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Neuropsychologia
journal homepage: www.elsevier.com/locate/neuropsychologia
A hitchhiker's guide to lesion-behaviour mapping
Bianca de Haana,⁎,1
, Hans-Otto Karnatha,b
a
Center of Neurology, Division of Neuropsychology, Hertie-Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
b
Department of Psychology, University of South Carolina, Columbia, USA
A R T I C L E I N F O
Keywords:
Lesion analysis
Voxel based lesion symptom mapping
VLSM
VLBM
Brain behaviour inference
Human
A B S T R A C T
Lesion-behaviour mapping is an influential and popular approach to anatomically localise cognitive brain
functions in the human brain. Multiple considerations, ranging from patient selection, assessment of lesion
location and patient behaviour, spatial normalisation, statistical testing, to the anatomical interpretation of
obtained results, are necessary to optimize a lesion-behaviour mapping study and arrive at meaningful conclusions.
Here, we provide a hitchhiker's guide, giving practical guidelines and references for each step of the
typical lesion-behaviour mapping study pipeline.
1. Introduction
In a classical lesion analysis, the aim is to infer the cognitive function
of an area of the human brain by observing the behavioural consequences
of damage to that brain area. In the early days, the lesion
method was the only approach available to study the functional architecture
of the brain and these early studies have contributed tremendously
to our understanding of a wide variety of cognitive functions
(e.g. Damasio and Damasio, 1989). Nowadays, functional brain
imaging and transient neuroinhibition/neurostimulation techniques
have complemented this traditional approach. Nevertheless, the lesion
method continues to be an essential and influential approach for neuroscientists
aiming to study the functional architecture of the brain (see
Rorden and Karnath (2004) for a review). This continued importance of
the lesion method as an approach to aid understanding of cognitive
function is also illustrated by the steadily increasing number of scientific
publications featuring this method over the years (see Fig. 1).
Surprisingly, despite this continued and increasing popularity of the
lesion method, there currently are virtually no papers or books to assist
scientists interested in using this method (see Wilson (2016) for a notable
exception). This is in stark contrast to the many excellent papers
and books available to guide scientists interested in using other neuroscientific
methods. Thus, inspired by similar recent guides for diffusion
tensor imaging (DTI; Soares et al., 2013) and functional magnetic
resonance imaging (fMRI; Soares et al., 2016), we here compile a
hitchhiker's guide detailing the necessary considerations at each step of
the typical lesion study pipeline (see Fig. 2), with a primary focus on
‘classical’ univariate lesion analysis approaches (Bates et al., 2003;
Rorden et al., 2007). New multivariate lesion analysis approaches have
also been proposed (Smith et al., 2013; Mah et al., 2014a; Zhang et al.,
2014; Pustina et al., in press, this issue) and, in light of known drawbacks
associated with the univariate approach, such as limited statistical
power and potential spatial bias (Mah et al., 2014a; Inoue et al.,
2014; for a review, see also Sperber and Karnath, in press, this issue), it
has been suggested that these multivariate approaches should be preferred
over univariate lesion analysis approaches (Mah et al., 2014a).
However, multivariate approaches have their own issues requiring future
improvements, such as the feature selection method (Yourganov
et al., 2015; Rondina et al., 2016). Moreover, the spatial bias in univariate
approaches can be reduced considerably by correcting for lesion
volume and ensuring sufficient minimum lesion overlap (Sperber and
Karnath, 2017; see also Section 5.2.1 below), and the possibility that
multivariate approaches are not likewise affected by a spatial bias has
not been ruled out yet. As such, a more nuanced view would be to
consider univariate and multivariate lesion analysis approaches as
complementary (Karnath et al., in press).
2. Patient selection
The first decision a researcher planning a lesion-behaviour mapping
study has to make, is which patients to select for the study. Patient
assessment is time-consuming and, as such, it is generally most efficient
to restrict patient assessment to those patients that will allow both
meaningful conclusions on the functional architecture of the brain and
a meaningful investigation of the research hypothesis.
http://dx.doi.org/10.1016/j.neuropsychologia.2017.10.021
Received 6 June 2017; Received in revised form 16 October 2017; Accepted 17 October 2017
⁎
Correspondence to: Division of Psychology, Department of Life Sciences Brunel University London Kingston Lane, Uxbridge UB8 3PH, UK.
1
Present address: Division of Psychology, Department of Life Sciences, Centre for Cognitive Neuroscience, Brunel University London, Uxbridge, UK.
E-mail address: bianca.dehaan@brunel.ac.uk (B. de Haan).
Neuropsychologia 115 (2018) 5–16
Available online 21 October 2017
0028-3932/ © 2017 Elsevier Ltd. All rights reserved.
T
2.1. Lesion aetiology
One frequently used patient selection criterion is lesion aetiology.
Of the 181 studies depicted in Fig. 1(i.e. the publications between 1995
and 2015 that used the lesion method found searching Pubmed), the
vast majority (66.3%) were conducted with stroke patients.
2.1.1. Use acute stroke patients to investigate the functional architecture of
the brain
In the acute phase, strokes are associated with clear behavioural
consequences that can, for the large part, be directly linked to the
original function of the functionally impaired part of the brain (as
strokes are sudden and as such the brain has not yet had time to
functionally reorganise, see also Shahid et al. (2017)). As long as patients
with mass shifts due to extensive haemorrhage or extensive oedema
are excluded, all brain structures are typically still at their
original locations and the parts of the brain affected by stroke can be
reliably visualised in computed tomography (CT) (Mohr et al., 1995;
Mayer et al., 2000) and/or magnetic resonance (MR) (Genovese et al.,
2002) images (see Section 3.1.1 below). As such, acute stroke patients
are highly suitable for studies where the aim is to investigate the
functional architecture of the brain.
2.1.2. Use chronic stroke patients to investigate the neural correlate of
chronic dysfunction
Unfortunately, acute stroke patient data is not always easy to acquire.
Access to stroke units and acute stroke patients may be restricted
and the behavioural assessment of acute stroke patients is frequently
difficult due to their often poor general state of health. As a consequence,
many lesion-behaviour mapping studies have relied on more
readily available chronic stroke patient data. However, in the chronic
stroke phase, functional reorganization of the brain in the course of
normal recovery can complicate the investigation of the functional architecture
of the brain (Karnath and Rennig, 2016). This is mostly due
to the fact that during lesion-behaviour mapping analyses, chronic
stroke patients who have recovered from their initial cognitive deficit
are grouped together with chronic stroke patients that never had a
deficit. As a consequence, the lesion-behaviour mapping analysis (incorrectly)
assumes that the parts of the brain damaged in chronic stroke
patients who have recovered are not, or less critically, associated with
the cognitive function of interest. Karnath and Rennig (2016) recently
tested the consequences of this effect, comparing the three most
common combinations of structural imaging data and behavioural
scores used in previous lesion-behaviour mapping studies. Only the
combination of acute behavioural scores and acute structural imaging
precisely identified the targeted brain areas. In contrast, lesion-behaviour
mapping analyses based on chronic behaviour, in combination
with either chronic or acute imaging, hardly detected any of the targeted
substrates.
