Lessons of ALS imaging: Pitfalls and future directions – A critical review

Abstract

Background: While neuroimaging in ALS has gained unprecedented momentum in recent years, little progress has been made in the development of viable diagnostic, prognostic and monitoring markers.
Objectives: To identify and discuss the common pitfalls in ALS imaging studies and to reflect on optimal study designs based on pioneering studies.
Methods: A “PubMed”-based literature search on ALS was performed based on neuroimaging-related keywords. Study limitations were systematically reviewed and classified so that stereotypical trends could be identified.
Results: Common shortcomings, such as relatively small sample sizes, statistically underpowered study designs, lack of disease controls, poorly characterised patient cohorts and a large number of conflicting studies, remain a significant challenge to the field. Imaging data of ALS continue to be interpreted at a group-level, as opposed to meaningful individual-patient inferences.
Conclusions: A systematic, critical review of ALS imaging has identified stereotypical shortcomings, the lessons of which should be considered in the design of future prospective MRI studies. At a time when large multicentre studies are underway a candid discussion of these factors is particularly timely.

Full text

NeuroImage: Clinical 4 (2014) 436–443
Contents lists available at ScienceDirect
NeuroImage: Clinical
j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / y n i c l
Review Article
Lessons of ALS imaging: Pitfalls and future directions —A critical
review
Peter Bede
a , *
, Orla Hardiman
a
a
Academic Unit of Neurology, Trinity College Dublin, Room 5.43, Biomedical Sciences Building, Pearse Street, Dublin 2, Ireland
a r t i c l e i n f o
Article history:
Received 22 December 2013
Received in revised form 23 February 2014
Accepted 23 February 2014
Keywords:
Amyotrophic lateral sclerosis
Biomarker
MRI
PET
Spectroscopy
a b s t r a c t
Background: While neuroimaging in ALS has gained unprecedented momentum in recent years, little progress
has been made in the development of viable diagnostic, prognostic and monitoring markers.
Objectives: To identify and discuss the common pitfalls in ALS imaging studies and to reflect on optimal study
designs based on pioneering studies.
Methods: A “PubMed”-based literature search on ALS was performed based on neuroimaging-related key-
words. Study limitations were systematically reviewed and classified so that stereotypical trends could be
identified.
Results: Common shortcomings, such as relatively small sample sizes, statistically underpowered study
designs, lack of disease controls, poorly characterised patient cohorts and a large number of conflicting
studies, remain a significant challenge to the field. Imaging data of ALS continue to be interpreted at a
group-level, as opposed to meaningful individual-patient inferences.
Conclusions: A systematic, critical review of ALS imaging has identified stereotypical shortcomings, the
lessons of which should be considered in the design of future prospective MRI studies. At a time when large
multicentre studies are underway a candid discussion of these factors is particularly timely.
c
2014 The Authors. Published by Elsevier Inc.
This is an open access article under the CC BY-NC-ND license
( http: // creativecommons.org / licenses / by-nc-nd / 3.0 / ).
1. Introduction
An exponential increase in high-impact imaging publications of
ALS has been seen in recent years. However, the majority of re-
cent systematic reviews on the topic are technique-based ( Turner
et al., 2012 ), classifying and discussing studies based on the specific
imaging method utilised, rather than highlighting common themes
and shared conclusions. Furthermore, comprehensive reviews of ALS
imaging have focused primarily on the achievements of landmark
studies, and are insufficiently critical of shortcomings, discussion of
which may contribute to improved study designs.
ALS imaging has been relatively successful as a descriptive tool,
characterising features of specific ALS phenotypes and genotypes
Abbreviations: AD, axial diffusivity; C9orf72, chromosome 9 open reading frame 72;
DTI, diffusion tensor imaging; FA, fractional anisotropy; MD, mean diffusivity; MEG,
magnetoencephalography; MRS, magnetic resonance spectroscopy; MUNE, motor unit
number estimation; PET, positron emission tomography; PNS, peripheral nervous sys-
tem; RD, radial diffusivity; ROI, region of interest; SPECT, single photon emission com-
puted tomography; TMS, transcranial magnetic stimulation; VBM, voxel-based mor-
phometry.
* Correspondence to: Peter Bede, Tel.: + 353 1 8964497; fax: + 353 1 2604787.
E-mail address: bedepeter@hotmail.com (P. Bede).
( Chang et al., 2005 ; Stanton et al., 2009a ; Bede et al., 2013a ). Ad-
ditionally, the anatomical bases of recent clinical observations, such
as the concept of cortical focality, neuropsychological deficits, ex-
trapyramidal dysfunction, and sensory deficits, have been elucidated.
Imaging studies of ALS have also contributed to our understanding
of active biological processes, such as confirmation of inflammatory
mechanisms ( Corcia et al., 2012 ), spread along functional connec-
tions ( Verstraete et al., 2013 ), and dysfunction of inhibitory circuits
( Douaud et al., 2011 ). Recent work has provided evidence of network
degeneration as opposed to preferential, focal white and grey mat-
ter pathology ( Douaud et al., 2011 ). Landmark studies of presymp-
tomatic genetic variants such as SOD-1 mutation carriers have high-
lighted structural and metabolic changes prior to symptom onset and
have offered unprecedented insights into the presymptomatic phase
of the disease ( Ng et al., 2008 ; Carew et al., 2011 ). PET and fMRI
studies have revealed compensatory processes, suggestive of an at-
tempted functional adaptation in the face of relentless neurodegen-
eration ( Schoenfeld et al., 2005 ).
However as in the case of Alzheimer’s disease and multiple scle-
rosis, the development of viable diagnostic, prognostic and disease
progression markers at an individual level remains as one of the pri-
mary aspirations of ALS. Despite years of research, progress on this
front has been relatively slow, results inconsistent, and the outcomes
2213-1582/ $ - see front matter
c
2014 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license ( http: // creativecommons.org /
licenses
/ by-nc-nd / 3.0 / ).
http://dx.doi.org/10.1016/j.nicl.2014.02.011

P. Bede, O. Hardiman / NeuroImage: Clinical 4 (2014) 436–443 437
not readily transferable to the clinic. The aims of this study are to
explore the factors that have led to a large number of inconsistent re-
sults and to reflect on optimal study designs which could be utilised
in future multi-centre studies.
2. Methods
A formal literature review was conducted on PubMed with the
individual search terms ‘Imaging ’ , ‘Neuroimaging ’ , ‘Magnetic reso-
nance imaging ’ , ‘Positron emission tomography ’ , ‘Single photon emis-
sion computed tomography ’ , ‘Diffusion tensor imaging ’ , ‘Voxel-based
morphometry ’ , ‘Spectroscopy ’ in combination with ‘ALS ’ and ‘Mo-
tor neuron disease ’ separately. Publications were searched during a
2 month period between November 2013 and December 2013. Both
original contributions and review papers ( Turner et al. , 2009 , 2012 ,
2013 ; Bede et al., 2012 ; Bowser et al., 2011 ; Turner and Modo, 2010 ;
Foerster et al., 2013b ; Wang et al., 2011 ; Agosta et al., 2010a ; Pradat
and Dib, 2009 ; van der Graaff et al., 2009 ; Dengler et al., 2005 ; Kalra
and Arnold, 2003 ; Karitzky and Ludolph, 2001 ; Comi et al., 1999 ;
Kollewe et al., 2012 ; Kassubek et al., 2012 ; Prell and Grosskreutz,
2013 ) were selected, but only articles published in English were
reviewed. Where relevant, references of identified papers were also
evaluated. Based on the above search criteria, a total of 184 original re-
search papers and 21 review papers were identified ( Supplementary
Table 1 ). Each original contribution was individually reviewed for
author-reported and reviewer-identified study limitations, based on
which distinct trends of common methodological shortcomings were
observed. An additional objective was to identify reports of seem-
ingly inconsistent results or potentially contradicting conclusions.
Thirdly, constructive examples of innovative methods were sought
in response to the identified stereotypical pitfalls, so that recommen-
dations for optimised ALS study designs can be presented.
3. Results
3.1. Common methodological limitations
While disease heterogeneity is an inherent challenge of the field,
common methodological study limitations can also be identified
across individual studies, such as small sample sizes, lack of dis-
ease controls, suboptimal patient characterisation, technique-driven
rather than clinical problem-driven studies, lenient statistical models
and insufficient discussion of laterality and symmetry of pathology
( Table 1 ). In addition to the methodological shortcomings of single
studies, fairly well-defined gaps in the ALS imaging literature as a
whole can also be observed, indicating pressing, yet promising re-
search opportunities ( Table 1 ).
