1. Lesion-Symptom Mapping (and Biological Motion)

2. Brain and “Behavior” ~1700 BC ~1861 ~1820
Humans have been interested in how their behaviors, thoughts, feelings, sensations, actions and consciousness are possible. For centuries, we have been fascinated by the brain and have tried to explore links between brain and behavior using many approaches, some of which are still in use today. Patient studies, lesion mapping in particular, have been criticial in the advance of neurology and cognitive neuroscience because it is these studies that can allow us to explore causal links between specific brain areas and specific symptoms or outcomes.

3. Lesion-Symptom Mapping
Patient K: Male, 68. CVA. Wernicke’s aphasia. Fluent, speech. Language comprehension severely impaired.
Patient L: Female, 66. CVA. Mild anomia. Fluent speech, no comprehension problems. No other deficits measured.
Patient M: Male, 65. CVA. Mild anomia. Fluent speech. No speech comprehension deficits but non-verbal auditory agnosia.
K, L, and M had nearly identical left temporoparietal lesions
Saygin et al., 2010, Neuropsychologia
Yet, as many of you know, this is not easy work… There is a huge amount of variability in the effects of many kinds of brain damage, as well as in recovery patterns. I’d like to tell you about my first encounter with this. This will be familiar to many of you.. But when I first started working in this field all I knew about neuropsyhcology and neurology seemed very simple. I was working on language and it seemed like we knew pretty much all there was about aphasias. I mean Broca’s area, Broca’s aphaisa, Wernicke’s area, Wernicke’s aphasia - it’s gonna be hard to come up with a trick question there! Then I went out to test some patients and these are the individuals I encountered on my first day:

4. Lesion-Symptom Mapping
Patient K: Male, 68. CVA. Wernicke’s aphasia. Fluent, speech. Language comprehension severely impaired.
Patient L: Female, 66. CVA. Mild anomia. Fluent speech, no comprehension problems. No other deficits measured.
Patient M: Male, 65. CVA. Mild anomia. Fluent speech. No speech comprehension deficits but non-verbal auditory agnosia.
K, L, and M had nearly identical left temporoparietal lesions
Saygin et al., 2010, Neuropsychologia
Yet, as many of you know, this is not easy work… There is a huge amount of variability in the effects of many kinds of brain damage, as well as in recovery patterns. I’d like to tell you about my first encounter with this. This will be familiar to many of you.. But when I first started working in this field all I knew about neuropsyhcology and neurology seemed very simple. I was working on language and it seemed like we knew pretty much all there was about aphasias. I mean Broca’s area, Broca’s aphaisa, Wernicke’s area, Wernicke’s aphasia - it’s gonna be hard to come up with a trick question there! Then I went out to test some patients and these are the individuals I encountered on my first day:

5. Lesion-Symptom Mapping
It is difficult to infer lesion-behaviour relationships from single case studies or small groups
Fortunately, lesion-behaviour relationships are not random…
Fortunately it is not

6. Two Common Approaches: Lesion-based Selection
Patients selected based on lesion site
e.g. dorsolateral prefrontal cortex
Behavioral (or neural) measures of interest are compared to controls, or other groups
Fellows & Farah, 2003, Brain
Powerful approach
Clear testing of hypotheses and possible dissociations
Only pre-hypothesized lesion sites tested

7. Two Common Approaches: Behaviour-based Selection
Patients selected based on diagnosis/deficit
e.g. apraxia of speech (Dronkers, 1996)
Overlays of “impaired” or “spared” patients
Possible to not have lesion hypotheses at outset
Need more precise lesion information
Where to draw the line?
Dronkers, 1996, Nature
Saygin et al., 2003, Brain

8. Limitations
These methods are not ideal for every question a neuropsychologist might want to probe
Can lead to potential loss of information
- How to determine ‘impaired’
- How to select lesion locations to be tested

9. What is VLSM? Lesion mapping method and software
Bates, Wilson, Saygin, Dick, Sereno, Knight, & Dronkers, 2003, Nature Neuroscience
Free and open source Matlab Toolbox:
Voxel-based approaches to lesion mapping in general

10. VLSM: Goals neglect vs not, impaired vs. normal
No need to know lesion regions of interest
No need to categorise patients
neglect vs not, impaired vs. normal
Use the ‘voxel-based statistics’ idea
And facilitate link between lesion mapping and neuroimaging

11. VLSM: The Procedure A group of patients and a behaviour of interest
Patients’ lesions on a common space
e.g., MNI “Colin”
Statistics over voxels using behaviour(s) of interest
Plot statistics over space
+ Behaviour

12. Lesion Information
Binary – each voxel is marked as ‘lesioned’ or ‘not lesioned’
Methods can generalise to continuously marked lesions (e.g., ANOVA  Regression)
Lesion reconstruction methods vary
Outlined by trained neurologist on template

13. What we have… LESION BEHAVIOUR Patient 1 68 Patient 2 74 . Patient N93

14. A Given Voxel
Each patient’s lesion either includes or excludes that voxel
Voxel INTACT
Voxel
LESIONED
Patient 1
Patient 2
Patient 5
Patient 3 .
Patient N
Patient M

15. A Given Voxel
Each patient’s lesion either includes or excludes that voxel
Each patient has (one or more) behavioural measures
Voxel INTACT
Voxel
LESIONED 68 74 89 71 . 93 65

16. A Given Voxel
Each patient’s lesion either includes or excludes that voxel
Each patient has (one or more) behavioural measures
Compare Intact and Lesioned to get a statistic (e.g., t and p)
Voxel INTACT
Voxel
LESIONED 68 74 89 71 . 93 65

