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Transcript
What are we measuring in
fMRI?
Ruth Stavy
David Carmel
Outline
•
Neuronal activity
– Physiology and Haemodynamics
• Physics of fMRI
• Use of physics to “see” neural activity
• What fMRI really tells us (?) about neural
activity
Physiology and haemodynamics
functional Magnetic Resonance Imaging
(fMRI) measures brain activity indirectly
through changes in blood vasculature that
accompany neural activity.
Neuronal activity results in:
1.
An initial increase in oxygen
consumption owing to increased
metabolic demand.
2.
After a delay of ~2 secs, a large increase
in local blood flow. which
overcompensates for the amount of
oxygen being extracted
3.
Local increase in cerebral blood volume
The increase in blood oxyhaemoglobin is
what we measure in fMRI
This is called the BOLD (Blood Oxygen
Level Dependent) response
MRI Images: What do we see?
• MRI images are
usually based on the
signal from protons
• A Proton is the
nucleus of the
hydrogen atom
• Hydrogen is the
most common
element in tissue
• The signal from
protons is due to
their „spin“
Atomic nuclei with an odd number of neutrons and/or
protons have:
- a small magnetic moment
- an angular momentum called nuclear spin
Spinning protons are little magnets which are
frequently referred to as just spins.
If an external magnetic field B0 is applied,
they align with it.
An MRI machine’s B0 is always kept on!
M
A slight excess number of protons align with B0
producing an additional magnetic field in the
direction of B0 - we will refer to this field as M.
In addition, when placed in the magnetic field B0
the hydrogen proton can absorb energy and turn
(precess) around the applied magnetic field much
like a spinning top when placed in the earth’s
gravitational field:
Each type of nucleus has a unique frequency of spin.
This is also proportional to the strength of the magnetic field,
and is known as its resonance.
The resonance frequencies
characteristic of today’s MRI
machines are in the range of FM
radio waves, which is why they are
referred to as RF (Radio
Frequency)
Excitation: If a second magnetic
field (another RF) is applied,
orthogonal to the static field and at
the precessing frequency of the
atomic nuclei, the nuclei will all
precess about the static field
together, in-phase.
A nearby coil can detect this
precession: the precession
causes a current through the coil,
which has this characteristic
frequency.
Decay (relaxation): What Happens when the
additional magnetic field is turned off?
1. Protons emit the energy they absorbed (they do so at their RF)
- This is the "NMR“ (Nuclear Magnetic Resonance) signal detected by the nearby
coil
- At the radio frequency
- Signal proportional to Proton Density
2. M begins to return to its original orientation (B 0)
- At an Exponential rate defined by a certain formula
- This formula contains a “Time constant” - T1
- Signal intensity decreases with increasing T1
- T1 is unique to every tissue. This is the basis of
structural MRI.
- T1 is quite long: tens of milisecs to secs.
T1 Weighted Image
Caudate Nucleus
(T1=1200ms)
CSF
(T1=4300ms)
T1-Weighted Image
Corpus Callosum
(T1=700ms)
3. Spins begin to dephase
- At an Exponential rate
- defined by a certain formula
- This formula contains a different
“Time constant” – T2
- Signal intensity decreases with
decreasing T2
- The T2 rates are different for
different tissues
- The observed signal
decay T2 is fast: a few
msec to tens of msec
T2 Weighted Image
Caudate Nucleus
(T2=100ms)
CSF
(T2=2000ms)
T2 Weighted Image
Corpus Callosum
(T2=90ms)
To sum up,
Relaxation:
• In the real world, the NMR signal decays
faster than T2 would predict.
• There are many factors creating
imperfections in the homogeneity of a
magnetic field.
• Every tissue has a different magnetic
susceptibility which distorts the field at
tissue borders, particularly at air/tissue
interfaces.
