even hundreds, and also allows to resolve the ori-
entations of multiple fi bre populations coexisting in
the same voxel (24). Among the several HARDI
techniques, Q-ball imaging (QBI) has shown to be
the most sensitive to multimodal diffusion along
WM fi bres (25).
The present work is based on a QBI HARDI
approach, aiming to investigate the integrity of motor
and extramotor WM pathways in a group of patients
with sporadic ALS also in advanced stages of disease
and to correlate generalized FA (GFA), the most
prominent and widely accepted QBI parameter, with
clinical measures of UMN impairment, disability
and frontal dysfunction.
Materials and methods
Patients and controls
We studied 19 patients with ALS (nine males, ten
females; mean age 61.1 ⫾ 11.2 years), with defi nite
( n ⫽ 9) or probable ( n ⫽ 10) sporadic ALS, according
to the revised El Escorial criteria of the World
Federation of Neurology (26). The mean disease
duration, estimated from the time of symptoms onset
to scanning, was 4.1 ⫾ 3.6 years. All had a ‘ classic ’
phenotype with bulbar signs present in nine patients
including also long-term survivors. Specifi cally, eight
patients had a survival ⱖ 4 years, two of whom were
receiving artifi cial respiratory support (non-invasive
ventilation; mean duration, 68 months). We excluded
patients with non-classical phenotypes (i.e. domi-
nant lower motor neuron impairment, such as pro-
gressive muscular atrophy, fl ail leg syndrome or
pseudopolyneuritic form) or with other motor neu-
ron diseases (i.e. progressive bulbar palsy, primary
lateral sclerosis, post-poliomyelitis ALS and ALS-
dementia).
In all patients we assessed the ALS Functional
Rating Scale-Revised (ALSFRS-R) for evaluating
and monitoring ALS related disability (27); the Fron-
tal Systems Behaviour (FrSBe) scale for estimating
the degree of frontal dysfunction through the evalu-
ation of total or T-score, derived from the caregiver
form and referring to the present time (28); and the
UMN score, a measure of pyramidal dysfunction
based on a scale used in previous ALS neuroimaging
studies (14,29). Specifi cally, this score evaluates the
number of pathologic refl exes, elicited from 15 body
sites – glabellum, orbicularis oris, masseter (jaw jerk),
biceps, triceps and fi nger jerks bilaterally, and knee,
ankle, and Babinski responses bilaterally.
We have not administered our patients the frontal
assessment battery (FAB) (30), because the ALS
related physical symptoms could have signifi cantly
affected the score. However, all patients underwent a
prescreening neuropsychological evaluation, includ-
ing the Mini-Mental State Examination (MMSE)
(31) and phonemic and semantic fl uency tasks
(32,33), and performed in the normal range (for more
details see Table I). Moreover, in our sample we
found a mild prevalence of UMN signs and a frontal
executive and behavioural impairment (Table I).
All patients were treated with riluzole (50 mg ⫻
two/day). No patient had familial ALS or was posi-
tive for mutations of most genes causative for ALS
(i.e. superoxide dismutase-1 or SOD1, transactive
response DNA-binding protein or TARDBP, fused
Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration Downloaded from informahealthcare.com by 89.97.220.66 on 04/17/13
For personal use only.
HARDI in amyotrophic lateral sclerosis 3
in sarcoma/translocated in liposarcoma or FUS/TLS
and C9orf72).
The control group comprised 19 healthy controls
(nine males, ten females) aged from 46 to 78 years
(mean age 62.1 ⫾ 8.5 years) with no history of
neurological or psychiatric diseases and without any
abnormalities detected on conventional MRI T1
and T2 weighted images. All 38 subjects included in
the study were right-handed.
The study was approved by the ethics committee
of the Second University of Naples and was in
accordance with the Helsinki Declaration of
1975. Informed consent was received from all
participants.
MRI scanning protocol
Magnetic resonance images were acquired on a 3T
GE Medical System scanner (Signa HDxt3T
twinspeed GE) equipped with an eight-channel par-
allel head coil. Whole brain DTI was performed
using a GRE EPI sequence (repetition time ⫽ 10,000
ms, echo time ⫽ 88 ms, fi eld of view ⫽ 320 mm, iso-
tropic resolution ⫽ 2.5 mm, b value ⫽ 1000 s/mm
2 ,
32 isotropically distributed gradients, frequency
encoding RL).
Q-ball analysis
QBI analysis was performed by using the Functional
MRI of the Brain (FMRIB) Software Library (FSL)
software package (www.fmrib.ox.ac.uk/fsl).
Preprocessing included eddy current and
motion correction and brain tissue extraction. After
preprocessing, Q-ball images were averaged and
concatenated into 33 volumes and a Q-ball model
was fi tted at each voxel, generating GFA maps.
Q-ball group analyses included voxel based TBSS
and atlas-based volume of interest (VOI) analyses.
