voxel-based lesion-symptom...

Voxel-based Lesion-Symptom Mapping

Céline R. Gillebert

Paul Broca (1861)

“Mr. Tan”

• no productive speech

• single repetitive syllable

‘tan’

Broca’s aphasia: problems with

fluency, articulation, word-finding,

repetition, production and

comprehension of complex

grammatical structures

Broca’s area: speech

production

Lesion-Symptom Mapping

= inferring the function of a brain area by

observing the behavioural consequences

of damage to that area

advantages

• stronger inference: Is brain area necessary for task?

fMRI, EEG, MEG: Does activity in brain area correlate with

task?

• infer function of node in network of areas

fMRI: difficult to understand the differential contribution of

areas that are simultaneously activated by the task

• clinical relevance: predict recovery or select best protocol

for rehabilitation of behavioural deficits

disadvantages

• Lesions do not respect the boundaries of functional

areas…

… and do not cover the whole brain, even not in the largest

possible sample of patients

• Lesions are permanent….

… although their relation to behavioural function depends on

the time to stroke (neuroplasticity)

• Lesions can cause dysfunction of structurally intact areas

at the distance

… lesion-symptom mapping is inherently a “localizationist

approach” http://www.strokecenter.org/

disadvantages

• Lesions do not respect the boundaries of functional

areas…

… and do not cover the whole brain, even not in the largest

possible sample of patients

• Lesions are permanent….

… although their relation to behavioural function depends on

the time to stroke (neuroplasticity)

• Lesions can cause dysfunction of structurally intact areas

at the distance

… lesion-symptom mapping is inherently a “localizationist

approach”

Example:

What brain injury leads to hemispatial

neglect?

Mort et al, 2003

Example: hemispatial neglect

Karnath et al. (2012).

Neuropsychologia

Example: hemispatial neglect

. . . . .

. .

.

. .

Demeyere et al. (under review). Psychological Assessment.

lesion overlap

• We can overlay the lesions of patients with a deficit on the

cancellation task.

• Example: Karnath et al. (2004). Cerebral Cortex

n=78

lesion subtraction

Patients with similar brain

damage but without the deficit

are critical to identify areas

related to the function on top

of areas that are commonly

damaged!

Karnath et al. (2004). Cerebral Cortex.

voxel-based lesion-symptom mapping

• Statistics to evaluate whether differences in lesion

frequency are reliable predictors of behavioural deficits.

• Example: Karnath et al. (2004). Cerebral Cortex

How to run a VLSM analysis?


1. Acquisition of brain scan with visible

lesion

2. Delineation of the lesion

3. Normalization of lesion to a common

template

4. Statistics across a group of patients

CT versus MR scans

MRI scans

• no radiation (control data)

• higher spatial resolution

• different images with different

contrasts

CT scans

• clinical: acute haemorrhage visible

• when contraindication for MRI

• not ideal for research… but large database

Case RR, Oxford CNC Case RR, Oxford CNC

MR scans: different contrasts

T2-weighted scans

• Slower to acquire

• Excellent for finding lesions

• FLAIR attenuates CSF

T1-weighted scans

• Fast to acquire

• Good contrast between

WM and GM

• Excellent structural detail


acute or chronic stroke?

• acute stroke: widespread dysfunction

• structurally intact brain areas are disrupted as they are

connected to the lesioned brain areas

• more clinically relevant

• chronic stroke: brain is plastic

• difficult to infer what a brain region used to do

• more stable, identifies functions that cannot be compensated


1. Acquisition of brain scan with visible lesion



template


lesion delineation

• Manual delineation of the lesion: “gold standard”

• requires experience and knowledge about brain anatomy

• time-consuming, only feasible for relatively small sample

sizes (but power of VLSM…)

• susceptible to operator bias

• Fully/semi-automated delineation

• replicable

• suitable for large sample sizes

• errors are inevitable

• “normal” signal varies from individual to individual

• lesions are heterogeneous in signal, also within an individual

Automated lesion delineation

• CT scans: Gillebert, C.R.,

Humphreys, G.W., & Mantini, D.

(2014). Automated delineation of

stroke lesions using brain CT

images. Neuroimage: Clinical,

4:540-548.

• MRI scans: Mah, Y.H., Jager, R.,

Kennard, C., Husain, M., & Nachev,

P. (2014). A new method for

automated high-dimensional lesion

segmentation evaluated in vascular

injury and applied to the human

occipital lobe. Cortex, 56:51-64.

Manual lesion delineation

Manual delineation of the lesion, slice by slice, using e.g.

