Transcript Jody Culham Brain and Mind Institute Department of Psychology Western University http://www.fmri4newbies.com/ Group Analyses in fMRI Last Update: November 9, 2014 Last Course: Psychology 9223, F2014, Western.

Jody Culham
Brain and Mind Institute
Department of Psychology
Western University
http://www.fmri4newbies.com/
Group Analyses in fMRI
Last Update: November 9, 2014
Last Course: Psychology 9223, F2014, Western University
Group Analyses
1. Get all the subjects’ brains into a common space
– Talairach space
– MNI space
– cortex-based alignment
2A. Do group statistics
– Random Effects GLM
and/or
2B. Use a Region of Interest Approach
Combining Group Data
Brains are Heterogeneous
Slide from Duke course
Talairach Coordinate System
Talairach & Tournoux, 1988
• made an atlas based on one brain
… from an alcoholic old lady
• any brain can be squished or stretched to
fit hers and locations can be described
using a 3D coordinate system (x, y, z)
Note: That’s TalAIRach, not TAILarach!
Rotate brain into ACPC plane
Corpus Callosum
Fornix
Find anterior commisure (AC)
Find posterior commisure (PC)
ACPC line
= horizontal axis
Note: official Tal says to use top of AC
and bottom of PC but I suspect few
people actually do this
Pineal Body
“bent asparagus”
Source: Duvernoy, 1999
AC = Origin
x=0
+
y=0
z
z=0
-
L
R
+
y
-
x
+
Left is what?!!!
Neurologic (i.e. sensible) convention
• left is left, right is right
L
R
-
+
x=0
Radiologic (i.e. stupid) convention
• left is right, right is left
R
L
+
x=0
Note: Make sure you know what
your magnet and software are
doing before publishing
left/right info!
Tip
Put a vitamin E capsule on the one side of
the subject’s head or coil.
Deform brain into Talairach space
Mark 8 points in the brain:
• anterior commisure
• posterior commisure
• front
• back
• top
• bottom (of temporal lobe)
• left
• right
Squish or stretch brain to fit in “shoebox”
of Tal system
y<0
AC=0
y
y>0
z
y>0
ACPC=0
y<0
x
Extract 3 coordinates
Talairach Tables
Source: Culham et al.,
2003, Exp. Brain Res.
• Talairach coordinates can be useful for other people to check
whether their activation foci are similar to yours
• Often it’s easiest to just put coordinates in a table to avoid
cluttering text
Do We need a “Tarailach Atras”?
Variability between Japanese
and European brains, both male
(red > yellow > green > blue)
Variability between male and female brains,
both European
(red > yellow > green > blue)
Source: Zilles et al., 2001, NeuroImage
Talairach Pros and Cons
Advantages
• widespread system
• allows averaging of fMRI data between subjects
• allows researchers to compare activation foci
• relatively easy to use
Disadvantages
• does better for central regions of cortex, but not great for most of cortex
• not appropriate for all brains (e.g., group variability, patients may not fit well)
• ignores left- vs. right-hemisphere asymmetries
• activation foci can vary considerably – other landmarks like sulci may be more
reliable
MNI Space
•
Researchers at the Montreal Neurological Institute (MNI) created a better
template based on a morphed average of hundreds of brains
– many different versions
• http://en.wikibooks.org/wiki/MINC/Atlases/Atlases/History
MNI Space
•
Benefits
– MNI Space is based on many subjects not just one brain like Talairach space
– MNI transformations use nonlinear warping, which leads to better
intersubject alignment
Image Source
Converting Between MNI and Tal space
• You many want to convert between the systems
– Only Tal provides Brodmann’s areas
– Need to convert for meta-analyses
• The MNI and Talairach coordinates are similar but not identical
– e.g., temporal lobes extend 10 mm lower in MNI brain
• Caveat: careful comparison requires a transformation
-- converters can be found online http://www.brainmap.org/icbm2tal/
Source: http://www.mrc-cbu.cam.ac.uk/personal/matthew.brett/abstracts/MNITal/mniposter.pdf
Anatomical Localization
Sulci and Gyri
CROWN
BANK
gray matter
(dendrites &
synapses)
white matter
(axons)
neuron
FISSURE
FUNDUS
Source: Ludwig & Klingler, 1956,
in Tamraz & Comair, 2000
Variability of Sulci
Source: Szikla et al., 1977, in Tamraz & Comair, 2000
Effects of Sulcal Variability
Source: Frost & Goebel, 2012, NeuroImage