For these group analyses, Q-ball images were warped
to the Montreal Neurological Institute (MNI) 152
template, available as standard T1 data set in the
FSL software package.
TBSS was run with GFA maps to create the
‘ skeleton ’ , which represents the centre of all fi bre
bundles in common with all subjects, and which was
used for all other maps. For this purpose, GFA
images of all subjects ( n ⫽ 38) were aligned to a
common target (FMRIB58_FA standard space)
using non-linear registration, and thereby GFA maps
were calculated using the FSL FMRIB’s Diffusion
Toolbox (FDT) tool and aligned to a 1 ⫻ 1 ⫻ 1 mm
MNI 152 standard space. A mean GFA skeleton was
then created with threshold of GFA ⬎ 0.1. Voxelwise
correlations were performed to relate GFA to the
clinical scores of disability (UMN score, ALSFRS-R,
FrSBe scale). The resulting statistical maps were
thresholded at p ⬍ 0.05 corrected for multiple com-
parisons at a cluster level using the threshold-free
cluster enhancement (TFCE) approach (34). Age
was used as covariate. A VOI analysis was also per-
formed to correlate the TBSS results with standard
anatomic VOI data. VOIs were defi ned by anatomic
marks obtained from the International Consortium
of Brain Mapping DTI-81 WM labels atlas (Johns
Hopkins University, Baltimore, Maryland) (35,36).
Individual GFA values were extracted by fi rst
aligning the specifi c VOI to the GFA maps of all
subjects with the Non-linear Image Registration Tool
in FMRIB; then, all subject maps were masked with
this VOI, and the mean values of GFA were obtained.
GFA was evaluated in the CSTs, the CC (divided
into splenium, body, and genu), and the superior
longitudinal, fronto-occipital and uncinate fasciculi.
Table I. Detailed patient characteristics.
n 19
Mean age (years ⫾ SD) (r ange) 61.1 ⫾ 11.2 (34 – 80)
Gender (male: female) 9:10
El Escorial criteria (probable:defi nite) 10:9
Mean disease duration (months ⫾ SD) (ra nge) 49.2 ⫾ 43.2 (12 – 168 )
Age of onset (mean ⫾ SD) (range) 57.3 ⫾ 12.2 (30 – 75)
Site of initial symptom onset (upper limbs:
lower limbs: bulbar)
12:7:0
UMN score (mean ⫾ SD) (range) 7.1 ⫾ 4.4 (16 – 1)
ALSFRS-R (mean ⫾ SD) (r ange) 34.2 ⫾ 9.1 (47 – 18)
Rate of disease progression
∗ (mean ⫾ SD) (ra nge) 0.31 ⫾ 0.57 (1.83 – 0.083)
FrSBe scale (T-score, derived from caregivers)
∗
∗
(mean ⫾ SD ) (ra nge)
106.6 ⫾ 22.7 (143 – 74)
MMSE (cut-off 24) (mean ⫾ SD) (range) 28.5 ⫾ 1.7 (30 – 25.31)
Phonemic fl uency task (cut-off 17.35) (mean ⫾ SD)
(range)
27.1
⫾ 8.8 (51 – 18)
Semantic fl uency task (cut-off 7.25) (mean ⫾ SD)
(range)
15.2 ⫾ 4.3 (23.75 – 9.5)
∗ Calculated according to Ellis et al. (11) by the following formula: 48 ⫺ ALSFRS-R/
disease duration.
∗
∗ T-s cor e ⬎ 65 is defi ned as impaired behaviour and executive functions (28).
Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration Downloaded from informahealthcare.com by 89.97.220.66 on 04/17/13
For personal use only.
4 F. Trojsi et al.
Pearson ’ s correlation coeffi cients were calculated
to evaluate the relationship between the clinical vari-
ables (UMN score, ALSFRS-R, FrSBe scale, and
disease duration) and the mean values of GFA within
the VOIs. p -values ⬍ 0.05 were considered statisti-
cally signifi cant after correction for multiple com-
parisons with the Bonferroni method.
Results
Comparing the GFA maps of ALS patients and con-
trols in the TBSS analysis, the former showed a sig-
nifi cant reduction ( p ⬍ 0.05, corrected) of GFA
along: the CSTs, specifi cally within the rostral WM
tracts underneath the left and right precentral
gyri and the left ponto-mesencephalic junction; the
anterior thalamic radiations, with slight left lateral-
ization; the anterior cingulate; the splenium and
body of the CC; the fornix; the left uncinate fascicu-
lus; the left inferior fronto-occipital fasciculus; and
the left superior longitudinal fasciculus (Figure 1).
Within the patient group, signifi cant correlations
between GFA and clinical measures were observed.