Moreover, in the chronic stroke phase, the precise determination of
the parts of the brain that are functionally impaired in CT and/or MR
images is complicated by secondary morphological changes to the brain
due to tissue resorption following brain damage, such as structural
distortions, sulcal widening, and ventricle enlargement (Karnath and
Rorden, 2012). As such, chronic stroke patients are less suitable than
acute stroke patients for studies where the aim is to investigate the
functional architecture of the brain. However, if combined with CT
and/or MRI data obtained in the acute stroke phase, the behavioural
data from chronic stroke patients has been shown to be highly suitable
for studies where the aim is to investigate the neural correlates of
chronic cognitive deficits, i.e. studies where the aim is to determine
where in the brain acute damage results in a cognitive deficit that is still
present in the chronic stroke stage 6 months or more following stroke
onset (Karnath et al., 2011; Abela et al., 2012; Wu et al., 2015). This is a
question with a high clinical relevance, as it ultimately has the potential
to enable long-term clinical predictions based on the location of the
acute brain damage.
2.1.3. Avoid combining acute and chronic patients in the same lesionbehaviour
mapping analysis
Importantly, combining both acute and chronic stroke patients in
the same lesion-behaviour mapping study entails the risk of ending up
with the worst of two worlds: Even if CT and/or MRI data is obtained in
the acute stroke phase for all patients, the different amounts of cortical
reorganization in different patients are likely to confound the interpretation
of the lesion-behaviour mapping results, regardless of whether
the aim was to study the functional architecture of the brain, or to
study the neural correlates of chronic cognitive deficits. This problem is
most straightforwardly demonstrated with a thought experiment (see
Fig. 3): Imagine that both lesions depicted on the top of Fig. 3 equally
affect the brain area crucially related to a certain cognitive function of
interest. Thus, directly following stroke onset, the behavioural deficit in
Fig. 1. Number of publications per year that used the lesion method between the years
1995 and 2015. This result was obtained by running a Pubmed (https://www.ncbi.nlm.
nih.gov/pubmed) literature search with the search term “(‘lesion analysis’ OR ‘lesion
mapping’ OR ‘VLSM’ OR ‘VLBM’) AND brain”, followed by a manual exclusion of nonempirical
articles (i.e. methodological articles, review articles, etc). Note the clear and
steady increase in number of publications that used the lesion method per year over the
last 10 years.
Fig. 2. Illustration of the typical lesion study pipeline. When performing a lesion study,
the researcher has to decide which patients to select, how to assess the patient's lesion
location and behavioural status, how to spatially normalise the patient's brain image and
lesion map, how to perform the voxelwise (statistical) comparisons over all patients, and
finally, how to anatomically interpret the obtained results.
B. de Haan, H.-O. Karnath Neuropsychologia 115 (2018) 5–16
6
these two patients is maximal and equal (i.e. the behavioural deficit
score is ‘54’ for both cases). In both patients the severity of the behavioural
deficit decreases equally over time due to spontaneous recovery.
Now imagine that both patients are recruited for a lesion-behaviour
mapping study. However, whereas one of the patients is
recruited and behaviourally assessed in the acute stroke phase (and thus
shows a behavioural deficit score of ‘54’ in Fig. 3), the other patient is
first seen and assessed in the intermediate/chronic stroke phase following
considerable spontaneous recovery of his/her behavioural deficit
(let us assume by 50%, leading to a measured behavioural deficit
score of ‘27’ in Fig. 3). Although both brain lesions equally affect the
brain area crucially related to the cognitive function of interest, the
lesion analysis now erroneously weighs the lesion location of the patient
with the behavioural deficit score of ‘27’ as being less relevant for
the cognitive function of interest than the lesion location of the subject
with the behavioural deficit score of ‘54’. As such, the overall contribution
of this brain area to the cognitive function of interest is ultimately
underestimated. The main underlying issue is that lesion-behaviour
mapping analyses assume that each patient is assessed at the
same point in time following stroke onset and that thus the contribution
of a certain brain area to a certain cognitive function is directly reflected
in the behavioural scores in each patient. Combining both acute
and chronic stroke patients in the same lesion-behaviour mapping study
violates this assumption.
2.1.4. Lesion aetiologies other than stroke
Beyond strokes, lesion-behaviour mapping analyses have also been
conducted with other lesion aetiologies. In the 181 lesion-behaviour
mapping studies depicted in Fig. 1, the second and third most popular
lesion aetiologies were traumatic brain injury and brain tumour (10.5%
and 5.5% of the studies respectively). Moreover, a further 9.9% of these
studies combined patients with different lesion aetiologies in the same
study (e.g. included patients with stroke, traumatic brain injury and
brain tumour). There is, however, considerable debate concerning the
suitability of these patients with lesion aetiologies other than stroke for
lesion-behaviour mapping analyses. Specifically, a considerable body of
work suggests that traumatic brain injury and tumour patients might be
less suitable than stroke patients for lesion-behaviour mapping analyses
that aim to study the functional architecture of the healthy brain.
In traumatic brain injury patients, a major neuropathological component,
beyond focal brain damage and regardless of traumatic brain
injury severity (mild, moderate or severe) or mechanism (closed or
penetrating), is diffuse axonal injury (Gennarelli et al., 1982;
Povlishock and Katz, 2005; Büki and Povlishock, 2006; Su and Bell,
2016). Moreover, this diffuse axonal injury has been suggested to
contribute significantly to the cognitive impairments (and their recovery)
observed following traumatic brain injury (Povlishock and
Katz, 2005; Levine et al., 2013). However, while areas of diffuse axonal
injury of sufficient size can be detected in MRI (Su and Bell, 2016), the
full extent of diffuse axonal injury can only be detected histopathologically
(Adams et al., 1991; Povlishock, 1993; Johnson et al., 2013).
This presents a significant problem for lesion-behaviour mapping analyses,
as these analyses require an accurate in vivo determination of
which areas of the brain are functionally impaired and which areas of
the brain are functionally intact. Additionally, traumatic brain injury
patients are typically investigated in a chronic disease phase, in which
case the same caveats as noted above for chronic stroke patients hold.
In brain tumour patients, there is likewise evidence that an accurate
determination of functionally impaired and functionally intact areas of
the brain can be problematic. The most common type of malignant
primary brain tumour is glioma, accounting for 74.6% of all malignant
brain and other central nervous system tumours (Ostrom et al., 2016).
In gliomas, however, the precise spatial extent of the tumour is impossible
to determine. The vast majority of gliomas are characterised by
diffuse infiltration of surrounding tissue (Scherer, 1940) that can extend
considerably beyond the tumour border visible in conventional T1 or
T2 MRI images (Burger et al., 1988; McKnight et al., 2002; Swanson
et al., 2004). While more recent imaging modalities such as diffusion
tensor imaging and proton MR spectroscopy may improve the visualisation
of the tumour (Claes et al., 2007), many areas of tumour infiltration
occur at a spatial scale that cannot be detected even with these
newer imaging modalities. Critically, and presenting a significant problem
for lesion-behaviour mapping analyses, it is unclear whether or to
what extent brain function is impaired in these (in vivo not detectable)
areas of diffuse tumour infiltration (Karnath and Steinbach, 2011). In
the case of preoperative tumour patients, this problem in accurately
determining the functionally impaired and functionally intact areas of
the brain is further exacerbated by observations suggesting that brain
function can sometimes be preserved within the tumour, particularly
(but not exclusively) in patients with a slow-growing low-grade glioma
(Ojemann et al., 1996; Skirboll et al., 1996; Schiffbauer et al., 2001).