3.2. Inconsistencies of conclusions
The above factors are likely to have contributed to the inconsis-
tencies of various studies, particularly in the degree of extra-motor
involvement, laterality of pathology and the extent of brain changes
in lower motor neuron dominant conditions. Many studies have
highlighted right precentral gyrus changes ( Kassubek et al., 2005 ;
Grossman et al., 2008 ; Agosta et al., 2007 ; Grosskreutz et al., 2006
), while others have demonstrated bilateral motor cortex pathol-
ogy ( Chang et al., 2005 ; Filippini et al., 2010 ; Verstraete et al., 2012 ;
Thivard et al., 2007 ; Bede et al., 2013c ). . Unilateral left ( Bede et al.,
2013c ) and right ( Mezzapesa et al., 2007 ) parahippocampal patholo-
gies have both been reported. Similar discrepancies can be observed
in studies of specific phenotypes. For example, relative sparing of
corticospinal tract integrity has been reported in progressive mus-
cle atrophy by some studies ( Cosottini et al., 2005b ), while others
have identified extensive diffusivity changes in the brain, concluding
that widespread CNS involvement occurs ( Prudlo et al., 2012 ). And
while some drug-response studies have captured a Riluzole effect
( Kalra et al., 2006 ), others failed to replicate this ( Bradley et al., 1999
). Accounts of extra-motor grey matter pathology also show con-
siderable variation ranging from limited frontotemporal pathology
to widespread occipital, parietal and subcortical changes. This wide
range of inconsistent findings may reflect true disease heterogeneity,
but is more likely to be a function of small sample size, inadequate
power, and consequent over-interpretation of findings.
3.3. Sample size and statistical analysis
The challenges of recruiting large patient cohorts in ALS imaging
studies are obvious due to disease-specific factors such as orthopnoea,
dyspnoea, and sialorrhoea. Yet, despite these recognised limitations,
formal power calculations are seldom carried out. Methods for power
calculations depend on the specific imaging technique utilised. Recent
evidence suggests that the number of foci reported in small VBM
studies and even in meta-analyses with few studies may often be
exaggerated ( Fusar-Poli et al., 2013 ). In contrast, whole-brain meta-
analyses of large sample sizes identify fewer foci than single studies
( Fusar-Poli et al., 2013 ). Region of interest (ROI) based studies, on the
other hand, are susceptible to strong reporting bias ( Ioannidis, 2011 ).
Methods for bias-corrected power calculations have been specifically
developed for diffusion tensor imaging ( Lauzon and Landman, 2013 ).
Sample size and power calculations for fMRI studies are relatively well
established ( Mumford, 2012 ; Desmond and Glover, 2002 ). Several
commercial software packages also exist for power calculations of
imaging studies ( Joyce and Hayasaka, 2012 ).
The number of ALS patients included in SPECT studies varies be-
tween n = 14 ( Waldemar et al., 1992 ) and n = 26 ( Abe et al., 1997 ),
and those in PET studies varies between n = 7 ( Hoffman et al., 1992 )
and n = 32 ( Cistaro et al., 2012 ). Single-centre morphometric studies
of ALS also show considerable variation in sample size; from n = 12
( Minnerop et al., 2009 ) to n = 45 ( Verstraete et al., 2012 ). Similarly,
diffusivity studies report results from a range of sample sizes; from
n = 13 ( Ciccarelli et al., 2006 ) to n = 87 ( Rajagopalan et al., 2013 ).
In general, task (paradigm) based functional MRI studies are particu-
larly small; from n = 6 ( Schoenfeld et al., 2005 ) to n = 22 ( Mohammadi
et al., 2011 ). Resting state fMRI studies are somewhat larger; from
n = 12 ( Verstraete et al., 2010a ) to n = 25 ( Douaud et al., 2011 ). Spec-
troscopy studies range from n = 8 ( Yin et al., 2004 ) to n = 70 ( Pohl
et al., 2001 ; Block et al., 2002 ). Studies of specific genotypes and
phenotypes often draw conclusions from even smaller – frequently
single digit –sample sizes ( Table 2 ).
The majority of ALS imaging papers use age and gender matched
study groups. Age is sometimes included as a covariate in the anal-
yses, but gender, education and handedness are seldom considered.
The effect of gender on MR variables is well established in healthy
populations ( Chen et al., 2007 ; Menzler et al., 2011 ) and can also be
demonstrated in ALS cohorts ( Bede et al., 2013b ). Similarly, the link
between handedness and corticospinal tract / motor cortex asymme-
try has been confirmed in healthy individuals ( Herv
´
e et al., 2009 ). In
ALS, there is evidence that handedness may be associated with side
of onset in ALS ( Turner et al., 2011 ), therefore correction for handed-
ness in ALS imaging studies may be judicious. Moreover, neuroimag-
ing data from healthy aging cohorts also demonstrate the effect of
education on structural data, especially in older populations which
are typically studied in ALS ( Arenaza-Urquijo et al., 2013 ; Noble et al.,
2012 ). The typically small sample sizes of ALS imaging studies are
often further subdivided to characterise specific phenotypes, which is
likely to accentuate the confounding effects of the above demographic
factors even more.

438 P. Bede, O. Hardiman / NeuroImage: Clinical 4 (2014) 436–443
Table 1.
Common shortcomings for ALS imaging studies.
Common methodological limitations of individual ALS imaging studies
•Technique-driven rather than clinical problem-driven studies
•Confirmatory as opposed to original study designs
•Small to moderate sample sizes, lack of power calculations
•Inadequate discussion or interpretation of unilateral findings
•Suboptimal clinical patient characterisation
•Lack of comprehensive genotyping i.e. C9orf72 which may contribute to extra-motor changes
•Limited imaging methods i.e. white matter only, grey matter only studies, as opposed to multifaceted, multimodal structural
/ functional, cortical / subcortical characterisation
•Lack of disease controls and “ALS-mimic” controls
•Correlation of brain changes with clinical measures that also heavily depend on lower motor neuron function (ALSFRS-r, tapping rates)
•Lack of post-mortem validation of imaging findings
•Lenient statistical models, insufficient correction for demographic factors (education, handedness, age, gender)
•Reports of statistical “trends” uncorrected for multiple testing
Shortcomings of the current literature of ALS imaging
•Paucity of presymptomatic studies
•Paucity of classifier (diagnostic) studies
•Paucity of meta-analyses
•Paucity of high-field MRI studies
•Lack of large, cross-platform, multi-centre studies
•Lack of post-mortem imaging studies in ALS
•Relative paucity of spinal cord studies
•Lack of quantitative LMN
/ plexus / PNS imaging studies
•Paucity of muscle imaging studies
Table 2
A selection of sample size examples from imaging studies characterising specific ALS phenotypes or genotypes. The highlighted studies also included larger reference groups of
controls or sporadic ALS patients.
ALS-dementia
n = 4 ( Neary et al., 1990 ; Tanaka et al., 1993 ), n = 8 ( Talbot et al., 1995 ), n = 17 ( Rajagopalan et al., 2013 )
ALS-PD-Guam complex
n = 4 ( Snow et al., 1990 )
ALS-FTD
n = 10 ( Chang et al., 2005 ), n = 12 ( Cistaro et al, 2014 )
D90a-SOD1 genotype
n = 6 ( Stanton et al., 2009b ), n = 7 ( Blain et al., 2011 ; Turner et al., 2007a ), n = 10 ( Turner et al., 2005 )
C9orf72 hexanucleotide repeat expansion
in ALS
n = 9 ( Bede et al., 2013a ; Bede et al., 2013d ), n = 15 ( Cistaro et al., 2014 )
Progressive lateral sclerosis
n = 4 ( Turner et al., 2007b ), n = 6 ( Ciccarelli et al., 2009 ; Mitsumoto et al., 2007 ), n = 12 ( van der Graaff et al., 2011 ), n = 19 ( Iwata et al., 2011 )
Progressive muscular atrophy
n = 8 ( Cosottini et al., 2005a ), n = 9 ( Mitsumoto et al., 2007 ), n = 12 ( van der Graaff et al.,
2011 )
Presymptomatic studies of homozygous D90A-SOD1
n = 2 ( Turner et al., 2005 ), n = 8 ( Ng et al., 2008 ), n = 24 ( Carew et al., 2011 )
Bulbar onset ALS
n = 8 ( Ellis et al., 2001 ; Ellis et al., 1998 ), n = 12 ( van der Graaff et al., 2011 ), n = 13 ( Cistaro et al., 2012 ), n = 13 ( Bede et al., 2013c )
Spinal onset ALS
n = 8 ( Ellis et al., 2001 ; Ellis et al., 1998 ), n = 12 (
van der Graaff et al., 2011 ), n = 19 ( Cistaro et al., 2012 ), n = 20 ( Bede et al., 2013c )
3.4. Disease controls
Neurological disease controls, patients with lower motor neu-
ron syndromes ( Sperfeld et al., 2005 ), Kennedy’s disease patients
( Sperfeld et al., 2005 ), Alzheimer’s disease cohorts ( Block et al., 2002
) and poliomyelitis groups ( Dalakas et al., 1987 ) have been previ-
ously included in ALS imaging studies. However, the large majority
of ALS imaging studies utilise healthy controls as a reference group
to highlight ALS-specific changes. For the development of diagnostic
markers capable of discriminating ALS from other neurological con-
ditions, the inclusion of disease controls, especially common mimics
of ALS, is essential.