Specifi cally, individual GFA data, extracted via
the VOI analysis selectively from the TBSS results,
showed a signifi cant inverse correlation with
UMN scores along the left CST at the ponto-
mesencephalic junction ( p ⫽ 0.031). This result was
also confi rmed by applying a voxelwise GLM analy-
sis on the group of patients, although at an uncor-
rected level of signifi cance ( p ⬍ 0.001).
Voxel-level GLM analysis showed that GFA in
the body of CC and, bilaterally, in WM tracts from
the central CC to the primary motor and premotor
cortices, also including, with slight prevalence in
the left hemisphere, the anterior cingulate and the
superior longitudinal fasciculus, was inversely cor-
related with UMN score ( p ⬍ 0.001, uncorrected)
(Figure 2).
Additionally, GFA in WM underneath the left
primary motor cortex, the body of CC, the left
superior longitudinal fasciculus and the CSTs at
the ponto-mesencephalic junction was negatively
correlated with FrSBe scale T-scores ( p ⬍ 0.001,
uncorrected) (Figure 3).
No signifi cant correlations were observed between
GFA and ALSFRS-R, disease duration or disease
progression rate in the CSTs, the CC and the supe-
rior longitudinal, the inferior fronto-occipital and
the uncinate fasciculi.
Discussion
ALS has been commonly regarded as a neuro-
degenerative disorder primarily involving the pyra-
midal system, but there is growing evidence that
Figure 1. GFA decrease in ALS patients compared with controls: prominent involvement of rostral CST, CC and fornix (red-yellow
scale; blue shows the CST derived from the JHU White-Matter Tractography atlas (35,36) – upper panels) and frontal (associative)
tracts (uncinate, superior longitudinal and inferior fronto-occipital fasciculi, with left prevalence; green VOIs were derived from
the JHU White-Matter Tractography atlas (35,36) – lower panels) ( p ⬍ 0.05, corrected).
CC: corpus callosum; CST: corticospinal tract; IFOF: inferior fronto-occipital fasciculus; L : left; SLF: superior longitudinal
fasciculus; UNC: uncinate fasciculus.
Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration Downloaded from informahealthcare.com by 89.97.220.66 on 04/17/13
For personal use only.
HARDI in amyotrophic lateral sclerosis 5
Figure 2. Voxelwise correlation analysis between GFA and UMN score: signifi cant inverse correlation in the left CST (upper panels)
and the body of CC (lower panels) ( p ⬍ 0.001, uncorrected).
Figure 3. Voxelwise correlation analysis between GFA and FrSBe scale T-scores: signifi cant inverse correlations in the body of
CC and at the left ponto-mesencephalic junction (upper panels) and in the left SLF (red shows the SLF derived from the JHU
White-Matter Tractography atlas (35,36) – lower panels) ( p ⬍ 0.001, uncorrected).
degenerative changes can occur elsewhere in the
central nervous system.
Recently, the extensive application of advanced
MRI techniques to the study of ALS has undoubtedly
improved the understanding of the pathophysiology
of the disease and in particular the spreading of
the neurodegenerative process from motor toward
extramotor systems (4,7).
Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration Downloaded from informahealthcare.com by 89.97.220.66 on 04/17/13
For personal use only.
6 F. Trojsi et al.
In our study we depicted whole brain GFA
changes in advanced stages of ALS by using a QBI
model as the HARDI method and provided addi-
tional information on the extent of the neurodegen-
erative process with respect to earlier DW-MRI
studies. To the best of our knowledge, there is only
one other paper addressing WM pathways ’ abnor-
mality in ALS by applying HARDI along with
high resolution sMRI and whole brain probabilistic
tractography (23).
Interestingly, comparing ALS patients with
controls, our voxelwise analysis of GFA maps resem-
bles the intra- (i.e. between the left and right precen-
tral gyri and the brainstem) and inter-hemispheric
(i.e. between the right and left anterior cingulate
gyri) abnormalities of WM pathways previously
detected by a different HARDI approach (23).
The need for using a multitensorial DWI
approach, such as HARDI, for depicting and
tracking axonal fi bre tracts was originated by the
limitations of DTI, which is based on a conven-
tional single-tensor model and then does not take
into account multiple fi bres crossing within single
voxels (37). The Gaussian tensor model assumes
a unimodal displacement probability distribution,
where the principal eigenvector is coaligned with
the dominant fi bre orientation within a voxel.
Therefore, considering that over one-third of vox-
els in the brain exhibits marked deviations from
the Gaussian diffusion behaviour, DTI is not ideal
for completely characterizing the location and ori-
entation of brain fi bres (24,38). This diffi culty is
particularly evident when attempting to recon-
struct the motor pathways, especially regarding
the face and tongue regions (39). The major fi bre
bundles, such as the callosal fi bres and superior
longitudinal fasciculi, cross the motor tracts at the
level of the centrum semiovale and, therefore,
when these fi bre bundles intersect within a voxel,
the DTI model is not adequate to comprehen-
sively describe the anatomical connectivity of this
voxel (37).