Variability of Functional Areas
Watson et al., 1995
- motion-selective area, MT+ (=V5) is quite variable in stereotaxic space
- however, the area is quite consistent in its location relative to sulci
- junction of inferior temporal sulcus and lateral occipital sulcus
- see also Dumoulin et al., 2000
Cortical Surfaces
segment gray-white
matter boundary
render cortical surface
inflate cortical surface
sulci = concave = dark gray
gyri = convex = light gray
Cortical Inflation Movie
Movie: unfoldorig.mpeg
http://cogsci.ucsd.edu/~sereno/unfoldorig.mpg
Source: Marty Sereno’s web page
Cortical Flattening
2) make cuts along
the medial surface
(Note, one cut
typically goes along
the fundus of the
calcarine sulcus
though in this
example the cut was
placed below)
1) inflate the brain
3) unfold the medial
surface so the
cortical surface lies
flat
Source: Brain Voyager Getting Started Guide
4) correct for the
distortions so that the
true cortical distances
are preseved
Spherical Averaging
Future directions of fMRI: Use cortical
surface mapping coordinates
Inflate the brain into a sphere
Use sulci and/or functional areas to match
subject’s data to template
Cite “latitude” & “longitude” of spherical
coordinates
Source: Fischl et al., 1999
Spherical Averaging
Movie: brain2ellipse.mpeg
http://cogsci.ucsd.edu/~sereno/coord1.mpg
Source: Marty Sereno’s web page
Source: Fischl et al., 1999
Movie: morph-curv1.mpg
http://www.cogsci.ucsd.edu/~sereno/morph-curv1.mpg
Source: Marty Sereno’s web page
Before and After CBA
Source: Frost & Goebel, 2012, NeuroImage
Before and After CBA
hand motor area (M1)
hand somatosensory area (S1)
Source: Frost & Goebel, 2012, NeuroImage
Gains in Overlap
Source: Frost & Goebel, 2012, NeuroImage
Voxelwise Group Analyses
Fixed vs. Random Effects
Example
• Three subjects
• Three conditions: Baseline, Faces, Objects
• For simplicity, just consider one voxel in FFA
Stupid Way: Concatenated Fixed Effects (FFX)
C
o
n
c
a
t
e
n
a
Stupid Way: Concatenated Fixed Effects
(FFX)
• Make one predictor for Faces and one for Objects (2 df)
• Scale predictors (by beta weights)
• Note why this is stupid
– Assumes all subject show same magnitude of activation
– Errors in this assumption   residuals
Better Way: Separate Subjects FFX
• Don’t concatenate
• Separate predictors for 3 subjects x 2 conditions
– 6 df
Problem: Separate Subjects FFX
• We could do business-as-usual GLM and see if
predictors account for significant variance
considering noise
• Effectively, we are asking how confident we are that
this effect is true (not due to chance) in these three
subjects (and only these three subjects)
• BUT usually, we want to generalize to the population
from which we sampled
Best Way: Random Effects (RFX)
Second-level analysis
• Nothing too complicated… it’s effectively just a paired t-test
Subject
βFaces
βObjects
Difference
βFaces βObjects
S1
0.552
0.105
0.447
S2
2.061
1.121
0.940
S3
1.019
0.247
0.772
Mean
0.719
SD
0.250
SEM [=SD/sqrt(N)]
0.145
tcrit(df=3)
4.3
95%CI lower (= mean – (t*SEM))
0.097
95%CI lower (= mean + (t*SEM))
1.34
Estimated Distribution
of Differences
• does not include zero
 significant (p<.05)
95% CI
0 0.097
0.719
1.34
Repeat for the other 60,000 voxels…
Huettel, Song & McCarthy, 2008
First-level analysis
Second-level analysis
If you really want to be correct…
• We often refer to this type of analysis as random
effects (RFX)
• Since subjects is a random effect but other aspects
(e.g., stimulus categories) are fixed effects,
technically the proper term is Mixed Effects Analysis
• Other common jargon = Second-level Analysis
Take-home Message
• RFX enables us to generalize to the population from
which we sampled subjects
for most fMRI studies,
this means underpaid
graduate students in
need of a few bucks!
• Degrees of freedom comes from number of subjects,
not number of time points
– No need to worry about correction for serial correlations
Examples from a real data set
Concatenated FFX
• Example 17 Subjects x 2 runs with Faces & Houses
df = 17 Ss x 2 runs/S
x 264 vols/run - 1
one predictor per condition
FFX Separate Subjects
•
•
•
…
If you’re looking at pilot data from a
few Ss, with RFX it will be hard to
see any effects.