MRIcron


overview




template


normalization

• Alignment of brains to ‘template’ image in stereotaxic

space, necessary to compare lesions between individuals

• Linear and non-linear transformation to minimize

difference with template

normalization

• Alignment of brains to ‘template’ image in stereotaxic

space, necessary to compare lesions between individuals

• Linear and non-linear transformation to minimize

difference with template

• Use an appropriate (age- and modality-matched)

template:

Rorden et al. (2012). Neuroimage.

n=50,

73yrs

N=152,

25yrs

MNI152, SPM and FSL

n=30,

61yrs

n=366,

35yrs

Winkler et al. FLAIR Templates.

Available at http://glahngroup.org

http://www.glahngroup.org/Members/anderson/flair-templates

normalization of CT scans: Gillebert et al. (2014) Neuroimage: Clinical

normalization

• ! Region of lesion appears different in image and template, and software will attempt to warp lesioned region

→ Solution: ignore the lesioned brain tissue in the process

→ Masked normalization: Brett et al., (2001) Neuroimage

→ Less of a problem with unified segmentation-normalization approach (Crinion et al. (2007) Neuroimage)

• Clinical toolbox for SPM

Clinical Toolbox in SPM Rorden et al. (2012). Neuroimage

http://www.mccauslandcenter.sc.edu/CRNL/clinical-toolbox

overview




template


visualization of lesion distribution

Molenberghs, Gillebert, et al., 2009

0 5 10 15 20 25 30 35 40 45 500

5

10

15

20

25

number of cancelled complete hearts

num

ber

of patients

Operationalization of behaviour

Demeyere*, Gillebert*, et al. (in preparation)

cut-off = 42

n=180

N=132

0 5 10 15 20 25 30 35 40 45 500

5

10

15

20

25

number of cancelled complete hearts

num

ber

of patients

Operationalization of behaviour

Demeyere*, Gillebert*, et al. (in preparation)

performance

Parametric or non-parametric statistics

• traditional: t-test for continuous data

• assumptions: data are normally distributed, two groups have similar variance, and

data represent interval measurements

• but

• assumptions difficult to test across the thousands of voxel-wise comparisons

• measures differences in the mean between two groups, not appropriate for

skewed distributions

• dependent variables often measured using an ordinal scale

• alternative: Brunner Munzel rank order test

• assumption free, also for variables on an ordinal scale

• Approaches normal distribution if n>= 10

correction for multiple comparisons

• Bonferroni-correction

• Strong protection against false alarms

• Overly conservatives when comparisons are not independent

• Permutation thresholding

• randomly relabeling and resampling the data, computing the maximum

observed statistic within the entire brain volume for each permutation

• lesions are formed from large contiguous regions, where each voxel is not

truly independent

• False discovery rate (FDR)

• controls the ratio of false alarms to hits

• sensitive where a signal is present in a substantial portion of the data

Some considerations…

• A t-test requires two groups and one continuous variable.

• The VLSM t-test is orthogonal to t-tests used for fMRI/VBM:

• fMRI/VBM t-tests:

• Deficit defines two groups.

• Voxel intensity provides continuous variable.

• VLSM

• Voxel intensity (lesion/no lesion) defines two groups.

• Behavioral performance provides continuous variable.

• Note VLSM group size varies from voxel-to-voxel.

• Statistical tests provide optimal power both groups have the same number of

observations (balanced).

• Therefore, VLSM power fluctuates across voxels

• We can not make inferences of voxels that are rarely damaged or always damaged (also true for

binomial tests).

Beyond VLSM…

• Track-wise “Hodological” Lesion-Deficit Analysis

• Thiébaut de Schotten et al. (2012) Cerebral Cortex

• maps of white matter tracts representing a probability of a given voxel

belonging to that tract

• calculating the size of the overlap (in cubic centimetres) between each

patient’s lesion map and each thresholded (50%) pathway map

→ Can these continuous measure of the pathway disconnection predict

behavioural deficits?

Beyond VLSM…

Chechlacz, Mantini, Gillebert, & Humphreys (under review). Cortex

Beyond VLSM…

• Track-wise “Hodological” Lesion-Deficit Analysis • Thiébaut de Schotten et al. (2012) Cerebral Cortex

• maps of white matter tracts representing a probability of a given voxel belonging to that tract

• calculating the size of the overlap (in cubic centimetres) between each patient’s lesion map and each thresholded (50%) pathway map

→ Can these continuous measure of the pathway disconnection predict behavioural deficits?

• Voxel-wise Bayesian Lesion-Deficit Analysis • Chen et al. (2008) Neuroimage

• Multivariate Lesion-Symptom Mapping (MLSM) • Zhang et al. (2014) Human Brain Mapping: Modelling the relation of the deficit to the entire

lesion map as opposed to each isolated voxel, using support vector regression

• Mah et al. (2014) Brain: capturing high-dimensional structure of lesion data using machine learning techniques

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