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Catastrophic structural changes

- Chris Rorden
- Voxelwise Lesion Symptom Mapping
- Motivation: strengths and limitations.
- Should we examine acute or chronic injury?
- Visualizing injury.
- Mapping brain injury.
- Normalization lesion maps.
- Lesion mapping statistics.

“George Miller coined the term ‘cognitive neuroscience’…we already knew that neuropsychology was not what we had in mind…the bankruptcy and intellectual impoverishment of that idea seemed self evident.”

-Michael S. Gazzaniga, 2000

Lesion symptom mapping

- Lesion symptom mapping infers brain function by observing consequences of brain injury.
- Historically, one of the most influential tools in understanding brain function
- Language: Broca’s and Wernickes Area
- Memory: anterograde amnesia
- Vision: Prosopagnosia, achromatopsia
- Emotions, Motor Control, etc.

Lesion mapping

- Perhaps lesion mapping was historically important because it was the only tool.
- New tools address some of the weaknesses.
- Disadvantages of lesion mapping:
- Poor spatial precision: lesions are messy: location and extent influenced by vasculature, not functional areas.
- Poor temporal precision: lesions are permanent.
- Sequence of information processing difficult to assess.
- Dilemma of acute vs chronic lesion mapping.

Acute vs chronic lesion mapping

- Acute lesion mapping:
- Initially after a stroke, one sees widespread dysfunction. Intact areas are disrupted as they depend on damaged areas.
- Chronically, the brain is plastic. So it is difficult to infer what a brain region used to do.
- Ideally, we would examine both acute and chronic effects.
- Acute injury is more clinically relevant.
- Chronic deficits: more stable and identifies functions that can not be compensated.

Lesion mapping

- Advantages:
- Stronger inference:
- Activation techniques (fMRI, ERP): is area involved with task?
- Disruption techniques (TMS, lesions): is area required by task?
- Understand function of tightly coupled network.
- Activation techniques like listening to whole orchestra: hard to identify differential contribution of musicians.
- Example: highly connected visual attention network tends to be activated in concert, even though lesions suggest different functions.
- Lesion method: How does music change if individual stops playing?
- Clinically relevant:
- can we predict recovery or best treatment?

Lesion-symptom mapping requires us to identify which bit of the brain is injured.

Different MRI modalities show different aspects of injury.

The quality of the MRI scans dramatically influences the analysis.

Lesion visualizationCrucial clinical tool

Detect acute hemorrhage

Can be conducted when MRI contraindicated

Limited research potential

Exposes individual to radiation

Difficult to collect control data

Typically very thick slices, impossible to normalize

Little contrast between gray and white matter

CT scansMRI scans are very flexible

Various protocols can contrast different properties

T1: Excellent spatial resolution

T2/FLAIR: Pathology

DWI: White matter, acute stroke

PWI: Acute perfusion deficits

T2*: ‘fMRI’ ~ brain function

Does not expose participant to radiation

MRI scansT1 (anatomical): fast to acquire, excellent structural detail (e.g. white and gray matter).

T2 (pathological): slower to acquire, therefore usually lower resolution than T1. Excellent for finding lesions.

Conventional MRI scansT2

T1

T1 scans offer good spatial resolution.

T2 scans better for identifying extent of injury, but poor spatial resolution.

Solutions:

Acquire chronic T1 (>8 weeks)

Acquire both T1 and T2, use T2 to guide mapping on T1.

Acquire T2, map on normalized iconic brain (requires expert lesion mapper).

Aquire high resolution T2 image, use for both mapping and normalization (e.g. 1x1x1mm T2 ~9min). Requires latest generation MRI.

Note: Many clinicians like FLAIR as it attenuates CSF. Lesion signal similar to T2. Normalization tricky (thick slices, no standard template).

T1

Lesion mapping: T1 vs T2T2

FLAIR

T1/T2 MRI and x-rays can not visualize hyperacute ischemic strokes.

Acute: Subtle low signal on T1, often difficult to see, and high signal (hyperintense) on spin density and/or T2-weighted and proton density-weighted images starting 8 h after onset. Mass effect maximal at 24 h, sometimes starting 2 h after onset.

Subacute (1 wk or older): Low signal on T1, high signal on T2-weighted images. Follows vascular distribution. Revascularization and blood-brain barrier breakdown may cause enhancement with contrast agents.