• The sum total of all of these random and
fixed effects is called T2*
BOLD (Blood Oxygen Level Dependent)
contrast
• Takes advantage of the different magnetic
properties of oxyhemoglobin (HbO) and
deoxyhemoglobin (Hb)
Hb is paramagnetic and introduces an inhomogeneity into
the nearby magnetic field, whereas HbO is weakly
diamagnetic and has little effect.
Neuronal activity
local blood flow increases
overcompensating for oxygen consumption
oxygen level
in venous blood is elevated
larger MR signal.
How do we know where the signal comes
from?
The frequency f emitted by the protons is
proportional to the magnetic field B0
f =  B0
 is proportionality constant for specific
atomic nuclei
B0 is the strength of the static magnetic
(e.g.,1.5T)
If we modify the magnetic field B0 by applying a
„gradient field“ (B rises from left to right):
B
B1 B2 B3 B4…
Different B values 
Different f values
Values of frequencies indicate Position.
This is done in all three axes to enable
3-dimensional localisation (blobs)
What exactly does fMRI tell us?
• From sluggish
Haemodynamic response to
inferences on neural activity
• The central assumption:
the fMRI signal is
approximately proportional
to some measure of the
local neural activity,
averaged over several
millimeters and several
seconds. This is sometimes
referred to as the linear
transform model
(Buckner, 2002)
altered neuronal activity
changes in local haemodynamics
How the three are related is unclear
fMRI signal
• We know that the fMRI signal is triggered by the metabolic
demands of increased neuronal activity – but the details of this
process are only partially understood
• Altered neuronal activity  Changes in local haemodynamics:
- need for glucose?
- For oxygen?
- Some combination of both?
Various conflicting bits of evidence
(Heeger & Ress, 2002)
• In this talk: altered neuronal activity  fMRI signal
(the linear transform model)
• The success of fMRI as a research method depends on
understanding the relationship between the fMRI signal
and the underlying neuronal activity
• If the linear transform model were satisfactory, this would
be great (!):
• it would mean we could reliably estimate the underlying
neural activity from the Haemodynamic Response
Function (HRF)
• But can we? most studies simply assume we can; if the
model weren’t a good approximation, this would make
HRF measurements worthless
• This is important because some fMRI and
neurophysiology experiments have yielded conflicting
results
For example, the role of V1 in
spatial attention:
• Visual discrimination improves when
cued to attend, without moving the
eyes, to the stimulus location
X
• Does the attentional effect occur early in visual pathways
(V1)?
• Single-cell recording in monkeys: NO
• fMRI in humans: YES
• attentional effects in further regions (e.g. V4) much
larger in human fMRI than in monkey electrophysiology
• Is this due to species difference, or a difference in what
is being measured?
Things the fMRI signal MAY reflect
(but we don’t know):
• Firing rates of the local neurons
but also activity that doesn’t evoke spikes:
• Sub-threshold activity
• Simultaneous excitation and inhibition
• Modulatory inputs (e.g. top-down and
feedback from higher cortical areas)
and other effects:
• Changes in neuronal synchrony without a concomitant
change in mean firing rate
• Large changes in the firing rates of a few neurons
OR
small changes in the firing rate of many neurons
• To estimate the validity of the linear
transform model, it is necessary to see how
the fMRI signal correlates with measures of
neural activity.
• But doing this is not so straightforward
• The relationship of fMRI data and neural
activity depends on a few factors:
1. How ‘Neural activity’ is measured and quantified:
• fMRI: simultaneous activity of MANY neurons in a LARGE region of
cortex (millimeters) over a LONG period (seconds). What component of
the neural activity most predicts the fMRI signal?
•
•
•
•
Average firing rate of all / a subpopulation of neurons?
Degree of synchronous spiking?
The Local Field Potential (LFP), believed to reflect dendritic currents?
The Multi Unit Activity (MUA), believed to reflect spiking near the
electrode tip?
• The current source density?
• Some measure of local average synaptic activity?
• Some measure of subthreshold electrical activity?
• All the above may correlate with each other under some circumstances,
but can also vary independently of each other.