These constraints have been overcome by
HARDI methodology, which proved to be effi cient
in the detection of subtle axonal connections in the
brain (25), as well as in the spinal cord (40), mainly
by using a novel reconstruction model, such as QBI
(25). Specifi cally, this reconstruction method is
considerably more time-effi cient and practical for
routine investigative purposes than other q-space
imaging techniques, although less informative about
complex tissue microstructure properties (25). The
study of congenital (41,42), neoplastic (39) and
post-traumatic (43) brain and spinal cord abnor-
malities by HARDI is relatively recent, and in par-
ticular there are few applications to the investigation
of the neurodegenerative process in experimental
(43) and clinical (23) conditions.
A number of non-ALS studies have shown the
advantages of using the HARDI approach. In healthy
subjects, a magnifi cation of the frontopontine-
crossing motor fi bres in the midbrain has been
observed by using HARDI (25). In brain tumour
patients, the depiction of the motor pathway by
using a conventional DTI model resulted to be less
sensitive than HARDI in detecting corticospinal
tracts abnormalities (39). In cases of partial agen-
esia of the CC, HARDI allowed to depict many
more heterotopic tracts compared to DTI (42).
Furthermore, with regard to extramotor fi bre tracts,
in healthy subjects HARDI proved superior to DTI
in depicting microstructural changes in regions sup-
posed or known to have more crossing fi bres, such
as cingulate gyrus, superior longitudinal fasciculus
and uncinate fasciculus (38).
Against this background we designed our study,
believing that HARDI could be a valuable tool for
investigating in vivo the pathological process in
ALS. However, although the present work does not
aim to compare QBI and DTI models when applied
to ALS, we have preliminarily generated FA maps
submitted to the same TBSS pipeline of GFA maps,
and have observed that the main differences between
the two models in TBSS results were in regions sup-
posed or known to have many crossing fi bres, par-
ticularly in anterior thalamic radiations, cingulate
gyrus and uncinate fasciculus, more impaired in
ALS patients compared with controls in GFA maps
(data not shown).
With respect to GFA measures within the CTSs,
the signifi cant decrease of GFA in ALS patients
compared with controls, observed within both ros-
tral (underneath the primary motor cortices) and
caudal (at the left ponto-mesencephalic junction)
WM tracts, confi rms and strengthens previous DTI
TBSS results (14,15,18,34). This pattern of wide-
spread microstructural impairment of the CSTs
might be interpreted as an advanced stage of pathol-
ogy characterized by a wider extension of the
degenerative process, similar to what has been
reported in post mortem examinations of patients
with sporadic ALS, especially in case of long sur-
vival (3,44). Moreover, the signifi cant correlation
between GFA at the left ponto-mesenchephalic
junction along the CSTs and UMN score is con-
sistent with previous DTI correlation analyses
reporting signifi cant correlations between FA and
clinical indices of pyramidal impairment in the
brainstem (11,34).
In our group of patients with ALS we showed a
reduced GFA also in the CC, mainly in its body,
which negatively correlated with clinical UMN
involvement. These results are consistent with previ-
ous DTI studies about the CC impairment in ALS
(12,14 – 16,18,19,23), although signifi cant correla-
tions between changes of FA and UMN impairment
have been mostly detected in the CSTs but not in
the CC (14,16,18). We argue that these diverging
results depend on: the different clinical characteris-
tics of the patients enrolled (i.e. predominantly
Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration Downloaded from informahealthcare.com by 89.97.220.66 on 04/17/13
For personal use only.
HARDI in amyotrophic lateral sclerosis 7
upper or lower motor neuron signs and different
disease duration or disability); the weakness of clin-
ical scores for the detection of specifi c UMN impair-
ment (45); and the different methodologies used.
Although our results should be further validated,
they underline the crucial role of CC impairment as
a neuroradiologic marker of UMN disease.
The reduction of GFA in the body of CC, includ-
ing its midbody that is substantially formed by inter-
hemispheric fi bres connecting the motor cortices
(46), and in both cranial and caudal parts of the
CSTs, is probably the result of a prevalent axonal loss
within the motor fi bre pathway, as also demonstrated
by neuropathological evidence (3,45). Specifi cally,
our data corroborate the recent concept of the CC
as a conduit for the spread of pathology from one
hemisphere to the other (47). In agreement with our
fi ndings, several clinical studies have demonstrated
an impaired interhemispheric inhibition in ALS even
at an early stage of disease progression, which could
be mediated by CC impairment and associated with
detection of mirror movements (48,49).