Old (several weeks to years): Low signal on T1, high signal on T2. Mass effect disappears after 1 mo. Loss of tissue with large infarcts. Parenchymal enhancement fades after several months.

www.strokecenter.org/education/ct-mri_criteria/

Imaging acute strokeacute

+3days

CT

T2

www.med.harvard.edu/AANLIB/

T1/T2 scans do not show acute injury.

Diffusion and Perfusion weighted scans show acute injury:

Diffusion images show permanent injury. Perhaps good predictor of eventual recovery.

Perfusion scans show functional injury. Best correlate of acute behavior.

Difference between DWI and PWI is tissue that might survive.

Diaschisis: regions connected to damaged areas show acute hypoperfusion and dysfunction.

Hypoperfused regions may have enough collateral blood supply to survive but not function correctly (misery perfusion).

Imaging Hyperacute StrokeT2

DW

Allows us to measure perfusion

Static images can detect stenosis and aneurysms (MRA)

Dynamic images can measure perfusion (PWI)

Measure latency – acute latency appears to be strong predictor of functional deficits.

Measure volume

Perfusion imaging uses either Gadolinium or blood as contrast agent.

Gd offers strong signal. However, only a few boluses can be used and requires medical team in case of (very rare) anaphylaxis.

Arterial Spin Labelling can be conducted continuously (CASL). Good CASL requires good hardware.

Perfusion imagingDiffusion tensor imaging is an extension of Diffusion Weighted Imaging.

DTI allows us to examine integrity and direction of fiber tracts.

This will allow us to examine disconnection syndromes (see Catani).

Analysis of DTI still in infancy.

DTI in strokeDTI - stroke

Healthy

fMRI analysis of stroke difficult.

Hemodynamic response often disrupted.

Misery perfusion: system always compensating for reduced bloodflow, so no dynamic ability to increase.

Luxury perfusion: destroyed tissue no longer requires blood, so regulation not required for surviving tissue.

fMRI in strokeModality of scanning depends on age of lesion.

Hyperacute imaging will require PWI/DWI.

Older injuries seen on T1 and T2.

Different modalities provide different information: can we combine information across modalities?

Our analysis should be based on individuals with similar delay between injury and observation.

SummaryThought experiment

- What brain injury leads to visual field injury?

Mapping Lesions

- With my software it is easy to trace injured area.
- We can create an overlay plot of damaged region.
- For example: here are the lesion maps for 36 people with visual field defects:

The problem with overlay plots

- Overlay plots are misleading:
- Highlight areas involved with task (good)
- Highlight areas commonly damaged (bad)
- Brain damage is not random: some brain areas more vulnerable. Overlay plots highlight these areas of common damage.
- Solution: collect data from patients with similar injury but without target deficit:

Value of control data

- Solution: collect data from patients with similar injury but without target deficit:

Statistical plots

- We can use statistics to identify areas that reliably predict deficit
- E.G. Damage that results in visual field cuts

fMRI – many low resolution volumes per individual

Typical stages (SPM):

Slice Time Correct

Motion correct

Normalize

Smooth (spatial, temporal)

Statistics

Lesions – one high quality scan per indivdiual

Typical stages:

Map Lesion

Normalize

Statistics

Lesion analysisWe need to map the region of brain injury.

We can use MRIcron to draw the location of the lesion.

Note that our high-resolution T1 scans may not show full extent of injury (must refer to other scans).

Map lesionNormalization adjusts size and shape of brain.

Aligns brains to ‘template’ image in stereotaxic space.

Allows us to compare brains between individuals.

NormalizationNonlinear Basis Functions

- Basis functions can have local influence.
- Allow better fit of scans.
- Can cause distortion in some case (e.g. stroke)

Normalization works by adjusting the image orientation until ‘difference’ with template is minimized

Typically, variance between image and template is used as a measure of difference [variance= (image-template)2 ]

However, region of lesion appears different in image and template

Therefore, normalization will attempt to warp lesioned region

Image Template Variance

Lesions disrupt normalizationHow to normalize images with lesions?

Only calculate normalization on healthy tissue – ignore regions of injury.

Healthy Lesion-NoMask Lesion-Mask

Lesion masked normalizationTwo classes of analysis:

Binomial analysis: if behavior falls into two mutually exclusive groups (e.g. Broca’s Aphasia, No Broca’s Aphasia).