An additional problem with using glioma patients in lesion-behaviour
analyses is that these tumours tend to develop on a relatively long
time-scale. That is, unlike strokes or traumatic brain injury, gliomas do
not have a sudden onset. Instead, gliomas slowly grow and are only
diagnosed when they are both large enough to be detected in CT or MR
images and clinically symptomatic (Swanson et al., 2003; Pallud et al.,
2013). This relatively long time-scale of development means that, by
the time the tumour is diagnosed, most glioma patients‘ brains have
undergone a considerable amount of (compensatory) functional reorganization
(Wunderlich et al., 1998; Fandino et al., 1999; Thiel et al.,
2001; Holodny et al., 2002; Meyer et al., 2003; Taniguchi et al., 2004;
Shaw et al., 2016). This means that in tumour patients, the behavioural
Fig. 3. Illustration of the thought experiment described in the manuscript text. Both lesions
depicted on the top equally affect the brain area crucially related to a certain cognitive
function of interest. The diagram on the bottom, reflects the rate of spontaneous
recovery over time of this function. The difference in measured behavioural deficit scores
of these patients (‘54’ vs. ‘27’) does not result from differences in the relevance of the
individual lesions for the cognitive function of interest. Instead, this difference is the
consequence of including the two patients at different time points following stroke-onset:
one patient is recruited and behaviourally tested in the acute phase after stroke (and thus
shows a max. behavioural deficit score of ‘54’) while the other patient is first seen and
tested in the intermediate/chronic stroke phase, following partial recovery of the behavioural
deficit (from an initial behavioural deficit score of ‘54’ down to a behavioural
deficit score of ‘27’).
B. de Haan, H.-O. Karnath Neuropsychologia 115 (2018) 5–16
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consequence of brain damage may no longer reflect the original function
of the damaged part of the brain, which presents a serious problem
for lesion-behaviour mapping analyses that aim to study the functional
architecture of the brain.
2.2. Lesion location
Beyond lesion aetiology, another patient selection criterion is lesion
location. For example, based on a convincing body of previous findings,
we might a priorily expect certain language functions (e.g. language
production) to be associated with a certain part of the brain (e.g. the left
hemisphere). As such, when interested in, e.g., language production,
our research question might be „which areas of the left hemisphere
causally contribute to language production?“. In this case, it would
make little sense to also assess patients with right hemispheric brain
damage. Likewise, on a smaller spatial scale, we might know from
various previous investigations that a certain function of interest is
located in a particular region of the brain, e.g., that a specific executive
function is governed by prefrontal cortex. As such, when interested in
describing where exactly within the prefrontal cortex this specific
function is located, it would make little sense to also assess patients
with posterior brain damage. However, while restricting patient selection
to patients with damage to only a certain part of the brain is valid
in these cases, it is also important to realise that this then by definition
means that no inferences can be made about the potential (and perhaps
even larger) contributions of not-investigated areas of the brain.
Moreover, caution should be used when extending this logic to multiple
parts of the brain (see also Section 5.3 below). In patients with larger
strokes, brain lesions are likely to encompass more than just one of
these areas of interest. This creates a significant problem during classification
of these cases. Exclusion is no solution, as this would create a
bias towards smaller lesions and potentially milder cognitive symptoms,
ultimately leading to different anatomical conclusions.
2.3. General criteria
Finally, beyond these main patient selection criteria, there are a few
typical general exclusion criteria. Firstly, patients with evidence of
clinically relevant cognitive impairments such as dementia or mental
retardation and/or evidence of psychiatric disorders are usually excluded.
In these patients, a valid assessment of the behaviour of interest
would be difficult. Secondly patients with evidence of additional (preexisting)
neurological disorders beyond the neurological disorder of
interest, such as Parkinson's disease, infections of the central nervous
system, or older and/or additional diffuse brain lesions due to, e.g.,
previous strokes or chronic hypertension, are also usually excluded
(although up to 2–5 pre-existing silent lacunes are typically allowed). In
these patients it would be difficult to determine which aspect(s) of the
behavioural deficit (if observed) can be attributed to the neurological
disorder of interest and which aspect(s) of the behavioural deficit might
instead be due to these additional neurological disorders. Finally, patients
with mass shifts due to extensive haemorrhage or oedema should
be excluded. In these patients, brain areas are no longer at their original
positions, which potentially confounds the interpretation of lesion-behaviour
mapping analysis results. Importantly, however, absence of the
behavioural deficit of interest should not be used as an exclusion criterion.
The inclusion of patients that do not have the behavioural deficit of
interest (control patients) is essential, as this allows us to differentiate
between areas of the brain where damage is associated with the deficit
of interest and areas of the brain where damage merely reflects increased
vulnerability to injury (Rorden and Karnath, 2004). Moreover,
restricting patient selection solely to patients that show the behavioural
deficit of interest reduces variance in the behavioural data and so ultimately
reduces statistical power to detect an effect in lesion-behaviour
mapping analyses (see also Section 3.2 below). Instead, researchers
should ideally a priorily decide on a reasonable patient recruitment
time period and unselectively include all suitable (i.e. matching all
inclusion criteria and none of the exclusion criteria) patients that present
during that time period in the study.
3. Patient assessment
The next decision a researcher planning a lesion-behaviour mapping
study has to make, is how to assess lesion location and behavioural
status in each patient. Given the problems associated with lesion aetiologies
other than stroke (see Section 2.1.4 above), the following
section and the rest of this manuscript will focus on stroke patients.
3.1. Assessing lesion location
As mentioned in Section 2.1 above, imaging data should be obtained
in the acute stroke phase, regardless of whether the aim is to study the
functional architecture of the brain, or to study the neural correlates of
chronic cognitive deficits. In acute stroke patients, the lesion can be
visualised using either CT (Mohr et al., 1995; Mayer et al., 2000) or MRI
(Neumann-Haefelin et al., 1999; Ricci et al., 1999; Schlaug et al., 1999).
The development of CT templates for spatial normalisation of individual
patient images (Rorden et al., 2012; see also Section 4.1
below) has removed the main reason to disregard CT for lesion-behaviour
mapping studies. Moreover, modern spiral CT scanners provide
high resolution images and in many clinical institutions CT remains the
dominant imaging modality of choice at admission. Importantly, the
choice between administering CT or MRI to patients at admission is not
random, but follows specific clinical criteria. As a consequence, the
systematic exclusion of patients with CT images only, implements a
selection bias, typically influencing important factors such as lesion
size, general clinical status, severity of cognitive deficits etc. (for a
detailed discussion, see Sperber and Karnath, (in press, this issue)). As
such, both CT and MRI data can and should be used for lesion analysis
studies.
We suggest the following practical guidelines for the assessment of
lesion location: In patients with CT imaging only, use noncontrast CT
images to visualise the brain lesion. In noncontrast CT images, acute
haemorrhagic strokes appear as hyperintense areas within minutes to
hours following stroke onset (Bergström et al., 1977). Ischemic strokes,
on the other hand, appear as hypointense areas between 24 and 36 h
following stroke onset (Mohr et al., 1995; von Kummer et al., 2001). In
patients with MR imaging only, use diffusion-weighted images (DWI) to
visualise the lesion if imaging is performed less than 48 h following
stroke onset, and use T2FLAIR images (ideally supplemented with DWI,
particularly during the first 5 days following stroke onset (Ricci et al.,
1999)) if imaging is performed more than 48 h following stroke onset.