With respect to extramotor WM pathways, we
found a more widespread pattern of WM impair-
ment in the medial frontal lobes compared to what
was revealed by previous DWI measures (i.e. GFA
decrease in CC, anterior cingulate and uncinate, infe-
rior fronto-occipital and superior longitudinal fas-
ciculi), while we found an inverse correlation between
the FrSBe T-score and GFA changes only in the
CC and superior longitudinal fasciculus. It is worth
speculating that such a pattern of predominantly
frontal WM injury, also described in other cohorts of
patients with ALS (50,51), may refl ect the executive
and behavioural dysfunctions that characterize our
patients. Along this line of evidence, our fi ndings
reinforce the patterns of degeneration detected by
DTI analyses in patients with ALS (13,15,52,53) or
frontotemporal dementia (FTD) (53) or both (53,54),
supporting the concept of a continuum between ALS
and FTD not only on a pathological and genetic
level, but also on a microstructural degeneration
level (1). In this regard, a recent structural neuroim-
aging study using voxel based morphometry (VBM)
and DTI in the three subtypes of the ALS-FTD
continuum (i.e. ALS, ALS-FTD and FTD) revealed
that anterior cingulate, premotor and motor areas as
well as their underlying WM tracts were equally
affected across the three diseases, in keeping with
features common to the spectrum of frontotemporal
lobar degeneration (53). Speculatively, this overlap
of structural changes across the three ALS-FTD
subtypes may be related to the speed of evolution of
the common cortical TAR DNA binding protein
43 (TDP-43) pathology that leads to rapid dissolu-
tion of cortico-cortical connections and might infl u-
ence the clinical presentation with motor symptoms
and/or behavioural manifestations (8).
The correlation between GFA and FrSBe T-scores
in the left superior longitudinal fasciculus found in
the present study might corroborate previous evi-
dence about the linkage between extramotor WM
tracts degeneration and neuropsychological defi cits
detected in patients with ALS (20,52,55).
Importantly, a recent structural study showed
that anterior cingulate atrophy was closely related to
the degree of apathy in ALS patients (55), apathy
being the most prominent behavioural symptom
reported in ALS (51). However, only Tsujimoto
et al. (20) observed in early stages of ALS a signifi -
cant inverse correlation between the FrSBe scale
(considering the subscore of apathy alone) and FA
values within the WM underneath the frontal lobes,
mainly in the right medial frontal gyri, without
detecting in these regions a signifi cant decrease of
WM volumes. In agreement with the latter results,
in a more advanced stage of disease and contrary to
what was revealed in terms of diffusivity, we did not
detect signifi cant WM atrophy in the medial frontal
lobes when comparing patients with controls by a
VBM analysis (SPM8 software package (www.fi l.
ion.ucl. ac.uk/spm) (data not shown). We speculate
that this discrepancy between VBM and diffusivity
fi ndings in the medial frontal lobes might be indica-
tive of an early active degeneration process detected
by DWI at a stage when volumetric changes have not
yet occurred.
The lack of signifi cant correlations between GFA
and ALSFRS-R score or disease progression rate in
our work is in agreement with previous studies
reporting an absence or low levels of signifi cance for
the correlations between DTI parameters and clini-
cal assessment (12,16,17,19,23). The limited num-
bers of patients studied, the heterogeneity of their
clinical presentations, and in particular the variance
in lower motor neuron (LMN) involvement that
contributes signifi cantly to the ALSFRS-R score,
may probably impact on the signifi cance levels of
these correlations.
Our study has some limitations. First, to fully
evaluate the power of this structural analysis method,
our QBI analysis needs to be expanded on a larger
cohort of patients, possibly scanned with more dif-
fusion gradient directions. We must take into account
that our sample of ALS patients, albeit small, covers
many disease features, especially in the advanced
stages. Secondly, we cannot determine with cer-
tainty the correlation between GFA changes and
cognitive impairment, as we did not perform a com-
plete neuropsychological assessment.
In summary, this study shows that the QBI
HARDI analysis holds important promise for gain-
ing additional insights into knowledge of the neuro-
degenerative process affecting the brain of patients
with ALS. The major fi ndings are the motor and
extramotor frontal diffusivity abnormalities poten-
tially underlying the behavioural and cognitive dys-
functions and the evidence of a neurodegenerative
pattern showing a spatial distribution similar
to what was previously described in the ALS-FTD
Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration Downloaded from informahealthcare.com by 89.97.220.66 on 04/17/13
For personal use only.
8 F. Trojsi et al.
continuum. Future studies should aim to correlate
these structural fi ndings, involving both motor and
extramotor WM pathways, with clinical and func-
tional data, by analysing groups of several ALS
subtypes or phenotypes in early stages of disease
to identify non-invasive markers of ALS neurode-
generation in larger serial and multimodal MRI
analyses.
Acknowledgements
The authors are grateful to Antonella Paccone for
her expert technical assistance.
Declaration of interest: The authors report no
confl icts of interest. The authors alone are respon-
sible for the content and writing of the paper.
References
1. Ludolph AC , Brettschneider J , Weishaupt JH . Amyotrophic
lateral sclerosis . Curr Opin Neurol. 2012 ; 25 : 530 – 5 .