3.5. Laterality of findings
Unilateral or asymmetrical imaging findings are frequently re-
ported in ALS, yet they are seldom discussed comprehensively. Sim-
ilarly to other neurodegenerative conditions, at an individual level,
asymmetrical symptoms and brain pathology are established features
of early stage ALS ( Turner et al., 2011b ). However, few imaging stud-
ies have examined the relationship of sidedness of symptoms and
brain changes. Metabolite ratio changes in the motor cortex have
been shown to correspond to the lateralisation of clinical symptoms
( Pohl et al., 2001 ; Block et al., 2002 ). Morphometric studies report
unilateral pathological changes in the left cingulum ( Abrahams et al.,
2005 ), left middle frontal gyrus ( Kassubek et al., 2005 ), left inferior
frontal gyrus ( Agosta et al., 2007 ), left thalamus ( Chang et al., 2005 ),
left medial frontal region ( Kassubek et al., 2005 ), left insula ( Thivard
et al., 2007 ), left anterior temporal region ( Grossman et al., 2008 ), left
parahippocampal gyrus ( Bede et al., 2013c ), right parahippocampal
gyrus ( Mezzapesa et al., 2007 ), right precentral gyrus ( Kassubek et al.,
2005 ; Grossman et al., 2008 ; Agosta et al., 2007 ; Grosskreutz et al.,
2006 ), right superior temporal gyrus ( Bede et al., 2013c ; Mezzapesa
et al., 2007 ), right cerebellum ( Thivard et al., 2007 ), and right premo-
tor regions ( Grossman et al., 2008 ). Diffusivity studies have reported
unilateral pathology in the left inferior frontal lobe ( Canu et al., 2011
) and right uncinate fasciculus ( Agosta et al., 2010b ). However, sam-
ple size effects, handedness, disability profile, disease duration and
physiological CNS asymmetry are rarely considered in the interpre-
tation of these unilateral findings. This is despite the recognition of
physiological brain asymmetry in right-handed healthy populations
( Takao et al., 2011 ) and that asymmetry of the primary motor cortex
and corticospinal tract architecture is particularly well established

P. Bede, O. Hardiman / NeuroImage: Clinical 4 (2014) 436–443 439
(
Westerhausen et al., 2007 ). Sample size limitations, disability pro-
file and disease duration are likely to be the key factors contributing
to asymmetrical findings. It is probable that asymmetry decreases
on longitudinal follow-up. Until large meta-analyses and prospec-
tive studies with extensive data sharing are undertaken, reports on
laterality should be interpreted with caution, and emphasis should
be placed on the specific structure affected rather than the side of
involvement.
3.6. Patient characterisation
Multifaceted characterisation of patients is of importance given
the unique clinical, psychological and imaging profile of specific ALS
genotypes, such as those with SOD1 mutations ( Stanton et al., 2009a ;
Blain et al., 2011 ; Turner et al., 2005 ) and those carrying the hexanu-
cleotide expansion in C9orf72 ( Bede et al., 2013a ). Imaging studies
of ALS often provide in-depth characterisation in selected domains
e.g. detailed psychological and limited genetic or post-mortem pro-
filing, or vice versa. This is frequently a function of local expertise and
is likely to improve with the shared infrastructure of international
collaborations.
3.7. Multimodal studies
In studies using whole-brain, functional imaging modalities, such
as PET, SPECT or fMRI, single-technique approaches may be suffi-
cient. However, in studies using region-of-interest (ROI) or segmen-
tation based MRI techniques such as VBM, DTI or cortical thickness
measurements, multimodal approaches may be superior by providing
comprehensive characterisation of disease-specific pathology. Stud-
ies combining multiple imaging techniques that evaluate multiple
measures of both grey and white matter integrity are more likely
to capture the full spectrum of network degeneration in ALS. Mul-
timodal papers have highlighted increased functional and decreased
structural connectivity in ALS, suggesting inhibitory dysfunction in
ALS ( Douaud et al., 2011 ). The benefit of using multiple imaging
parameters can be further illustrated with the use of multiple diffu-
sivity variables. Many DTI studies only use fractional anisotropy (FA)
or mean diffusivity (MD), despite the fact that these are composite
measures of eigenvalues and are not associated with the specific na-
ture of white matter pathology. Conversely, axial diffusivity (AD) and
radial diffusivity (RD) are independent variables; AD is broadly con-
sidered an axonal marker ( Sun et al., 2006 ) and RD a myelin marker
( Song et al., 2005 ). Multimodal biomarker studies are also ideal as a
method to compare the sensitivity and specificity profiles of various
techniques. For example, multimodal studies have suggested that MR
spectroscopy may be more sensitive in detecting UMN degeneration
than TMS ( Kaufmann et al., 2004 ).
A large longitudinal multimodal ALS study utilising DTI, MUNE,
MRS and TMS has concluded that MUNE changes considerably over
time in comparison with other markers (DTI, TMS) that showed less
significant longitudinal changes ( Mitsumoto et al., 2007 ). Multimodal
studies are also optimal cross-validation platforms, establishing novel
imaging approaches such as whole-brain MRS against more recog-
nised techniques ( Govind et al., 2012 ). Whole brain MR spectroscopy
demonstrated that metabolic changes along the corticospinal tracts
correlate with more established measures of CST integrity ( Stagg et al.,
2013 ). Multimodal approaches are also essential in diagnostic, clas-
sifier analyses. Discriminant analyses utilising multiple imaging vari-
ables have been consistently shown to improve the sensitivity and
specificity of group classification ( Filippini et al., 2010 ).
3.8. Presymptomatic studies
Very few studies have examined presymptomatic carriers of ALS
causing mutations to date ( Ng et al., 2008 ; Carew et al., 2011 ; Turner
et al., 2005
). In a large spectroscopy study of presymptomatic SOD1
carriers, metabolic changes were detected in the spinal cord prior
to development of symptoms ( Carew et al., 2011 ). A landmark DTI
study of asymptomatic SOD1 carriers identified decreased fractional
anisotropy and increased radial diffusivity in the posterior limb of
the internal capsule compared to healthy SOD1 negative controls ( Ng
et al., 2008 ). These pioneering studies should help to pave the way
for future studies, so this relatively arcane, presymptomatic phase
of ALS, representing a crucially important diagnostic and therapeutic
window, can be explored.
3.9. Multicentre ALS imaging studies
The Neuroimaging Society in ALS (NISALS) had its founding meet-
ing in 2010 attracting considerable technical, clinical, psychology,
and imaging expertise from various centres around the world. The
challenges, objectives and potential benefits of multicentre collab-
oration in ALS imaging have been candidly discussed ( Turner et al.,
2011a ). Another example of a multicentre ALS imaging and biomarker
initiative is the SOPHIA 99 Consortium of the European Union Neu-
rodegenerative Disease Research Programme (JPND). The obvious ad-
vantage of such collaborations is generating large patient numbers
of relatively rare ALS phenotypes. The challenges of such initiatives
include harmonisation across different scanner field-strengths and
manufacturers, funding and authorship issues, time contribution of
participating individuals, data management, storage and protection,
ethics approvals, etc. Despite these difficulties however, multicentre
neuroimaging is routinely used in clinical trials of multiple sclerosis
drugs with established cross-platform harmonisation and calibration
protocols ( Moraal et al., 2009 ). Multicentre MR studies have also
been successfully conducted in Alzheimer’s disease, as evidenced by
the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The cross
platform calibration of ADNI, utilising travelling MRI phantoms has
been comprehensively described ( Gunter et al., 2009 ). By contrast,
few cross-platform ALS imaging studies have been published to date.
A large two-centre imaging study of ALS and ALS-FTD has been con-
ducted in Germany using identical scanners and imaging protocol
( Schuster et al., 2013 ), and the First NISALS coordinated DTI project is
currently underway with the participation of 12 European and North
American centres.