In DWI, ischemic strokes appear as hyperintense areas within 2–6 h
following stroke onset (Warach et al., 1992; González et al., 1999),
while the initial T2FLAIR infarct hyperintensity might be too subtle for
accurate lesion visualisation within the first 48 h (Lansberg et al.,
2001). Finally, while not suitable for the visualisation of the lesion in
the acute phase of a stroke, a T1 image might aid spatial normalisation
(see Section 4.2 below). In patients with both CT and MR images, the
researcher is in the privileged situation to choose the best from both
modalities, i.e. to use those images where the lesion is most con-
spicuous.
The information provided by structural imaging data could be
meaningfully complemented with imaging data that allows visualisation
of areas of the brain that are structurally intact, but may function
abnormally (e.g. the ischemic penumbra and/or areas of diaschisis).
That is, multimodal imaging of brain damage, where structural and
functional information are combined, might provide a more accurate
picture of the full extent of brain damage than structural imaging alone.
In clinical settings, visualisation of areas of the brain that are structurally
intact, but may function abnormally can be done using perfusion
CT (Mayer et al., 2000; Koenig et al., 2001) or MR perfusion-weighted
B. de Haan, H.-O. Karnath Neuropsychologia 115 (2018) 5–16
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imaging (PWI; Schlaug et al., 1999; Schaefer et al., 2002; Zopf et al.,
2012). Shahid et al. (2017) were recently able to show that, when patient
assessment was performed within the first 48 h following stroke
onset, lesion analysis inferences were more accurate when based on
both structural and perfusion imaging than when based on structural
imaging alone. Unfortunately, however, effective usage of perfusion
data is often difficult due to the fact that the precise relationship between
the severity of hypoperfusion and the severity of the functional
impairment is largely unknown. While guidelines have been posited
concerning the degree of hypoperfusion likely to lead to a behaviourally
relevant functional impairment for some areas (Neumann-Haefelin
et al., 1999; Hillis et al., 2001; Motta et al., 2014), it is currently unclear
whether these guidelines are equally applicable to all areas of the brain.
This is in contrast to areas that are lesioned, where we know that
function is completely lost. Moreover, while there is evidence for the
behavioural relevance of the ischemic penumbra (Shahid et al., 2017),
evidence for the behavioural relevance of remote diaschisis is still
mixed. While several studies suggest that subcortical damage may result
in behaviourally relevant remote cortical hypoperfusion (Hillis
et al., 2001, 2002, 2005; Karnath et al., 2005; Ticini et al., 2010),
evidence that cortical damage results in behaviourally relevant remote
cortical hypoperfusion is so far lacking (Zopf et al., 2009).
3.1.1. Lesion delineation
Following CT or MR data acquisition, the lesion needs to be delineated
on each slice of the patient's brain image. The standard is
manual lesion delineation, which can be done using programs like
MRIcroN (https://www.nitrc.org/projects/mricron; Rorden and Brett,
2000), or ITK-SNAP (http://www.itksnap.org; Yushkevich et al., 2006).
However, manual lesion delineation is time-consuming and potentially
observer-dependent (Ashton et al., 2003). To address these disadvantages,
both fully automated and semi-automated lesion delineation
methods have been developed. Fully automated lesion delineation
methods can roughly be divided in unsupervised (e.g. Gillebert et al.,
2014; Mah et al., 2014b) and supervised (e.g. Griffis et al., 2016;
Pustina et al., 2016) classification algorithms. As the name implies,
fully automated methods do not require any user interaction. As such,
these methods are substantially less time-consuming and observer-dependent
than manual lesion delineation, which improves replicability
and reproducibility across labs. A considerable downside of these fully
automated methods, however, is that they may be more susceptible to
imaging artefacts and thus less precise than the current gold standard,
manual lesion delineation. Importantly, this reduced precision associated
with fully automated lesion delineation methods may influence
subsequent lesion-behaviour mapping results (Pustina et al., 2016).
Given this downside, semi-automated lesion delineation methods that
combine fully automated steps with mandatory user interaction might
be able to provide an optimal compromise. While several semi-automated
lesion delineation approaches exist (e.g. Wilke et al., 2011), the
semi-automated lesion delineation approach Clusterize (https://www.
medizin.uni-tuebingen.de/kinder/en/research/neuroimaging/
software/; Clas et al., 2012) has recently been shown to be capable of
significantly speeding up lesion delineation, without loss of either lesion
delineation precision or lesion delineation reproducibility in acute
stroke patients scanned in both CT and a range of common MRI modalities
(de Haan et al., 2015). The principle of Clusterize is simple: On
the basis of local intensity maxima and iterative region growing, the
whole CT or MR brain image is fully automatically clusterized, including
the lesioned area. The user subsequently manually selects those
clusters that correspond to the lesion. Clusterize may thus combine the
best of two worlds: the presence of fully automated steps makes it less
time-consuming than manual lesion delineation, while mandatory user
interaction results in lesion delineation that is more precise and/or less
error-prone (in the sense that they are closer to the results from the
current gold standard, manual delineation) than that obtained by fully
automated lesion demarcation methods (de Haan et al., 2015).
3.2. Assessing behavioural status
In a lesion-behaviour mapping analysis, the behavioural status
needs to be assessed for each patient. Typically, this means that the
cognitive function of interest needs to be operationalised. Several important
considerations concerning this operationalisation of cognitive
functions, such as taking care to distinguish between impaired performance
on a test used to operationalise a cognitive function and the
clinical syndrome of interest, and ensuring that the test used to operationalise
a cognitive function is as specific to the cognitive function of
interest as possible, are discussed in Sperber & Karnath (in press, this
issue). Additionally, it is important to ensure that the test used to
characterize the behavioural deficit has sufficient sensitivity. To help
ensure this, the range of scores used in a test should be chosen to
measure the full range of the underlying behaviour with both sufficient
and meaningful resolution. That is, the optimal range of scores would
allow both a measurement of the full possible range of behaviour and a
measurement of the smallest meaningful difference in the behaviour of
interest. Insufficient sensitivity both reduces the ability to detect a true
effect in lesion-behaviour mapping analyses, and reduces the likelihood
that an observed significant effect reflects a true effect (Button et al.,
2013; Ingre, 2013). Moreover, given that cognitive performance is typically
expressed on a continuous scale, dichotomization of this inherently
continuous performance should be avoided. Dichotomization
of continuous behaviour is known to result in a significant loss of information
and thus of statistical power (Cohen, 1983). In the rare cases
where dichotomization of continuous behaviour is unavoidable (for
example when performing lesion subtraction analyses to study exceedingly
rare deficits, see Section 5.1 below), the dichotomization and
subsequent classification of individual patients as ‘impaired’ or ‘nonimpaired’
should be performed with proper statistical procedures
(Crawford and Howell, 1998; Crawford and Garthwaite, 2005). Finally,
when assessing the behavioural status of a patient, potential neuropsychological
co-morbidity should be taken into account (see also
Bonato et al., 2012; Sperber and Karnath, in press, this issue). Care
should be taken to ensure that the test used to operationalise the cognitive
function of interest does not introduce a systematic bias against
patients with certain co-occurring deficits. Moreover, frequently cooccurring
deficits (e.g. reduction of general cognitive status, language
impairments) whose severity might correlate with the severity of the
cognitive deficit of interest should ideally be assessed in addition to the
cognitive function of interest, so that they can be controlled for during
the lesion-behaviour mapping analyses (e.g. by including them as nuisance
covariates, see Section 5.2.1 below).