2. Phukan J , Pender NP , Hardiman O . Cognitive impairment
in amyotrophic lateral sclerosis . The Lancet Neurology.
2007 ; 6 : 994 – 1003 .
3. Smith MC . Nerve fi bre degeneration in the brain in amyo-
trophic lateral sclerosis . J Neurol Neurosurg Psychiatry.
1960 ; 23 : 269 – 82 .
4. Agosta F , Chiò A , Cosottini M , de Stefano N , Falini A ,
Mascalchi M , et al . The present and the future of neuro-
imaging in amyotrophic lateral sclerosis . AJNR Am J
Neuroradiol. 2010 ; 31 : 1769 – 77 .
5. Eisen A , Weber M . The motor cortex and amyotrophic
lateral sclerosis . Muscle Nerve. 2001 ; 24 : 564 – 73 .
6. Ravits JM , La Spada AR . ALS motor phenotype
heterogeneity, focality, and spread . Neurology. 2009 ; 73 :
805 – 11 .
7. Tsermentseli S , Leigh PN , Goldstein LH . The anatomy of
cognitive impairment in amyotrophic lateral sclerosis: more
than frontal lobe dysfunction . Cortex. 2012 ; 48 : 166 – 82 .
8. Geser F , Martinez-Lage M
, Robinson J , Uryu K , Neumann
M , Brandmeir NJ , et al . Clinical and pathological continuum
of multisystem TDP-43 proteinopathies . Arch Neurol.
2009 ; 66 : 180 – 9 .
9. Pierpaoli C , Basser PJ . Toward a quantitative assessment of
diffusion anisotropy . Magn Reson Med. 1996 ; 36 : 893 – 906 .
10. Basser PJ . Inferring microstructural features and the physi-
ological state of tissues from diffusion-weighted images .
NMR Biomed. 1995 ; 8 : 333 – 44 .
11. Ellis CM , Simmons A , Jones DK , Bland J , Dawson JM ,
Horsfi eld MA , et al . Diffusion tensor MRI assesses corticos-
pinal tract damage in ALS . Neurology. 1999 ; 53 : 1051 – 8 .
12. Metwalli NS , Benatar M , Nair G , Usher S , Hu X ,
Carew JD . Utility of axial and radial diffusivity from diffu-
sion tensor MRI as markers of neurodegeneration in amyo-
trophic lateral sclerosis . Brain Res. 2010 ; 1348 : 156 – 64 .
13. Agosta F , Pagani E , Petrolini M , Caputo D , Perini M ,
Prelle A , et al . Assessment of white matter tract damage in
patients with amyotrophic lateral sclerosis: a diffusion tensor
MRI imaging tractography study . AJNR Am J Neuroradiol.
2010 ; 31 :
1457 – 61 .
14. Filippini N , Douaud G , Mackay CE , Knight S , Talbot K ,
Turner MR . Corpus callosum involvement is a consistent
feature of amyotrophic lateral sclerosis . Neurology.
2010 ; 75 : 1645 – 52 .
15. Cirillo M , Esposito F , Tedeschi G , Caiazzo G , Sagnelli A ,
Piccirillo G , et al . Widespread microstructural white matter
involvement in amyotrophic lateral sclerosis: a whole brain
DTI study . AJNR Am J Neuroradiol. 2012 ; 33 : 1102 – 8 .
16. Agosta F , Pagani E , Rocca
MA , Caputo D , Perini M ,
Salvi F , et al . Voxel based morphometry study of brain
volumetry and diffusivity in amyotrophic lateral sclerosis
patients with mild disability . Hum Brain Mapp. 2007 ; 28 :
1430 – 8 .
17. Senda J , Kato S , Kaga T , Ito M , Atsuta N , Nakamura T , et al .
Progressive and widespread brain damage in ALS: MRI
voxel based morphometry and diffusion tensor imaging
study . Amyotroph Lateral Scler. 2011 ; 12 : 59 – 69 .
18. Sage CA , van Hecke W , Peeters RR , Sijbers J , Robberecht W ,
Parizel P
, et al . Quantitative diffusion tensor imaging in
amyotrophic lateral sclerosis: revisited . Hum Brain Mapp.
2009 ; 30 : 3657 – 75 .
19. Ben Bashat D , Artzi M , Tarrasch R , Nefussy B , Drory VE ,
Aizenstein O . A potential tool for the diagnosis of ALS based
on diffusion tensor imaging . Amyotroph Lateral Scler.
2011 ; 12 : 398 – 405 .
20. Tsujimoto M , Senda J , Ishihara T , Niimi Y , Kawai Y ,
Atsuta N , et al . Behavioural changes in early ALS correlate
with voxel based morphometry and diffusion tensor imaging .
J Neurol Sci. 2011 ; 307 : 34 – 40 .