Traditional test: Fisher-exact or Chi-squared test

Alternative test: Liebermeister quasi-exact test

Continuous analysis: if data is not binomial. (e.g. number of words starting with ‘b’ spontaneously reported in two minutes).

Traditional test: t-test

Alternative test: Brunner-Munzel test.

Lesion analysisVisual neglect: ask patients for find each letter ‘A’ in cluttered display (60 possible).

People who detect <55 are considered to have a deficit.

Binomial DataDeficit

Control

Our goal is to see if brain anatomy predicts behavior.

We collect data scans and behavioral data from many stroke patients.

Consider 24 patients – half with deficits.

Statistics will identify regions that predict deficit.

12 people w. cancellation deficit

Statistics12 people wo cancellation deficit

We will compute statistics at every voxel of the brain.

The statistical test will differ if the behavioral data is binomial (deficit present or absent) or continuous (performance is graded continuum).

Voxelwise statisticsMRIcron tutorial

Binomial Tests

- Traditional tests: The Fisher and Chi-squared tend to be very conservative (they assume fixed marginals).
- The Liebermeister measure offers near-optimal performance.
- Rorden et al. (in press)

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).

T-test lesion analysis.Visual neglect: ask patients for find each letter ‘A’ in cluttered display (60 possible).

Continuous measure is number of ‘A’s detected.

Performance on neuropsychological tasks is rarely normal.

Skewed distribution: many at ceiling

Performance

Neuropsychological DataSkewed data causes the t-test to lose power.

If our data is skewed, the t-test may fail to detect real differences.

Solution: Rorden et al (in press) propose using non-parametric Brunner-Munzel test for skewed data.

T-test and skewed dataMonte-Carlo simulation to compare t-test with BM test.

Data: cancellation performance from 63 stroke patients.

Permutations:

Randomly select 30 patients and analyze, then threshold with FDR.

Repeat 1000 times.

Mean detection shown below: BM test is more sensitive.

Brunner-Munzel test- Note: t-test slightly more sensitive for normal data; BM test slightly more sensitive for skewed data.

Lesion volume always correlates with deficits.

Small lesions unlikely to knock out entire functional region.

Large lesions knock out more regions.

Therefore, previous tests can not distinguish between equipotentiality and localized function.

Logistic regression can covary out lesion volume: is location still a good predictor independent of volume? (Karnath et al., 2004).

As lesion volume correlates well with deficits, LR analysis offers poor statistical power but strong inference.

Advanced VLSM statisticsThe bad news:

Typical fMRI study uses 3x3x3mm voxels.

Typical VLSM study uses 1x1x1 voxels.

x27 the number of voxels.

Bonferroni correction leads to exceptionally low power.

The good news:

Lesions are large, contiguous across individuals, therefore less within-subject variability than fMRI.

Ideally suited for Permutation Thresholding (Frank et al. 1997; Kimberg et al. in press).

Solution: use either FDR or permutation thresholds.

My NPM provides both.

Statistical thresholdingVoxelwise Lesion Symptom Mapping has low power.

Lesions large and variable.

You will need to test many people to find effects.

You can dramatically increase power by conducting a region of interest analysis, pooling data within a pre-defined area.

E.G. Aron et al. (2003) examine medial, orbital, inferior, middle and superior frontal regions. Find IFG (red) strongly predicts go-nogo performance.

Region of Interest AnalysisVLSM applied to surgery

- Lesion analysis typically used to assess brain function.
- Technique can also refine neurosurgery, e.g. epilepsy.
- Sustained uncontrolled seizures are a problem:
- Seizures lead to more seizures (vicious circle)
- Leads to brain damage and cognitive decline
- Impairs lifestyle and job options (driving car, tractor, etc.)
- If drugs do not stop seizures, surgery can be used.
- Surgery attempts to remove origin of seizures.
- Current surgery attempts to remove hippocampus and amygdala
- Fails to stop seizures in around 30% of patients
- Can we map lesions to identify brain regions crucial to stopping seizures?

Epilepsy Surgery

- Current surgery attemps to remove hippocampus.
- Large anterior portion of temporal lobe removed.
- Some variability in which regions are removed.