3.10. Meta-analyses
While meta-analyses could potentially presage the sort of infor-
mation large multicentre studies can offer, surprisingly few meta-
analyses have been carried out in ALS. An individual patient data (IPD)
meta-analysis of DTI data of 221 ALS patients and 187 healthy controls
suggested that corticospinal tract DTI alone lacked diagnostic speci-
ficity ( Foerster et al., 2013a ). However, a voxel-based meta-analysis
of DTI date from eight studies, comprising 143 ALS patients and 145
healthy controls highlighted bilateral corticospinal tract changes in
the posterior limb of the internal capsule as well as bilateral frontal
and cingulate diffusivity changes ( Li et al., 2012 ). A meta-analysis of 5
VBM studies demonstrated that right precentral grey matter atrophy
is an important feature of ALS ( Chen and Ma 2010 ). The data reposito-
ries of multicentre MRI initiatives of ALS, such as NISALS and SOPHIA,
will be ideal platforms for individual patient data meta-analyses.
3.11. Correlative studies
A number of ALS imaging studies have sought to correlate com-
mon clinical variables with various MRI measures. Decreased corti-
cospinal tract FA ( Thivard et al., 2007 ; Cosottini et al., 2005a ) has
been associated with decreased ALSFRS-r ( Cedarbaum et al., 1999 ),
composite upper motor neuron scores ( Stanton et al., 2009a ; Filippini
et al., 2010 ; Blain et al., 2011 ) and disease progression rates ( Ciccarelli

440 P. Bede, O. Hardiman / NeuroImage: Clinical 4 (2014) 436–443
et al. , 2006 , 2009 ; Agosta et al., 2010b ). Grey matter density measures
( Grosskreutz et al., 2006 ; Bede et al., 2013c ) and NAA / Cr ratios ( Siv
´
ak
et al., 2010 ) have been correlated with disability scores. In addition
to motor variables, cognitive ( Grossman et al., 2008 ; Sarro et al., 2011
) and behavioural ( Tsujimoto et al., 2011 ) deficits have also been
correlated to structural changes in ALS.
Despite the abundance of clinically-correlated neuroimaging stud-
ies in ALS, important conceptual factors must be considered. The
ALSFRS-r is heavily influenced by lower motor neuron degenera-
tion which is not captured by current imaging technology. Correla-
tion of disease duration with structural changes is relatively difficult
to interpret, as progression rates vary considerably at an individual
level. Contrary to the conclusions of some studies, imaging should
not be proposed as an alternative clinical assessment tool. Clinical
disability scales and neuropsychological tests can be easily and rou-
tinely applied in a clinic room, home or bedside setting. They reflect
on key functional aspects of the disability and with minimal train-
ing, excellent inter-rater and test–retest reliability can be achieved.
The role of imaging in ALS on the other hand points beyond simpli-
fied clinico-structural correlations and could be regarded as a sensi-
tive and objective descriptive tool, able to capture subtle, phenotype-
defining pathology in cross-sectional and longitudinal, group-level
and individual-level analyses.
3.12. Diagnostic applications
There is considerable interest in developing imaging technology
that can discriminate ALS from non-ALS and mimic syndromes at in-
dividual level. Discriminant analyses of diffusivity measures ( Ben
Bashat et al., 2011 ), machine-learning and support vector machine
classifier-analyses ( Wang and Summers, 2012 ), are increasingly used
in other neurodegenerative conditions ( Orr
`
u et al., 2012 ) and show
considerable promise in the interpretation of individual imaging data.
In ALS, a discriminant analysis, combining radial diffusivity, fractional
anisotropy and voxel-based morphometry, achieved study group clas-
sification with 92% sensitivity, 88% specificity, and 90% accuracy
( Filippini et al., 2010 ). The use of the disease state classifier ma-
chine learning approach (support-vector machine) on resting-state
fMRI data achieved over 71% accuracy for disease state classification
( Welsh et al., 2013 ).
3.13. Future directions
The purpose of ALS imaging is twofold. The first is to further
progress our understanding of disease pathology and pathophysi-
ology, in which group analysis is appropriate; and the second is to
develop an imaging based technology that enhances individualised
diagnostic accuracy beyond best clinical practice. Based on the crit-
ical appraisal of the shortcomings and achievements of recent ALS
imaging studies, optimised study recommendations can be outlined.
ALS imaging studies should ideally encompass genetically, neuropsy-
chologically, electrophysiologically, and pathologically characterised
patient cohorts, a healthy reference group and disease controls. Mul-
tiple complementary imaging techniques should be ideally utilised
in the same study to provide multifaceted grey and white matter as-
sessments. The effect of demographic variables, such as age, gender,
education and handedness, should be strictly accounted for, and com-
parisons of ALS sub-cohorts should be corrected for disease duration
and disability. Correlative studies should take the network degen-
eration aspect of ALS into account and assess network integrity as
opposed to selected grey or white matter measures. Individual pa-
tient data meta-analyses are required prior to initiating harmonised
multicentre studies, which in turn are eagerly awaited and are likely
to generate sufficiently large sample sizes for meaningful data inter-
pretation.
From a technological standpoint, high-field MRI scanners i.e. 7 T
systems are increasingly available, promising unprecedented resolu-
tion and detailed spectroscopic evaluation. Nonetheless, only a few
ALS studies have been carried out on these systems to date ( Verstraete
et al., 2010 ; Kwan et al., 2012 ). Similarly, no post-mortem MRI stud-
ies have been conducted in ALS, a method increasingly used in other
neurodegenerative conditions. Quantitative muscle MRI is another
relatively overlooked field of ALS biomarker research ( Bryan et al.,
1998 ). Whole-brain MRS is a particularly promising technique and
its potential in ALS is far from being fully explored ( Stagg et al., 2013 ).
Despite a number of very successful spinal cord MRI studies ( Valsasina
et al., 2007 ), quantitative spinal imaging methods seem surprisingly
underutilised in ALS11. Finally, the emergence of combined PET / MRI
scanners and access to magnetoencephalography (MEG) are other
exciting developments which are likely to contribute to our under-
standing of ALS pathophysiology.
4. Conclusions
A critical review of ALS imaging has identified stereotypical short-
comings, the lessons of which should be considered in the design
of future prospective MRI studies. At a time when large multicentre
studies are underway a candid discussion of these factors is particu-
larly timely.
Author contributions
Peter Bede and Orla Hardiman have drafted and reviewed the
manuscript for intellectual content.
Conflict of interest statement
We have no conflicts of interests to disclose.
Role of the funding source
The sponsors of the study had no role in study design, data anal-
ysis or interpretation, writing or decision to submit the report for
publication.
Acknowledgements
Peter Bede has received research funding from the Elan Fellowship
in Neurodegeneration and the Health Research Board (HRB-Ireland).
Professor Hardiman’s research group has also received funding from
the Health Research Board (HRB-Ireland), the European Community’s
Seventh Framework Programme ( FP7 / 2007-2013 ) under grant agree-
ment no. [ 259867 ] (EUROMOTOR) and the EU-Joint Programme For
Neurodegeneration (JPND) SOPHIA project.
Appendix A. Supplementary Material
Supplementary material associated with this article can be
found, in the online version, at http: // dx.doi.org / 10.1016 /
j.nicl.2014.02.011 .
References
Abe, K., Fujimura, H., Toyooka, K., et al. 1997. Cognitive function in amyotrophic lateral
sclerosis. Journal of the Neurological Sciences 148 (1), 95–9100. http://dx.doi.org/
10.1016/S0022- 510X(96)05338- 5 , 9125395 .
Abrahams, S., Goldstein, L.H., Suckling, J., et al. 2005. Frontotemporal white matter
changes in amyotrophic lateral sclerosis. Journal of Neurology 252 (3), 321–31.
http://dx.doi.org/10.1007/s00415- 005- 0646- x , 15739047 .
Agosta, F., Chi
`
o, A., Cosottini, M., et al. 2010. The present and the future of neuroimag-
ing in amyotrophic lateral sclerosis. AJNR. American Journal of Neuroradiology 31
(10), 1769–77. http://dx.doi.org/10.3174/ajnr.A2043 , 20360339 .

P. Bede, O. Hardiman / NeuroImage: Clinical 4 (2014) 436–443 441
Agosta, F., Pagani, E., Petrolini, M., et al. 2010. Assessment of white matter tract dam-
age in patients with amyotrophic lateral sclerosis: a diffusion tensor MR imaging
tractography study. AJNR. American Journal of Neuroradiology 31 (8), 1457–61.
http://dx.doi.org/10.3174/ajnr.A2105 , 20395382 .