4. Spatial normalisation of patient brain and lesion map
Following patient imaging and lesion delineation, we have a 3D
binary lesion map reflecting the voxels where brain function is impaired
for each patient that we can use for lesion-behaviour mapping analysis.
However, all brains differ in orientation, size, and shape. As such, before
we can perform voxelwise (statistical) comparisons, we need to
spatially normalise the patient brains and lesion maps to ultimately
ensure that a given voxel (roughly) represents the same anatomical
structure in each patient. Thus, the third decision a researcher has to
make, is how to spatially normalise the patient brain and lesion map.
Spatial normalisation can be performed with programs such as
BrainVoyager (http://www.brainvoyager.com/; Goebel, 2012), SPM
(http://www.fil.ion.ucl.ac.uk/spm/), FSL (https://fsl.fmrib.ox.ac.uk/
fsl/fslwiki; Jenkinson et al., 2012), AFNI (https://afni.nimh.nih.gov/;
Cox, 1996, 2012), or ANTs (http://stnava.github.io/ANTs/; Avants
et al., 2011). The analysis package SPM is widely used due to its platform
independence, free obtainability, and availability of many addons.
As such, we here will focus on the spatial normalisation routines of
SPM, as implemented in the Clinical Toolbox (https://www.nitrc.org/
projects/clinicaltbx/; Rorden et al., 2012). This Clinical Toolbox
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provides specialised templates that allow spatial normalisation of both
CT and MR brain images of elderly, stroke-aged populations (see
Section 4.4 below). As such, the Clinical Toolbox is ideally suited to be
used in lesion-behaviour mapping studies where the patients included
are typically older, and where different modalities, i.e. CT as well as MR
images, are present in different patients. Beyond routines for traditional
spatial normalisation and unified segmentation and normalisation approaches
(see Sections 4.1 and 4.2 below), the Clinical Toolbox also
provides processing steps that aid the spatial normalisation of scans
from stroke patients, including corrections for the presence of a lesion
(see Section 4.3 below).
4.1. Spatial normalisation of imaging data with low radiometric resolution
Imaging data with a low radiometric resolution is imaging data
where the image intensity values offer a poor differentiation between
different tissue types, particularly between grey and white matter brain
tissue. In acute stroke patients, this typically applies to CT and T2FLAIR
data. Here, the typical approach is to perform spatial normalisation by
matching the orientation, size, and shape of each patient brain to the
orientation, size, and shape of a template brain in standard stereotaxic
space. This matching is done with an automated algorithm that aims to
find the necessary image transformations that minimize the least mean
square difference between the voxel intensities of the patient and
template brain (Ashburner and Friston, 2003). In a first step, the patient
brain is matched to the template brain using linear (affine) image
transformations that can include translations, rotations, zooms, and/or
shears. These affine transformations change the entire patient brain in
the same way and so result in a global match between the patient brain
and the template brain. Subsequently, in a second step, the patient
brain is further matched to the template brain using nonlinear (nonaffine)
image transformations consisting of cosine basis functions.
These nonaffine transformations allow local changes to the patient
brain and so improve the match between patient and template brain. To
avoid overfitting during this second step, the difference in the amount
of nonlinear transformations of adjacent areas is simultaneously minimized
(known as ‚regularisation‘). As such, spatial normalisation matches
the overall orientation, size and shape of the patient brain to that
of the template brain, but not individual sulci. Once the necessary
image transformations have been estimated, they can be applied to both
the patient's brain image and the lesion map, bringing both in standard
stereotaxic space.
4.2. Spatial normalisation of imaging data with high radiometric resolution
Imaging data with a high radiometric resolution is imaging data
where the image intensity values offer a good differentiation between
different tissue types, particularly between grey and white matter brain
tissue. This typically does not apply to the imaging data used to visualise
the lesion in acute stroke (see Section 3.1 above), but does
usually apply to an additionally collected T1 image. Likewise, when
DWI data is collected, this is usually collected with different b-values
(typically b0, b500, and b1000). While the b1000 image best suited to
visualise the lesion in acute stroke patients has a low radiometric resolution,
the additionally collected b0 image often has a relatively high
radiometric resolution (Mah et al., 2014b). In these cases, the typical
approach is to first coregister the image with a low radiometric resolution
(e.g. the T2FLAIR or the b1000 DWI), used to visualise the
lesion, to the image with a high radiometric resolution (e.g. the T1 or
the b0 DWI). Subsequently, the image with a high radiometric resolution
is normalised using the unified segmentation and normalisation
approach (Ashburner and Friston, 2005). This approach has been
shown to be superior to the traditional normalisation approach discussed
in Section 4.1 above in both healthy (Crinion et al., 2007; Klein
et al., 2009) and stroke (Crinion et al., 2007) populations, but requires
an image with a high radiometric resolution. The unified segmentation
and normalisation approach combines tissue classification (i.e. segmentation),
bias correction, and image registration (i.e. spatial normalisation)
in a single model. Estimation of the model parameters is
done by repeatedly alternating between tissue classification (to assign
each voxel to a tissue class on the basis of its intensity), bias correction
(to correct for image intensity nonuniformity due to magnetic field
inhomogeneity), and image registration (to bring patient image and
template-based tissue probability maps into common space using regularised
affine and non-affine transformations and so derive the image
transformations necessary to bring the patient image into standard
stereotaxic space) steps. As this model accounts for the conditional
dependencies between the steps (i.e. tissue classification aids the bias
correction and image registration steps, and bias correction and image
registration aid the tissue classification step), the results are superior to
those obtained following serial application of the same steps. Once the
necessary image transformations have been estimated, they are applied
to the patient's brain image(s) and the lesion map, bringing all images
in standard stereotaxic space.
4.3. Correcting for the lesion during spatial normalisation
While both spatial normalisation approaches described above work
well with imaging data from neurologically healthy subjects, the presence
of a lesion in imaging data from stroke patients presents a challenge,
as lesions are characterised by abnormal image intensity. This
area of abnormal image intensity in the patient brain locally creates a
large mismatch between the patient brain and the template brain /
template-based tissue probability maps, ultimately leading to local
overfitting in the lesion area during the minimization of this mismatch
(Brett et al., 2001; Andersen et al., 2010). The two dominant solutions
to this overfitting problem are cost function masking (Brett et al., 2001)
and enantiomorphic normalisation (Nachev et al., 2008). During cost
function masking, lesioned voxels are excluded during spatial normalisation
using (a typically slightly smoothed version of) the binary lesion
map. As such, the image transformations necessary to bring the patient's
brain image(s) and the lesion map in standard stereotaxic space are
derived from intact areas of the brain only. During enantiomorphic
normalisation, on the other hand, the lesion is ‚corrected‘ by replacing it
with brain tissue from the lesion homologue in the intact hemisphere of
the brain. As such, the image transformations necessary to bring the
patient's brain image(s) and the lesion map in standard stereotaxic
space are effectively derived from a brain image without a lesion.
Logically, one would expect enantiomorphic normalisation to perform
better than cost function masking when lesions are large and
unilateral, as spatial normalisation with cost function masking becomes
less accurate as lesion size increases (as the area from which the image
transformations can be derived decreases with increasing lesion size),
while enantiomorphic normalisation does not. Cost function masking
would, however, be expected to perform better than enantiomorphic
normalisation when lesions are bilateral and affect similar areas in both
hemispheres, as enantiomorphic normalisation would in this case replace
the lesion with the likewise lesioned homologue. Moreover, as
enantiomorphic normalisation assumes that the brain is essentially
symmetric, enantiomorphic normalisation might be suboptimal in areas
known to be considerably asymmetric (e.g. the planum temporale).
