Voxel-based Lesion-Symptom Mapping. Céline R. Gillebert

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1 Voxel-based Lesion-Symptom Mapping Céline R. Gillebert

2 Paul Broca (1861) Mr. Tan no productive speech single repetitive syllable tan Broca s area: speech production Broca s aphasia: problems with fluency, articulation, word-finding, repetition, production and comprehension of complex grammatical structures

3 Lesion-Symptom Mapping = inferring the function of a brain area by observing the behavioural consequences of damage to that area

4 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

5 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

6 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

7 Example: What brain injury leads to hemispatial neglect?

8 Example: hemispatial neglect Karnath et al. (2012). Neuropsychologia Mort et al, 2003

9 Example: hemispatial neglect Demeyere et al. (under review). Psychological Assessment...

10 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

11 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.

12 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

13 How to run a VLSM analysis?

14 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

15 CT versus MR scans Case RR, Oxford CNC CT scans clinical: acute haemorrhage visible when contraindication for MRI not ideal for research but large database Case RR, Oxford CNC MRI scans no radiation (control data) higher spatial resolution different images with different contrasts

16 MR scans: different contrasts Case RR, Oxford CNC T1-weighted scans Fast to acquire Good contrast between WM and GM Excellent structural detail Case RR, Oxford CNC T2-weighted scans Slower to acquire Excellent for finding lesions FLAIR attenuates CSF

17 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

18 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

19 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

20 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: 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.

21 Manual lesion delineation Manual delineation of the lesion, slice by slice, using e.g. MRIcron Case RR, Oxford CNC Case RR, Oxford CNC

22 overview 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

23 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

24 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: N=152, 25yrs n=50, 73yrs n=30, 61yrs n=366, 35yrs MNI152, SPM and FSL Rorden et al. (2012). Neuroimage. Winkler et al. FLAIR Templates. Available at

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

26 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

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

28 overview 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

29 visualization of lesion distribution Molenberghs, Gillebert, et al., 2009

30 number of patients Operationalization of behaviour N= n= Demeyere*, Gillebert*, et al. (in preparation) number of cancelled complete hearts cut-off = 42

31 number of patients Operationalization of behaviour Demeyere*, Gillebert*, et al. (in preparation) number of cancelled complete hearts performance

32 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

33 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

34 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).

35 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?

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

37 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