Agosta, F., Pagani, E., Rocca, M.A., et al. 2007. Voxel-based morphometry study of
brain volumetry and diffusivity in amyotrophic lateral sclerosis patients with mild
disability. Human Brain Mapping 28 (12), 1430–8. http://dx.doi.org/10.1002/hbm.
20364 , 17370339 .
Arenaza-Urquijo, E.M., Landeau, B., La, Joie R., et al. 2013. Relationships between years
of education and gray matter volume, metabolism and functional connectivity
in
healthy elders. NeuroImage 83, 450–7. http://dx.doi.org/10.1016/j.neuroimage.
2013.06.053 , 23796547 .
Bede, P., Bokde, A., Elamin, M., et al. 2013. Grey matter correlates of clinical variables in
amyotrophic lateral sclerosis (ALS): a neuroimaging study of ALS motor phenotype
heterogeneity and cortical focality. Journal of Neurology, Neurosurgery, and Psychi-
atry 84 (7), 766–73. http://dx.doi.org/10.1136/jnnp- 2012- 302674 , 23085933 .
Bede, P., Bokde, A.L., Byrne, S., et al. 2013. Multiparametric MRI study of ALS stratified
for the C9orf72 genotype. Neurology 81 (4), 361–9. http://dx.doi.org/10.1212/WNL.
0b013e31829c5eee , 23771489 .
Bede, P., Bokde, A.L., Byrne, S., et al. 2012. Spinal cord markers in ALS: diagnostic and
biomarker considerations. Amyotrophic Lateral
Sclerosis: Official Publication of
the World Federation of Neurology Research Group on Motor Neuron Diseases 13
(5), 407–15. http://dx.doi.org/10.3109/17482968.2011.649760 , 22329869 .
Bede, P., Elamin, M., Byrne, S., et al. 2013. Basal ganglia involvement in amyotrophic
lateral sclerosis. Neurology 81 (24), 2107–15. http://dx.doi.org/10.1212/01.wnl.
0000437313.80913.2c , 24212388 .
Bede, P., Elamin, M., Byrne, S., et al. 2013. Sexual dimorphism in ALS: exploring gender-
specific neuroimaging signatures. Amyotrophic Lateral Sclerosis & Frontotemporal
Degeneration.
Ben, Bashat D., Artzi, M., Tarrasch, R., et al. 2011. A potential tool for the diagnosis
of ALS based on diffusion tensor imaging. Amyotrophic Lateral Sclerosis 12 (6),
398–405. http://dx.doi.org/10.3109/17482968.2011.582646 .
Blain, C.R.V., Brunton, S., Williams, V.C., et al. 2011. Differential corticospinal tract de-
generation in homozygous ‘D90A ’ SOD-1 ALS and sporadic. ALS. Journal of Neurol-
ogy, Neurosurgery, and Psychiatry 82 (8), 843–9. http://dx.doi.org/10.1136/jnnp.
2010.236018 .
Block, W., Tr
¨
aber, F., Flacke, S., et al. 2002. In-vivo proton MR-spectroscopy of
the human brain: assessment of N-acetylaspartate (NAA) reduction as a marker
for neurodegeneration. Amino Acids 23 (1–3), 317–23. http://dx.doi.org/10.1007/
s00726- 001- 0144- 0 , 12373553 .
Bowser, R., Turner, M.R., Shefner, J., 2011. Biomarkers in amyotrophic lateral sclerosis:
opportunities and limitations. Nature Reviews. Neurology 7 (11), 631–8. http://dx.
doi.org/10.1038/nrneurol.2011.151 , 21989244 .
Bradley, W.G., Bowen, B.C., Pattany, P.M., et al. 1999.
1
H-magnetic resonance spec-
troscopy in amyotrophic lateral sclerosis. Journal of the Neurological Sciences 169
(1–2), 84–6. http://dx.doi.org/10.1016/S0022- 510X(99)00221- X , 10540013 .
Bryan, W.W., Reisch, J.S., McDonald, G., et al. 1998. Magnetic resonance imaging of
muscle in amyotrophic lateral sclerosis. Neurology 51 (1), 110–13. http://dx.doi.
org/10.1212/WNL.51.1.110 , 9674787 .
Canu,
E., Agosta, F., Riva, N., et al. 2011. The topography of brain microstructural damage
in amyotrophic lateral sclerosis assessed using diffusion tensor MR imaging. AJNR.
American Journal of Neuroradiology 32 (7), 1307–14. http://dx.doi.org/10.3174/
ajnr.A2469 , 21680655 .
Carew, J.D., Nair, G., Andersen, P.M., et al. 2011. Presymptomatic spinal cord neu-
rometabolic findings in SOD1-positive people at risk for familial ALS. Neurology 77
(14), 1370–5. http://dx.doi.org/10.1212/WNL.0b013e318231526a , 21940617 .
Cedarbaum, J.M., Stambler, N., Malta, E., et al. 1999. The ALSFRS-R: a revised ALS
functional rating scale that incorporates assessments of respiratory function. BDFN
ALS Study Group (Phase III). Journal of the Neurological
Sciences 169 (1–2), 13–21.
http://dx.doi.org/10.1016/S0022- 510X(99)00210- 5 , 10540002 .
Chang, J.L., Lomen-Hoerth, C., Murphy, J., et al. 2005. A voxel-based morphometry
study of patterns of brain atrophy in ALS and ALS
/ FTLD. Neurology 65 (1), 75–80.
http://dx.doi.org/10.1212/01.wnl.0000167602.38643.29 , 16009889 .
Chen, X., Sachdev, P.S., Wen, W., et al. 2007. Sex differences in regional gray mat-
ter in healthy individuals aged 44–48 years: a voxel-based morphometric study.
NeuroImage 36 (3), 691–9. http://dx.doi.org/10.1016/j.neuroimage.2007.03.063 ,
17499524 .
Chen, Z., Ma, L., 2010. Grey matter
volume changes over the whole brain in amyotrophic
lateral sclerosis: a voxel-wise meta-analysis of voxel based morphometry studies.
Amyotrophic Lateral Sclerosis 11 (6), 549–54. http://dx.doi.org/10.3109/17482968.
2010.516265 .
Ciccarelli, O., Behrens, T.E., Altmann, D.R., et al. 2006. Probabilistic diffusion trac-
tography: a potential tool to assess the rate of disease progression in amy-
otrophic lateral sclerosis. Brain: A Journal of Neurology 129 (7), 1859–71. http:
//dx.doi.org/10.1093/brain/awl100 , 16672290 .
Ciccarelli, O., Behrens, T.E., Johansen-Berg, H., et al. 2009. Investigation of white matter
pathology in ALS and PLS using tract-based spatial statistics. Human Brain Mapping
30 (2), 615–24. http://dx.doi.org/10.1002/hbm.20527 , 18172851 .
Cistaro,
A., Pagani, M., Montuschi, A., et al. 2014. The metabolic signature of C9ORF72-
related ALS: FDG PET comparison with nonmutated patients. European Journal of
Nuclear Medicine and Molecular Imaging, 24445987 .
Cistaro, A., Valentini, M.C., Chi
`
o, A., et al. 2012. Brain hypermetabolism in amyotrophic
lateral sclerosis: a FDG PET
study in ALS of spinal and bulbar onset. European
Journal of Nuclear Medicine and Molecular Imaging 39 (2), 251–9. http://dx.doi.
org/10.1007/s00259- 011- 1979- 6 , 22089661 .
Comi, G., Rovaris, M., Leocani, L., 1999. Review neuroimaging in amyotrophic lat-
eral sclerosis. European Journal of Neurology: the Official Journal of the Euro-
pean Federation of Neurological Societies 6 (6), 629–37. http://dx.doi.org/10.1046/
j.1468-1331.1999.660629.x , 10529749 .
Corcia, P., Tauber, C., Vercoullie, J., et al. 2012. Molecular imaging of microglial activa-
tion in amyotrophic lateral sclerosis. PLOS One 7 (12), e52941. http://dx.doi.org/
10.1371/journal.pone.0052941 , 23300829 .
Cosottini, M., Giannelli, M., Siciliano, G., et al. 2005. Diffusion-tensor MR imaging
of corticospinal tract in amyotrophic lateral sclerosis and progressive muscular
atrophy. Radiology 237 (1), 258–64. http://dx.doi.org/10.1148/radiol.2371041506 ,
16183935 .
Cosottini, M., Giannelli, M.,
Siciliano, G., et al. 2005. Diffusion-tensor MR imaging
of corticospinal tract in amyotrophic lateral sclerosis and progressive muscular
atrophy. Radiology 237 (1), 258–64. http://dx.doi.org/10.1148/radiol.2371041506 ,
16183935 .