4.4. Choosing the right template
Finally, an important consideration during spatial normalisation
concerns the choice of template / template-based tissue probability
maps. Firstly, when using the traditional spatial normalisation approach
described in Section 4.1 above, the template image should
ideally have the same image modality as the patient image that is
spatially normalised, as the accuracy of this approach depends on how
similar the voxel intensities of a given brain area are between the patient
and the template image. The unified segmentation and
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normalisation approach described in Section 4.2 above, on the other
hand, is modality-independent. Secondly, regardless of the spatial
normalisation approach chosen, the population from which the template
image or template-based tissue probability maps are derived
should roughly match the population of the lesion mapping study. That
is, if the lesion-behaviour mapping study is performed in elderly stroke
patients, the template or template-based tissue probability maps used
should ideally have been derived from an elderly population.
For elderly adults (mean age 61.3 years), a template is available for CT
imaging data, as well as template-based tissue probability maps for MR
imaging data (Rorden et al., 2012). For young adults (mean age 25 years),
templates are available for T1 and T2 imaging data, as well as templatebased
tissue probability maps (Mazziotta et al., 1995, 2001a, 2001b). For
paediatric populations, various templates are available for T1 imaging
data, as well as template-based tissue probability maps (e.g. Wilke et al.,
2008; http://jerlab.psych.sc.edu/neurodevelopmentalmridatabase/). Finally,
a template derived from a wide range of adults (mean age 35.4,
range 18–69 years) is available for T2FLAIR imaging data (https://
brainder.org/download/flair/; Winkler et al.,). Given that the average
age of acute stroke patients included in a lesion-behaviour mapping study
is typically over 60, the Clinical Toolbox (https://www.nitrc.org/projects/
clinicaltbx/) includes the above-mentioned CT template and templatebased
tissue probability maps derived from elderly adults, as well as the
T2FLAIR template. This toolbox can, however, easily be modified for use
with other templates or template-based tissue probability maps.
5. Performing voxelwise (statistical) comparisons
Following lesion delineation and spatial normalisation, we have a
spatially normalised binary lesion map for each patient. Moreover, we
have a behavioural measurement for each patient. With these two
sources of information, we are ready to perform a voxelwise lesionbehaviour
mapping analysis to relate lesion location and patient behaviour.
Over the years, the methods to perform voxelwise analyses
have continuously improved, from early subtraction analyses, to voxelwise
statistical analyses (with correction for multiple comparisons),
and ultimately to voxelwise statistical analyses that account for nuisance
covariates such as lesion volume. Each of these methods will be
discussed in the sections below.
5.1. Lesion subtraction analyses
The simplest type of voxelwise analysis is a lesion subtraction
analysis. Here, the lesion overlap map of patients without the cognitive
deficit of interest is subtracted from the lesion overlap map of patients
showing the cognitive deficit of interest. This can be performed with
programs such as MRIcroN (https://www.nitrc.org/projects/mricron).
To account for potential sample size differences between the two patient
groups, these subtraction analyses need to use proportional values.
That is, for each voxel the percentage of patients without the cognitive
deficit of interest that have a lesion at the voxel is subtracted from the
percentage of patients with the cognitive deficit of interest that have a
lesion at the voxel. The result of the subtraction analysis is then a map
with the percentage relative frequency difference between these two
groups for each voxel. For example, say we have 10 patients with the
cognitive deficit of interest and 20 patients without the cognitive deficit
of interest, where at a given voxel 9 of the 10 patients with the deficit
have a lesion (i.e. 90%), while 10 of the 20 patients without the deficit
have a lesion (i.e. 50%). In this case, the percentage relative frequency
difference at that voxel would be 90% − 50% = 40%, indicating that
this voxel is damaged 40% more frequently in patients with the cognitive
deficit of interest than in patients without this deficit. These
subtraction analyses are superior to simple overlap analyses that focus
on only those patients that show the disorder of interest, because
overlap analyses might simply highlight regions that reflect increased
vulnerability of certain regions to injury (see Rorden and Karnath,
2004). In contrast, a lesion subtraction analysis highlights those areas of
the brain where lesions are more frequent in patients with than in patients
without the cognitive deficit of interest, and so distinguishes
between regions that are merely often damaged in strokes and regions
that are specifically associated with the deficit of interest (Rorden and
Karnath, 2004). To control for neuropsychological co-morbidity, the
two patient groups contrasted in a lesion subtraction analysis need to be
comparable with respect to additional neurological impairments (of no
interest) such as, e.g., paresis, visual field defects, etc. While subtraction
analyses have merit in the study of exceedingly rare cognitive deficits
(where it would be near to impossible to obtain a larger sample size), it
is important to realise that these analyses are purely descriptive and
allow no statistical inference. For statistical inference, voxelwise statistical
analyses are necessary.
5.2. Voxelwise statistical lesion-behaviour mapping analyses
In a voxelwise statistical lesion-behaviour mapping analysis, we
perform a statistical test at each voxel to relate voxel status (lesioned/
nonlesioned) and patient behaviour. These voxelwise statistical analyses
can be performed with programs such as VLSM (https://
langneurosci.mc.vanderbilt.edu/resources.html; Bates et al., 2003),
VoxBo (https://www.nitrc.org/projects/voxbo/, Kimberg et al., 2007),
NPM (https://www.nitrc.org/projects/mricron; Rorden et al., 2007),
NiiStat (https://www.nitrc.org/projects/niistat/) and/or LESYMAP
(Pustina et al., in press, this issue). When the behavioural data is continuous,
the behavioural data of the group of patients in whom a given
voxel is damaged is statistically compared to the behavioural data of
the group of patients in whom that same voxel is intact. This is traditionally
done with a two-sample t-test, which assumes that the behavioural
data is normally distributed and measured on an interval scale.
Unfortunately, however, behavioural data from patient populations is
often not normally distributed. During behavioural assessment, patients
without the deficit of interest will typically all demonstrate close to
maximum performance, whereas performance in patients with the
deficit of interest will typically be poorer and more variable over patients.
As a consequence, the distribution of the behavioural data from
patient populations is often negatively skewed. Moreover, behavioural
data from patient populations is often not measured on an interval
scale. Instead, many tests designed to assess cognitive function in patient
populations measure patient behaviour on an ordinal scale, where
the behavioural data is ordered (e.g. higher scores denote better performance),
but the distances between the individual measurements are
not known (e.g. the difference between a score of ‚1′ and ‚2′ is not
necessarily the same as the difference between a score of ‚2′ and ‚3′).
Unfortunately, the t-test tends to be overly conservative when its test
assumptions are violated, resulting in a reduction of statistical power to
detect an effect in lesion-behaviour mapping analyses. Instead, the assumption
free rank order test proposed by Brunner and Munzel (2000)
might be more appropriate in these situations. In a lesion-behaviour
mapping analysis with simulated data, this so-called Brunner-Munzel
test has been shown to have higher statistical power than the t-test,
while offering similar protection against false positives, in situations
where the distribution of the behavioural data is skewed (Rorden et al.,
2007).