You can do FFX with separate
subjects.
It has the same problems as basic
FFX but at least enables you to
examine the consistency between Ss
RFX
S1
S2
S3
…
S17
This contrast is just like doing a paired t-test
between Faces and Objects with 17 Ss
df = 17 Ss - 1
Now that our df no longer depends on # volumes,
we don’t have to worry about correction for serial correlations with RFX
Smoothing and Averaging
anatomical variability of
activation for 3 Ss
without spatial smoothing
anatomical variability of
activation for 3 Ss
with spatial smoothing
each subject shows an effect but
there’s not enough spatial
overlap to find any voxels in an
RFX analysis
now there’s enough overlap
between Ss that some voxels will
be found with RFX analysis
Random Effects Analysis
• Brain Voyager recommends you don’t even toy with random effects
unless you’ve got 10 or more subjects (and 50+ is best)
• Random effects analyses can really squash your data, especially if you
don’t have many subjects.
• Though standards were lower in the early days of fMRI, today it’s virtually
impossible to publish any group voxelwise data without RFX analysis
Strategies for Exploration vs. Publication
•
Deductive approach
–
–
–
–
•
Have a specific hypothesis/contrast planned
Run all your subjects
Run the stats as planned
Publish
Inductive approach
– Run a few subjects to see if you’re on the right track
– Spend a lot of time exploring the pilot data for
interesting patterns
– “Find the story” in the data
– You may even change the experiment, run additional
subjects, or run a follow-up experiment to chase the
story
• While you need to use rigorous corrections for publication, do not be overly
conservative when exploring pilot data or you might miss interesting trends
• Random effects analyses can be quite conservative so you may want to do
exploratory analyses with fixed effects (and then run more subjects if
needed so you can publish random effects)
How can we identify activation foci?
Talairach coordinates
• Example: The FFA is at x = 40, y = -55,
z = -10
Anatomical localization
• Example: The FFA is in the right
fusiform gyrus at the level of the
occipitotemporal junction
Kanwisher, McDermott & Chun,
1997, J Neurosci
Functional localization
• Example: The FFA includes all voxels
around the fusiform gyrus that are
activated by the comparison between
faces and objects
Talairach Daemon
• http://www.talairach.org
Brodmann’s Areas
Brodmann (1905):
Based on cytoarchitectonics: study of
differences in cortical layers between areas
Most common delineation of cortical areas
More recent schemes subdivide
Brodmann’s areas into many smaller
regions
Monkey and human Brodmann’s areas not
necessarily homologous
Definition of an “Area”
• Neuroimager’s definition of an area: Some blob
vaguely in the vicinity (+/- ~3 cm) of where I think it
ought to be that lights up for something I think it
ought to light up for
• Neuroanatomist’s definition of an area: A
circumscribed region of the cerebral cortex in which
neurons together serve a specific function, receive
connections from the same regions, have a common
structural arrangement, and in some cases show a
topographic arrangement
• may also be called a cortical field
50
Cortical Fields: Multiple Criteria
1. Function
– an area has a unique pattern of responses to different stimuli
2. Architecture
– different brain areas show differences between cortical
properties (e.g., thickness of different layers, sensitivity to
various dyes)
3. Connectivity
– Different areas have different patterns of connections with
other areas
4. Topography
– many sensory areas show topography (retinotopy, somatotopy,
tonotopy)
– boundaries between topographic maps can indicate
boundaries between areas (e.g., separate maps of visual
space in visual areas V1 and V2)
51
Can We Use Multiple Criteria in Human Imaging?