Dalakas, M.C., Hatazawa, J., Brooks, R.A., et al. 1987. Lowered cerebralglucose utilization
in amyotrophic lateral sclerosis. Annals of Neurology 22 (5), 580–6. http://dx.doi.
org/10.1002/ana.410220504 , 3501273 .
Dengler, R., von Neuhoff, N., Bufler, J., et al. 2005. Amyotrophic lateral sclerosis: new
developments in diagnostic markers. Neuro-degenerative Diseases 2 (3–4), 177–
84. http://dx.doi.org/10.1159/000089623 , 16909023 .
Desmond, J.E., Glover, G.H., 2002. Estimating sample size in functional MRI (fMRI) neu-
roimaging studies: statistical power analyses. Journal of Neuroscience Methods 118
(2), 115–28. http://dx.doi.org/10.1016/S0165- 0270(02)00121- 8 , 12204303 .
Douaud, G., Filippini, N., Knight, S., et al. 2011. Integration of structural and func-
tional magnetic resonance imaging in amyotrophic lateral sclerosis. Brain : A
Journal of Neurology 134 (12), 3470–9. http://dx.doi.org/10.1093/brain/awr279 ,
22075069 .
Ellis, C.M., Simmons, A., Andrews, C., et al.
1998. A proton magnetic resonance spectro-
scopic study in ALS: correlation with clinical findings. Neurology 51 (4), 1104–9.
http://dx.doi.org/10.1212/WNL.51.4.1104 , 9781537 .
Ellis, C.M., Suckling, J., Amaro, E., et al. 2001. Volumetric analysis reveals corticospinal
tract degeneration and extramotor involvement in ALS. Neurology 57 (9), 1571–8,
11706094 .
Filippini, N., Douaud, G., Mackay, C.E., et al. 2010. Corpus callosum involvement is a
consistent feature of amyotrophic lateral sclerosis. Neurology 75 (18), 1645–52.
http://dx.doi.org/10.1212/WNL.0b013e3181fb84d1 , 21041787 .
Foerster, B.R., Dwamena, B.A., Petrou, M., et al. 2013. Diagnostic accuracy of diffusion
tensor imaging in amyotrophic lateral sclerosis: a systematic review and individual
patient
data meta-analysis. Academic Radiology 20 (9), 1099–106. http://dx.doi.
org/10.1016/j.acra.2013.03.017 , 23931423 .
Foerster, B.R., Welsh, R.C., Feldman, E.L., 2013. 25 years of neuroimaging in amyotrophic
lateral sclerosis. Nature Reviews Neurology.
Fusar-Poli, P., Radua, J., Frascarelli, M., et al. 2013. Evidence of reporting biases in
voxel-based morphometry (VBM) studies of psychiatric and neurological disorders.
Human Brain Mapping, 24123491 .
Govind, V., Sharma, K.R., Maudsley, A.A., et al. 2012. Comprehensive evaluation of
corticospinal tract metabolites in amyotrophic lateral sclerosis using whole-brain
1
H MR spectroscopy. PLOS One 7, e35607, 22539984 .
Grosskreutz, J., Kaufmann, J., Fr
¨
adrich, J., et al. 2006. Widespread sensorimotor and
frontal cortical atrophy in amyotrophic lateral sclerosis. BMC Neurology 6, 17.
http://dx.doi.org/10.1186/1471- 2377- 6- 17 , 16638121 .
Grossman, M., Anderson, C., Khan, A., et al. 2008. Impaired action knowledge in amy-
otrophic lateral sclerosis. Neurology 71 (18), 1396–401. http://dx.doi.org/10.1212/
01.wnl.0000319701.50168.8c , 18784377 .
Gunter, J.L., Bernstein, M.A., Borowski, B.J., et al. 2009. Measurement of MRI scanner
performance with the ADNI phantom. Medical Physics 36 (6), 2193–205. http:
//dx.doi.org/10.1118/1.3116776 , 19610308 .
Herv
´
e, P.Y., Leonard, G., Perron, M., et al. 2009. Handedness, motor skills and maturation
of the corticospinal tract in the adolescent brain. Human Brain Mapping 30 (10),
3151–62. http://dx.doi.org/10.1002/hbm.20734 , 19235881 .
Hoffman, J.M., Mazziotta, J.C., Hawk, T.C., et al. 1992. Cerebral glucose utilization in
motor neuron disease. Archives of Neurology 49 (8), 849–54. http://dx.doi.org/10.
1001/archneur.1992.00530320077014 , 1524517 .
Ioannidis, J.P., 2011. Excess significance bias in the literature on brain volume abnor-
malities. Archives of General Psychiatry 68 (8), 773–80. http://dx.doi.org/10.1001/
archgenpsychiatry.2011.28 , 21464342 .
Iwata, N.K., Kwan, J.Y., Danielian, L.E., et al. 2011. White matter alterations differ in
primary lateral sclerosis and amyotrophic lateral sclerosis. Brain: A Journal of Neu-
rology 134 (9), 2642–55. http://dx.doi.org/10.1093/brain/awr178 , 21798965 .
Joyce, K.E., Hayasaka, S., 2012. Development of PowerMap: a software package for
statistical power calculation in neuroimaging studies. Neuroinformatics 10 (4),
351–65. http://dx.doi.org/10.1007/s12021- 012- 9152- 3 , 22644868 .
Kalra, S., Arnold, D., 2003. Neuroimaging in amyotrophic
lateral sclerosis. Amyotrophic
Lateral Sclerosis and Other Motor Neuron Disorders: Official Publication of the
World Federation of Neurology, Research Group on Motor Neuron Diseases 4 (4),
243–8. http://dx.doi.org/10.1080/14660820310011269 , 14753658 .
Kalra, S., Tai, P., Genge, A., et al. 2006. Rapid improvement in cortical neuronal integrity
in amyotrophic lateral sclerosis detected by proton magnetic resonance spectro-
scopic imaging. Journal of Neurology 253 (8), 1060–3. http://dx.doi.org/10.1007/
s00415- 006- 0162- 7 , 16609809 .
Karitzky, J., Ludolph, A.C., 2001. Imaging and neurochemical markers for diagnosis and
disease progression in ALS. Journal of the Neurological Sciences 191 (1–2), 35–41.
http://dx.doi.org/10.1016/S0022- 510X(01)00628- 1 , 11676990 .
Kassubek, J., Ludolph, A.C., M
¨
uller, H.P., 2012. Neuroimaging of motor neuron diseases.
Therapeutic Advances in Neurological Disorders 5 (2), 119–27. http://dx.doi.org/

442 P. Bede, O. Hardiman / NeuroImage: Clinical 4 (2014) 436–443
10.1177/1756285612437562 , 22435076 .
Kassubek, J., Unrath, A., Huppertz, H-J, et al. 2005. Global brain atrophy and corti-
cospinal tract alterations in ALS, as investigated by voxel-based morphometry of
3-D MRI. Amyotrophic Lateral Sclerosis and Other Motor Neuron Disorders: Offi-
cial Publication of the World Federation of Neurology, Research Group on Motor
Neuron Diseases 6 (4), 213–20. http://dx.doi.org/10.1080/14660820510038538 ,
16319024 .
Kaufmann, P., Pullman, S.L., Shungu, D.C., et al. 2004. Objective tests for upper motor
neuron involvement in amyotrophic lateral sclerosis (ALS). Neurology 62 (10),
1753–7. http://dx.doi.org/10.1212/01.WNL.0000125182.17874.59 , 15159473 .
Kollewe, K., K
¨
orner, S., Dengler, R., et al. 2012. Magnetic resonance imaging in
amyotrophic lateral sclerosis. Neurology Research International 2012, 608501,
22848820 .
Kwan, J.Y., Jeong, S.Y., Van Gelderen, P., et al. 2012. Iron accumulation in deep cor-
tical layers accounts for MRI signal abnormalities in ALS: correlating 7 tesla MRI
and pathology. PLOS One 7 (4), e35241. http://dx.doi.org/10.1371/journal.pone.
0035241
, 22529995 .
Lauzon, C.B., Landman, B.A., 2013. Correcting power and p-value calculations for bias
in diffusion tensor imaging. Magnetic Resonance Imaging 31 (6), 857–64. http:
//dx.doi.org/10.1016/j.mri.2013.01.002 , 23465764 .
Li, J., Pan, P., Song, W., et al. 2012. A meta-analysis of diffusion tensor imaging studies
in amyotrophic lateral sclerosis. Neurobiology of Aging 33 (8), 1833–8. http://dx.
doi.org/10.1016/j.neurobiolaging.2011.04.007 , 21621298 .