When the behavioural data is binomial (i.e. when the deficit is either
present or absent, as in e.g. hemianopia), we statistically assess, for
each voxel, whether the variables ‘voxel status’ (voxel lesioned vs. voxel
intact) and ‘behavioural status’ (deficit present vs. deficit absent) are
associated or independent. The statistical test typically used in these
situations is the Pearson's chi-squared test. In many lesion-behaviour
mapping analyses, however, expected cell frequencies are lower than
5–10 in at least some voxels, resulting in inflated false positive rates
when using Pearson's chi-squared test. Traditional solutions to this
problem are to use Yates's correction for continuity or a Fisher's exact
test. These solutions, however, both assume fixed marginals, meaning
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that they assume that the column totals (in how many patients is this
voxel lesioned and in how many patients is this voxel intact) and row
totals (how many patients have a deficit and how many patients do not)
are fixed in advance before data collection starts. Obviously, this is not
the case in a typical lesion-behaviour mapping analysis, and as a consequence,
both Yates's correction for continuity and Fisher's exact test
tend to be overly conservative. A statistical test that might be more
appropriate in these situations is the quasi-exact test proposed by
Liebermeister (1877). In a lesion-behaviour mapping analysis with simulated
data, observed false positive rates for this so-called Liebermeister
test closely approximated the set false positive threshold,
whereas observed false positive rates for Fisher's exact test tended to be
too low (Rorden et al., 2007).
5.2.1. Inclusion of nuisance covariates and ensuring sufficient minimum
lesion overlap
One variable known to correlate strongly with the severity of behavioural
deficit in stroke populations is lesion volume (the larger the
lesion, the more likely it is that a patient will show a behavioural
deficit). Thus, to avoid identifying brain areas where damage is related
simply to lesion volume instead of patient behaviour, lesion volume
should be included as a nuisance covariate in a voxelwise statistical
lesion analysis. This can be done using regression approaches (e.g. logistic
regression or the general linear model). Moreover, statistical
power varies over voxels as a function of the amount of lesions that
overlap at each voxel, with statistical power theoretically being maximal
at voxels that are lesioned in half of the patient sample.
Importantly, statistical power is absent at voxels that are damaged in
none of the patients (as this would result in one empty group for the ttest
and Brunner-Munzel test, or two empty cells for the Liebermeister
test). Thus, to ensure sufficient statistical power, voxels damaged in a
very low percentage of the patient sample should be excluded.
Correcting for lesion volume as well as ensuring sufficient minimum
lesion overlap has been shown to reduce the spatial bias and so improve
the anatomical validity in univariate voxelwise statistical analyses
(Sperber and Karnath, 2017). Finally, similarly as done for lesion volume,
other nuisance covariates can additionally be included, such as
the severity of frequently co-occurring deficits that may correlate with
the cognitive function of interest (see also Section 3.2 above), fiber tract
disconnection likelihood (Rudrauf et al., 2008), etc.
5.2.2. Correcting for multiple comparisons
During a voxelwise statistical lesion-behaviour mapping analysis,
the same statistical test is performed at many individual voxels.
However, if each statistical test has the typical false positive probability
of 5%, performing a statistical test at e.g. 100 voxels will be expected to
result in 5 false positives. That is, as more and more statistical tests are
performed, the probability of observing at least one false positive increases.
In fact, performing 100 independent statistical tests, each with
the typical false positive probability of 5%, will increase the overall
probability of at least one false positive to 99.4%. In these situations,
we do not want to control the probability of observing a false positive in
each individual voxel. Instead, we want to control the overall probability
of observing a false positive (over all voxels tested), also known
as the family-wise error rate. To do this, we need to correct for multiple
comparisons.
The traditional method to correct for multiple comparisons is the
Bonferroni correction. Here, we simply divide our desired false positive
probability by the amount of tests that we perform. Thus, if we assess
100 voxels and want to ensure that the family-wise error rate does not
exceed 5%, we would set the false positive probability threshold for
each individual voxel at 5/100=0.05%. While this method offers excellent
control of the family-wise error rate, it is also very conservative
(particularly in voxelwise lesion-behaviour mapping analyses where the
individual voxels are not truly independent), and thus severely reduces
the statistical power to detect an effect. As such, considerable efforts
have been made to develop alternative, less conservative, ways to
correct for multiple comparisons.
A more exact way to correct for multiple comparisons and control
the family-wise error rate, without sacrificing statistical power, is permutation
thresholding. Permutation thresholding aims to determine
whether an observed test statistic at a voxel (e.g. a t-test, BrunnerMunzel
or Liebermeister statistic) is truly due to the difference in voxel
status (lesioned or non-lesioned) or not. The underlying logic is that if
the observed test statistic is truly due to the difference in voxel status,
similar or more extreme test statistics would be unlikely to arise in situations
where the pairing of behavioural data and voxel status is
scrambled (i.e. situations where there is no association between behavioural
data and voxel status). To determine how likely certain test
statistics are under this null hypothesis of no association between behavioural
data and voxel status, the behavioural scores of the patients
with and the patients without damage at a certain voxel are randomly
scrambled (i.e. permuted) thousands of times, each time calculating a
new test statistic. With this, a distribution of permuted test statistics is
created, reflecting the probability of observing certain test statistics
under the null hypothesis. Using this null distribution of permuted test
statistics, the 5% threshold value can be determined, with test statistics
exceeding this threshold value having a probability of less than 5%
under the null hypothesis. Finally, by comparing the originally observed
test statistic to this null distribution of permuted test statistics,
we can determine whether the original test statistic was extreme enough
to allow rejection of the null hypothesis. In the context of voxelwise
statistical lesion-behaviour mapping, this approach is extended
by using the maximum test statistic (over all voxels) obtained in each
permutation to create the null distribution, instead of individual voxel
test statistics. As such, the 5% threshold value is not exceeded anywhere
in the brain in more than 5% of the permutations, that is, permutation
thresholding offers the same control of the family-wise error
rate as the Bonferroni correction. Importantly, however, in situations
where the individual voxels are not truly independent, permutation
thresholding offers better statistical power than the Bonferroni correction.
Moreover, while permutation thresholding typically focusses on
the maximum test statistic obtained in each permutation (controlling
the probability of observing a single false positive), this approach can
also be generalised by focussing on the n-th extreme test statistic, where
n > 1 (Mirman et al., in press, this issue). This so-called continuous
permutation-based family-wise error rate correction method (controlling
the probability of observing n false positives) might allow for a
better balance between false positives and false negatives in typical
lesion-behaviour mapping studies where the anatomical interpretation
of the results rarely depends on a single voxel.
Finally, an alternative, less conservative approach to correcting for
multiple comparisons is offered by false discovery rate thresholding
(Benjamini and Hochberg, 1995; Genovese et al., 2002). Here, the goal
is not to control the family-wise error rate, but to control the proportion
of false positives amongst observed positives. As a consequence, a false
discovery rate threshold of 5% means that up to 5% of the observed
positives might be false positives. In situations where no positives are
observed, false discovery rate thresholding will provide the same control
of the family-wise error rate as the Bonferroni correction. However,
in situations where positives are observed, false discovery rate thresholding
will result in more positives surviving the correction for multiple
comparisons than either the Bonferroni correction or permutation
thresholding. In fact, as the amount of observed positives increases, the
false discovery rate threshold decreases. This adaptiveness of false
discovery rate thresholding, however, comes at the price of reduced
control of the family-wise error rate (as up to 5% of the positives surviving
the correction for multiple comparisons could be false positives).