21. Song SK , Sun SW , Ramsbottom MJ , Chang C , Russell J ,
Cross AH . Dysmyelination revealed through MRI as
increased radial (but unchanged axial) diffusion of water .
NeuroImage. 2002 ; 17 : 1429 – 36 .
22. Song SK , Yoshino J , Le TQ , Lin SJ , Sun SW , Cross AH ,
et al . Demyelination increases radial diffusivity in corpus
callosum of mouse brain . NeuroImage. 2005 ; 26 : 132 – 40 .
23. Rose S , Pannek K , Bell C , Baumann F , Hutchinson N ,
Coulthard A , et al . Direct evidence of intra- and inter-
hemispheric corticomotor network degeneration in amyo-
trophic lateral sclerosis: an automated MRI structural
connectivity study . NeuroImage. 2012 ; 59 : 2661 – 9 .
24. Tuch DS , Reese TG , Wiegell MR , Makris N , Belliveau JW ,
Wedeen VJ . High angular resolution diffusion imaging
reveals intravoxel white matter fi bre heterogeneity . Magn
Reson Med. 2002 ; 48 : 577 – 82 .
25. Tuch DS . Q-ball imaging . Magn Reson Med. 2004 ; 52 :
1358 – 72 .
26. Brooks BR , Miller RG , Swash M , Munsat TL , World
Federation of Neurology Research Group on Motor
Neuron Diseases. El Escorial revisited: revised criteria for
the diagnosis of amyotrophic lateral sclerosis . Amyotroph
Lateral Scler Other Motor Neuron Disord. 2000 ; 1 :
293 – 9 .
27. Cedarbaum JM , Stambler N , Malta E , Fuller C , Hilt D ,
Thurmond B , et al . The ALSFRS-R: a revised ALS
functional rating scale that incorporates assessments of
respiratory function: BDNF ALS Study Group (Phase III) .
J Neurol Sci. 1999 ; 169 : 13 – 21 .
28. Grace J , Stout JC , Malloy PF . Assessing frontal lobe behav-
ioural syndromes with the frontal lobe personality scale .
Assessment. 1999 ; 6 : 269 – 84 .
29. Turner MR , Cagnin A , Turkheimer FE , Miller CC ,
Shaw CE , Brooks DJ , et al . Evidence of widespread cerebral
microglial activation in amyotrophic lateral sclerosis: an
[(11)C](R)-PK11195 positron emission tomography study .
Neurobiol Dis. 2004 ; 15 : 601 – 9 .
30. Dubois B , Slachevsky A , Litvan I , Pillon B . The FAB: a
frontal assessment battery at bedside . Neurology. 2000 ; 55 :
1621 – 6 .
31. Folstein MF , Folstein SE , McHugh PR . ‘ Mini-mental
state ’ . A practical method for grading the cognitive state of
patients for the clinician . J Psychiatr Res. 1975 ; 12 :
189 – 98 .
32. Borkowski JG , Benton AL , Spreen O . Word fl uency and
brain damage . Neuropsychologia. 1967 ; 5 : 135 – 40 .
33. Carlesimo GA , Caltagirone C , Gainotti G . The Mental
Deterioration Battery: normative data, diagnostic reliability
and qualitative analyses of cognitive impairment. The Group
Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration Downloaded from informahealthcare.com by 89.97.220.66 on 04/17/13
For personal use only.
HARDI in amyotrophic lateral sclerosis 9
for the Standardization of the Mental Deterioration Battery .
Eur Neurol. 1996 ; 36 : 378 – 84 .
34. Smith SM , Jenkinson M , Johansen-Berg H , Rueckert D ,
Nichols TE , Mackay CE , et al . Tract based spatial statistics:
voxelwise analysis of multi-subject diffusion data .
NeuroImage. 2006 ; 31 : 1487 – 505 .
35. Hua K , Zhang J , Wakana S , Jiang H , Li X , Reich DS , et al .
Tract probability maps in stereotaxic spaces: analysis of
white matter anatomy and tract-specifi c quantifi cation .
NeuroImage. 2008 ; 39 : 336 – 47
.
36. Wakana S , Caprihan A , Panzenboeck MM , Fallon JH ,
Perr y M , Gollub RL , et al . Reproducibility of quantitative
tractography methods applied to cerebral white matter .
NeuroImage. 2007 ; 36 : 630 – 44 .
37. Jones DK . Studying connections in the living human brain
with diffusion MRI . Cortex. 2008 ; 44 : 936 – 52 .
38. Behrens TEJ , Johansen Berg H , Jbabdi S , Rushworth MFS ,
Woolrich MW . Probabilistic diffusion tractography with
multiple fi bre orientations: what can we gain? NeuroImage.
2007 ; 34 : 144 – 55 .