Menzler, K., Belke, M., Wehrmann, E., et al. 2011. Men and women are different:
diffusion tensor imaging reveals sexual dimorphism in the microstructure of the
thalamus, corpus callosum and cingulum. NeuroImage 54 (4), 2557–62. http://dx.
doi.org/10.1016/j.neuroimage.2010.11.029 , 21087671 .
Mezzapesa,
D.M., Ceccarelli, A., Dicuonzo, F., et al. 2007. Whole-brain and regional brain
atrophy in amyotrophic lateral sclerosis. AJNR. American Journal of Neuroradiology
28 (2), 255–9, 17296989 .
Minnerop, M., Specht, K., Ruhlmann, J., et al. 2009. In vivo voxel-based relaxometry in
amyotrophic lateral sclerosis. Journal of Neurology 256 (1), 28–34. http://dx.doi.
org/10.1007/s00415- 009- 0947- 6 , 19267168 .
Mitsumoto, H., Ulug, A.M., Pullman, S.L., et al. 2007. Quantitative objective markers for
upper and lower motor neuron dysfunction in ALS. Neurology 68 (17), 1402–10.
http://dx.doi.org/10.1212/01.wnl.0000260065.57832.87 , 17452585 .
Mohammadi, B., Kollewe, K., Samii, A., et al. 2011. Functional neuroimaging at differ-
ent
disease stages reveals distinct phases of neuroplastic changes in amyotrophic
lateral sclerosis. Human Brain Mapping 32 (5), 750–8. http://dx.doi.org/10.1002/
hbm.21064 , 20836159 .
Moraal, B., Meier, D.S., Poppe, P.A., et al. 2009. Subtraction MR images in a multiple
sclerosis multicenter clinical trial setting. Radiology 250 (2), 506–14. http://dx.doi.
org/10.1148/radiol.2501080480 , 19037018 .
Mumford, J.A., 2012. A power calculation guide for fMRI studies. Social Cognitive
and Affective Neuroscience 7 (6), 738–42. http://dx.doi.org/10.1093/scan/nss059 ,
22641837 .
Neary, D., Snowden, J.S., Mann, D.M., et al. 1990. Frontal lobe dementia and motor
neuron disease. Journal of Neurology, Neurosurgery, and Psychiatry 53 (1), 23–32.
http://dx.doi.org/10.1136/jnnp.53.1.23 ,
2303828 .
Ng, M.C., Ho, J.T., Ho, S.L., et al. 2008. Abnormal diffusion tensor in nonsymptomatic
familial amyotrophic lateral sclerosis with a causative superoxide dismutase 1
mutation. Journal of Magnetic Resonance imaging: JMRI 27 (1), 8–13. http://dx.doi.
org/10.1002/jmri.21217 , 18022844 .
Noble, K.G., Grieve, S.M., Korgaonkar, M.S., et al. 2012. Hippocampal volume varies with
educational attainment across the life-span. Frontiers in Human Neuroscience 6,
307, 23162453 .
Orr
`
u, G., Pettersson-Yeo, W., Marquand, A.F., et al. 2012. Using support vector machine
to identify imaging biomarkers of neurological and psychiatric disease: a critical
review. Neuroscience and Biobehavioral Reviews 36 (4), 1140–52. http://dx.doi.
org/10.1016/j.neubiorev.2012.01.004 , 22305994 .
Pohl, C., Block, W., Karitzky, J., et al. 2001. Proton magnetic resonance spectroscopy of
the motor cortex in 70 patients with amyotrophic lateral sclerosis. Archives of Neu-
rology 58 (5), 729–35. http://dx.doi.org/10.1001/archneur.58.5.729 , 11346367 .
Pradat, P.F., Dib, M., 2009. Biomarkers in amyotrophic lateral sclerosis: facts
and future
horizons. Molecular Diagnosis & Therapy 13 (2), 115–25. http://dx.doi.org/10.1007/
BF03256320 , 19537846 .
Prell, T., Grosskreutz, J., 2013. The involvement of the cerebellum in amyotrophic lateral
sclerosis. Amyotrophic Lateral Sclerosis & Frontotemporal Degeneration 14 (7–8),
507–15. http://dx.doi.org/10.3109/21678421.2013.812661 , 23889583 .
Prudlo, J., Bißbort, C., Glass, A., et al. 2012. White matter pathology in ALS and lower
motor neuron ALS variants: a diffusion tensor imaging study using tract-based
spatial statistics. Journal of Neurology 259 (9), 1848–59. http://dx.doi.org/10.1007/
s00415- 012- 6420- y , 22349938 .
Rajagopalan, V., Yue, G.H., Pioro, E.P., 2013. Brain white matter diffusion tensor metrics
from clinical 1.5 T MRI distinguish between ALS phenotypes. Journal of Neurology
260 (10), 2532–40. http://dx.doi.org/10.1007/s00415- 013- 7012- 1 , 23864396 .
Sarro, L., Agosta, F., Canu, E., et al. 2011. Cognitive functions and white matter tract
damage in amyotrophic lateral sclerosis: a diffusion tensor tractography study.
AJNR. American Journal of Neuroradiology 32 (10), 1866–72. http://dx.doi.org/10.
3174/ajnr.A2658 , 22016410 .
Schoenfeld, M.A., Tempelmann, C., Gaul, C., et al. 2005. Functional motor compensation
in amyotrophic lateral sclerosis. Journal of Neurology 252 (8), 944–52. http://dx.
doi.org/10.1007/s00415- 005- 0787- y , 15750701 .
Schuster, C., Kasper, E., Dyrba, M., et al. 2014. Cortical thinning and its relation
to cognition in amyotrophic lateral sclerosis. Neurobiology of Aging 35, 240–6,
23992619 .
Siv
´
ak, S., Bitt
ˇ
sansk
´
y, M., Kur
ˇ
ca, E., et al. 2010. Proton magnetic resonance spectroscopy
in patients with early stages of amyotrophic lateral sclerosis. Neuroradiology 52
(12), 1079–85. http://dx.doi.org/10.1007/s00234- 010- 0685- 6 , 20369234 .
Snow, B.J., Peppard, R.F., Guttman, M., et al. 1990. Positron emission tomo-
graphic scanning demonstrates a presynaptic dopaminergic lesion in Lytico-Bodig.
The amyotrophic lateral sclerosis–parkinsonism–dementia complex of Guam.
Archives of Neurology 47 (8), 870–4. http://dx.doi.org/10.1001/archneur.1990.
00530080052010 , 2375693 .
Song, S.K., Yoshino, J., Le, T.Q., et al. 2005. Demyelination increases radial diffusivity in
corpus callosum of mouse brain. NeuroImage 26 (1), 132–40.
http://dx.doi.org/10.
1016/j.neuroimage.2005.01.028 , 15862213 .
Sperfeld, A.D., Bretschneider, V., Flaith, L., et al. 2005. MR-pathologic comparison of
the upper spinal cord in different motor neuron diseases. European Neurology 53
(2), 74–7. http://dx.doi.org/10.1159/000084650 , 15785072 .
Stagg, C.J., Knight, S., Talbot, K., et al. 2013. Whole-brain magnetic resonance spectro-
scopic imaging measures are related to disability in ALS. Neurology 80 (7), 610–15.
http://dx.doi.org/10.1212/WNL.0b013e318281ccec , 23325907 .
Stanton, B.R., Shinhmar, D., Turner, M.R., et al. 2009. Diffusion tensor imaging in
sporadic and familial (D90A SOD1) forms of amyotrophic lateral sclerosis. Archives
of Neurology 66 (1), 109–15, 19139308 .
Stanton, B.R., Shinhmar, D., Turner,
M.R., et al. 2009. Diffusion tensor imaging in
sporadic and familial (D90A SOD1) forms of amyotrophic lateral sclerosis. Archives
of Neurology 66 (1), 109–15, 19139308 .
Sun, S.W., Liang, H.F., Trinkaus, K., et al. 2006. Noninvasive detection of cuprizone in-
duced axonal damage and demyelination in the mouse corpus callosum. Magnetic
Resonance in Medicine 55 (2), 302–8. http://dx.doi.org/10.1002/mrm.20774 .
Takao, H., Abe, O., Yamasue, H., et al. 2011. Gray and white matter asymmetries in
healthy individuals aged 21–29 years: a voxel-based morphometry and diffusion
tensor imaging study. Human Brain Mapping 32 (10), 1762–73. http://dx.doi.org/
10.1002/hbm.21145 , 20886579 .