Moreover, in smaller samples (n = 30–60), false discovery rate
thresholding might considerably underestimate the proportion of false
positives amongst observed positives (Mirman et al., in press, this
issue). In situations where control of the family-wise error rate is
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paramount and/or where the test assumptions of false discovery rate
correction may be violated, permutation thresholding (see above)
should thus be the preferred approach to correct for multiple comparisons
in lesion-behaviour mapping analyses.
5.3. Avoid dividing samples into subsamples on the basis of an a priori
hypothesis
Often, we have an a priori hypothesis concerning the parts of the
brain that might contribute to a certain cognitive process. Accordingly,
it might seem intuitive to divide the patients and their brain lesions into
different subsamples and perform separate lesion-behaviour mapping
analyses for each of these subsamples. For example, based on the a
priori hypothesis that action-related aspects of cognition are represented
in anterior parts of the brain while perception-related aspects
of cognition are located in posterior brain regions, one might divide an
unselectively recruited patient sample into a subsample of patients with
more anteriorly located brain damage and a subsample of patients with
more posteriorly located brain damage. There are, however, several
problems with this approach (see Fig. 4). Firstly, patients with large
lesions (for example covering both anterior and posterior parts of the
brain) are difficult to categorise. This might lead to an extra category
(e.g., ‘anterior & posterior’) for which no clear a priori hypothesis exists.
As mentioned before (see Section 2.2 above), exclusion of these patients
is no solution, as this would not only result in a significant loss of valuable
information, but would also create a bias towards smaller lesions
and potentially milder cognitive symptoms, ultimately leading to different
anatomical conclusions. Secondly, dividing a single patient
sample into subsamples will anatomically bias the results of a lesionbehaviour
mapping analysis into the direction of this a priori hypothesis.
Dividing a patient sample into, for example, a subsample with
more anterior brain damage and a subsample with more posterior brain
damage, will reveal two neural correlates: one neural correlate somewhere
in the more anterior regions of the brain, and one neural correlate
somewhere in the more posterior regions (see Fig. 4B, left side).
This result is a priorily expected and simply a consequence of dividing
the sample into these two anatomical subsamples, regardless of the
cognitive deficit displayed by the patients. The a priori hypothesis of an
Fig. 4. The consequences of dividing an unselectively recruited patient sample into subsamples on the basis of an a priori anatomical hypothesis. A: Sketch where one and the same stroke
patient sample (middle image, simple lesion overlap in yellow) is divided into either an anterior and posterior subsample (left image), or a ventral and dorsal subsample (right image), in
order to perform separate lesion-behaviour mapping analyses in each of these subsamples. This division of a single patient sample into subsamples will generate results that match the a
priori hypothesis. B: Illustration of this effect in an unselectively recruited sample of 20 stroke patients (patients taken from Sperber and Karnath (2016)). One and the same patient
sample (top middle image, simple lesion overlap) is divided into either an anterior and posterior subsample (left images), or a ventral and dorsal subsample (right images). Whereas
dividing the entire patient sample into an anterior and posterior subsample results in an area of maximum lesion overlap at a z-coordinate of 6 and 24 respectively, dividing the same
patient sample into a ventral and dorsal subsample results in an area of maximum lesion overlap at a z-coordinate of −12 and 33 respectively. Moreover, when dividing the entire patient
sample into an anterior and posterior subsample, 7 patients (i.e. 35% of the sample) could not be categorised. When dividing the entire patient sample into a ventral and dorsal subsample,
11 patients (i.e. 55% of the sample) could not be categorised. The number of overlapping lesions is illustrated by colour, from violet (n=1) to red (n=maximum lesion overlap). The
numbers at the bottom of the Figure indicate MNI z-coordinates. Images are in neurological orientation.
B. de Haan, H.-O. Karnath Neuropsychologia 115 (2018) 5–16
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anterior vs. posterior dissociation of perception- vs. action-related
cognitive processes will thus lead to an observation that corresponds to
this hypothesis. Had we taken the same data sample of stroke patients
as before, but instead divided this sample into one subsample with more
ventrally located brain damage and one subsample with more dorsally
located brain damage (based on the equally defensible a priori hypothesis
that perception-related aspects of cognition are represented in
more ventral brain areas whereas action-related aspects of cognition are
represented in more dorsal areas of the brain), we would have revealed
a different result (see Fig. 4B, right side). In this case, the lesion-behaviour
mapping analysis of the sample with more ventral brain damage
would have revealed a neural correlate somewhere in more
ventrally located regions of the brain, while the lesion-behaviour
mapping analysis of the subsample with more dorsal brain damage
would have found a neural correlate somewhere in more dorsally located
regions. The problem illustrated here, is that the results of a lesion-behaviour
mapping analysis can be biased by the categorization of
patients on the basis of their lesion location. That is, the categorization
of an unselected patient sample on the basis of an a priori anatomical
hypothesis will lead to anatomical results that correspond to this hypothesis.
As such, this should be avoided.
6. Anatomical interpretation of lesion analysis results
Following a voxelwise statistical lesion-behaviour mapping analysis,
we obtain a statistical map highlighting the voxels where voxel status
(lesioned vs. non-lesioned) and patient behaviour are significantly related.
In the case of a lesion subtraction analysis, on the other hand, we
obtain a map highlighting areas of the brain where lesions are descriptively
more frequent in patients with than in patients without the
cognitive deficit of interest (often thresholded to isolate those percentage
relative frequency difference values thought to be meaningful
[with typical threshold values of 20–50%]). Anatomical interpretation
then consists of describing the location of these significant or meaningful
voxels, typically with the help of a brain atlas. For convenience,
coordinates of peak voxels or a coordinate ranges of a cluster can be
provided to describe the location of the results of the voxelwise lesionbehaviour
mapping analysis or subtraction analysis. It is, however,
important to realise that all voxels identified as statistically significant
in a voxelwise statistical lesion-behaviour mapping analysis, or meaningful
in a subtraction analysis, have the same importance, and thus
should be given equal weights when interpreting the results.
Nowadays, there are many different cortical atlases to choose from.
The first division that can be made is between atlases derived from
single-subject data and atlases derived from multi-subject data.
Whereas atlases derived from single-subject data remain popular (i.e.
the Brodmann atlas, or the AAL atlas of Tzourio-Mazoyer et al., 2002),
probabilistic atlases derived from multi-subject data should be preferred,
as these are able to quantify the intersubject variability in location
and extent of each anatomical area. Within these multi-subject
atlases, a second division can be made based on the brain characteristics
used to parcellate distinct areas in different atlases. Whereas some
probabilistic multi-subject atlases are based on macroscopical landmarks
such as gyri and sulci (e.g. Hammers et al., 2003; Shattuck et al.,
2008), others are based on histology (Zilles et al., 1997), or on functional
connectivity patterns (e.g. Joliot et al., 2015). Finally, in addition
to these cortical atlases, multi-subject atlases exist for white matter
fiber tracts, based on either DTI fiber tracking (e.g. Zhang et al., 2010;
Thiebaut de Schotten et al., 2011), or on histology (Bürgel et al., 2006).
Which atlas to choose for the anatomical interpretation of the results of
a lesion-behaviour mapping study is not a trivial issue. It is important to
realise that different atlases might result in different anatomical interpretations
of the same lesion-behaviour mapping results (de Haan and
Karnath, 2017).
Acknowledgements
This work was supported by the Deutsche Forschungsgemeinschaft
(HA 5839/4-1 to BdH; KA 1258/20-1, KA 1258/23-1 to HOK). We
would like to thank Christoph Sperber for helpful and inspiring dis-
cussions.
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