Transcript bcs513_lecture_week1

Goals
Get a handle on how fMRI can be used to ask
interesting questions about the brain
• Learn to play music
Get some practical fluency in how to do fMRI
analysis, including pattern-based analyses
• Learn to play scales
Become a better-informed reader of fMRI papers
and news reports
• Not every #1 hit is the Beatles. But some are
My favourite science quote
It’s better to have an approximate answer to
the right question than an exact answer to the
wrong question - John Tukey
What’s a good question?
What’s a mechanism?
Eye movement demo
What’s a mechanism? (continued)
From outside the head, we can figure out
something about what’s going on inside the
head
So, if we look inside the head, we must have
a royal road to finding mechanisms, right?
Problem:
This picture does not show any
neural mechanisms
Overview of course
Main goal: course project
• Analyse a publicly available dataset
• Design a new expt
• Or something else that we agree on
• 40% of grade, but there’ll be sub-chunks
• Might even turn into a publishable paper!
Computational exercises (20%)
Mini-essays (20%)
Tweet-length Qs about readings (10%)
Thanks for the feedback from mini-Qs
Helped me to see a couple of frequent stumbling
blocks
• Blood flow vs Blood oxygenation
• Reverse inference
Please e.mail as plain body-text if possible (not
Word attachments). Not a big deal, though
Anybody send in answers that I didn’t reply to yet?
Piazza
My hope: good for class discussion
Feel more than free to post anonymously
I’ll be posting polls, seeking feedback
Quick Q: who here in room isn’t on my
mailing list? Who didn’t get a Piazza invite
Will use to figure out good office hours time
Computer practicals
Hands-on experience doing data analysis
Will be using Matlab and SPM
• Can also use your own favourite tools if you like
With the invaluable assistance of Dan Cole
Let’s try all to squeeze into one session
MRI
Magnetic Resonance Imaging
• Takes a 3D picture of the inside of
body, completely non-invasively
• One picture, just shows the structure
http://www.coppit.org/brain/
fMRI
functional Magnetic Resonance Imaging
• Shows brain activity (indirectly)
• Takes a series of pictures over time,
e.g. one every three seconds
• The “f” in fMRI means functional, i.e.
you get a movie of brain function,
not a still image of brain structure
http://www.fmrib.ox.ac.uk/image_gallery/av/
What are we actually measuring with fMRI?
• An MRI machine is just a big magnet (30,000 times
stronger than Earth’s magnetic field)
• The only things it can measure are changes in the
magnetic properties of things inside the magnet: in this
case, your head
• When neurons are active, they make electrical activity,
which in turns creates tiny magnetic fields
• BUT far too small for MRI to measure (100 million
times smaller than Earth’s magnetic field)
• So, how can we measure neural activity with MRI?
What makes fMRI possible:
Don’t measure neurons, measure blood
Two lucky facts make fMRI possible
• When neurons in a brain area become active,
extra oxygen-containing blood gets pumped to
that area. Active cells need oxygen.
• Oxygenated blood has different magnetic
properties than de-oxygenated blood.
Oxygenated blood gives a bigger MRI signal
End result: neurons fire => MRI signal goes up
This fMRI method is known as BOLD imaging:
Blood-Oxygenation Level Dependent imaging.
Invented in 1992.
But neurons do the real work, not blood.
Neurons represent and process information
Individual nerve cells (neurons) represent information
• Sensitive to “preferred stimuli”, e.g. /ba/
• These stimuli make them active
• Firing activity: send electrical spikes to other neurons
/ba/
/ba/-sensitive neuron
Populations of neurons
process information together
Information is distributed across
large populations of neurons, and
across brain areas
There’s no “grandmother cell”:
the one single cell that
recognizes your grandmother
To really understand the brain,
we’d need somehow to read the
information from millions of
individual neurons at once!
The basic design of an fMRI experiment
Aim:
• Find which brain areas are active during a given task
• E.g. discriminating speech sounds, producing speech
Typical design:
• Present blocks, e.g. 30s of task, 30s of rest
• Measure fMRI activity regularly every few seconds
• Look for brain areas which are more active during the
task periods, compared to rest periods
Example time-courses
Time-course of task versus rest periods
Task
Rest
Task
Rest
Rest
MRI signal from voxel that correlates well with task: Active
Signal from voxel that does NOT correlate with task: Inactive
TIME
What are those little coloured blobs, actually?
Colour represents
statistical significance of
how well the voxel’s
activation correlates with
the task.
The hi-res grayscale
anatomical picture
underneath the coloured
blobs is a completely
different type of image,
from a different type of
scan. Shows the anatomy
at the spot where the
significant voxel’s timecourse was recorded.
The key problem
Interpreting what brain activation means
Reverse inference
Why it’s hard to infer processing from activation:
Brain areas are multi-functional
???
Attention
Intention
Spatial reasoning
Numerical magnitude
Parietal cortex
A famously horrible example
“You love your iPhone, literally”
http://www.nytimes.com/2011/10/01/opinion/you-love-your-iphone-literally.html
“But most striking of all was the flurry of activation in the insular
cortex of the brain, which is associated with feelings of love and
compassion. The subjects’ brains responded to the sound of
their phones as they would respond to the presence or proximity
of a girlfriend, boyfriend or family member.
In short, the subjects didn’t demonstrate the classic brain-based
signs of addiction. Instead, they loved their iPhones.”
Distinct representations may produce
same overall activation
/ba/-sensitive
population of neurons
Speech area
Subtraction:
/ba/ minus /da/ = zero
/da/-sensitive
population of neurons
Representations that standard fMRI can handle:
Localised and segregated
If you do a PubMed search for
representations and fMRI,
you find figures like these:
Hand
Lips
Faces
Houses
Representations that are more difficult:
Distributed and overlapping
Distinct but overlapping representations:
same average activation,
but different local patterns
Stimuli A and B activate the same neural population,
both activating shared neurons to differing degrees,
so they elicit different activation patterns
Stimulus A
B
Average local activation is the same for both stimuli
Representational pile-up
Standard analysis:
• Smoothed local average activations all look the same
Pattern-based analysis
• Maybe can tell the unsmoothed spatial patterns apart