Talbot, P.R., Goulding,
P.J., Lloyd, J.J., et al. 1995. Inter-relation between “classic” mo-
tor neuron disease and frontotemporal dementia: neuropsychological and sin-
gle photon emission computed tomography study. Journal of Neurology, Neu-
rosurgery, and Psychiatry 58 (5), 541–7. http://dx.doi.org/10.1136/jnnp.58.5.541 ,
7745399 .
Tanaka, M., Kondo, S., Hirai, S., et al. 1993. Cerebral blood flow and oxygen metabolism
in progressive dementia associated with amyotrophic lateral sclerosis. Journal of
the Neurological Sciences 120 (1), 22–8. http://dx.doi.org/10.1016/0022-510X(93)
90019-U , 8289076 .
Thivard, L., Pradat, P-F, Leh
´
ericy, S., et al. 2007. Diffusion tensor imaging and voxel based
morphometry study in amyotrophic lateral sclerosis: relationships with motor
disability. Journal of Neurology, Neurosurgery, and Psychiatry 78 (8), 889–92. http:
//dx.doi.org/10.1136/jnnp.2006.101758 , 17635981 .
Tsujimoto, M., Senda, J., Ishihara, T., et al. 2011. Behavioral changes in early ALS cor-
relate with voxel-based morphometry and diffusion tensor imaging. Journal of
the Neurological Sciences 307 (1–2), 34–40. http://dx.doi.org/10.1016/j.jns.2011.
05.025 , 21641004 .
Turner,
M.R., 2011. MRI as a frontrunner in the search for amyotrophic lateral sclerosis
biomarkers? Biomarkers in Medicine 5 (1), 79–81. http://dx.doi.org/10.2217/bmm.
10.120 , 21319968 .
Turner, M.R., Modo, M., 2010. Advances in the application of MRI to amyotrophic lateral
sclerosis. Expert Opinion on Medical Diagnostics 4 (6), 483–96. http://dx.doi.org/
10.1517/17530059.2010.536836 , 21516259 .
Turner, M.R., Hammers, A., Al-Chalabi, A., et al. 2005. Distinct cerebral lesions in
sporadic and ‘D90A ’ SOD1 ALS: studies with [11C]flumazenil PET. Brain a Journal
of Neurology 128 (6), 1323–9. http://dx.doi.org/10.1093/brain/awh509 .
Turner, M.R., Hammers, A., Al-Chalabi, A., et al. 2007. Cortical involvement in four cases
of
primary lateral sclerosis using [(11)C]-flumazenil PET. Journal of Neurology 254
(8), 1033–6. http://dx.doi.org/10.1007/s00415- 006- 0482- 7 , 17294065 .
Turner, M.R., Hammers, A., Allsop, J., et al. 2007. Volumetric cortical loss in spo-
radic and familial amyotrophic lateral sclerosis. Amyotrophic Lateral Sclerosis:
Official Publication of the World Federation of Neurology Research Group on Mo-
tor Neuron Diseases 8 (6), 343–7. http://dx.doi.org/10.1080/17482960701538734 ,
18033592 .
Turner, M.R., Kiernan, M.C., Leigh, P.N., et al. 2009. Biomarkers in amyotrophic
lateral sclerosis. Lancet Neurology 8 (1), 94–9109. http://dx.doi.org/10.1016/
S1474- 4422(08)70293- X , 19081518 .
Turner, M.R., Grosskreutz, J., Kassubek, J., et al. 2011. Towards a neuroimaging
biomarker for amyotrophic lateral sclerosis. Lancet Neurology 10 (5), 400–3.
http://dx.doi.org/10.1016/S1474- 4422(11)70049- 7 , 21511189 .
Turner, M.R., Wicks, P., Brownstein, C.A., et al. 2011. Concordance between site of
onset and limb dominance in amyotrophic lateral sclerosis. Journal of Neurology,
Neurosurgery, and Psychiatry 82 (8), 853–4. http://dx.doi.org/10.1136/jnnp.2010.
208413 , 20562391 .
Turner, M.R., Agosta, F., Bede, P., et al. 2012. Neuroimaging in amyotrophic lateral
sclerosis. Biomarkers in Medicine 6 (3), 319–37. http://dx.doi.org/10.2217/bmm.
12.26 , 22731907 .
Turner, M.R., Bowser, R., Bruijn, L., et al. 2013. Mechanisms, models and biomarkers
in amyotrophic lateral sclerosis. Amyotrophic Lateral Sclerosis & Frontotempo-
ral Degeneration
14 (Suppl. 1), 19–32. http://dx.doi.org/10.3109/21678421.2013.
778554 , 23678877 .
Valsasina, P., Agosta, F., Benedetti, B., et al. 2007. Diffusion anisotropy of the cervical
cord is strictly associated with disability in amyotrophic lateral sclerosis. Journal
of Neurology, Neurosurgery, and Psychiatry 78 (5), 480–4, 17030586 .
van, der Graaff M.M., de Jong, J.M., Baas, F., et al. 2009. Upper motor neuron and extra-
motor neuron involvement in amyotrophic lateral sclerosis: a clinical and brain

P. Bede, O. Hardiman / NeuroImage: Clinical 4 (2014) 436–443 443
imaging review. Neuromuscular Disorders: NMD 19 (1), 53–8. http://dx.doi.org/
10.1016/j.nmd.2008.10.002 , 19070491 .
van, der Graaff M.M., Sage, C.A., Caan, M.W., et al. 2011. Upper and extra-motoneuron
involvement in early motoneuron disease: a diffusion tensor imaging study. Brain:
A Journal of Neurology 134 (4), 1211–28. http://dx.doi.org/10.1093/brain/awr016 ,
21362631 .
Verstraete, E., Biessels, G.J., van, den Heuvel M.P., et al. 2010. No evidence of microbleeds
in ALS patients at 7 Tesla MRI. Amyotrophic Lateral Sclerosis: Official publication
of the World Federation of Neurology Research Group on Motor Neuron Diseases
11 (6), 555–7. http://dx.doi.org/10.3109/17482968.2010.513053 .
Verstraete, E., van, den Heuvel M.P., Veldink, J.H., et al. 2010. Motor
network degen-
eration in amyotrophic lateral sclerosis: a structural and functional connectivity
study. PLOS One 5 (10), e13664, 21060689 .
Verstraete, E., Veldink, J.H., Hendrikse, J., et al. 2012. StructuralMRI reveals cortical thin-
ning in amyotrophic lateral sclerosis. Journal of Neurology, Neurosurgery, and Psy-
chiatry 83 (4), 383–8. http://dx.doi.org/10.1136/jnnp- 2011- 300909 , 21965521 .
Verstraete, E., Veldink, J.H., van, den Berg L.H., et al. 2013. Structural brain network
imaging shows expanding disconnection of the motor system in amyotrophic lat-
eral sclerosis. Human Brain Mapping, 23450820 .
Waldemar, G., Vorstrup, S., Jensen, T.S., et al. 1992. Focal reductions of cerebral
blood flow in amyotrophic lateral sclerosis: a
[99mTc]-d,l-HMPAO SPECT study.
Journal of the Neurological Sciences 107 (1), 19–28. http://dx.doi.org/10.1016/
0022- 510X(92)90204- X , 1578230 .
Wang, S., Melhem, E.R., Poptani, H., et al. 2011. Neuroimaging in amyotrophic lateral
sclerosis. Neurotherapeutics : the Journal of the American Society For Experimental
NeuroTherapeutics 8 (1), 63–71. http://dx.doi.org/10.1007/s13311- 010- 0011- 3 ,
21274686 .
Wang, S., Summers, R.M., 2012. Machine learning and radiology. Medical Image Analysis
16 (5), 933–51. http://dx.doi.org/10.1016/j.media.2012.02.005 , 22465077 .
Welsh, R.C., Jelsone-Swain, L.M., Foerster, B.R., 2013. The utility of independent compo-
nent analysis and machine learning in the identification of the amyotrophic lateral
sclerosis diseased brain. Frontiers in Human Neuroscience 7, 251,
23772210 .
Westerhausen, R., Huster, R.J., Kreuder, F., et al. 2007. Corticospinal tract asymme-
tries at the level of the internal capsule: is there an association with handedness?
NeuroImage 37 (2), 379–86. http://dx.doi.org/10.1016/j.neuroimage.2007.05.047 ,
17601751 .
Yin, H., Lim, C.C.T., Ma, L., et al. 2004. Combined MR spectroscopic imaging and dif-
fusion tensor MRI visualizes corticospinal tract degeneration in amyotrophic lat-
eral sclerosis. Journal of Neurology 251 (10), 1249–54. http://dx.doi.org/10.1007/
s00415- 004- 0526- 9 , 15503106 .