1. Function
– this is often the only criterion in fMRI
2. Architecture
– there are now probabilistic maps of human brain areas
available (Zilles lab)
3. Connectivity
– DTI and functional connectivity now give us options here
4. Topography
– topography is useful in imaging, especially for early and midlevel sensory areas
52
Brodmann Area 17
53
Brodmann Area 17 Meets 21st Century
Anatomical MRI
Logothetis fMRI data:
image from http://www.bruker-biospin.com/imaging_neuroanatomy.html
Functional MRI
Goense, Zappe & Logothetis, 2007, MRI
Layer 4 fMRI activation (0.3 x 0.3 x 2 mm spin echo)
54
Retinotopic Maps
EXPANDING
RINGS
ROTATING
WEDGES
DTI in V1
Saentz & Fine, 2010, NeuroImage
56
Maps, Maps, Maps
… even in parietal lobe …
… even in frontal lobe …
Hagler & Sereno, 2006,
NeuroImage
Wandell et al., 2007, Neuron
57
Other Sensory “-topies”
Touch:
Somatotopy
Servos et al., 1998
red = wrist; orange = shoulder
Audition:
Tonotopy
Sylvian fissure
temporal lobe
cochlea
Movie: tonotopy.mpeg
http://cogsci.ucsd.edu/~sereno/downsweep2.mpg
Source: Marty Sereno’s web page
Learning Brain Anatomy
Duvernoy, 1999, The Human Brain: Surface, Blood Supply, and Three-Dimensional
Sectional Anatomy
• beautiful pictures
• good schematic diagrams
• clear anatomy
• slices of real brain
• Springer, US$439
• DISCONTINUED
Ono, 1990, Atlas of the Cerebral Sulci
• great for showing intersubject variability
• gives probabilities of configurations and stats on sulci
• Theime, US$199
Damasio,2005, Human Brain Anatomy in Computerized Images, 2nd edition
• good for showing sulci across wide range of slice planes
• 2nd edition much better than 1st edition
• Oxford University Press, US$100
Tamraz & Comair, 2000, Atlas of Regional Anatomy of the Brain Using MRI with
Functional Correlations
• good overview
• Springer, US$203
Talairach & Tournoux, 1988. Co-Planar Stereotaxic Atlas of the Human Brain
• just because it’s the standard doesn’t mean it’s good
• Theime, US$240
Brain Tutor
• Mac/PC: free
• iOS App: $1.99
Jody Culham
Brain and Mind Institute
Department of Psychology
Western University
http://www.fmri4newbies.com/
Proposal Guidelines
Last Update: November 9, 2014
Last Course: Psychology 9223, F2014, Western University
Research Proposal
Due December 8, 2014
• Goals
– give students an opportunity to demonstrate what they’ve
learned and apply ideas to their research area
– give students practice in writing grants/papers
• 16 double-spaced pages + figures
• must be original
– not thesis
– not something your advisor totally worked out for you
• can get some suggestions from advisor but core of proposal
should be your work
Research Proposal
• partially like a grant
– proposal for experiment
– make case for why experiment should be done
• “hasn’t been done before” is not good enough
– clear question, hypotheses
– conclusion: so what?
– immunization against potential criticism
• partially like a paper
– just one experiment, not 5 years of experiments
– in-depth methods
• be clear about design (e.g., protocol) and analyses
• be clear about contrasts
• Appendix with budget and time line
• don’t worry about formatting – spend your time on
content not formatting
Range of Approaches
• Standard univariate fMRI with hypothesis-driven GLM
– Block or Event-related
• Advanced designs
– e.g., MVPA
• Data-driven fMRI
– e.g., ICA on resting state data
• Approaches we’ve touched on in class
Be aware that we
won’t discuss these
in too much detail in
class; therefore you
would need to have
some prior exposure
or to do some extra
reading
– e.g., intersubject correlations
• Anatomical approaches
– DTI
• For the more computationally inclined
– better ways to analyze data
– if you must use equations, explain them intuitively in text
(and consider putting them in an appendix)
Two questions to consider whenever you
write a paper or give a talk
• Who is my audience?
– a math-phobic professor who will be checking whether you
understood the core ideas of fMRI
• What is my goal?
– show professor that you can find a way to use neuroimaging
in your research
– show professor that you understand jargon and concepts
Bonus
– be clever and creative
– write clearly and concisely
– solidify your understanding of neuroimaging approaches
– think more deeply about how to apply neuroimaging
Sections
•
•
•
•
•
•
•
Introduction
– Give enough information to put the research in context and lead the reader to the conclusion that the
experiment you’re proposing is a reasonable next step
– You don’t need to cite every paper in the history of neuroimaging
– Do enough of a lit search to be fairly certain proposal hasn’t been done
– Replication attempts discouraged (but may be considered with sufficient justification)
Methods
– Include enough detail to demonstrate that you understand jargon and key concepts
– Be clear about specific contrasts
Results
– How could it turn out?
– May want to include graph of hypotheses
Conclusions
– What would it mean if the results turned out one way or another?
– Are there any caveats that should be acknowledged?
– What is the broader significance of the research?
References
– whatever format you like
– don’t go overboard
Figures (optional)
Appendix
– How much will it cost?
– How long will it take?
Example of Hypothesis Figure