17. Repeat… Calculate statistics across all voxels Plot the statistics
A massive univariate approach similar to fMRI analysis and subject to similar challenges
Bates et al., 2003, Nature Neuroscience

18. Applications Many different domains
Attention/Space: e.g., Committeri et al, 2007; Molenberghs et al, 2008; Verdon et al., 2010; Karnath et al., 2009; Language: e.g., Dronkers et al, 2004; Borovsky et al, 2007; Piras & Marangolo, 2007; Leff et al., 2009; Schwartz et al., 2009; Motor: e.g., Lo et al., 2010; Schlaug et al., 2009; Memory: Tsuchida & Fellows, 2010; Haramati et al, 2008; Mathematical cognition: Baldo & Dronkers, 2007; Piras & Marangolo, 2009; General intelligence: Glascher et al., 2010; Action and body perception: Saygin et al, 2004; Saygin, 2007; Moro et al., 2008

19. Example: WAB Subscales
Western Aphasia Battery
N=101
Fluency
Insula, parietal white matter; also IFG
Auditory comprehension
MTG, also STG
Bates et al., 2003, Nature Neuroscience

20. Example: Neglect
Verdon et al., 2010, Brain

21. Example: Neglect
Committeri et al., 2007, Brain

22. Example: Biological Motion Perception

23. Biological Motion Motion – Dorsal System
But you also see Form – Ventral System?
Form-from-Motion, Structure-from-Motion
You see an action – Action Perception (or “Mirror Neuron” system)

24. Stimuli and Task ‘Point to the person’
And the correct answer is…
‘Point to the person’
Behavioural measure: Number of noise dots at 82% accuracy
Adaptive estimation (Quest, Watson & Pelli, 1983)

25. Behavioural Data Both LHD and RHD patients significantly impaired
Limitation caused by lesions in the sample
Sufficient lesion reconstructions available only for the left hemisphere
Also insufficient power, e.g. occipital cortex
N=60
Chronic stroke
Normal/corrected vision
Controls > LHD p<0.0001
Controls > RHD p<0.01
RHD, LHD n.s. p=0.7

26. Patient Lesions Voxel INTACT Voxel LESIONED Patient 1 Patient 2.
Patient n
Patient m

27. Biological Motion Thresholds
Voxel INTACT
Voxel
LESIONED
12.9
14.8
21.6
19.4 .
17.1
11.9

28. Statistics Voxel INTACT Voxel LESIONED 12.9 14.8 21.6 19.4 . 17.1 11.9
Now we do this over all voxels and plot in the brain. Going back to biological motion, we do this, our behav measure being the threshold estimated for each subject.

29. VLSM: Biological Motion
Map of the t-statistic at each voxel
Large region in temporal and parietal (BA 21, 22, 37, 39, 40)
Smaller area in inferior frontal (BA 44, 45, 6)
Saygin, 2007 Brain

30. Independence of Multiple Foci
Covary FRONTAL
ANCOVA map – Biological Motion
Saygin, 2007 Brain

31. Independence of Multiple Foci
Covary FRONTAL
Covary POSTERIOR
ANCOVA map – Biological Motion
Saygin, 2007 Brain

32. Combining Lesion and fMRI
Saygin, 2007 Brain

33. Combining Lesion and fMRI
Saygin, 2007 Brain; Saygin et al., 2004, J. Neurosci

34. Combining Lesion and fMRI
Lesion and fMRI data in same space and format – quantitative analyses facilitated
Voxel by Voxel lesion and fMRI data are correlated (r=0.55)
LESION
FMRI
Some differences:
- Involved vs. Necessary
Lesion data limited to lesion distributions
Lesion data provides more information from white matter

35. Another Example: Conversational Speech Production
Structured interviews with 50 LH patients
Transcripts coded for measures from computational linguistics
Tokens – overall fluency
Mean Length of Utterance (MLU) in morphemes – grammatical complexity
Type/Token ratio – lexical diversity of speech

36. VLSM
Borovsky, Saygin, Bates & Dronkers, 2007, Neuropsychologia

37. Primary Methodological Issues
Anatomy
Multiple comparisons
Power

38. Anatomy Lesions can be:
Represented binary (as we did) or continuous (Leff et al, 2009; Ashberger & Friston, 2000)
Drawn directly on a standardised template
Manually marked on structural scans (e.g. with MRICro) and registered to standard space
Automatically or semi-automatically derived from structural scans (e.g. intensity cutoffs)

39. Multiple Comparisons A pervasive problem in imaging analysis
Bonferroni is too conservative
Resolution is not necessarily same as voxel size
Voxels often not “independent tests”
Alternatives
Random Field Theory (Worsley, 1996)
Requirements often not true of lesion datasets
False discovery rate (Benjamini & Hochberg, 1995)
Controls the expected proportion of false positives
Calculating the actual number of comparisons
Permutation (or Monte Carlo) based approaches
See also: Kimberg, Coslett & Schwartz, 2007; Rorden, Karnath & Bonilha, 2007; Chen & Herskowitz, 2010; Medina et al., 2010

40. Power No lesion  No inference
Some areas are more likely to be lesioned than others (e.g., perisylvian for MCA strokes)
Supplement data with power in regions of interest
e.g., if lack of an effect is theoretically important
Voxel-based map of power across the brain
Power is optimal in voxels where N(lesioned)=N(intact)
Exclude voxels where either N is small
Use N
See also: Kimberg, Coslett & Schwartz, 2007, JOCN; Medina et al., 2010, Neuropsychologia; Rudrauf et al., 2008

41. Summary: VLSM
Eliminates reliance on behavioural categories, clinical diagnoses, or predefined lesion regions of interest
Facilitates (quantitative) comparisons between lesion and imaging results
Is flexible and can be extended to run complex models
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