Behavioral paradigm development for fMRI and EEG
Jason Zevin
Sackler Institute
What’s your problem?
What’s your problem?
Clinical: “Does this treatment alleviate a particular symptom?”
Translational: “Is activity in this region related to some feature of a
disorder/disease?”
Basic: “How does the brain accomplish some function?”
What’s your problem?
Are you interested in a particular region or network?
Are you interested in a particular behavior or function?
Are you interested in a particular population?
Do you care more about spatial or temporal resolution?
Different approaches have different strengths/weaknesses, and
are suited to different kinds of problems.
Electrophysiology (EEG)
- high temporal resolution
- low spatial resolution
Analysis approaches
- event related potentials (ERPs)
- topographic/source analysis
- continuous EEG
Different approaches have different strengths/weaknesses, and
are suited to different kinds of problems.
fMRI
- low temporal resolution
- high spatial resolution
Analysis approaches
- block designs
- event-related designs
- correlation analyses
- fancy stuff we won’t have time for
EEG
256-Channel Geodesic Sensor
Net
EEG Signals
ERPs
EEG is averaged
Time locked to a stimulus
event
May also be averaged to a
response event
(Response Potential)
Increases signal-to-noise
Development Print Processing
in the first 200 milliseconds
Age 6.5
Age 8.3
220 ms
Age 26
220 ms
150 ms
Words
Symbols
Difference
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-7/14.0 µV
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-11.4
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11.4
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Maurer et al. (2007)
What’s actually being measured:
Current related to local field potentials in cortex
Measurements are taken at the scalp, though…
Locating activity can be tricky! Orientation plays a role in
what you see at the scalp…
Graphics from
http://www.mrc-cbu.cam.ac.uk/EEG/img/Physiological_basis_EEG.gif
http://ww2.heartandstroke.ca/images/english/english_brain.jpg
The Inverse problem
Given a dipole source inside the
head, we can solve for what it
would look like on the scalp.
But the pattern of activity at the
scalp has a one-to-many
mapping back onto possible
dipole sources.
So, what’s the upside?
A more “direct” measure of neural activity.
Lets you look at neat stuff like oscillation
frequency:
Alpha - strong in relaxed,
awake states.
Figures from Wikipedia entry on EEG, of all places
Theta - may be largely
driven by hippocampus,
prominent in short term
memory tasks
Figure from: Klimesch, W. (1999) EEG alpha and theta oscillations reflect cognitive and
memory performance: A review and analysis, Brain Research Reviews, Volume 29,169-195.
Temporal
resolution
allows finegrained
inferences
about the timing
of neural
events.
QuickTime™ and a
decompressor
are needed to see this picture.
Molholm, S., Ritter, W., Murray, M.M., Javitt, D.C., Schroeder,C.E., and Foxe, J.J.
(2002) Multisensory auditory-visual interactions during early sensory processing in
humans: a high-density electrical mapping study, Cognitive Brain Research, 14, 115128.
Some considerations for designing EEG
studies:
How important is spatial resolution?
Maybe the process
you care about is
related to a general
“brain state,” e.g. a
stage of sleep.
QuickTime™ and a
decompressor
are needed to see this picture.
Some considerations for designing EEG studies:
Can you get enough data to do ERPs? (typically ~100 trials)
Interestingly, kids and infants, with their thin skulls and little
heads, give better EEG data and need fewer trials (but they
wiggle around more).
QuickTime™ and a
decompressor
are needed to see this picture.
Some considerations for designing EEG studies:
How important is temporal resolution?
Here, the argument
that multisensory
integration happens
early depends on
rapid responses
very short stimuli.
But what if your
stimuli are naturally
long, or vary in
duration?
fMRI
I’ll skip over the basics, because they’ve been covered earlier.
But let’s think about time.
Block designs can maximize power.
response to a single brief
stimulus
activation intervals
summed up HRFs from
activations
QuickTime™ and a
GIF decompressor
are needed to see this picture.
(adapted from the afni regression
tutorial)
Why would you
ever sacrifice
power?
QuickTime™ and a
decompressor
are needed to see this picture.
(audience participation)
Slow event-related designs
Stimulus (“Neural”)
HRF

Predicted Data
=
You can recover the HRF nicely, but you don’t get much
data.
http://www.columbia.edu/cu/psychology/tor/
Fast event related designs without “jitter”
Stimulus (“Neural”)
HRF

Predicted Data
=
Lots of data, but no idea where it’s coming from in time.
http://www.columbia.edu/cu/psychology/tor/
QuickTime™ and a
decompressor
are needed to see this picture.
Let’s get (sort of) random.
Stimulus (“Neural”)
HRF

Predicted Data
=
A little randomness in the timing of events permits better recovery
of hemodynamic responses, with relatively rapid events.
Still nowhere close to the temporal resolution of EEG.
http://www.columbia.edu/cu/psychology/tor/
You don’t have to get
super fancy, though, to
see interesting things.
In this study, Singer et al.
administered shocks to
women and their partners.
Even a slow, event related
design shows interesting
overlap between getting
shocked and watching
your honey get shocked.
They also took measures
of empathy using paper
and pencil outside the
scanner.
QuickTime™ and a
decompressor
are needed to see this picture.
These correlations are
probably bogus (we can
talk about it if there’s
time).
BUT, the principle of
measuring some
behavioral trait and
relating it to brain
responses is sound.
To do a study like this you
probably want a very
powerful design (or
stimulus) in order to be
sure you’re driving activity
in the regions of interest.
QuickTime™ and a
decompressor
are needed to see this picture.
But you might want to do something subtler with timing…
QuickTime™ and a
decompressor
are needed to see this picture.
Here, the authors (including next week’s lecturer) wanted to
measure the effect of context on responses to ambiguous
faces.
They used temporal jitter to separate activity due to the context
out from activity related to seeing the face.
Some considerations for designing fMRI studies:
Do you want “power” or “subtlety?”
- how much time do you have to collect data? (kids,
patients sometimes kinda hate being in the scanner)
- are you trying to characterize the function of a
region/network, or relate activity in some well-characterized
region to a population variable?
Does dealing with the timing of stimulus presentation to get a
nice HRF make your experiment awkward? Slow? What’s the
impact on behavior? (This happens in EEG, too.)