• Logothetis et.al. (Nature, 2001): simultaneous fMRI, LFPs and MUAs in
rats. Concluded that BOLD fMRI signals “reflect the input and
intracortical processing of a given area rather than its spiking output.”
2. fMRI acquisition technique
• BOLD (Blood Oxygen Level Dependent), the most
common, provides a mixed signal dependant on:
– blood FLOW
– blood VOLUME
– blood OXYGENATION.
• Variations on the technique can be used to emphasize or
de-emphasize one or another of these components:
- Pefusion-based fMRI  blood flow
- Injections of various compounds  blood volume
- Diffusion-based fMRI  cell swelling (after excitation)
• So far, only little work has been done to quantify the
relationship between different fMRI techniques.
3. Experimental protocol & data analysis
• Early days of fMRI:
worry that the signal
arises entirely/mostly
from large draining veins
 misleading on localization
• For example,
visual stimuli at nearby locations 
activity in nearby (but distinct) loci in V1
• But if the fMRI signal were only evident in a large vessel
draining blood from V1, The activity from both points
would seem to occur in the location of that vessel
• The solution: appropriate
experimental protocols
• For visual stimuli, retinotopic maps
are produced by using stimuli that
move through the visual field slowly,
evoking a traveling wave of neural
activity across the grey matter
• Large veins draining a large portion
of visual cortex are de-emphasized
because the blood flow and
oxygenation in these vessels are
roughly constant throughout the
experiment
• Hence the experimental design can
be crucial for precise localization
• So it is important to consider this
each time a new protocol is
developed
Temporal summation
• According to the linear transform model, it
should be possible to predict the response to a
long stimulus presentation by summing the
responses to shorter stimuli
• For example, the response to a 12-second
stimulus should be the same as summing the
response to two identical consecutive 6-second
stimuli
• This temporal summation holds up well in some
experiments, but fails in others
• Why?
Two possibilities:
1.
Blood flow is proportional to neural activity, but
BOLD fMRI signals have a non-linear
dependence on flow:
The BOLD signal might saturate at high levels of
blood flow, as further increases in flow would
cause negligible decreases in the concentration
of deoxyhaemoglobin.
So a moderately-strong stimulus could evoke a
near-maximal fMRI response, leaving little room
for further increase in response (even to a
stronger stimulus).
Two possibilities:
2. Temporal summation works, but experiments may miss it:
These experiments observed primary sensory and motor
areas. In such areas, Short-duration stimuli are expected to
evoke disproportionately large neural responses for ~3s.
This is because such neurons:
- Show large transient responses after stimulus onset
- Adapt during prolonged stimulation
- Their responses are boosted by attention, which is
likely to be engaged automatically by stimulus onset
Experiments have not been carried out yet to measure the
temporal summation of fMRI responses while explicitly
controlling for these factors
Colocalization of fMRI and neural activity
•
The linear transform model implies that the fMRI response should
be at the same location as the underlying neural activity
•
However, this may depend on the specific method of data analysis.
For example:
1. The specific HRF used may pinpoint the center of fMRI activity
2mm-1cm from the center of intrinsic optical-imaging activity
(Cannestra et.al., 2001).
2. Dibrow et.al. (2000) report a large discordance between fMRI and
electrophysiological recordings in somatosensory cortex of
anesthetized monkeys.
On the other hand, Wandel et.al. (2000) report excellent agreement
between fMRI and the electrophysiological literature for early visual
areas.
This difference between somatosensory and visual maps might
reflect a fundamental difference in the basis of the Haemodynamic
response in the two areas.
Conclusions
• Is the linear transform model a reasonable and useful
approximation of the way fMRI data reflects neural
activity?
• Heeger & Ress (2002):
YES,
- for some recording sites / measures of neural activity
- in some brain areas
- using certain experimental protocols
• NO, under other circumstances.
• We need to know more about the relationship between
various measures of neural activity, and determine whether
they reflect different aspects of neural function.
Thank you