Bonilha et al, Arq Neuropsiquiatr 2004;62(1):15-20

Behavioral Measure: Engel Outcome Scale

- Class I - seizure-free;
- Class II - rare seizures;
- Class III - worthwhile improvement, with a reduction of more than 90% of seizures;
- Class IV – no worthwhile improvement (<90% reduction in seizure frequency).

B

% 88

% 88

59

59

29

29

1 2 3 4

1 2 3 4

66

66

A Hippocampal voxel (-26,-8,-22)

44

44

B Entorhinal cortex voxel (-20,-8,-30)

22

22

Surgically Resected

1 2 3 4

1 2 3 4

Not Surgically Resected

Engel Outcome Scale

Engel Outcome Scale

StatisticsConclusions

- Our behavioural measure (Engel outcome) is not binomial
- People are not cured/not cured, rather some have a degree of improvement.
- Data is ordinal, not interval, so t-test is not appropriate.
- We use the non-parametric Brunner-Munzel test for this data.
- This work shows that the surgery can be refined to target the entorhinal cortex.
- Future work: make sure removal of entorhinal cortex does not lead to further deficits.

Fisher’s Exact Test

- Fisher Exact Test gives the precise probability of an occurrence.
- Psychic claims to predict our behavior.
- To test, we will place four coins on table, two heads and two tails.
- Psychic will tell us the order we placed the coins, choosing 2 tails and two heads.
- There are 6 ways we could order the coins.
- 1/6 chance of correctly guessing all correctly.
- We could also compute probability of getting at least half your choices correct.
- This is a ‘permutation’ test – we can derive all possible combinations to estimate odds.

Fisher’s Exact Test

- Strengths:
- Nonparametric: does not assume normal distribution.
- Precise: Reports exact probability (as the precise number of permutations are known).
- Mathematically simple and elegant (solved using factorials).
- 4! = 4*3*2*1 = 24
- 5! = 5*4*3*2*1 = 120
- Weaknesses:
- Difficult with large numbers of trials
- factorials become huge quickly).
- use chi-squared for larger values.
- In practice, Fisher’s test is very conservative.

Permutation Tests

- Permutation tests attempt to compute exact probability.
- Example: sum of two dice. There is only one combination for 2 (1+1), two combinations for 3 (1+2,2+1), etc.
- Probability for rolling 11 or higher is 3/36.

2 3 4 5 6 7 8 9 10 11 12

Fisher’s Exact Test

- In our example, we MUST present two heads and two tails, and the observer MUST name two as being heads and two as being tails.
- Note: The observer can only make 0,2,4 mistakes – they can not make only one error, or precisely 3 errors.
- One combination of guessing all correct, 4 combinations of 2 errors, and one combination of guessing all incorrectly.

Fisher’s Exact Test

- Fisher’s exact test is precise, but assumes fixed marginals.
- In our example, the sum of each column must equal 2, and the sum of each row must equal 2.

2

2

2

2

Most experiments do not have fixed marginals.

For example, if we flip a coin 4 times, we will not always observe exactly two heads and two tails.

There are 16 combinations.

Also, observer responses are not typically fixed. They will not always want to say heads twice and tails twice.

The chance of a psychic precisely guessing all four coin tosses correctly is 1/16.

Fisher’s exact test is much more conservative.

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Power of Fisher’s Exact TestIn the real world, Fisher’s test is very conservative. When it says something is a 6% chance, it is often much more significant.Fisher’s Exact Test

In the 1960’s, Lancaster proposed a modification of Fisher’s exact test.

This mid-P is in practice much more accurate than the exact test.

Mathematicians dislike this test, as it is an inelegant kludge.

Lancaster’s mid-P2 3 4 5 6 7 8 9 10 11 12

Analogy:

Fisher’s approach reports the chance of rolling a 7 or higher is (6+5+4+3+2+1)/36 = 58%

Lancaster’s approach says the percentile of 7 or higher is ((6/2)+5+4+3+2+1)/36 = 50%

In other words, there are 6 ways to roll a 7, and Lancaster assumes our observation is in the middle of these possibilities.

Liebermeister’s measure

- In 1877 Liebermeister proposed permutation test for situations without fixed marginals.
- Forgotten until around 2001!
- Liebermeister was an MD
- Did not offer an elegant, easy mathematical solution.
- Fisher’s Factorial algorithm can solve Liebermiester’s measure.

Binomial Tests

- Binomial Tests: Fisher is too conservative. MidP and Liebermeister are more accurate.

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