39. Yamada K , Sakai K , Hoogenraad FGC , Holthuizen R ,
Akazawa K , Ito H , et al . Multitensor tractography enables
better depiction of motor pathways: initial clinical experience
using diffusion-weighted MR imaging with standard b-value .
AJNR Am J Neuroradiol. 2007 ; 28 : 1668 – 73 .
40. Cohen-Adad J , Descoteaux M , Rossignol S , Hoge RD ,
Deriche R , Benali H . Detection of multiple pathways in
the spinal cord using Q-ball imaging . NeuroImage. 2008 ; 42 :
739 – 49 .
41. Wahl M , Barkovich AJ , Mukherjee P . Diffusion imaging and
tractography of congenital brain malformations . Pediatr
Radiol. 2010 ; 40 : 59 – 67 .
42. Wahl M , Strominger Z , Jeremy RJ , Barkovich AJ , Wakahiro
M , Sherr EH , et al . Variability of homotopic and heterotopic
callosal connectivity in partial agenesis of the corpus
callosum: a 3T diffusion tensor imaging and Q-ball tracto-
graphy study . AJNR Am J Neuroradiol. 2009 ; 30 : 282 – 9 .
43. Cohen-Adad J , Leblond H , Delivet-Mongrain H , Martinez
M , Benali H , Rossignol S . Wallerian degeneration after spinal
cord lesions in cats detected with diffusion tensor imaging .
NeuroImage. 2011 ; 57 : 1068 – 76 .
44. Nishihira Y , Tan CF , Toyoshima Y , Yonemochi Y , Kondo H ,
Nakajima T , et al . Sporadic amyotrophic lateral sclerosis:
widespread multisystem degeneration with TDP-43 pathol-
ogy in a patient after long-term survival on a respirator .
Neuropathology. 2009 ; 29 : 689 – 96 .
45. Ince PG , Wharton SB . Cytopathology of the motor neuron.
In: Eisen AA , Shaw PJ , editors . Motor Neuron Disorders
and Related Diseases: Handbook of Clinical Neurology .
3rd edn. Amsterdam, the Netherlands: Elsevier ; 2007.
pp. 89 – 119 .
46. Chao YP , Cho KH , Yeh CH , Chou KH , Chen JH , Lin CP .
Probabilistic topography of human corpus callosum using
cytoarchitectural parcellation and high angular resolution
diffusion imaging tractography . Hum Brain Mapp. 2009 ;
30 : 3172 – 87 .
47. Eisen A . Amyotrophic lateral sclerosis: evolutionary
and other perspectives . Muscle Nerve. 2009 ; 40 :
297 – 304 .
48. Karandreas N , Papaopoulou M , Kokotis P , Papapostolou A ,
Tsivgoulis G , Zambelis T . Impaired interhemispheric inhibi-
tion in amyotrophic lateral sclerosis . Amyotroph Lateral
Scler. 2007 ; 8 : 112 – 8 .
49. Krampfl K , Petri S , Gotz F , Mohammadi B , Bufl er J .
Amyotrophic lateral sclerosis (ALS) and mirror movements
in a patient with polymicrogyria . Amyotroph Lateral Scler.
2003 ; 4 : 266 – 9 .
50. Abrahams S , Goldstein LH , Suckling J , Ng V , Simmons A ,
Chitnis X , et al . Frontotemporal white matter changes
in amyotrophic lateral sclerosis . J Neurol. 2005 ; 252 :
321 – 31 .
51. Lillo P , Mioshi E , Zoing MC , Kiernan MC , Hodges JR . How
common are behavioural changes in amyotrophic lateral
sclerosis? Amyotroph Lateral Scler. 2011 ; 12 : 45 – 51 .
52. Sarro L , Agosta F , Canu E , Riva N , Prelle A , Copetti M ,
et al . Cognitive functions and white matter tract damage
in amyotrophic lateral sclerosis: a diffusion tensor
tracto graphy study . AJNR Am J Neuroradiol. 2011 ; 32 :
1866 – 72 .
53. Lillo P , Mioshi E , Burrell JR , Kiernan MC , Hodges JR ,
Hornberger M . Grey and white matter changes across
the amyotrophic lateral sclerosis/frontotemporal dementia
continuum . PLoS One. 2012 ; 7 : e43993 .
54. Mahoney CJ , Beck J , Rohrer JD , Lashley T , Mok K ,
Shakespeare T , et al . Frontotemporal dementia with the
C9orf72 hexanucleotide repeat expansion: clinical, neuro-
anatomical and neuropathological features . Brain. 2012 ;
135: 736 – 50 .
55. Woolley SC , Zhang Y , Schuff N , Weiner MW , Katz JS .
Neuroanatomical correlates of apathy in ALS using 4 Tesla
diffusion tensor MRI . Amyotroph Lateral Scler. 2011 ;
12 : 52 – 8 .
Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration Downloaded from informahealthcare.com by 89.97.220.66 on 04/17/13
For personal use only.
